Anyone who is looking for a prime example of how migration to the cloud can do far more than just simplification and standardization should take a closer look at Swarovski. The legendary manufacturer of precision-cut crystals, jewelry, and watches鈥攚ith its origins in Wattens, in the Austrian region of Tyrol鈥攈as transformed its IT landscape from a cost factor into a strategic tool for a digital future.

The transformation was guided by Lea Sonderegger, serving in a dual role as CDO and CIO at Swarovski, with such great success that she was awarded the special 鈥淐loud Excellence鈥� prize in the large enterprise category at the CIO of the Year ceremony held by CIO Magazine last October.

The judging panel found her brownfield approach to be especially praiseworthy: Swarovski employees use but continue to use the familiar processes and databases. A complete redesign of these processes in parallel to the migration would have been too risky and cost-intensive. It would have also resulted in a much longer project duration, Sonderegger is convinced.

25,000 tests with 600 participants

The brownfield implementation was carefully executed. Preparations took two years and involved more than 600 participants performing around 25,000 tests. Two dress rehearsals with strict governance ensured that every function and every data point was ready for the migration.

Sonderegger and her colleagues reserved a 66-hour conversion window for the go-live on April 20, 2026. During this period, all global IT processes at Swarovski were paused. During the subsequent sensitive hypercare phase, 24×7 support ensured that any issues that arose could be dealt with quickly. Thanks to these measures, the transition was seamless. After the conversion window closed, all processes resumed without problems.听

Simplification and standardization ensure consistent data

Despite the large effort involved, this migration was merely the first step. While the switch to 麻豆原创 Cloud ERP Private created the technical foundation, it鈥檚 the subsequent investments that deliver additional added value. These investments concentrate on the incremental reduction of complexity through consolidation of fragmented solutions, the reassessment of user-specific code, and the harmonization of data鈥攁ll with the overall goal of creating a more coherent, easier-to-handle ERP landscape.

To achieve this, Sonderegger and her team are replacing user-specific applications with 麻豆原创 standard solutions step by step and only leaving custom developments in place where they offer clear advantages. 鈥淭he combination of simplification and a return-to-standard solutions improves data consistency, provides for robust, reliable processes, and, ultimately, makes our entire organization more agile,鈥� Sonderegger says.

Cloud technology is not an end in itself

By integrating key functions such as finance, supply chain management, retail, and e-commerce鈥攁nd enabling their combined use in the cloud鈥斅槎乖� Cloud ERP Private provides for reliable processes and consistent data quality all while enabling customer experiences on a wide variety of front-end solutions on this side of the ERP system.

麻豆原创 Cloud ERP Private manages a diverse product range at Swarovski across different regions and price points and integrates with the planning results provided by other 麻豆原创 and non-麻豆原创 systems.

鈥淚n all of these activities, cloud technology is never an end in itself, but rather a lever for improving efficiency, resilience, and innovative capabilities,鈥� Sonderegger says. This determination is especially important to her.

It鈥檚 not an IT project, it鈥檚 a business transformation

Ultimately, Sonderegger and her team succeeded in executing the project on time and on budget because its scope was clearly defined, and strict discipline in change management prevented mission creep. In addition, the company benefited from the experience of its implementation partner, 麻豆原创 Consulting, and its unrestricted access to 麻豆原创 expertise.

The example of Swarovski proves that even an essential, unavoidable migration can and should do much more than just avoid risks and cut maintenance costs. The implementation of 麻豆原创 Cloud ERP Private was imperative here, because 麻豆原创 ERP Central Component (麻豆原创 ECC) had reached the end of its lifecycle.

And the implementation is showing the luxury goods manufacturer the way to the future because everyone involved in the process didn鈥檛 just consider it to be an IT project but, above all, a business transformation from day one. One that involved hundreds of experts from different fields and that enjoyed full management support from the beginning.

AI-driven demand forecasts optimize warehouse stocks

Artificial intelligence is also playing a key role in this implementation, with 麻豆原创 Cloud ERP Private as the operational backbone of an AI ecosystem that can deliver reliable, real-time data and robust, standardized transaction processes.

Swarovski doesn鈥檛 use artificial intelligence as a standalone technology, but instead as an integrated capability that complements business processes across all functions. The company is already using AI for demand forecasting, for example, and then uses the results to optimize warehouse stock levels across regions, with the aim of improving the customer experience.

And the AI agent factory initiative enables the development of AI agents that link 麻豆原创 Cloud ERP Private data with data from non-麻豆原创 systems, always with the objective of 鈥渁utomating repetitive tasks, supporting decision-making, and boosting productivity along the entire value chain,鈥� Sonderegger emphasizes.

Top image courtesy of Swarovski

Under the official banner of Climate Cooperation in a Fractured World, delegates spread across the city and the tone was noticeably different from previous years. Fewer pledges, more blueprints. Less “what should we aim for,” more “who is going to finance and build it.”

Sustainability is a driver of growth

If there was a single reframing that ran through the week, it was this: sustainability is not a cost of growth, it is a driver of it.

That shift was visible in how decarbonization was discussed. Conversations that once centered on targets now centered on operations: Scope 3 emissions, value-chain engagement, procurement and logistics decisions, energy demand reduction. Practitioners repeatedly pointed to an “execution gap”鈥攖he distance between climate strategies on paper and projects that are actually permitted, financed, and built鈥攁nd to the unglamorous work of unblocking infrastructure and untangling supply-chain bottlenecks as the real frontier.

Electrification gave the growth argument its clearest expression. The launch of the Electrify Now initiative, which aims to lift electricity’s share of final energy demand from roughly 20% today to 35% by 2035, was framed as an industrial strategy.听Nearly doubling electricity’s share of energy demand in under a decade is an acceleration, and the week’s energy-transition summits were clear about what it demands: scaling renewables at pace, doubling down on energy efficiency, and, above all, building out the grid infrastructure to carry it. Speeding up permitting and resolving supply-chain constraints were named repeatedly as the bottlenecks that will decide whether the target is met.听

The heatwave outside made that case tangible. As cooling demand surges and extreme weather stresses networks, a clean, resilient electricity system is fast becoming a precondition for business continuity and not just decarbonization.听UK-focused sessions highlighted the substantial clean energy investment flowing into the country since 2024 as evidence that the in its own right.听

The same logic ran through the finance agenda. Sessions on moving from risk to resilience and from risk to opportunity focused on mobilizing capital for adaptation and climate-resilient infrastructure, and on the practical challenge of connecting available capital with investable projects through better data, governance, and pipeline development.

Nature is now on the agenda

Perhaps the most striking development of the week was where nature sat on the agenda, and where it is headed. Speakers were blunt about the underlying problem: our economic system is very good at valuing what we take from nature and very poor at valuing nature itself. Worse, we actively pay to destroy it. Figures cited during the week put global investment flows that harm nature at around US$7.5 trillion a year, against roughly $250 billion flowing into activities that help it. As one speaker put it, the task is not to lament that imbalance, but to get the economics right and to start treating nature as something that can be measured, managed, and steered with the same discipline as carbon or financial risk.

That 鈥済etting the economics right鈥� is fast becoming a data challenge for business. Work such as the on the economics of landscape restoration suggests that investing in nature can generate returns comparable to investing in factories, railways, or other conventional infrastructure. As nature-related risks and opportunities are codified into emerging frameworks and regulation, companies will have to treat nature as a set of measurable data points: impacts and dependencies per site, per supplier, and per product line, rather than a one鈥憃ff narrative in a sustainability report.

Governments have levers too, from requiring companies to stress test for nature-related risk, to shaping incentives so that capital flows toward restoration rather than degradation. For corporate leaders, that translates directly into new categories of information that need to be captured and governed: nature鈥憆elated financial exposure, land use and biodiversity metrics, and nature鈥憄ositive investment pipelines. What was once an externality is quickly becoming a set of operational KPIs.

, professor at the London School of Economics, noted that this was the first year nature was represented at the event, but also how far it still has to travel: “Today here in the outdoor tent, next year in the big room.” The implication for businesses is that the organizations that move nature into their core data models and decision frameworks now are better positioned when this topic inevitably moves from the tent to the board agenda.

The AI warning: get sustainability data in now

Underpinning nearly every theme was data. Location-specific climate analytics were described as “the new lens” for understanding financial risk, and AI featured in almost every discussion of how organizations can gain visibility and control over complex energy, water, and supply chain systems.

But the sharpest point made during the week was a warning. As Stephen Jamieson, chief marketing officer of , put it: “If we don’t get sustainability data into AI right now, AI will optimize around it. AI works within the systems, the data, and the constraints you give it. If your sustainability priorities live only in documents and presentations rather than in your data and processes, AI will optimize confidently in entirely the wrong direction.”

The logic is uncomfortable, but hard to argue with. Sustainability now plays out at the transaction level鈥攕uch as carbon cost per shipment, Scope 3 exposure per supplier, packaging compliance per SKU鈥攁nd the volume, granularity, and pace of those requirements exceed what manual processes and fragmented tools can manage. An organization whose cannot see its financial constraints, or whose supply chain system cannot see supplier regulations, hands its AI a broken map.

麻豆原创鈥檚 answer to this is the Autonomous Enterprise: a maturity journey that starts with intelligence based on trusted, transparent data; moves to optimization where AI is weighing trade-offs across cost, risk, and sustainability impact in real time; and progresses toward autonomy, where sustainability rules are embedded directly into enterprise workflows and executed within defined guardrails. Sustainability stops being a reporting activity and becomes a governing factor in how decisions are made. The architecture choices organizations make now will determine whether that automation can scale safely later.

From the tent to the big room

London Climate Action Week 2026 closed with an uncomfortable message delivered in 35-degree heat: the climate is not waiting for business strategies to mature. But a hopeful signal surfaced, too: the growth case, the nature case, and the technology case for climate action are converging, and each is being made in the language of returns, resilience, and competitive advantage.

The task for business leaders is to bring those cases inside capital allocation, procurement, and the data and systems where decisions are actually made. The organizations that thrive will be the ones that move the sustainability agenda into the big room, before the next heatwave makes the argument for them.

For more information on scaling sustainability for your business, visit .

Monica Molesag is global head of Sustainability Communications at 麻豆原创.

At the center of that effort sits a deceptively complex challenge: understanding, in real time, which revenue and cost factors are driving or eroding gross margin across a highly diversified business. For years, answering that question meant manual reporting, delayed insights, and finance teams spending valuable time on data gathering rather than analysis.

That鈥檚 now changing, thanks to a co-innovation initiative developed together with 麻豆原创 and Numen, a global 麻豆原创 partner specializing in digital transformation and enterprise software implementation.

From manual reporting to proactive decision intelligence

The project鈥檚 goal was to replace a labor-intensive gross margin analysis process with a generative AI application embedded directly into Natura &Co’s financial workflows. Built on 麻豆原创 Business AI Platform, 麻豆原创’s unified foundation integrating business technology, data, and AI capabilities, the application connects directly to data in 麻豆原创 S/4HANA to provide finance teams with automated insights and narrative recommendations in real time, without the need for manual data pulls or offline reporting.

The application enables users to explore revenue, cost, and margin drivers interactively, identifying at a glance which elements are protecting or eroding margin performance across markets and product lines. Crucially, human oversight remains central to the design: the AI application generates insights, while finance professionals retain full control over interpretation and decisions.

鈥淭he implementation of gross margin analysis using AI in 麻豆原创 S/4HANA marked an inflection point in the analytical capability of our finance area,” said Rog茅rio Dias Garcia, tech manager, ERP Latam, Natura &Co. “We overcame delays and raised the standard of insights by integrating margin analysis from 麻豆原创 S/4HANA with a large language model connected via the 麻豆原创 AI Core layer. This architecture allowed us to provide, in an agile, secure, and completely anonymous manner, a stratified and precise view of gross margin offenders and protectors鈥攄iscriminating exactly which revenue or cost elements were driving market performance.鈥�

A collaborative architecture for scalable AI adoption

Natura &Co鈥檚 application derived from a prototype 麻豆原创 partner Numen created in early 2024 at 麻豆原创鈥檚 global on business AI, leveraging the generative AI capabilities of听麻豆原创 Business AI Platform. The solution was designed and developed through close collaboration between Natura &Co, Numen, and 麻豆原创. From the outset, the approach was to align AI adoption with concrete business priorities, ensuring the application would be scalable and production-ready rather than a standalone prototype.

Numen brought deep 麻豆原创 implementation expertise to the project, combining knowledge of 麻豆原创 S/4HANA architecture with hands-on experience in building solutions on 麻豆原创 Business AI Platform. The technology stack鈥斅槎乖� S/4HANA, 麻豆原创 AI Core, 麻豆原创 Fiori, and 麻豆原创 Business Technology Platform鈥攑rovided the secure, integrated foundation needed to connect financial data with generative AI capabilities in an enterprise context.

鈥溌槎乖� enabled the transformation by providing the technological foundation and expert support,鈥� said Carlos Aravechia, head of Data Design & Intelligence at Numen.

The success of the project has validated a broader conviction at Natura &Co: that generative AI, embedded directly in ERP workflows, can fundamentally reposition finance from a transactional function to a strategic business partner.

A blueprint for other businesses

The Natura &Co project demonstrates a pattern that other organizations can replicate, particularly those running 麻豆原创 S/4HANA. The combination of structured ERP data with the contextual reasoning capabilities of large language models creates a foundation for decision intelligence that goes well beyond traditional business intelligence tools.

The project was built within a six-month co-innovation sprint and went live in August 2025. It is currently in use across Natura &Co鈥檚 Equador operations.

Looking ahead, Natura &Co is already planning the next phase: integrating Joule Agents to further automate the extraction of standard analytical content and deepen the AI-driven optimization of financial processes.

鈥淭he success of this initiative validates the transformative potential of embedded AI within our ERP,鈥� Dias Garcia noted. 鈥淲e are now ready to move forward鈥攄eepening these insights and integrating the capability of Joule Agents to maximize the extraction of standard content and further optimize our business decisions.鈥�

For 麻豆原创 customers evaluating how to move from AI experimentation to AI in production, the Natura &Co project offers a concrete, replicable model: start with a high-value, well-defined business process, embed AI directly into existing workflows, and build in human oversight from the start.

West made a point that stayed with me. Using AI simply to generate more output is only the beginning. The more powerful use case is to treat AI as a collaborator in the thinking process: something that can challenge assumptions, ask questions we might not have asked, and help us see a problem from a different angle. In that role, AI is not replacing judgment, it is creating useful friction around it.

That shift matters because the work of building technology is changing. Developers are no longer just writing deterministic logic and controlling every outcome in advance. Increasingly, they are working with systems that are creative, probabilistic, and less predictable by design. That opens up huge possibilities, but it also raises the bar. Quality, guardrails, metrics, and trust become even more important when software starts to reason in ways that are not always fully scripted.

The advice West offered to developers was simple and probably more durable than any specific tool or framework: fall in love with the problem, not the solution. The technologies will change. The models will change. The implementations will change. But the ability to understand a problem deeply, stay curious, and apply AI with judgment will remain valuable.

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At a time when so much of the conversation around AI feels breathless, that feels like a good way to approach this moment: not with blind enthusiasm, and not with fear, but with curiosity and discipline. What are we trying to understand? What are we trying to improve? Where would better questions, better feedback, or better judgment make the biggest difference?

That is where AI starts to feel less like a wave we have to chase, and more like a capability we can shape with purpose. AI gives us new ways to build, learn, and decide. The question is not just how much more we can produce with it, but how much better our thinking can become when we use it well.

When suppliers send order confirmations as PDF files, even minor discrepancies in price, quantity, or delivery dates can trigger significant manual effort within procurement. For Lemvigh鈥慚眉ller, one of Denmark鈥檚 largest wholesalers within steel, plumbing, heating and electrical products, this has long been a familiar challenge, consuming substantial time and resources.

The company has now tackled the very point where earlier automation initiatives often stalled. With a new solution based on several specialized AI agents, developed on 麻豆原创 technology and implemented in close collaboration with NTT DATA Business Solutions, supplier PDF order confirmations can now be read, interpreted, compared, and processed automatically鈥攄irectly against 麻豆原创 systems.

鈥淲e have previously tried both RPA and traditional automation approaches without really achieving the desired effect. The key difference this time is that we broke the task down into multiple independent AI agents, each responsible for a specific part of the process. Together, they now handle what previously required manual review,鈥� says Frederik Aakerlund, IT director at Lemvigh鈥慚眉ller.

10 weeks from idea to AI agents in production

The project originated with an e-mail from Jess Frederiksen, an AI鈥憇avvy project manager in Lemvigh鈥慚眉ller鈥檚 Market and Procurement organization. After successfully matching an order confirmation with a purchase order using ChatGPT as an experiment, he approached the IT director to explore whether this could be turned into a fully integrated system solution.

From the initial tests to production deployment, the entire project took just 10 weeks. According to Lemvigh鈥慚眉ller, this short implementation timeline was critical in allowing the solution to demonstrate tangible business value quickly and build internal support.

鈥淭his was not a long-running project. In 10 weeks, we moved from idea to AI agents in production, already delivering measurable value to our procurement officers,鈥� Aakerlund says.

Over time, Lemvigh鈥慚眉ller expects the solution to free up resources equivalent to three to four full-time employees. These resources will instead be redeployed to higher-value activities, including handling the most complex and exception鈥慸riven orders.

鈥淭he objective is not to reduce headcount, but to use our expertise more effectively. The AI agents take care of routine tasks, enabling procurement officers to focus on cases where their experience genuinely matters,鈥� Aakerlund adds.

More than 100,000 order confirmations automated

Each year, Lemvigh鈥慚眉ller sends approximately 175,000 purchase orders to more than 2,000 suppliers. While part of this volume is handled in a structured manner via EDI, around 60% of supplier order confirmations are still received as unstructured documents.

With the coordinated AI agents in place, the company can now automatically identify delays, quantity changes, and price discrepancies鈥攁nd respond significantly faster.

鈥淧reviously, when order confirmations were handled manually, it could take hours or even days before changes were reflected across the organization. Today, the AI agents update the data almost immediately, allowing customers to receive a much more accurate picture of deliveries far sooner,鈥� says Klaus Heinemann, head of 麻豆原创 ERP at Lemvigh鈥慚眉ller, who led the development together with the project team. 鈥淚n addition, we now identify price discrepancies before the final invoice is issued, saving time both for us and for our suppliers.鈥�

Multiple AI agents orchestrated in a single workflow

The solution is built around three cooperating AI agents, each with a clearly defined role in the process. One agent handles incoming e-mails and attachments, a second extracts and structures data from PDF documents, and a third compares the extracted information against purchase orders in 麻豆原创 to determine whether there is a match or a deviation.

As a result, complex and unstructured supplier data can be processed in a unified, automated workflow without requiring procurement officers to open and manually review lengthy PDF files.

鈥淲hat makes this solution robust is the interaction between the agents. Each agent is highly specialized, but they are orchestrated in a way that ensures the process flows seamlessly from start to finish,鈥� Heinemann explains.

During the project, the importance of master data quality also became increasingly clear.

鈥淚n areas such as Incoterms and other master data, we identified improvements that need to be addressed. This has been an important learning not just for this initiative, but for our broader work with AI,鈥� he says.

While it is still too early to measure the full impact on customer experience, error rates, or claims, expectations are that faster and more precise handling of supplier confirmations will, over time, lead to fewer surprises and significantly improved delivery transparency. Internally, the solution has been met with strong interest and curiosity among employees.

鈥淧rocurement officers clearly recognize the value of being relieved from the most tedious routine work. This has sparked a constructive dialogue about how technology can best support their day鈥憈o鈥慸ay responsibilities,鈥� Heinemann says.

Business AI with a clear business outcome

According to Lemvigh鈥慚眉ller, the investment is expected to deliver a return within a relatively short timeframe.

鈥淲e are talking about quarters rather than years when it comes to ROI. That is why it was essential for us to get the solution into production quickly and focus on processes with a clear and measurable impact,鈥� Aakerlund says.

For 麻豆原创, the project serves as a concrete example of how artificial intelligence can be embedded directly into core business processes rather than remaining a disconnected experiment.

鈥淢any companies talk about AI agents primarily in terms of automation. Lemvigh鈥慚眉ller demonstrates that the real challenge鈥攁nd the real opportunity鈥攍ies in coordination,鈥� says David Pontoppidan, head of AI at 麻豆原创 for the Nordics and Baltics. 鈥淚t is the orchestration of three specialized agents directly within the core process that makes this solution robust. This is also where many multi鈥慳gent initiatives fail, not due to limitations of individual agents but because of insufficient coordination. Lemvigh鈥慚眉ller has succeeded by anchoring the solution in its 麻豆原创 landscape, where data, business rules, and governance frameworks are already firmly established.鈥�

He continues: 鈥淚nnovation is not about company size. Lemvigh鈥慚眉ller shows that a Danish organization with short decision paths and a pragmatic approach to technology can move faster than many large global enterprises that are still in the planning stage. Ten weeks from idea to production is far from the norm, but perhaps it should be.鈥�

Designed for operations and scalability

The solution was implemented in close collaboration with NTT DATA Business Solutions, which was responsible for making the solution production鈥憆eady and fully integrated into Lemvigh鈥慚眉ller鈥檚 麻豆原创 landscape.

鈥淏y distributing responsibilities across multiple AI agents, Lemvigh鈥慚眉ller has been able to automate a complex process without losing transparency or control. This has enabled a fast and secure transition from pilot to production and ensures a more robust solution that can easily be expanded as new requirements emerge,鈥� says Kristian Dahl, 麻豆原创 UX manager at NTT DATA Business Solutions.

According to Dahl, the modular, agent鈥慴ased architecture was a key enabler in moving efficiently from proof of concept to live operation.

First step in a broader AI agent strategy

Initially, the AI agents have been deployed for selected supplier inboxes and business areas. However, Lemvigh鈥慚眉ller already sees significant potential in applying the same agent鈥慴ased approach across additional administrative processes.

鈥淭his is the first AI agent solution we have put into production. The experience has given us the confidence to consider similar approaches across other areas, including invoice processing and order management,鈥� Aakerlund concludes.

Ellen Vig Nelausen is a Nordic Integrated Communications Expert at 麻豆原创.

AI infrastructure as strategic asset 

Although infrastructure ownership is just one element of digital sovereignty strategy, European politicians and policymakers have argued that without domestic data centers, Europe risks dependence on U.S. and Chinese providers for critical AI capabilities.

This concern is echoed by some industry leaders鈥攑articularly those in finance and regulated sectors鈥攚ho increasingly view AI infrastructure as a foundation of economic security. Specifically, they argue that sovereign data centers enable companies to comply with stringent European regulations on data protection and AI governance. They say that locally operated infrastructure ensures that data remains under European jurisdiction, reducing exposure to foreign legal regimes and enhancing trust among customers and regulators.

Security and compliance imperatives 

European leaders also frame AI infrastructure as a hedge against geopolitical risk. They argue that dependence on external providers introduces vulnerabilities, whether through legal exposure, supply chain disruptions, or political tensions.

As Christian Klein, CEO of 麻豆原创 SE, noted at the 麻豆原创 Sapphire Madrid event last month, many European customers operate in the public sector or other highly regulated industries. 鈥淕eopolitical risk is a growing concern,鈥� he said. 鈥淲hat if sanctions suddenly block data flows across borders? Or if the latest LLMs can鈥檛 be deployed in certain regions?鈥�

Christine Lagarde, president of the European Central Bank, also highlighted this concern in her November 2025 speech titled noting that Europe must 鈥渁void single points of failure鈥� in critical areas such as data centers and compute capacity.听听

Proponents of sovereign AI infrastructure also argue that it can stimulate broader economic growth. Data centers often anchor the ecosystems of startups, research institutions, and industrial applications, enabling Europe to capture more value from the AI stack.

From a technical standpoint, proximity also matters. Locally sited data centers reduce latency and improve performance for AI applications, particularly those requiring real-time processing or integration with industrial systems.

But despite these perceived advantages, many European business leaders have urged policymakers to take a more moderate, nuanced approach towards sovereign data. Their concerns are not about the need for data sovereignty itself, but about how it is implemented鈥攑articularly the push to rapidly build new, domestically controlled AI data centers. They emphasize that that data residency (location) is only one element of the four standard pillars of a sovereign data strategy, which also include legal sovereignty (jurisdictional control), operational sovereignty (independent operations), and technical sovereignty (data control).

In discussions with policymakers, European business leaders from diverse sectors have been warning that reducing reliance on U.S. technology too quickly is unrealistic. This reflects a structural reality: Europe remains deeply dependent on non-European providers for cloud infrastructure, chips, and AI platforms.

Research from Swiss cloud provider Proton suggests that around 75% of publicly listed European companies rely on U.S. tech services, (primarily Microsoft and Google) for critical infrastructure, including e-mail, cloud, and software. Therefore, attempting rapid substitution risks disrupting operations without delivering viable alternatives.

Barriers and concerns

Even the most ardent proponents of sovereign AI infrastructure acknowledge that there are major practical barriers to building massive AI data centers in Europe, including energy. AI data centers are extremely power-intensive, and Europe already faces grid constraints, high electricity prices, and long permitting timelines.

Without significant investment in energy systems, some European business leaders warn that new data center projects risk delays, cost overruns, or cancellation.

Another concern is that infrastructure-focused, sovereignty-driven policies may distort markets. Critics warn that infrastructure subsidies could flow to less competitive domestic providers resulting in slower innovation and the misallocation of capital resources to politically driven projects rather than economically viable ones.

In this view, sovereignty risks becoming industrial policy for its own sake, rather than a driver of efficiency or innovation. But perhaps the most significant critique is that the focus on infrastructure may distract from a more pressing issue: AI adoption.

Europe has historically lagged in deploying digital technologies. Some business leaders, including 麻豆原创鈥檚 Klein, argue that the priority should be accelerating AI use across industries and point out that infrastructure alone will not drive productivity gains. Over-emphasis on the infrastructure component of sovereignty could slow deployment through added complexity and cost. As Klein has noted, focusing primarily on infrastructure is a mistake if it is at the expense of developing AI applications and software.

Europe, he said recently, should prioritize 鈥渃ode over concrete.鈥� At the World Economic Forum in Davos earlier this year, senior executives from major European firms, including Capgemini and Ericsson, also warned against an overly protectionist approach. They argued that excluding or limiting global providers would raise prices, slow tech adoption, and reduce competitiveness.  

The business view 

From a business standpoint, AI is rapidly becoming a general-purpose technology, and the costs of AI infrastructure directly impacts productivity. If European AI infrastructure is more expensive, European companies risk falling behind global peers.

While data residency and the other elements of digital sovereignty are essential for some businesses operating in sensitive and highly regulated sectors, the sovereignty debate in Europe risks oversimplifying a fundamentally global industry. As Henna Virkkunen, the European Commission鈥檚 technology chief, noted: 鈥淣obody can be competitive alone.鈥�  

Indeed, since AI development depends on globally integrated supply chains, including semiconductors, software, and talent, fully localized infrastructure may be neither feasible nor desirable.

Rather than building duplicative infrastructure to support AI development, Europe鈥檚 real competitive advantage may lie in its treasure trove of operational data鈥攁 resource that is often difficult to access because of overly restrictive regulation and data access rules, prompting growing calls for reform from business leaders across Europe.

Easing and standardizing data access rules would help European businesses tap into this resource and compete more effectively with international rivals as they move into the next phase of AI enablement鈥攖he .

Over the past several years, procurement has steadily expanded its influence inside the enterprise. As supply chains faced unprecedented disruption, procurement leaders became trusted advisors to the C-suite on resilience, risk management, and sustainability. Their visibility increased and so did the expectations placed upon them.

Now, the playbook is being rewritten once again.

Research from the 2026 Economist Enterprise Report titled , sponsored by 麻豆原创, finds that financial performance has reemerged as the primary benchmark of procurement鈥檚 success. Drawing on a global survey of 2,648 C-suite executives, the report found that 54% cite cost control as procurement鈥檚 greatest contribution to the business, up from 43% just one year earlier. The findings point to a function that is increasingly stretched, yet positioned to deliver measurable business outcomes鈥攊f it can navigate a difficult balancing act.

Cost control returns to center stage

The shift is not surprising given the environment. Persistent inflation, tariff uncertainty, and continued investment in supply chain resilience have pushed cost management back to the top of the executive agenda. At the same time, companies are investing in dual sourcing, nearshoring, and inventory buffering to reduce exposure鈥攕trategies that strengthen resilience but often raise costs. Procurement is expected to offset those increases elsewhere.

What makes this moment particularly challenging is that procurement鈥檚 broader responsibilities have not diminished. Teams are still expected to manage geopolitical risk, advance sustainability, and support digital transformation. The mandate has expanded significantly, often without a corresponding increase in capacity, tools, or operating model support. Delivering savings, limiting cost increases, and managing input costs while fulfilling a growing strategic role is the defining tension facing procurement leaders today.

AI is becoming procurement’s digital imperative

Technology, and AI in particular, is increasingly seen as the key to resolving tension. In the Economist Enterprise study, 60% of executives identified digital transformation as procurement’s top strategic priority over the next 12 to 18 months, up sharply from 38% in 2025. More than half (56%) identified AI as the primary driver of that transformation.

The emergence of agentic AI is accelerating expectations further. More than half of executives are planning to implement or evaluate agentic AI capabilities within the next 12 to 18 months. Unlike earlier generations of AI that focused primarily on generating insights, agentic AI introduces the ability to execute workflows鈥攆rom guided buying experiences to automated purchase order creation鈥攅nabling procurement to move beyond recommendations and drive actions.

Even so, executive expectations remain grounded. Only 9% of survey respondents want AI to lead most procurement decisions within three years. Procurement鈥檚 highest-value work continues to rely on human judgment, strong supplier relationships, and the ability to navigate complex trade-offs. AI plays a critical role in strengthening these capabilities, but it does not replace them.

Realizing that potential, however, requires the right foundation. Connected data, clear governance, and close collaboration across procurement, finance, IT, and operations are prerequisites for generating AI outputs that are reliable and accountable.

Category management takes on greater importance

As procurement balances cost pressures with broader business priorities, category management is emerging as a critical discipline. The report found that category and demand management are expected to receive the second highest level of digital investment among procurement disciplines over the next three years, trailing only spend and performance analytics.

This reflects the growing complexity of procurement decisions. Category leaders are no longer simply awarding projects to the lowest-cost supplier. They are expected to weigh cost, risk, sustainability, and supplier performance simultaneously鈥攁 level of complexity that demands better analytics and faster insight-to-action capabilities.

That level of nuance can strengthen procurement鈥檚 impact, but it can also slow execution. More sophisticated category strategies require better data, sharper analytics, and faster insight-to-action capabilities. The report found that category strategy has become the third most common source of process delays, behind only contracting and sourcing.

Success increasingly depends not on collecting more data, but on turning data into confident decisions quickly. Organizations that close that gap will be better positioned to execute strategy, not simply develop it.

Procurement’s strategic value is being tested

Perhaps the most striking finding in the report is a growing confidence gap. While nearly three-quarters of executives still believe procurement collaborates effectively across the organization, that figure dropped from 90% in 2025 to 74% in 2026. Confidence in procurement鈥檚 role in shaping digital transformation strategy also declined meaningfully over the same period.

These numbers do not signal a retreat from procurement鈥檚 strategic importance. Rather, they reflect a broader shift in how enterprise decisions are being evaluated. As AI democratizes access to data across the organization, more stakeholders have the information to question decisions and demand clearer evidence of value.

Procurement leaders are now expected to control costs, manage risk, strengthen resilience, and help guide AI adoption, often simultaneously and without additional resources. The question is no longer whether procurement belongs at the leadership table. It is whether procurement can consistently deliver the value expected of it across an expanding and increasingly complex set of priorities.

Procurement鈥檚 next chapter

The Economist Enterprise findings paint a picture of a function at a pivotal moment. Cost savings has returned as the primary mandate, yet procurement is still expected to manage risk, protect supply continuity, and lead digital transformation.

Meeting those expectations will require more than layering AI onto existing processes. It demands connected data foundations that make AI outputs trustworthy, visibility across suppliers and spending, and technology that enables teams to move from reactive decision-making to proactive intervention.

The leaders who will define procurement鈥檚 next chapter are those who can turn AI, data, and connected processes into faster decisions, stronger resilience, and measurable business impact.

Gordon Donovan is vice president of Research for Procurement and External Workforce at 麻豆原创.

Travelers nowadays have very strict requirements. From booking a ticket to arriving at a destination, they expect fast, convenient, and prompt assistance when they face issues along the way. No matter how perfectly a system is designed to meet needs, there will always be situations that can鈥檛 be avoided. Delays, confusing booking systems, long customer service wait times, and much more create frustration for travelers.

AI is reshaping how the travel and transportation industry is doing business. It delivers more avenues to provide customer care aside from the typical communication channels such as e-mail, short messaging services, and social media. AI provides smarter, faster, and more personalized customer experiences, leading to happier customers and therefore growth in revenue for the business. AI is no longer optional, it is now becoming a necessity.

Beyond tickets and timetables: how AI orchestrates the customer journey

Previously, travelers preferred travel agents over booking apps, relied on printed tickets, and valued personal service and human interaction. However, mobile apps for bookings, check-ins, and payments are now widely used. Travelers also expect real-time updates and personalized recommendations that provide seamless, end-to-end experiences.

麻豆原创 delivers an ecosystem that can provide the tools needed to meet these expectations. Using 麻豆原创 Service Cloud, organizations can manage cases, complaints, and information requests efficiently. AI can respond within seconds, unlike traditional customer service processes that rely on manual handling of support tickets. AI-powered chatbots can also manage a high volume of customer conversations simultaneously. Here is an example integration strategy:

Intelligent selling services for 麻豆原创 Commerce Cloud

Intelligent selling services for 麻豆原创 Commerce Cloud are AI-powered services that leverage machine learning and artificial intelligence to help deliver personalized customer experiences and optimize booking strategies. These services help analyze customer booking patterns, provide contextual data across customer touchpoints, and offer recommendations that can lead to increases in revenue.

Travel accelerator

The travel accelerator for the 麻豆原创 Commerce solution is an industry-specific solution designed to enable travel companies to deliver omnichannel digital traveler engagement through 麻豆原创 Commerce Cloud. For customers that already have an existing 麻豆原创 Commerce Cloud solution, they can use the travel accelerator to help tailor it for travel business demand. It can provide real-time information to offer personalized customer experiences and reinforce customer loyalty.

Loyalty management program through integration

Organizations may need a system to reward customers for coming back, like earning points, perks, or special treatment when you repeatedly book with the same travel company. For this, an integration to a loyalty management program, either 麻豆原创 Customer Loyalty Management or a third-party solution, can be used.

The way forward

can help you achieve your business goals. As a starting point, we can create a service engagement plan that provides a tailored approach to meeting your KPIs. During this phase, we also deliver sessions to help you set up 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, and 麻豆原创 Commerce Cloud while working to ensure that travel and transportation industry best practices are followed.

With AI capabilities available across every solution, you can now categorize your customer base based on travel behaviors and patterns, as well as perform sentiment analysis on customer reviews and support tickets. The Advanced Success Plan can serve as a strategic partner in helping achieve AI objectives. Our experts, backed by deep industry knowledge, can provide guidance on the most effective path forward.

To deliver services that are aligned with each customer’s specific goals, we have organized our offerings into four phases: implementation, pre-go-live, post-go-live, and continuous improvement.

Implementation

During the implementation phase, our focus is on providing adoption guidance to help set up the solutions, from front-end applications to back-end systems. We work alongside the team to establish core capabilities needed for a successful implementation and to help ensure the solution is aligned with business requirements.

This includes, but is not limited to, application user management, key user extensibility, the 麻豆原创 CX AI Toolkit, integrations, security considerations, and other essential platform capabilities. Our goal is to help build a solid foundation that supports scalability, maintainability, and future growth while enabling teams to get the most value from the platform.

Pre-go-live

In the pre-go-live phase, our focus is to validate and safeguard the solutions that have been built throughout the implementation. The goal is to make sure systems are configured correctly, performing as expected, and ready at go-live.

This includes conducting detailed reviews of business configuration settings, evaluating system performance, validating integrations, reviewing security and user access setups, and assessing analytics and reporting capabilities. We also help identify potential risks, gaps, or areas for optimization before launch, working to ensure issues are addressed proactively.

In addition, we work with teams to confirm readiness across key functional and technical areas, helping ensure that testing has been completed successfully, critical business scenarios have been validated, and the solution is aligned with operational requirements. Performing an adoption checkpoint during this phase helps reduce risk, improve system stability, and support a smoother go-live experience.

Post-go-live

During the post-go-live phase, we work closely with the team to help ensure that recommendations and best practices identified throughout the implementation have been properly configured and are delivering the intended results.

As users begin working in the production environment, new questions, opportunities for optimization, and minor challenges often emerge. During this stage, we provide continued guidance and support to help address those items, whether they are related to business processes, system configuration, integrations, extensibility, analytics, or overall solution adoption.

Our functional and technical experts remain available to review issues, provide recommendations, and help navigate any areas that require additional attention. We also help identify opportunities for further improvements and knowledge transfer, working to ensure the organization is well-positioned to maintain, enhance, and scale the solution over time.

Continuous improvement

Finally, as part of the continuous improvement phase, we help stakeholders remain informed about new innovations and enhancements introduced through 麻豆原创 release cycles. By staying up-to-date with the latest capabilities, the team can continue to maximize the value of the solutions and drive ongoing business success.

Tara Tracey is global product owner of the Advanced Success Plan at 麻豆原创.
Geoffrey Arado is product manager for 麻豆原创 Customer Experience.

They see a personalized ad, explore a product through an AI assistant, respond to an offer in their inbox, and open your app expecting everything to simply work. They expect relevance, continuity, and immediacy across every interaction in their entire journey.

Instead, they hit friction:

• They鈥檙e asked to repeat information they already shared.
• Promotions don鈥檛 reflect inventory availability.
• Fulfillment updates arrive late or not at all.
• And when support is needed, no one has the complete picture.

These are not isolated breakdowns. They are systemic customer experience failures.

In the era of agentic AI, those moments compound quickly. Customers now assume brands understand who they are, what they need, and what is happening in real time, and they expect businesses to act on that intelligence instantly across the entire customer journey.

The agentic era is accelerating this shift dramatically.

This goes beyond surfacing insights or recommendations. Agentic AI systems are increasingly capable of planning, reasoning, decision-making, and coordinated action across networks of agents. AI is fundamentally reshaping how customer experiences are created, delivered, and optimized in real time.

But it also exposes a fundamental truth: when AI moves faster than your data, systems, and processes, it reveals everything that鈥檚 broken.

That tension鈥攂etween rising customer expectations and disconnected CX reality鈥攊s exactly what 麻豆原创 and Google Cloud are solving together with .

The customer experience reality: ambition outpacing execution

Most organizations want to deliver seamless, connected experiences, but they struggle to operationalize those moments.

According to 麻豆原创 research, 78% of businesses say AI will be essential for retaining customers in 2026. Yet fewer than two in five share their customer data across CX (37%) or CRM (39%) platforms.

This is an enterprise operations problem, and its impact is felt across the entire CX business. And when these touchpoints are disconnected, the customer feels it first. And by the time the company feels that friction, it may be too late to win that customer back.

A new model for customer experience built on trusted enterprise data

麻豆原创 Customer Experience plays a key role in the expansion of 麻豆原创 and Google Cloud’s partnership to enable a fundamentally new approach to CX鈥攐ne that connects data, AI, engagement, and commerce in real time.

鈥淎gentic customer experience starts deeper in the stack than the customer ever sees鈥攊n the quality of the data, the way processes run, and the strength of the platform underneath. With 麻豆原创 and Google Cloud, we鈥檙e building that foundation together, so AI moves beyond insight to action. This delivers continuous, intelligent experiences with the control, context, and execution required at enterprise scale.鈥�

Muhammad Alam, Member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering

麻豆原创 Commerce Cloud endorses Universal Commerce Protocol: powering the next era of agentic commerce

As digital commerce enters the age of AI-driven experiences, a new standard is emerging to reshape how consumers discover and purchase products.

The Universal Commerce Protocol is an open standard designed to enable AI agents to manage the entire shopping journey, from product discovery to checkout and even post-purchase support.

Unlike traditional commerce integrations that rely on fragmented APIs and bespoke connections, UCP creates a shared language for retailers, payment providers, and AI systems. This allows intelligent agents to interact directly with commerce platforms, dramatically simplifying how products are surfaced, evaluated, and purchased.

In essence, UCP transforms commerce from a series of disconnected steps into a unified, agent-driven experience.

, long known for powering enterprise-grade digital commerce for global brands, is now aligning with this new paradigm. As part of the growing UCP ecosystem, 麻豆原创 plans to work with Google to enable its customers to participate in AI-native commerce experiences that extend beyond traditional storefronts and help its merchants鈥� products be discovered and purchased across the Gemini app and Google Search, including AI Mode.

鈥淥ur goal with UCP is to build an open, trusted standard for the future of AI-driven commerce,鈥� said Ashish Gupta, VP/GM, merchant shopping at Google. 鈥淗aving a leader like 麻豆原创 endorse the protocol is critical as we work toward bringing seamless, secure agentic commerce to everyone.鈥�

For merchants, this means dramatically lower integration costs, faster onboarding into new AI-driven channels, and the ability to reach new customers beyond their storefront experiences.

In addition, 麻豆原创 Commerce Cloud will leverage Google Gemini capabilities to power a Shopping Assistant that brands can deploy directly to their own customers. This enables organizations to offer a real-time, AI-driven shopping experience across their digital touchpoints. The Shopping Assistant engages shoppers through chat, voice, and text to help them discover products, answer questions, and turn intent into transactions.

Unlike traditional storefront interactions, the Shopping Assistant can create a persistent, conversational experience that follows the customer across the shopping journey, continuously refining recommendations and guiding decisions in real time.

It can also curate creative ideas鈥攕uch as themed outfits or complete event concepts鈥攂y intelligently combining products based on customer requests. By unifying behavioral signals, real-time inventory, and promotional intelligence, it can increase conversion rates, improve average order value, and ensure every recommendation is both relevant and fulfillable.

麻豆原创 Engagement Cloud and Google Cloud: how agents work together for marketing

This new expanded partnership comes alongside another historic milestone for 麻豆原创 and Google Cloud, announced earlier this year. A fundamentally different approach to marketing execution is now offering marketers a new model for engagement that is built on trusted enterprise data.

By combining customer data and real-time signals like inventory, orders, and fulfillment status with operational truth, marketing teams can now build, launch, and optimize personalized customer engagements, grounded in business context and executed at scale through an autonomous multi-agent framework.

At the heart of this partnership:

• 麻豆原创 Business Data Cloud听(麻豆原创 BDC) connects semantically rich data across the enterprise with AI to help enable real-time insights and drive personalized interactions grounded in business context. This includes 麻豆原创 Business Data Cloud Connect for Google BigQuery with bidirectional, zero-copy data access and built-in enterprise-grade governance and security.

• Google BigQuery听unlocks real-time signals across the Google ecosystem, such as geolocation, weather, ad engagement, and rich analytics, for AI-driven segmentation, personalization, activation and analytics.

• 麻豆原创 Customer Experience听solutions can provide the real-time behavioral context: customer profiles, transactions, orders, service interactions, and consented engagement data.

• 麻豆原创 Engagement Cloud can activate enterprise data, AI insights, and predictions through intuitive tools and AI agents to help securely orchestrate real-time, personalized interactions across the entire customer life cycle.

Why this partnership matters

The collaboration between 麻豆原创 and Google reflects a broader shift in how commerce and marketing teams operate.

For commerce leaders:

• From search to agents: Consumers are no longer just searching. AI agents are acting on their behalf, making decisions, and completing purchases.
• From channels to ecosystems: Commerce is moving beyond owned channels into distributed, AI-powered environments like search, assistants, and chat interfaces.
• From integration to interoperability: Open standards like UCP eliminate the need for one-off integrations, enabling scalable participation in the AI economy.

With UCP, AI agents can seamlessly access product catalogs, manage carts, process payments, and handle post-purchase workflows, all without forcing retailers to rebuild their infrastructure.

For marketing leaders:

• From prompt to performance: Agentic intelligence becomes operational where business goals, enterprise signals, and marketing processes direct AI agents, translating into real customer interactions and automated lifecycle journeys.
• From manual to generative: Advanced generative capabilities powered by Google Gemini models, such as Nano Banana 2, introduce new agentic skills that help marketing teams dynamically generate messaging, imagery, and campaign variations.
• From dark data to unified data context: With every interaction grounded in business context and continuous engagement signals, messages become truly dynamic. Text messages can turn into immersive conversations with Google Rich Communication Services (RCS) and advertising creative and offers can continuously evolve.

Agents collaborate across 麻豆原创 and Google Cloud to personalize, activate, and continuously optimize campaigns in real time across engagement channels and media networks.

鈥淲ith this partnership, 麻豆原创 and Google Cloud bring together connected AI and a unified data foundation to create real-time understanding of the customer and business context. This enables organizations and CX teams to move from fragmented interactions to continuous, intelligent execution鈥攅mbedding AI into end-to-end processes and unlocking meaningful gains in productivity, speed, and business impact.鈥�

Jan Gilg, Global President Customer Success & Americas, Member of the Extended Board

Unlocking new value for enterprises

For 麻豆原创 Commerce Cloud customers, this partnership can unlock several strategic advantages:

• Increased discoverability in AI-driven shopping experiences
• Faster time-to-market through standardized integrations
• Ownership of customer relationships, even in third-party AI environments
• Future-proof architecture aligned with emerging commerce standards

As AI continues to compress the distance between intent and transaction, accessibility to agents becomes just as important as visibility in search results. In this new model, success in commerce is no longer defined by storefront experience alone鈥攊t鈥檚 defined by how effectively your products, data, and systems can be accessed, interpreted, and transacted on by AI agents.

For customers, this partnership unveils a new network of interoperable AI agents, grounded in enterprise data and shared context听across 麻豆原创 and Google. Organizations can achieve measurable outcomes, including:

• Faster speed-to-market through autonomous campaign and content generation
• Lower operational overhead by eliminating manual execution steps
• Always鈥憃n optimization that continuously improves performance
• Higher ROI through relevant, timely, and consistent engagement at scale

Marketers can spend less time managing workflows and more time shaping strategy, creative direction, and customer value.

鈥淲hat matters is delivering real value to our customers. As customer experience becomes more agentic, organizations need to move faster, stay connected, and operate consistently across every interaction. By bringing commerce, marketing, and service together, we help our customers reduce complexity, respond more quickly, and deliver more relevant experiences that strengthen engagement and drive sustainable growth.鈥�

Manos Raptopoulos, Global President Customer Success Europe, APAC, Middle East & Africa and Member of the Extended Board 麻豆原创 SE

The future of AI-driven commerce and marketing: what this means for your CX strategy

The partnership between 麻豆原创 and Google for marketing and commerce听marks a foundational shift toward what many are calling an agentic revolution鈥攁 world where AI doesn鈥檛 just assist CX teams and shoppers but actively participates in the buying process and shapes their customer experience.

For enterprise CX leaders, the message is clear: success in this new era will depend on how well your commerce and marketing platform can communicate with AI agents. With 麻豆原创 and Google Cloud, 麻豆原创 Customer Experience is positioning itself鈥攁nd its customers鈥攖o thrive in that future.

These announcements bring AI-led transformation to focus and are very relevant as we approach the end of mainstream maintenance for 麻豆原创 Solution Manager on December 31, 2027*, many customers are transitioning to 麻豆原创 Cloud ALM to stay competitive and future-ready. 麻豆原创 recommends that customers complete the transition to 麻豆原创 Cloud ALM before this date.

We are proud that 麻豆原创 Solution Manager has served thousands of customers exceptionally well over two decades as a key element of 麻豆原创鈥檚 support offerings, providing the governance, monitoring, and lifecycle management capabilities needed to support mission-critical landscapes.

Twenty-five years in, the business environment that it was built for has significantly evolved to one where enterprises innovate continuously, scale globally, adopt AI, maintain a clean core, and deliver business outcomes at unprecedented speed. Market expectations have changed, technology stacks are running on cloud-ready architecture, and the revenue potential of businesses has exponentially grown. These realities require a fundamentally different approach and functional scope for application lifecycle management. 麻豆原创 Cloud ALM was designed with exactly these factors in mind. As a cloud-native solution coming with 麻豆原创 Enterprise Support, or any cloud subscription from 麻豆原创, it can close the gaps that modern organizations face in an increasingly fast-moving digital landscape.

All the information required for the transition from 麻豆原创 Solution Manager to 麻豆原创 Cloud ALM is available on the page. You can access essential tools for a seamless transition as well as recommendations based on your current landscape, project plans, and operational needs. You can also find focused .

Take action now:

• Request your 麻豆原创 Cloud ALM tenant via听.听
• Join the听, a 90-minute onboarding designed to help you get up and running quickly.听
• Run the听听to get an exact understanding of your current 麻豆原创 Solution Manager footprint and receive guidance for your transition journey.听
• Expand your knowledge through the听听and join our听.听
• Subscribe to the monthly听听to stay updated on innovations, tips, and use cases.听

While 麻豆原创 Solution Manager’s end of mainstream maintenance in itself is a call to action, it isn’t the primary business case. The real need for transitioning lies in the value that 麻豆原创 Cloud ALM delivers: accelerated implementations, AI-powered and autonomous operations, continuous feature innovation, lower TCO, and a platform purpose-built for modern, cloud-first landscapes. As every digital touchpoint around you is being modernized and optimized for value, your ALM landscape should not be an exception.

Stefan Steinle is executive vice president and head of Global Customer Support at 麻豆原创.

*Details related to maintenance options are covered in 麻豆原创 Notes 52505 and 3255311.

At the same time, the research also makes clear that ambition and vision are not enough.

The orchestration gap

Nearly half of the executives surveyed by IDC recognize the substantial benefits of end-to-end orchestration鈥攂ut many have yet to take decisive action. The true challenge lies not in understanding its value, but in bridging the gap between strategic intent and effective execution.

IDC surveyed C-level leaders across industries and regions. The message was consistent. Leaders understand the destination, but they want clearer guidance on the blueprint to execution. As the IDC white paper said, 鈥淎 majority of executives see value in an orchestrated supply chain and believe they must move in that direction, yet the precise steps are unclear, and they are not sure where best to start鈥攖hey need help.鈥�

That tension is familiar. Many organizations have made progress in operational silos, but far fewer have connected design, planning, procurement, manufacturing, logistics, and service into a truly coordinated operating model.

What orchestration really means

An orchestrated supply chain connects people, processes, and technology to provide agility and deliver continuous improvement, despite persistent disruption. Agentic AI is fundamentally reshaping orchestration, enabling systems to analyze, decide, and coordinate across functions in real time. From sourcing and procurement to planning, manufacturing, logistics, and delivery, the focus shifts from optimizing individual functions to achieving enterprise-wide alignment.

Traditional linear supply chain models were built for a more predictable world. Today鈥檚 reality is different. Geopolitical uncertainty, AI-driven disruption, climate pressures, regulatory complexity, and rising customer expectations are constant. Decisions made in one area now ripple quickly across the rest of the supply chain.

Orchestration addresses this reality by establishing a shared foundation of contextually relevant information, designing processes to operate together, and enabling systems that support end-to-end decision-making and execution. Technology plays a critical role, but orchestration ultimately depends on organizational alignment: clear roles, shared metrics, and coordinated processes. Instead of optimizing planning or execution in isolation, orchestration evaluates trade-offs based on their impact on the entire supply chain and the broader business.

Different leaders, shared outcomes

The IDC research also highlights how perspectives on orchestration vary by role. COOs focus on enterprise performance, CSCOs balance transformation with operational demands, and CPOs emphasize cost and risk exposure in direct materials, including mitigation strategies. Orchestration must deliver value across these perspectives while maintaining a unified, end-to-end view.

This diversity of perspective reinforces why orchestration matters. Success comes from respecting functional priorities without allowing silos to drive disconnected decisions, enabling coordinated decision-making that optimizes the whole, not just the parts.

Efficiency and agility, not trade-offs

Many executives perceive a trade-off between efficiency and preparedness, but orchestration changes the equation. With integrated data, shared context, and agile tools, companies can respond faster, reduce disruption response times, and make informed trade-offs鈥攄emonstrating that agility and efficiency can reinforce each other rather than compete.

As one procurement leader told in the IDC white paper, 鈥淲e have to be able to be both resilient and efficient, or at least be able to make informed trade-offs quickly. Right now, we have neither the necessary supply chain integration nor agile enough tools to be able to do that.鈥� Without integrated supply chains and shared context, that balance is difficult to achieve. When companies can identify issues earlier and respond faster, recovery times shrink, translating into lower costs, less expediting, and more reliable customer service.

The role of agentic AI

Agentic AI is emerging as a practical, transformational path to supply chain orchestration. AI-driven agents can monitor signals, evaluate scenarios, and recommend or initiate actions within defined guardrails. Across 麻豆原创 customer environments, AI delivers value in supplier onboarding, predictive maintenance, and rapid rebalancing of inventory or capacity. Leaders generally prefer AI as an advisor rather than a fully autonomous decision-maker, reflecting the continued importance of human judgment, accountability, and experience. Orchestration works best when AI strengthens decision-making while keeping people firmly in the loop.

Data, platforms, and the role of 麻豆原创 Supply Chain Management

Effective orchestration requires more than connectivity, it depends on harmonized data, coordinated processes, and contextual intelligence embedded where work happens. As supply chains extend across multi-tier supplier networks, logistics partners, and service providers, the challenge is no longer access to data, but making data usable, contextual, and actionable at scale.

The IDC research underscores that true end-to-end orchestration must span both internal operations and external ecosystems. Much of the most critical information鈥攔isk signals, capacity constraints, execution status鈥攍ives outside the enterprise. Without a common data foundation, organizations struggle to move from insight to action.

This is where plays a distinct role. 麻豆原创 brings together an end-to-end portfolio of supply chain applications, deeply integrated with ERP and line-of-business systems, and connected externally through . Planning, sourcing and procurement, manufacturing, logistics, and service operate as a coordinated system rather than isolated domains.

At the data layer, provides a normalized foundation that can harmonize operational, transactional, and network data. This shared context provides visibility, analytics, and AI. On top of it, embedded and extensible AI鈥攊ncluding agentic AI and Joule鈥攕upports orchestrated decision-making, helping to accelerate time to decision and time to recovery while keeping people engaged and in control.

Turning priority into practice

The move toward orchestrated supply chains is well underway, but progress remains uneven. Only a minority of organizations consider themselves close to full, end-to-end orchestration. Technology alone is not the constraint. Data readiness, clarity of outcomes, organizational alignment, and change management matter just as much.

Leading organizations start with clear objectives, invest in people alongside platforms, and build pragmatic road maps that prioritize time to value. Orchestration is not a single project. It is a progression that evolves as capabilities mature.

At 麻豆原创, our focus is on making orchestration practical and accessible. Through 麻豆原创 Supply Chain Management solutions and 麻豆原创 Business Network, we help organizations align teams, connect processes, integrate partners, and embed AI into core supply chain activities鈥攅nabling better decisions, faster execution, and sustained enterprise impact in a constantly changing world.

Hagen Heubach is chief marketing officer for Supply Chain Management at 麻豆原创.

IDC White Paper, sponsored by 麻豆原创, Orchestrating the End-to-End Supply Chain: Strategic Priority, Practical Reality, #US54385326-WP, April 2026

Fewer opportunities for early talent 

The job marketplace has contracted significantly for those with less than five years of professional experience. Research published by 麻豆原创 shows that openings in the 10 most common entry-level job titles declined by 35% in just one year, from 2024 to 2025.*

Budget constraints, hiring freezes, and uncertainty around the ROI of early talent as AI increasingly takes on routine and manual tasks are among the reasons cited by HR leaders today.  

Applications skyrocket  

AI is also having a major impact on the recruitment process. With such limited opportunities available, more than half of early talent candidates use AI to help them land a job鈥攁 process that now takes eight months on average and involves more than 300 job applications.*

HR is feeling the pressure managing the candidate pipeline, with听the number of applicants per early talent job opening doubling since 2021.听A听high volume of candidates听are听submitting听AI-generated r茅sum茅s and applications, and it鈥檚 becoming much harder to detect high-fidelity signals around skills, fit, and potential.听

It鈥檚听a painful听scenario for everyone involved. Fragile and unsustainable. And the听implications听will be听profound for enterprises that听don鈥檛听act quickly.听听

HR leaders voice concerns  

Playing out the current听trajectory听to its natural conclusion, what happens when all the听fresh talent eventually dries up? Already,听HR leaders听are alarmed by this risk.听鈥淯ltimately, if we stop investing in early talent, we will wind up eliminating our talent pipeline,鈥� one听global head of early talent听programs at a high-tech organization听told researchers.*

A senior HR director at a high-tech organization commented: 鈥淚f we continue down this path and don鈥檛 provide a way for early talent to get started, it鈥檚 going to lead to massive skill shortages in the future.鈥� 

Widespread reductions in early talent hiring will lead to skills gaps that听will听prove expensive to remedy. Organizations will struggle to build company capabilities,听retain听knowledge, and develop future leaders.听听

But by far the most common concern from leaders was around not seeing early talent as AI-native. If the AI capabilities of this cohort are overlooked, companies may miss out on a key opportunity to scale AI innovation and adoption across the business.  

What鈥檚 the answer?  

Today,听there鈥檚听an opportunity for HR leaders to be more intentional听and strategic, to reimagine听their approach听to early talent from the ground up. With the right early talent strategy, organizations can gain a competitive advantage.

Here are three steps to consider. 

Step 1: Rethink entry-level roles 

Traditionally, junior employees have mainly been given routine, repetitive tasks. Combined with frustratingly slow career progression, the result is eroding morale and commitment.  

This听approach must evolve.听The nature of work is changing rapidly,听and early talent no longer need to take on those routine tasks. These employees need the opportunity to develop at speed and to be supported in performing听work that听meaningfully addresses听business challenges.听听

HR has the chance to reshape entry-level positions,听to provide support, guidance, and tools to enable听junior staff听to contribute听in听more听impactful听ways. This may involve them听working听with proper guidance听to support听more critical听projects, interacting with customers,听and听even听owning some tasks end to end.听

This approach not only enables early talent to contribute more positively to the business at an earlier stage, but when combined with clear goals, regular feedback loops, and occasional coaching, it also fosters greater engagement and commitment. 

Step 2: Support your strategy with technology 

Hiring and developing early talent have become more complex鈥攆rom deciphering AI-generated applications, to redesigning roles and meeting their aspirations in a fast-changing business context. And with the nature of early talent work shifting, leaders need tools to help understand the new capabilities that will predict long-term success and demonstrate the value of early talent initiatives.  

Here鈥檚 where technology can help. During the hiring process,听technology听can help听employers听see beyond the听noise of听AI听applications听and rediscover the听meaningful听signals听they听need to create candidate shortlists and strengthen hiring decisions. Meanwhile, technology can also help to maintain engagement with other high-potential听candidates听who applied鈥攆or when the next opportunities arise.听听

Once early talent begin work, today鈥檚 technology can help you track their participation in early talent programs and progression towards their goals. It also helps facilitate individualized learning opportunities and demonstrate the ROI of your early talent investments. For research-based recommendations on the role of technology in early talent selection and development, check out this . 

Step 3: Reframe the business case for early talent 

As the nature of early talent work is changing alongside the technology used to support them, HR leaders agree that the old business case for early talent investments needs to be reimagined. Many organizations are focused on mitigating critical skill gaps and engaging in large-scale AI transformations. While early talent lack experience, they are eager to engage in continuous learning and understand how to work effectively alongside AI. 

Research also reveals that鈥攁s they work alongside modern tools and technologies鈥攅arly talent can contribute to high-value, meaningful work much faster than in the past.  

A modern early talent business case is one that involves focusing on faster time to meaningful work, reducing critical skill gaps, and leveraging the AI-native capabilities of today鈥檚 entry-level workers.  

Build your early talent strategy 

Will HR leaders watch on as a generation of AI-savvy talent remains underused, or act now and build the skills pipelines necessary for a future-ready workforce? 

Get further insights on this topic by reading our report, 鈥�.鈥�&苍产蝉辫;痴颈蝉颈迟&苍产蝉辫;辞耻谤&苍产蝉辫; to stay tuned for when phase two of this research gets published later this year. 

Dr. Autumn D. Krauss is chief scientist at 麻豆原创 SuccessFactors.

*, 麻豆原创, 2026. 

This recognition reflects 麻豆原创鈥檚 broader vision for AI-driven, skills-based workforce orchestration, where hiring is no longer a standalone process but part of a continuous, intelligent system connecting people, skills, and business outcomes. With innovations across 麻豆原创 SuccessFactors solutions and the addition of SmartRecruiters to the portfolio in September 2025, 麻豆原创 is enabling organizations to move beyond transactional recruiting toward agentic, autonomous talent strategies that continuously adapt to change.

SmartRecruiters for 麻豆原创 SuccessFactors: AI at the center of hiring

combines a modern, AI鈥憂ative recruiting platform with 麻豆原创鈥檚 core HCM to help create a more connected and intelligent hiring experience at global scale. Hiring becomes part of an integrated flow of workforce decisions鈥攍inked directly to skills, planning, mobility, and long-term business strategy.

With embedded AI across the hiring lifecycle, organizations can:

• Deliver intuitive, consumer鈥慻rade candidate experiences at scale, from first interaction through new hire onboarding.
• Empower recruiters and hiring managers with AI鈥慹nabled insights and automation directly within daily workflows.
• Strengthen hiring decisions by connecting recruiting data to skills, roles, and workforce plans.
• Scale globally with confidence, balancing centralized governance with local market flexibility.

SmartRecruiters鈥� AI hiring companion, Winston, works alongside to help automate repetitive tasks, surface top candidates, and explain recommendations transparently, which allows recruiters to focus on higher鈥憊alue decision鈥憁aking while moving faster and with greater confidence.

New capabilities, including agentic interviewing and real鈥憈ime candidate engagement, are helping organizations accelerate hiring while improving candidate experience at scale. For example, early adopters of AI鈥慸riven screening have seen up to a in time鈥憈o鈥慸ecision, demonstrating how AI can streamline evaluation without sacrificing quality.

At the same time, innovations like agentic CRM are helping organizations activate existing talent pools鈥攕urfacing, ranking, and re鈥慹ngaging candidates automatically鈥攖urning recruiting from a reactive process into a continuous, dynamic pipeline.

From recruiting to Autonomous HCM

SmartRecruiters plays a key role in 麻豆原创鈥檚 broader shift toward Autonomous HCM鈥攚here AI-driven agents can continuously orchestrate workforce decisions across hiring, planning, development, and mobility.

Rather than treating hiring as an isolated function, 麻豆原创 is embedding AI across 麻豆原创 SuccessFactors solutions to create a unified, skills-based system of action. Workforce planning, learning, and talent acquisition are directly connected, enabling organizations to align skills with business and financial priorities in real time.

At , this vision came to life through innovations that increase agility and responsiveness. Intelligent agents can identify skill gaps, recommend hiring or redeployment strategies, and take action鈥攖urning talent management into a dynamic, continuously optimized system.

Within this model, hiring becomes one of several coordinated levers in an adaptive workforce strategy, ensuring every decision is guided by real-time data, skills intelligence, and evolving business needs.

Customer impact: AI鈥慸riven hiring in action

Organizations across industries are already seeing the impact of AI鈥慸riven, connected hiring.

Retailer selected SmartRecruiters to improve the candidate experience and reduce drop-off in a high-volume hiring environment. Today, the company processes 50,000 applications each month, reducing the application processing time from 28 days to just four and cutting time-to-fill from 56 days to just 20鈥攄emonstrating how speed and experience directly improve hiring outcomes.

Global real estate services firm implemented SmartRecruiters to increase visibility into recruitment activity and create a more consistent experience across its EMEA business. As a result, Colliers achieved a direct hiring rate above 80%, reduced agency reliance to roughly 7% of hires, and improved first鈥憏ear retention by 25%, showing how connected hiring can lower costs while strengthening long鈥憈erm talent outcomes.

and discover how AI鈥慸riven, connected hiring can help turn talent acquisition into a true strategic advantage.

About the Fosway 9-Grid™
Fosway Group is Europe鈥檚 #1 HR industry analyst. The Fosway 9-Grid™ provides a unique assessment of the principal talent supply options available to organizations in EMEA. The analysis is based on extensive independent research and insights from Fosway鈥檚 Corporate Research Network of over 250 organizations, including BP, HSBC, PwC, Sanofi, Shell, and Vodafone. Visit the Fosway website at www.fosway.com for more information on Fosway Group鈥檚 research and services.

Across industries, customers are moving from pilots to production. They are investing in systems that can run pricing, orders, fulfillment, and service end-to-end, with AI actively driving decisions and outcomes. They are not looking for more tools. They are looking for results.

This is where 麻豆原创 has a clear point of view. Customer experience (CX) only works when it is connected directly to execution.

麻豆原创 Customer Experience is integrated with 麻豆原创 Cloud ERP across pricing, order management, fulfillment, billing, and service. AI operates inside these processes using real business data. That means every customer interaction can reflect what the business can deliver.

This is the shift now underway, and it is creating immediate opportunities for partners.

麻豆原创 partners are the ones who bring this to life. They take product capabilities and turn them into working solutions that improve conversion, increase fulfillment accuracy, and reduce service cost.

The next wave of growth will be led by partners who move quickly and build on this foundation.

Customer experience is now measured by what gets done

Customer experience is no longer judged by engagement metrics alone. It is judged by outcomes. Customers expect:

• Accurate pricing at the moment of purchase
• Real product availability, not estimates
• Orders that are fulfilled as promised
• Service that resolves issues without repetition or delay

When these things work, the experience works. When they fail, the problem is immediately visible.

AI is increasing the speed of every interaction. It is also exposing execution gaps faster than ever before. If pricing, inventory, or order data are inconsistent, customers see it instantly.

This is why customer experience and execution can no longer be separated.

AI is now driving actions, not just insights

AI is already acting inside key business processes. As seen in the , in 麻豆原创 CX today:

• Marketing, content, and campaign assistants can orchestrate segmentation, content creation, and optimization based on live performance signals.
• Commerce, merchandising, shopping, and order management assistants can connect discovery, conversion, and fulfillment to real-time inventory and pricing.
• Sales assistants help guide deal qualification and deal execution by linking pipeline signals to pricing, availability, and fulfillment data.
• Case and service management assistants help automate routine interactions while maintaining full context across orders, entitlements, and history.

These are not future scenarios. These capabilities are available and in use. But they only work when they are connected to trusted business data.

Without that, AI creates errors at scale. With it, AI drives measurable improvement.

Autonomous CX connects experience to execution

Autonomous CX connects core products and processes across the business, operating on a shared business context. It brings together 麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 CPQ, 麻豆原创 Service Cloud, 麻豆原创 Field Service, 麻豆原创 Engagement Cloud for marketing, and 麻豆原创 Cloud ERP across finance, supply chain, and order management.

This is not a set of disconnected applications. It is a unified system where customer interactions and operational processes run on the same data foundation. Pricing, inventory, orders, and service are consistent across every touchpoint.

As a result, AI can move from recommendation to execution, working to ensure that every interaction is grounded in what the business can deliver. It can also remove the integration gaps that slow down CX execution.

麻豆原创 CX partners are moving faster from projects to outcomes

This shift is changing what customers expect from partners. Customers are not asking for system implementations. They are asking for outcomes such as:

• Faster time to deploy
• Higher conversion rates
• Improved order accuracy
• Lower cost to serve

麻豆原创 provides a strong starting point with embedded assistants, standard integrations, and prebuilt industry scenarios. Partners are building on this to deliver complete solutions. This is where differentiation happens.

Where partners are creating value today

The opportunity is not theoretical. It is already visible in active partner work.

Across 麻豆原创 CX:

• The cloud ERP edition of 麻豆原创 Commerce Cloud can connect storefront, pricing, ordering, and fulfillment in one model.
• 麻豆原创 Revenue Growth Management and 麻豆原创 Retail Execution support trade planning and in-store performance.
• Intelligent applications help package AI use cases across marketing, sales, and service.
• 麻豆原创 Service Cloud with partner integrations such as Parloa enables automated, context-aware service interactions.

麻豆原创 CX partners are turning these capabilities into repeatable offerings. Examples include:

• Industry packages for retail and CPG combining commerce, pricing, and fulfillment
• Preconfigured deployments of 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud that reduce time to go-live
• Integration connectors linking 麻豆原创 CX with existing commerce, loyalty, and service platforms
• Extensions to CPQ and sales workflows that improve deal margin and approval speed
• Service automation scenarios that reduce manual case handling using real order and entitlement data
• AI-driven discovery connected directly to 麻豆原创 Commerce and 麻豆原创 Commerce, order management

These are practical, deployable solutions that can deliver measurable results.

The ecosystem is expanding what鈥檚 possible

麻豆原创 is strengthening this model through partnerships. Recently announced partnerships with companies such as Amazon Web Services, Google Cloud, Parloa, and Vercel enable new interaction models like conversational commerce, AI-driven search, and composable digital experiences.

What matters is that these experiences connect back to 麻豆原创 for execution. Orders, pricing, fulfillment, and service remain consistent across every channel. This gives partners the freedom to innovate on the experience layer while relying on 麻豆原创 for reliable execution.

A new economic model for partners

The economics for partners are changing. With Autonomous CX, partners can build:

• Industry solutions that can be reused and scaled
• Implementation packages that shorten delivery timelines
• Extensions and integrations that apply across customers
• Ongoing services for AI optimization and governance
• New offerings built around AI assistants, AI agents, and orchestration
• Higher-value transformation programs that combine AI, data, and process design

This creates a more predictable and repeatable revenue model. It also strengthens long-term customer relationships.

Now is the time to act with 麻豆原创 CX

Customers are making decisions now. They are selecting platforms and partners that can deliver AI-driven execution across customer experience. They are looking for partners who can:

• Connect CX to ERP processes
• Deliver solutions that work out-of-the-box and scale
• Improve measurable business outcomes

麻豆原创 provides the foundation. The platform is in place. The capabilities are real. The next step is execution.

Partners who move now can define the use cases, build the offerings, and lead in their industries. The momentum is already building. This is the moment to accelerate it.

What partners should do next

To move from opportunity to execution, partners can act now.

• See what is available today. Explore the latest AI-driven CX capabilities and partner opportunities in the .
• Understand the foundation for Autonomous CX. Learn how 麻豆原创 connects experience to execution with the .
• Deepen expertise and accelerate readiness. Gain a stronger understanding of Autonomous CX and partner use cases:
• Engage now and shape the next wave. Join the upcoming executive briefing to understand how to position and deliver these solutions:
  • June 18:
• Strengthen credibility and scale impact. Advance your go-to-market and positioning through certification:

麻豆原创 is not asking partners to start from scratch. The platform, capabilities, and ecosystem are already in place. The opportunity now is to build, differentiate, and lead.

Karl Fahrbach is chief partner officer at 麻豆原创.
Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience.

is a site where potential and existing 麻豆原创 customers can explore 麻豆原创 Business AI solutions, access pre-built applications and content, and discover use cases, services, reference architectures, and best practices鈥攁ll free of charge.

The self-service portal offers opportunities to not only explore solutions but get a feel for how 麻豆原创 technology could work in your organization. Cost and ROI estimator tools prompt users to input information unique to their organization鈥攃ustomizations, hyperscalers, and number of users, for example鈥攁nd get tangible, personalized use cases to bring back to the business. Maturity assessments use details about an organization鈥檚 current platform strategy and architecture landscape to guide users on next steps to achieve the Autonomous Enterprise.

The site also includes missions that provide step-by-step guidance on how to materialize 麻豆原创 solutions, including what solutions and services may be needed, what the architecture can look like, how other customers are deriving value, and more. For AI specifically, there are almost 400 features and agents available for customers to explore in the 麻豆原创 Business AI Catalog.

At 麻豆原创 Sapphire Orlando, two customers using 麻豆原创 Discovery Center shared the value they鈥檙e getting from the site and how it鈥檚 guiding their approach to digital transformation.

Agilent

is a global leader in life sciences, diagnostics, and applied chemical markets, producing and supplying analytical laboratory instruments, software, consumables, and services. Looking to avoid manual analysis and late detection in tariff and compliance changes, Agilent turned to 麻豆原创 Discovery Center. 鈥淲e have one core principle: do not solve the problem that has already been solved,鈥� Manthan Peshne, chief enterprise architect at Agilent Technologies, Inc., said. 麻豆原创 Discovery Center allowed Agilent to stay true to this principle.

鈥淲e started exploring 麻豆原创 Discovery Center and we found quite a few good building blocks, essentially in the form of missions,鈥� Peshne said. 鈥淲e also looked at some composable services which we could assemble together鈥t鈥檚 not a complete solution鈥ut we found a completely unrelated industry and use case that we could use.鈥� Using a mission set in the context of the oil and gas industry, the Agilent team was able to explore how an AI agent could interpret unstructured regulatory signals, extract the tariff context, determine its relevance, and convert that fragmented information into actionable alerts. Not only did Agilent find a solution to its problem, but the 麻豆原创 Discovery Center mission accelerated design thinking and development for the project.

鈥淲e also ended up with an enterprise pattern by building this solution, which gives us a platform where I could replicate this to other scenarios where I have external inputs or signals that we need to capture and put in the context of Agilent data,鈥� Peshne added.

Sutherland  

 is an AI-driven business transformation company that designs, runs, and automates enterprise operations at scale to help its clients achieve real, measurable business outcomes. A large part of this includes building and delivering production-grade agentic AI solutions on top of leading foundational models.

For Sutherland, 麻豆原创 Discovery Center has kick-started many projects. 鈥淚nstead of starting an MVP to see what the problem is and where to start, we have a ready-made solution from which we can pick up from,鈥� Amar Busireddy, 麻豆原创 enterprise architect at Sutherland Services, said. 鈥淚t gives us a start somewhere around 20%-30% depending on the scenario.鈥�

Having a starting point boosts confidence, Busireddy said, and enables Sutherland鈥檚 consultants to create solutions faster, which means faster time-to-value for its customers. Specifically, the missions available in 麻豆原创 Discovery Center have helped: 鈥淲e are using missions to kick-start educating our consultants and with implementing and helping our customers,鈥� Busireddy said. Missions around Joule are plentiful, including how to integrate the AI solution with 麻豆原创 SuccessFactors solutions, 麻豆原创 Ariba solutions, and 麻豆原创 S/4HANA.

鈥淭here are many reference architectures given for many problems, which might not be sufficient for us or might not fit our requirements 100%, but that definitely give us the direction on what to use and what not to use. From there, we can plan budget and time,鈥� he said.

麻豆原创 Discovery Center: The starting point

The experiences of Agilent and Sutherland show that 麻豆原创 Discovery Center is more than a resource hub鈥攊t is a catalyst for action. By offering practical guidance, reusable missions, reference architectures, and planning tools that help evaluate fit, cost, and value, the site enables organizations to accelerate innovation without starting from scratch. For organizations looking to turn AI ambition into actionable business outcomes, 麻豆原创 Discovery Center is the place to begin.

is the digital tool for customers and partners to easily interact with 麻豆原创 and get immediate guidance to the best solution. With this portal, users can access important alerts, metrics, and insights about their 麻豆原创 product portfolio from a single access point. The integration of Joule in 麻豆原创 for Me is not just a feature drop or a UX redesign. This is an inflection point in how customers access products, support, and self-service.听

We are moving from a click-and-search portal to a conversational, agent-driven enterprise ecosystem. For users, this is an experiential shift with significant business outcomes. By taking over the tedious work of clicking, searching, parsing, and diagnosing, Joule helps free up IT administrators, consultants, and business leaders to focus on what truly matters: driving innovation and scaling their business.

鈥淭he successful launch of Joule in 麻豆原创 for Me is the result of strong collaboration, innovation, and a shared commitment to improving customer experience. It demonstrates how 麻豆原创 continues to turn its AI strategy into real, tangible value, delivering solutions that are not only powerful but also practical and user-centric.鈥�

Gerlinde Wallner, Organizational Change Manager and Coach, Strategy & Operations, 麻豆原创

What can users expect from this new unified, AI鈥憄owered entry to 麻豆原创 for Me?

• Effortless navigation across the portal
• Fast access to relevant information
• Advanced self-service with guided support
• Accelerated task execution without needing to know where to click

In their fast-paced business environments, users don鈥檛 have to search through complex menus or multiple touchpoints. Joule in 麻豆原创 for Me can simplify their path to support. They can simply ask, explore, and act to experience personalized, conversational access to support, self-service, and key tasks in 麻豆原创 for Me.

鈥淛oule is transforming 麻豆原创 support by making it more intuitive and intelligent. We can guide users conversationally to the right outcome鈥攏o searching, no guesswork鈥攁ccelerate self-service and task execution, and deliver context-aware, personalized support directly within 麻豆原创 for Me.鈥�

Corinne Reisert, VP, Customer Support Experience 麻豆原创 for Me, Global Customer Support, 麻豆原创

In addition to introducing Joule in 麻豆原创 for Me, 麻豆原创 takes advantage of AI-powered agentic case resolution, which brings AI agents into support workflows to help analyze new cases, detect duplicates, suggest routings, and draft responses. For select priority cases, AI agents can recommend replies, which helps reduce manual effort, improve triage accuracy, and shorten resolution timelines. This is available now to 麻豆原创 customers.For more information, see .

Joule in 麻豆原创 for Me is being rolled out in phases as of May 2026, at no extra cost to customers.

鈥淲ith 麻豆原创 runs 麻豆原创, we show our customers how we scale agents across the enterprise to deliver real outcomes. Joule in 麻豆原创 for Me exemplifies how conversational and agentic AI can fundamentally transform the way users operate and offer customers a simple and intuitive path to access 麻豆原创鈥檚 services and support.鈥�

Benjamin Blau, Chief Process & Information Officer, 麻豆原创

While Joule in 麻豆原创 for Me already helps deliver a simpler and more intuitive way to access information, support, and guided actions, this is just the starting point. As 麻豆原创 continues to advance its AI strategy, customers can look forward to new scenarios, expanded agent capabilities, and deeper integration across services and support processes. This launch establishes the foundation for a more conversational and autonomous customer experience, one that will continue to evolve as 麻豆原创 brings the next generation of AI-powered innovations to life.

Stefan Steinle is executive vice president and head of Global Customer Support at 麻豆原创.

This journey cannot be defined by technology alone. It requires dialogue, shared learning, and real-world insight. That is exactly why we are launching the podcast, a new series we will be hosting together.

Why this conversation matters now

Organizations today face unprecedented volatility, from geopolitical uncertainty and supply chain disruptions to energy challenges and rising resilience requirements. In this environment, the cost of inaction is increasing. Businesses must become faster, more adaptive, and structurally more resilient to stay competitive.

The Autonomous Enterprise offers a response. It combines three critical capabilities:

• Data-driven decision-making
• Automated execution
• Governance-by-design

Together, these capabilities enable organizations to move beyond isolated AI pilots toward measurable outcomes and enterprise-wide impact.

The shift is not just technical, though. It is organizational and strategic. The leaders we talk to are no longer asking whether they should adopt AI. The question now is how fast they can scale it and how they can generate tangible business value.

From concept to operating model

At its core, the Autonomous Enterprise reframes AI鈥攏ot as a feature layered onto applications, but as an integral part of the operating model itself.

Three priorities define this model:

• Business value: focusing on measurable outcomes rather than experimental use cases
• Predictability: improving decision-making through trusted data and advanced forecasting
• Scalability: moving from proof-of-concept initiatives to enterprise-wide deployment

We are already seeing this shift change how organizations think about their systems and processes. Systems of record are evolving into systems of action. AI agents are moving from simple assistance toward execution. And AI is becoming embedded end-to-end, rather than confined to isolated scenarios.

At the same time, governance, auditability, and traceability are becoming non-negotiable. Enterprises must be able to stand behind every AI-driven decision with transparency and confidence.

What we are setting out to do

In this podcast series we want to create a space to explore these changes in depth and bring the voices shaping this transformation into the conversation. Each episode features discussions with 麻豆原创 leaders, customers, and industry experts who are actively building and operating autonomous capabilities today.

Some of the questions we will be digging into:

• What does the Autonomous Enterprise look like in practice?
• How are leading companies scaling AI across core business processes?
• What are the biggest barriers, and how can they be overcome?
• How do organizations balance automation with governance and trust?

鈥攏ow live鈥攆eatures 麻豆原创鈥檚 Peter Maier, responsible for Strategic Customer Engagements in the Office of the CEO at 麻豆原创, who brings these ideas into focus through practical, real-world context. In our conversation, he outlines how organizations are moving beyond experimentation toward measurable outcomes, more trusted and predictive decision-making, and scaling AI across the enterprise.

What we found particularly compelling is how clearly this reinforces a broader shift already underway: AI is no longer something applied on top of the business. It is becoming part of how the business runs.

How companies can get started

While the vision is ambitious, the path to becoming an Autonomous Enterprise does not require a 鈥渂ig bang鈥� transformation. The most effective approach is incremental and outcome driven.

Organizations can begin by focusing on a single high-value process, making it more intelligent, more automated, and more transparent. From there, they can expand step by step, scaling what works and continuously demonstrating measurable impact.

Success depends on more than technology, though. Trust plays a central role. Employees, executives, and stakeholders must understand and trust how AI decisions are made. This requires transparent and explainable systems, reliable high-quality data foundations, and strong governance frameworks embedded from the start.

Change management is equally critical. Becoming an Autonomous Enterprise is as much about people as it is about platforms. Organizations must align training, redesign roles, and empower employees to co-create how AI is integrated into their work.

A shared journey forward

The Autonomous Enterprise is not a branding concept. It is a new way of running a business鈥攐ne that is more automated, more data-driven, and ultimately more resilient. And no organization will navigate this journey alone.

That is the spirit behind this podcast. We want it to be a platform for shared learning, bringing together perspectives from across industries, functions, and geographies. Whether you are just beginning your AI journey or scaling enterprise-wide transformation, we hope these conversations give you practical insights and inspiration.

We would love for you to join us. Listen in, engage with the discussion, give feedback, and help shape what comes next.

Benedikt Gieger is AI strategy lead for 麻豆原创 Supply Chain Management.
Julia Kloppenburg is a technology consultant for Customer Engagement & Adoption at 麻豆原创.

鈥淟ast year, we were in the process of planning the optimization of our talent discovery tech stack and then something happened,鈥� Eric Goldstein, global head of Talent Discovery for 麻豆原创, said. 鈥淲e acquired SmartRecruiters in September, so we had to pivot in an agile way.鈥�

SmartRecruiters for 麻豆原创 SuccessFactors enables enterprises to manage the entire hiring lifecycle, from sourcing to onboarding, with AI-enabled recruiting capabilities that can result in faster time-to-hire, improved candidate experiences, and deeper analytics for workforce planning.

For 麻豆原创, this means adding much-needed rigor and precision to its global talent acquisition operations. This will not only elevate the quality of hires but also the candidate experience, which Goldstein identified as the 鈥渂iggest game changer.鈥�

Building a more intelligent hiring process

SmartRecruiters for 麻豆原创 SuccessFactors helps optimize processes, increasing transparency and personalization. This means improved experiences and processes for candidates, hiring managers, and recruiters. 鈥淲e have been through a time where we focused solely on the recruiter experience. Then it was fashionable to focus only on the candidates. Now we really see that with SmartRecruiters, it really is an enhanced experience for all stakeholders that are involved in the recruiting process,鈥� Ilka Sagner-David, global head of Talent Discovery Solutions and Innovations at 麻豆原创, said.

Candidate perspective

Seventy percent of candidates that apply for jobs are mindful to take their valuable time to do so, Goldstein shared, reiterating that it is important for companies to match that commitment when shaping and delivering the candidate experience. With SmartRecruiters for 麻豆原创 SuccessFactors as the foundation, it becomes possible for every pre-qualified applicant to interview, receive personalized and constructive feedback post-interview, and maintain 24×7 interaction with agentic AI built into SmartRecruiters.

鈥淚n our opinion, only responding with polite, automated rejection notes is not enough. [Candidates] need to be provided with some constructive, actionable feedback鈥攁nd that鈥檚 what we [at 麻豆原创] are going to be able to do,鈥� Goldstein said.

Hiring manager perspective

SmartRecruiters for 麻豆原创 SuccessFactors can give hiring managers a more precise and consistent way to identify strong candidates, helping to reduce time-to-hire while improving hiring quality. AI-prompted interview questions focused on skills can support more relevant and structured conversations while greater transparency across interview panelists can create better alignment throughout the evaluation process. In addition, AI-supported feedback collection can make it easier for interviewers at 麻豆原创 to capture timely, consistent insights, enabling its hiring teams to make more informed decisions with greater confidence. 

Recruiter perspective

Recruiters are often bogged down by manual tasks, such as outreach, prospect identification, and candidate screening, making it nearly impossible for them to step into the role of a trusted advisor. With SmartRecruiters for 麻豆原创 SuccessFactors, recruiters can experience automated internal and external prospect identification, personalized outreach and prioritization of candidates, and, therefore, the ability to focus on higher value-add advisory and relationship management.

鈥淚t鈥檚 going to allow the recruiters to focus on relationship management with candidates and hiring managers, really challenging the feedback of how well the interview panel measures skills proficiency,鈥� Goldstein said.

Bringing AI into the candidate journey

A key to the successful delivery of these benefits is SmartRecruiters Winston for 麻豆原创 SuccessFactors, an AI-driven, candidate-facing agentic experience. At 麻豆原创 Sapphire Orlando, Karl Baert, global head of People Solutions for 麻豆原创, demonstrated how Winston can facilitate the application experience for candidates.

In the demo, he acted as a candidate applying for an open position at 麻豆原创, showing how through a natural language conversation with Winston, he completed his application by uploading his CV and verifying some personal details with Winston. 鈥淎ll that information is very, very quickly brought together so with just a few questions my application is done,鈥� Baert said, adding that 鈥渢here鈥檚 also a few checks happening along the way because we want to make sure the data we are collecting is the right quality.鈥�

Winston also collects feedback from the applicant. 鈥淢easuring the quality of your agent and what鈥檚 happening with it is important. It鈥檚 something that really needs to be actively monitored just to ensure that the information provided by the agent is accurate,鈥� Baert said.

鈥淭he implementation of SmartRecruiters is the foundation for infusing AI into our processes,鈥� Sagner-David said. But, she added, 鈥渨e shouldn鈥檛 just plan to transfer everything tomorrow, but ensure we鈥檙e liberating AI when it makes sense.鈥�

The next step

Currently, SmartRecruiters for 麻豆原创 SuccessFactors is being implemented into 麻豆原创鈥檚 HR systems for two phases of user acceptance testing, with the global go-live expected in September.

麻豆原创 bringing SmartRecruiters for 麻豆原创 SuccessFactors to life across its own organization is more than a technology rollout, it鈥檚 a glimpse into the future of hiring at scale: more intelligent, more human, and more connected. By combining AI, better experiences, and real-word enterprise rigor, 麻豆原创 is not only transforming how it hires but also helping to define what modern hiring can look like for companies everywhere.

AI works when the foundation is right 

The energy and utilities sector is investing heavily in AI. Business听leaders worldwide are embracing artificial intelligence to increase efficiency, unlock new business models, and prepare for the energy transition. A successful proof of concept is often the first milestone鈥攂ut it marks only the beginning. The听real challenge听lies in scaling pilot projects across the entire听organization.听

In this context, the time and effort听required听for a full implementation听is听frequently听underestimated. Around six months are needed to build a robust data foundation. A further听12听months pass before initial results manifest in the form of a measurable return on investment. Large-scale rollout can take another three years. The reasons for this are manifold:听

• Unrealistic expectations: Many people use AI in their daily lives for simple tasks and expect similarly seamless effects in complex enterprise environments. 
• Legacy infrastructure: Historically grown system landscapes cannot be transformed overnight. 
• Regulatory complexity: In regulated industries such as electricity, gas, and water supply, compliance requirements are particularly high. They must be factored into every architectural decision from the very beginning. 
• Lack of AI-specific talent: What is needed are people who genuinely understand both the business and AI. This bridge between IT and the business side will become increasingly important in the future. 
• Organizational听change management:听Technology alone is not enough. Organizational transformation is and听remains听the decisive success factor.听

From AI hype to real value 

Building a new application is听only the first听step.听On the path to scaling, lifecycle management, identity and access management, security, compliance, and governance must all be consistently taken into account.听Release management, testing, and continuous improvement processes add further complexity.听鈥淭he听companies听that听invest in the right foundation today will benefit from AI to its full extent tomorrow,鈥� says Andre Bechtold,听president and听head of 麻豆原创 Industries & Experiences.听

For companies, this means overcoming fragmented data silos and developing an integrated data strategy. Legacy systems must be integrated into a modern data and AI platform on which AI models can genuinely create value. Torsten Welte,听head of Energy & Natural Resources Industries听at 麻豆原创,听summarizes听it as follows:听“AI is fundamentally transforming the energy industry. The business must understand what is technologically possible. And IT must understand what the business needs.”听

听can听provide听the听essential foundation for this. AI is already natively embedded in the suite in the form of Joule. This听can open up听concrete use cases for the energy industry:听in the area of asset management and predictive maintenance, utilities听can听proactively manage assets and grids before disruptions occur. The Utilities Customer Self-Service Agent, in turn, enables 24/7 self-service for customers and can reduce service costs by up to 90%.听

Distributed energy requires intelligent networking 

The topic of听distributed听energy听resources (DER) remains of听central importance. In the past, energy flowed in only one direction: from the power plant to consumers. In the future, it will be bidirectional. Consumers听that听generate their own energy will actively feed it back into the grid.听

DER听describes precisely听this principle: the generation of electricity through millions of decentralized resources such as solar panels, EV chargers, heat pumps, and battery storage systems听by听consumers and so-called听prosumers. These assets generate vast amounts of data. Their orchestration听represents听one of the key challenges of the energy transition.听

The 听solution听provides a platform听for听a听single source听of truth: technical assets, commercial contracts, and customer data are brought together in a coherent data model. This helps create the foundation for new business models such as smart tariffs, dynamic pricing, energy sharing, and demand response.

麻豆原创 consistently relies on a growing partner network built around its own data platform. Markus Bechmann,听global VP and听co-head听of听Industry Business Unit Utilities听at 麻豆原创, describes it this听way:听“Dynamic pricing and smart tariffs are no longer distant concepts.听They听are the business models听of听tomorrow. With 麻豆原创, energy providers already have the technological foundation today to seize these opportunities.”听

麻豆原创 Experience Centers: experiencing AI, not just discussing it 

To make AI tangible, 麻豆原创 Experience Centers offer visitors the opportunity to experience AI in real-world scenarios beyond classic demo environments. One central example is the 麻豆原创 Energy Park in Walldorf. Using real infrastructure on the campus, 麻豆原创 demonstrates how the company itself is implementing the energy transition. This includes e-mobility, intelligent asset management, and energy communities. 

A new chapter for the energy industry 

The 麻豆原创 for Energy & Utilities Conference in Toulouse has once again demonstrated that AI in the energy industry is no longer a topic for the future. However, the path from pilot project to company-wide transformation requires more than technological enthusiasm. To meet the challenges of the energy transition, what is needed鈥攁longside technological innovation鈥攊s a solid foundation of data, processes, and organization.

What made the event distinctive was its candor. Customers did not come to present polished success stories. They came to compare realities. And those realities were refreshingly honest.

Why direct procurement is hitting a breaking point

The pressure on direct procurement is not coming from one direction. Geopolitical instability and accelerating technological change are forcing sourcing decisions earlier in the product lifecycle, at precisely the moment when many organizations are least equipped to act. Meanwhile, institutional knowledge is leaving faster than systems are modernizing. The experienced individuals who once held fragile processes together are retiring or moving on, and the systems meant to replace that knowledge are not yet ready.

The result is an operating model that prevents procurement leaders from influencing value at the moments that matter most. Several customers described a tension they are actively dealing with. Sourcing is being pulled upstream into design and development, while the tools and processes that support it are still anchored downstream.

What customers shared about their reality

While all participants operate with a strong 麻豆原创 footprint, spanning and , many acknowledged that direct materials sourcing remains fragmented and disconnected from the digital core. The picture they described was familiar but worth stating plainly: engineers, buyers, and suppliers still collaborating through e-mail, local tools, and disconnected applications; there’s an overreliance on a small number of experienced individuals to make things work; and multiple ERP landscapes run in parallel, with direct sourcing living largely outside all of them.

One observation stood out clearly. The real friction is not the sourcing events themselves. It is the handoffs, the gaps between systems and teams where decisions get made too late, data is reconciled manually, and no single digital thread connects product intent to sourcing execution.

In other words, the process functions, but it functions in silos.

Participants also noted that traditional indirect source-to-pay approaches simply do not support direct materials adequately. They lack native support for procurement embedded in new product development, sourcing scenarios that evolve with engineering change, demand aggregation across programs, and contracts treated as executable objects rather than static documents. That last point came up repeatedly, particularly the need to treat contracts as executable objects. This is also where the add-on in 麻豆原创 S/4HANA is starting to resonate more strongly in ongoing customer discussions.

Where customers are focusing next

What emerged from the discussions wasn鈥檛 a long list of priorities, but a firm shift in where companies are focusing their efforts.

Moving sourcing upstream into product development鈥攔ather than reacting after design decisions are already locked鈥攚as a consistent theme. So was reducing dependency on hero buyers: individuals whose personal expertise and relationships are currently holding critical processes together.

Commodity volatility and renegotiations also came up as structural challenges, not one-time events. Organizations want to handle these systematically rather than heroically. And several participants raised the reality of managing multi-year 麻豆原创 S/4HANA journeys without stalling progress in the meantime鈥攁 genuine tension that demands honest road map planning.

While the direction is widely understood, most organizations do not yet have the setup to execute against it at scale.

These priorities help explain why customers are increasingly adopting the 麻豆原创 Ariba direct materials sourcing add-on alongside , , and 鈥攃apabilities that together can support the connected execution model direct procurement actually requires.

How AI fits into direct procurement

AI generated significant interest, but expectations were measured and, I would say, appropriately so.

The consistent message was this: AI only matters once the fundamentals are addressed. Agent-based capabilities depend on clean processes and consistent data. Without a unified digital thread across product design, sourcing, contracting, and execution, AI does not generate insight鈥攊t amplifies noise.

Leaders also expressed clear skepticism toward black-box automation. They want AI that is explainable and embedded directly into sourcing, negotiation, and execution workflows, not layered on top of broken processes and presented as a fix.

This thinking aligns closely with 麻豆原创鈥檚 vision for the Autonomous Enterprise, introduced at 麻豆原创 Sapphire just weeks after our Walldorf discussions. The vision anchors AI agents directly in transactional business processes, data, and governance鈥攅xactly what customers said they needed before they could trust AI in direct procurement environments. Hearing them articulate that requirement so clearly, before the announcement, felt like meaningful validation.

Where this is all heading

The Walldorf roundtable confirmed a clear trajectory. Direct procurement organizations are moving away from heroics and spreadsheets and toward system-led execution. They are aligning sourcing transformation with their 麻豆原创 S/4HANA road maps and preparing their organizations鈥攏ot just their systems鈥攆or a future where AI supports decision-making across the full procurement lifecycle.

Direct procurement, seen through this lens, is not a standalone transformation. It is a foundational building block. Connecting product, sourcing, contracts, and execution through a single digital thread is what enables AI to operate accurately, compliantly, and at scale. That connection has to exist before any of the more ambitious automation goals become realistic.

For 麻豆原创, conversations like the one in Walldorf directly inform our product direction and investment priorities. There is no substitute for sitting in a room with people navigating these challenges every day, without a script.

What was clear in Walldorf is that the direction is no longer in question for participating organizations. The challenge now lies in execution and in how quickly organizations can move from fragmented, person-dependent processes to cohesive models that reflect how direct procurement operates today.

Karolina Bombardelli is global go-to-market lead for Direct Procurement at 麻豆原创.

The conversation has moved beyond what AI can do into how it should be applied, placing HR at the center of decisions that will shape people, culture, and business outcomes for years to come.

While we have often talked about the 鈥渇uture of work,鈥� the simple fact is that future is already here. The question is whether organizations are ready to operate differently.

AI requires a fundamental rethinking of how work gets done, grounded in the data, systems, and processes that run today鈥檚 organizations. And getting it right starts with one clear principle: humans must remain firmly at the center鈥攏ot as operators of process, but as leaders of judgement, strategy, and change.

What Autonomous HCM means for HR leaders

At 麻豆原创, this is the foundation of our vision for the Autonomous Enterprise, announced at in Orlando, where AI assistants can run core HR processes end-to-end, so people are empowered to focus on their most meaningful work while staying firmly in control of outcomes.

brings together agentic AI, HR applications, and real business context鈥攇rounded in deep process expertise and enterprise-grade governance鈥攖o help organizations anticipate workforce needs and respond with greater precision as business priorities change.

With the new HCM innovations announced at 麻豆原创 Sapphire, we are building on the existing breadth and depth of 麻豆原创 SuccessFactors with new AI-native functionality that amplifies how HR can help shape the business and elevate what employees are capable of.

Automate work with Joule Assistants

The first shift is automation; not as task replacement, but as a new way of working. A new generation of , delivered through Joule as 麻豆原创鈥檚 AI engagement layer, bring this to life by orchestrating agents to execute work end-to-end and support decisions in real-time.

These assistants are not just automating tasks; they are guided by employees to reduce manual effort and support a growing range of HR scenarios:

• Payroll becomes proactive, not reactive: The coordinates multiple to prepare payroll runs, identify issues early, and guide administrators to faster resolution, shifting payroll from reactive process to proactive execution. Working alongside the Core HR Assistant and , it helps organizations manage employee data, track time and attendance, and pay employees with greater accuracy and less manual work.
• Talent acquisition flows more seamlessly end-to-end: The helps keep hiring moving from intelligent matching to interview coordination, providing real-time guidance to recruiters and hiring managers. Once a candidate accepts, the takes over to support a smooth transition for new employees. These new Joule Assistants connect talent acquisition processes between and the broader 麻豆原创 SuccessFactors HCM suite.
• HR services become faster and more intuitive: The helps administrators resolve common HR questions instantly, directing employees to the right next step and reducing service center volume while improving the overall employee experience.

Reimagine the workforce with AI-driven planning

As AI becomes part of how work gets done, organizations must rethink workforce planning as a continuous leadership discipline, not a periodic exercise. Today, 62% of C鈥憇uite executives say they are dissatisfied with how well people data connects to business performance, according to , making it harder to turn strategy into action. The new workforce planning capability within 麻豆原创 Enterprise Planning supports a shift toward strategic work redesign, inclusive of both agents and people, by helping leaders link workforce decisions directly to HR, business, and financial needs.

This workforce planning capability connects data from , , and 麻豆原创 SuccessFactors, creating a unified foundation for workforce decision鈥憁aking across employees and contingent labor. Together, this moves workforce planning beyond static models. Leaders gain clear scenario insight and the ability to combine human judgment with AI to align workforce and investment decisions.

At a more granular level, constant change means business and HR leaders are often dealing with organizational changes. The new AI鈥慹nabled organizational modeling for replaces slow, disconnected modeling approaches with an integrated experience that supports scenario planning and impact analysis, enabling leaders to evaluate organizational choices with greater accuracy and alignment. With this approach, leaders can quickly explore alternative organizational structures and understand implications before changes are implemented. Whether adjusting roles, teams, or reporting lines, organizational modeling becomes a practical leadership tool, supporting thoughtful change while maintaining data integrity and minimizing disruption. The result is a clearer, more proactive approach that helps organizations make smarter workforce decisions in a constantly evolving business landscape.

Elevate people through continuous upskilling

When it comes to skills, the rise of generative AI has once again accelerated the pace of change. New jobs are emerging, new skills are required, and processes that have worked for decades are being completely reimagined.  The new Workforce Upskilling Assistant delivers personalized, AI-driven learning directly where work happens, in collaboration tools, mobile, desktop and 麻豆原创 SuccessFactors鈥攈elping organizations keep skills aligned with where the business is headed. By orchestrating multiple Joule Agents, it supports content creation and generation, adaptive micro-learning, and reinforcement, enabling leaders and managers to identify critical skill gaps and accelerate upskilling, particularly in fast-moving areas such as AI.

By delivering learning in the tools and channels employees already use, the Workforce Upskilling Assistant turns workforce and business data into timely, bite鈥憇ized learning moments. Rather than relying on scheduled courses or standalone systems, HR learning teams can quickly convert existing content to deliver learning to the right person at the right time.

A new standard for human-centered Autonomous HCM

麻豆原创鈥檚 Autonomous Enterprise vision sets a new standard for how HR leads in an AI-driven world, one where AI assistants and agents take on the work of coordination, so people can focus on leading and shaping outcomes. As AI becomes embedded into how work runs, HR is uniquely positioned to guide what matters most, moving from coordinating processes to guiding decisions, building resilient teams, strengthening trust, and ensuring the workforce is ready for what鈥檚 ahead.

That is the promise of an Autonomous HCM platform: human expertise elevated by AI, delivering meaningful impact for both people and the business.

Learn more about how 麻豆原创 is delivering Autonomous HCM by catching the replay of the HCM Innovation .

Dan Beck is general manager and chief product officer for 麻豆原创 SuccessFactors.

Li茅nard is an information scientist and business informatics professional who joined 麻豆原创 in 2012. As senior vice president and head of ABAP platform at 麻豆原创, she is responsible for ABAP and all matters related to ABAP platform. In this role, she is also the head of ABAP AI and thus globally responsible for the latest developments and innovations in this domain.

In this interview, she discusses ABAP, the role of AI in development, how agentic AI will transform legacy applications, and what’s next.

Q: What is ABAP, exactly? How would you explain it to someone who might have heard of it but doesn鈥檛 really know what it is? And why is ABAP so important for enterprise software?

A: ABAP has a very long history at 麻豆原创. It is the company鈥檚 first and only proprietary programming language and turned 40 in 2023鈥攁n unusually long run in the fast-changing world of software.

What sets ABAP apart from other programming languages such as Java or C++ is that it was specifically designed for building and optimizing the business applications that large enterprises rely on every day. Among its many features is a high level of abstraction, which makes it very easy for developers to write or extend business software. It also reduces complexity because security concepts, authorization checks, and quality controls are already embedded in the language. This allows developers to focus entirely on the business logic鈥攖hat is, on the tasks they want the program to perform.

Over the years, ABAP has evolved to keep pace with how companies deploy software. The newest version is ABAP Cloud, which has a restricted language scope and is designed to support development in what 麻豆原创 calls a 鈥渃lean core.鈥� This is essential for running our cloud products. Enterprises still operating in a non-cloud environment can use ABAP Cloud to prepare the code in their on-premise systems or in 麻豆原创 S/4HANA Cloud Private Edition in such a way that it can also be run in the cloud.

Beyond its role as a programming language, ABAP is also a platform. ABAP platform is the foundation that underpins all of 麻豆原创’s core solutions, from older installations such as 麻豆原创 ERP Central Component (麻豆原创 ECC) to on-premise solutions, 麻豆原创听S/4HANA Cloud Private Edition, and 麻豆原创听S/4HANA Cloud Public Edition.

Q: Will ABAP continue to play a crucial role for 麻豆原创 customers?

A: Yes, both in terms of the programming language and the platform ABAP is still highly relevant. The programming language looks very different to the way it did 40 years ago of course鈥攂ecause we have continuously refined it over the years鈥攂ut it still forms the backbone of 麻豆原创鈥檚 core ERP solutions and extensions. There are roughly five million registered ABAP developers worldwide today, with around two million actively developing.

Through ABAP Cloud and our dedicated ABAP AI team, ABAP has evolved into a modern development language for business solutions. I don’t know of any other programming language that covers this scope. It is used globally. Almost all the world鈥檚 100 largest companies are 麻豆原创 S/4HANA customers, and underneath it always runs ABAP platform.

Q: How will AI shape ABAP development going forward?

A: For me as head of ABAP platform, this is one of the questions that intrigues me most. AI has completely disrupted the technology market. This of course also impacts the 麻豆原创 developer portfolio and how we customize and extend our solutions. We have therefore invested in AI-powered efficiency tools, such as a chat assistant that explains code on the fly. Another is 鈥済host texting,鈥� a feature that generates code suggestions while the developer types.

In the coming years, AI agents will be able to generate code鈥攊ncluding at the scale demanded of large enterprises鈥攁nd even build entire solutions. We believe that the next wave of AI will not just assist programmers but take on many of the routine tasks they perform today.

A crucial question for 麻豆原创 is: how can we leverage AI to translate legacy code into modern code without losing the underlying business logic that makes each system unique? A lot of our customers are still operating older solutions, including those based on 麻豆原创 ECC. So, we need to provide a clear migration strategy and the right tools to simplify and accelerate their move to the cloud.

That’s why we’re currently developing a service that will work for everyone鈥攔egardless of which system version they run. The aim is to bundle all of 麻豆原创鈥檚 ABAP AI capabilities into a single offering that can boost developer efficiency and allow custom code to be migrated. Ideally, this service will be agent-driven鈥攁s 鈥渁gentic AI.鈥�

Q: What is agentic AI?

A: Agentic AI works with so-called 鈥渁gents.鈥� Agents have specialized capabilities, can communicate with each other, exchange results, and thus solve highly complex tasks together. How they collaborate varies based on the complexity of the use case.

Most approaches involve an 鈥渙rchestrator,鈥� a lead agent that manages other agents to complete a particular task. The orchestrator does not have to call on the individual agents in a fixed order鈥攔ather, its greatest strength lies in intelligently combining the agents in dynamic, adaptive networks.

So, it鈥檚 no longer just about making human developers more efficient. When agents are powerful enough, they can build entire applications and thus take on part of the developer’s tasks. In our case, agentic AI can support the very complex task of transforming code, accelerating it significantly and reducing complexity.

This approach relies on different agents that focus on different aspects of the task: for instance, one agent specializes in explaining custom code; another makes code changes; and a third estimates the effort of a transformation project. When these agents collaborate, that’s when the real magic of agentic AI happens.

AI will radically change the role of developers. Despite continuing to set the direction, they will increasingly focus on business logic rather than on the coding itself. They will work with the code generated by AI systems, checking that it is correct, secure, and aligned with the problem they鈥檙e trying to solve. Thought leadership, however, will remain firmly with people. Developers will continue to decide what matters and communicate their instructions to AI through good prompts. The entire AI domain is extremely dynamic and evolving at astounding speed. Powerful solutions are already available today, so this isn’t a distant vision鈥攊t’s already upon us.

Q: How do customers benefit from agentic AI?

A: Agentic AI will deliver significant value in transforming legacy applications and custom extensions into cloud solutions from 麻豆原创, and thus the latest ERP versions. In February 2026, we extended our existing custom code management app with AI features that help developers understand what the code is doing and what changes are needed to future-proof it. And, of course, AI also provides recommendations on how the code can be extended. In the future, we will complement all this with agents. However, this will take some time, as we refuse to compromise on quality and security.

We are also investing in the developer experience with ABAP platform to make it as easy to use as possible. Here, agentic AI will help reduce the complexity that has built up over decades of development.

Q: Should we be worried about security?

A: No, we deliberately allow sufficient time before any release to make sure that quality and, above all, security meet a high bar. Don鈥檛 worry: AI won鈥檛 take control and generate or integrate solutions unilaterally or unchecked. Humans will remain in charge every step of the way and will always have the last word when it comes to ensuring that code complies with our standards.

Q: Where are we now and what鈥檚 next?

A: ABAP AI tools aimed at boosting developer productivity have been available since February 2025, and we are now building agentic AI in the ABAP context. However, it鈥檚 early days and agentic AI still must prove itself in practice. As I see it, though, it will transform the market.

As part of our road map, we released , a custom-trained, specialized AI model, on the generative AI hub in early January 2026. This model is specifically designed to explain ABAP program code.

Next, we plan to make all ABAP AI tools available as an independent side-by-side service. In a subsequent phase, we will transition the use cases embedded in those tools to agents.

In addition, we are expanding our cloud-based ABAP development into additional development environments (IDEs), especially ABAP development tools for Visual Studio Code. So, the team will also tap into the AI tools available there as part of our push toward agent-driven development.

This first appeared on the German 麻豆原创 News Center.

Held from April 20鈥�24, Hannover Messe is the world鈥檚 leading industrial trade fair.

On day one, Christian Klein, CEO of 麻豆原创 SE, stopped by the 麻豆原创 booth before joining German Chancellor Friedrich Merz and other industrial leaders on the center stage to discuss the importance of moving from AI ambition to real-world execution.

And visitors to the 麻豆原创 booth experienced that shift firsthand, following the production of the ginger shot.

Packaged in a neat blue box, the ginger shot was refreshing but that wasn鈥檛 the only takeaway. The real takeaway was how 麻豆原创鈥檚 new set of AI-powered manufacturing and supply chain innovations can deliver connected .

Supply chain orchestration

From AI and data and then using 麻豆原创鈥檚 agentic AI, visitors saw what supply chain orchestration looks like in practice. 麻豆原创 uses , trusted data, and applications to help manufacturers听sense, analyze, and act in real time.

At the booth, visitors saw human operators interact with an ANYbotics robot through Joule using natural language to run live, remote field service inspections; Uhlmann鈥檚 high-tech glass-fronted packing machine, PacXplorer, in action opposite the CNC machine from DMG MORI that was creating spare parts for the PacXplorer; and, at end of the production cycle, AIMBO鈥檚 robot handling the picking and packing of the ginger shot. Both AIMBO and ANYbotics are part of 麻豆原创鈥檚 growing network of physical AI partnerships.

In addition to many tours held in German and English, day one also saw tours in Japanese, Chinese, and Portuguese鈥擝razil was the partner country at Hannover Messe 2026.

Equipped with headphones to block out the noise of the crowds at the booth, visitors heard how 麻豆原创鈥檚 AI can deliver trusted orchestration and smarter execution for and .

Live AI use cases demonstrate functions and benefits

Operations and insights use case

Here, visitors experienced 麻豆原创鈥檚 vision of supply chain orchestration. In this vision, supply chain orchestration acts as the nerve center of the enterprise. It uses external alerts such as natural disasters, port congestions, or supplier routes to optimize enterprise logistics and planning using agents.

Benefits can include faster response times with AI-assisted monitoring and automated alerts; improved decision-making with data-driven, operational decisions powered by integrated business AI capabilities; and seamless integration with end-to-end connectivity from supply chain planning through to manufacturing execution and quality control.

Top AI functions

• can assist with order release and real-time monitoring.
• A physical AI robot inspects hazards, analyzes inspection data, and identifies root causes.
• Supply optimization analysis helps summarize insights, analyze, and explain the time-series optimization planning run.

Smart production use case

DMG MORI demonstrated production at its CNC machine鈥攁s part of an end-to-end process鈥攆rom engineering to planning to production.

As the white robotic arm of the CNC machine silently moved the pusher spare part听after the milling process, visitors learned about the benefits of integration, from design to tool management, CNC programs to as part of a seamless, integrated process. The production operator dashboard offers the operator on the machine AI capabilities and insights to operational and maintenance information.

The process then continues through to logistics execution with 麻豆原创 Logistics Management, which helps combine warehousing and transportation capabilities for smaller warehouses.听 This听features an AI-powered logistics assistant that can cut through the noise, automatically gathering, summarizing, and prioritizing critical shipment information. It can also provide real-time shipping prices, bringing to life trusted orchestration and smarter execution.

Top AI functions

• uses natural language to help streamline warehouse and transportation operations.
• can provide manufacturing information and support decision-making throughout the workflow.

Intelligent packaging use case

Uhlmann’s PacXplorer and 麻豆原创 highlighted a fully integrated, high-speed packaging line from 麻豆原创 S/4HANA, to 麻豆原创 Digital Manufacturing, down to Uhlmann鈥檚 automation layer to produce the packaged ginger shot. The ginger shots were moved away from the line by a mobile autonomous robot from Symovo. This use case showed visitors how 麻豆原创 supports regulated industries such as pharma and life sciences. 听

Highlighted benefits include increased operational speed with higher throughput thanks to decreased order processing time, built-in regulatory compliance, reduced manual intervention, inventory transparency, and data integrity across the entire production chain.

Top AI functions

• Condition monitoring-led services can enhance asset uptime and service efficiency by combining AI-driven insights and seamless collaboration across the service ecosystem.
• AI-empowered flow analysis enables quick process modeling and engineering optimization.
• Intelligent exception handling is embedded in agent-driven processes.
• Joule’s integrated AI agents can support decision-making throughout the workflow.
• Joule can help power order and line insights.

Humanoid use case

At the final stop before getting their ginger shots, visitors watched an intelligent humanoid robot perform physical tasks at the end of the packaging line, bridging the gap between digital planning and physical execution, highlighting 麻豆原创鈥檚 Project Embodied AI.

Benefits of humanoids include increased operational speed with higher throughput due to a decreased order processing time; increased business uptime and cost efficiency especially in areas dangerous or difficult for humans; inventory transparency with real-time data integrity across the warehouse; and physical-digital alignment eliminating misalignment between planning and execution.

Top AI functions

• Joule and Joule Studio can enable robots to understand the physical world, make autonomous decisions, and learn from their environment for smarter operations.

More than a quick refuel

At the end of their visit, visitors got so much more than a quick refuel to slake their thirst. Following the creation of the ginger shot from recipe development and planning to production with mixing, filling, and packing, visitors came away with a clear understanding of how 麻豆原创 is connecting insight to execution with trusted orchestration and smarter execution. And, it is this trusted orchestration and smarter execution that is building the resilience every manufacturer needs in today鈥檚 world.

Founded in 1899, NEC Corporation is one of Japan鈥檚 leading technology companies, operating globally across IT services, telecommunications, and digital infrastructure. Over the decades, the company has continuously adapted to new waves of technological change, but today鈥檚 shift is different. Artificial intelligence is not just another innovation cycle; it is redefining how organizations operate at their core.

For NEC, this means rethinking not only technology, but also how work gets done, how decisions are made, and how value is created.

In a recent conversation with Thomas Pfiester, head of Customer Engagement & Adoption and member of the Extended Board of 麻豆原创 SE, NEC CIO Toshihiko Nakata shared how the company is approaching this challenge and why becoming an AI-native enterprise requires more than technology.

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NEC鈥檚 journey toward becoming an AI-native enterprise did not begin with AI; it began with a fundamental shift in how the company operates. By rethinking systems, processes, data, and organization as one, NEC laid the groundwork for continuous transformation.

Over time, this meant building a strong digital foundation, standardizing processes, embracing a clean core strategy, increasing transparency, and enabling more connected, data-driven decision-making across the business.

With this in place, NEC moved early to modernize its core systems with . The ambition went beyond cloud migration. It was about creating a more flexible and resilient environment that can evolve with changing business needs while addressing the complexity that had built up over time.

鈥淏y moving to RISE with 麻豆原创, we鈥檝e laid the foundation to modernize our corporate infrastructure and speed up our use of AI agents like Joule,鈥� Nakata-san says.

Simplifying that landscape became a strategic priority. Through its clean core approach, NEC is reducing complexity and creating conditions for faster innovation, turning its core into a platform for continuous improvement rather than maintenance.

Building on this foundation, NEC is now accelerating its next phase: scaling AI across the enterprise. Rather than treating AI as isolated use cases, the company is embedding it into everyday work, supporting employees, streamlining processes, and enabling new ways of operating. In collaboration with 麻豆原创, NEC is bringing AI closer to where decisions are made, integrating capabilities such as 麻豆原创 Business AI and Joule directly into its business processes.

Looking ahead, NEC sees AI as a defining force for the next phase of enterprise transformation. At the same time, Nakata-san emphasizes that realizing that potential requires more than technology. It requires the ability to continuously adapt, and that鈥檚 the cornerstone of NEC success.

Panagiotis Moutas is part of Executive Communications, Customer Engagement & Adoption, at 麻豆原创.

Across many enterprises, HR, time, and payroll systems have evolved through years of growth, acquisitions, and regional customization. The result is a patchwork of disconnected tools, duplicated data, and manual handoffs that quietly slow decision-making and increase operational risk. These systems may still 鈥渨ork,鈥� but they carry a hidden cost on productivity, accuracy, and confidence, as expectations on HR continue to rise and AI becomes central to how work gets done.

Fragmentation is the hidden bottleneck behind 鈥渟low鈥� decisions

The impact of fragmentation isn鈥檛 always visible, but it shows up clearly in how decisions get made.

When decisions stall, leaders often point to approvals, governance, or external constraints. In reality, much of the friction happens earlier, when teams reconcile data across systems before decisions can even begin.

According to the research, organizations with unified HR foundations gained faster access to trusted workforce information, generating insights 60% faster and creating new position listings 53% faster. Rather than adding tools, these organizations removed friction by eliminating manual validation, shadow spreadsheets, and repeated checks to confirm data accuracy.

As organizations look to AI to accelerate workforce planning, surface risks, and guide decisions, this foundation becomes even more critical. AI is only as effective as the data it can access and trust. In disconnected environments, AI inherits the same inconsistencies, delays, and gaps, limiting its ability to generate reliable insights and recommendations.

Consider a simple workforce planning decision like headcount approval. In a fragmented environment, HR pulls data from one system, finance validates it in another, and managers reconcile discrepancies in spreadsheets. What should take hours stretches into days鈥攏ot because the decision is complex, but because the data is.

With real-time, consistent workforce information, leaders can act faster and with greater confidence in their decisions. More importantly, unified data allows AI to move beyond reactive reporting to deliver proactive, decision-ready intelligence.

Most payroll errors aren鈥檛 human鈥攖hey鈥檙e structural

Disconnected systems don鈥檛 just slow work; they also increase errors.

When employee data, time records, and payroll information live in different places, every handoff becomes an opportunity for mistakes. Manual reconciliation and corrective actions become routine, especially during high-pressure cycles like payroll close.

Organizations with unified platforms see a clear shift. Payroll error rates drop by 64% and payroll cycles are completed 44% faster by eliminating data gaps and automating validation across connected processes.

This is where AI begins to shift from reactive to preventative. With unified data, AI can identify anomalies before payroll runs, flag potential compliance risks, and continuously learn from patterns across the organization. Instead of fixing errors after the fact, HR and payroll teams can prevent them altogether.

That structural shift changes the nature of work for HR and payroll teams. Payroll teams saw a 21% productivity increase, while HR teams improved productivity by 14%, as time previously spent tracking down discrepancies, correcting entries, and responding to escalations was redirected toward oversight, compliance, and continuous improvement.

Fragmentation quietly erodes trust and limits AI adoption

When systems are fragmented, trust erodes quietly. Employees lose confidence when pay errors occur or self-service tools don鈥檛 reflect their reality. Managers hesitate to act when dashboards conflict. HR teams become intermediaries between systems rather than strategic partners to the business.

Integrated HR, time, and payroll systems reverse this dynamic. Employees gain easier access to self-service tools, with 28% more employees able to directly access HR and time entry platforms. Managers benefit from real-time visibility into approvals and team data. And HR teams regain credibility as the source of accurate, timely workforce information.

Over time, this trust compounds. When people trust the system, they use it. Increased usage improves data quality, and better data strengthens decision-making.

This foundation becomes even more important as organizations scale AI across HR. Employees and managers are far more likely to rely on AI-driven recommendations鈥攚hether for career growth, scheduling, or compensation鈥攚hen they trust the underlying data. Without that trust, even the most advanced AI capabilities remain underutilized.

Fragmentation doesn鈥檛 just slow execution鈥攊t narrows what leaders believe is possible, forcing decisions to be shaped by system constraints rather than business needs.

The cost of standing still

The cost of fragmentation isn鈥檛 just operational; it鈥檚 financial, and it compounds over time.

Across organizations studied, the average annual quantified benefit totaled US$649,400 per 1,000 employees supported, driven by productivity gains, reduced errors, faster cycles, and better business decisions. Over three years,organizations achieved a 284% return on investment, with a payback period of approximately 15 months.

Beyond these quantified gains, there is a growing competitive gap. Organizations operating on unified platforms are not only more efficient, but they are also better positioned to embed AI across the entire employee lifecycle, from hiring and onboarding to development and workforce planning. Those still operating with disconnected systems risk falling behind鈥攏ot just operationally, but strategically.

The real risk isn鈥檛 innovation

Innovation draws attention because it鈥檚 new, visible, and often disruptive. Fragmentation, by contrast, builds quietly in the background until it starts to limit how the organization operates. But as organizations ask HR to deliver more鈥攂etter insights, faster planning, stronger compliance, and improved employee experiences鈥攖he limits of disconnected systems become harder to ignore.

Modern HR outcomes don鈥檛 come from layering new tools on top of outdated foundations. They come from reducing complexity, unifying data, and creating consistency across the most essential people processes. This is where platforms like 麻豆原创 SuccessFactors are evolving鈥攏ot just to unify core HR, time, and payroll, but to embed AI directly into the flow of work. By combining a trusted data foundation with AI-driven insights and automation, organizations can move from reactive operations to predictive, insight-led workforce management.

The question isn鈥檛 whether organizations can afford to modernize HR. It鈥檚 whether they can afford to limit the impact of AI by building on fragmented foundations.

AI doesn鈥檛 transform HR on its own; it amplifies what鈥檚 already there. And without a unified, trusted core, even the most advanced AI will struggle to deliver on its promise.

Learn how leading organizations are reducing fragmentation and building a strong foundation for AI by unifying core HR, time, and payroll with .

*

Lara Albert is chief marketing officer for 麻豆原创 SuccessFactors.

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Artificial intelligence is creating new opportunities for social innovation鈥攂ut many young entrepreneurs are still navigating how to use it effectively.

According to a global study conducted by The Possibilists, a global alliance for youth innovation and partner of ChangemakerXchange, more than 60% of young impact entrepreneurs believe AI can benefit their work, society, and the broader economy. At the same time, 70% of these innovators say they lack support in navigating AI tools. Through a long鈥憇tanding collaboration, 麻豆原创 and ChangemakerXchange bring together young social entrepreneurs with 麻豆原创 volunteers and professionals to exchange knowledge, explore practical use cases, and build confidence in applying AI responsibly. Matthias Scheffelmeier, co-founder of ChangemakerXchange, and Alexia von Salomon, learning designer at Education Innovation Labs and a changemaker in the European cohort, discuss which skills are essential in an AI-driven future and how AI can help improve efficiency and scale social impact.

Learn more.

鈥淩eoassunto provides dedicated support for reintegration,鈥� Proietti Pantosti said. 鈥淥ur goal is to significantly reduce the rate of reoffending among first-time convicts.鈥� As processes for reintegration are complex and time consuming, he had the idea to set up an offline, server-based AI tool to help inmates with job applications as well as an AI agent to automate the complex tax paperwork for companies that offer jobs for inmates. But he and his organization didn鈥檛 have the skills or funds to create an AI prototype to realize his concept.

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A community of changemakers

This is when came in, a global support community for young social entrepreneurs and a long-standing partner of 麻豆原创. In 2025, this organization established a for social entrepreneurs and NGOs to experiment with AI capabilities and implement AI tools and features to fix one challenge common to all social enterprises: a lack of helping hands in combination with a large volume of small, sometimes repetitive tasks.

According to Matthias Scheffelmeier, co-founder of ChangemakerXchange, young changemakers are tackling the most pressing issues of our time but are often stretched and under-resourced. 鈥淲e believe helping them mindfully and ethically adopt AI tools allows them to focus on their key expertise and therefore scale their impact in the world,鈥� he said. 鈥淭o address this, the ChangemakerXchange AI program provides customized support, in-person gatherings, and a public toolkit to help young entrepreneurs navigate AI.鈥�

As the longest-standing corporate partner, 麻豆原创 has supported social enterprise ChangemakerXchange for more than eight years. Beyond just financial support from the company, 麻豆原创 employees joined local cohorts of social enterprises, shared knowledge on AI, and brainstormed how individual ideas could be brought to life.

ChangemakerXchange initiated the Possibilists, a global alliance for youth innovation. on the needs and challenges young change makers face with AI. The study showed that while 65% use AI almost daily, 70% lack knowledge on how to navigate AI tools proactively for their purpose.

From environment to politics

Entrepreneurs in the European cohort of the ChangemakerXchange AI program cover environmental, social, and political projects.

One of them, Romania-based social enterprise Station Europe, aims to make democracy accessible, especially for young people from rural areas. 鈥淲e empower young people to engage in participatory democracy, embrace creative activism, identify and address disinformation campaigns, and design policy recommendations that reflect their communities鈥� needs,鈥� said Alin Gramescu, president & co-founder of Station Europe. To support these goals, the organization launched a collaborative platform in 2024 called that allows young people to explore new formats of political participation, taking them right into the heart of the policymaking process. Participants in hands-on workshops learn how to start from an actual issue or need and create a policy recommendation鈥攚ith AI clustering and processing workshop findings. This results in recommendations for government authorities based on the input of thousands of young people.

鈥淎I will help connect policymakers and young people. Within one year, we condensed more than 1,400 papers from over 80 workshops,鈥� Gramescu said. 鈥淥pening the platform to additional countries will exponentially increase the volume of data we will be dealing with.鈥� When asked for the value ChangemakerXchange added for his organization, he said 鈥淚 knew what I wanted to build to manage this content, but I needed the step-by-step technical guidance to make it happen.鈥�

Education as foundation for progress

Education is often described as the cornerstone of progress, and for Alexia von Salomon, concept & learning designer at Education Innovation Lab, this belief is her daily motivation.

鈥淔or me, education is the foundation for social innovation,鈥� von Salomon said. As a leader in educational transformation, she sees a lack of relevant future skills conveyed at schools in Germany and aims as high as transforming Germany鈥檚 education system.

Besides conducting workshops at schools, she creates learning experiences for teachers and pupils, like the learning platform 鈥渄igital sparks for the future.鈥� To scale reach, Education Innovation Lab focuses on self-guided learning platforms and train-the-trainer sessions for school teachers.

von Salomon uses AI to co-create and validate new concepts. 鈥淭his helped me to be more creative and think outside the box,鈥� she said. 鈥淯sing AI for early testing how minors would interact with learning content and tools reduces the iterations we need before actually conducting tests in schools.鈥�

She said that being part of the ChangemakerXchange program not only gave her the opportunity to get to know the right people in the tech industry, but to shift her perspective on AI and increase her use of AI tools. Her personal goal is to shape a future where learning is not just about knowledge, but about empowerment and transformation. 鈥淔rom my perspective, key skills for minors in a future influenced by AI will be creativity and critical thinking鈥攖o use the opportunity AI offers without suffering from the potential negative impacts,鈥� she said.

Serving business and society

More than 1,500 social entrepreneurs in over 130 countries are part of ChangemakerXchange鈥檚 global community. 鈥淭rue innovation happens when changemakers challenge the status quo and create solutions that serve both business and society,鈥� Scheffelmeier emphasized. For him, social enterprises are not just businesses, but catalysts for inclusive growth and sustainable impact. 鈥淏y combining technology with the vision of social innovators, we can scale solutions that address global challenges and build a future where profit and purpose go hand in hand,鈥� he said.

To succeed, manufacturers need more than incremental improvements or siloed optimizations. They need to orchestrate their operations end to end with connected processes and trusted data, so they can respond faster to change, operate more efficiently, remain compliant, and continue to grow鈥攅ven as disruption is constant.

At Hannover Messe 2026, the world鈥檚 leading stage for industrial transformation, 麻豆原创 will introduce a new set of AI鈥憄owered manufacturing and supply chain innovations. These innovations help companies ensure business continuity by orchestrating people, processes, and technology across their extended enterprise, turning volatility into an opportunity for resilience, efficiency, and customer impact.

From AI insight to AI in execution

For years, manufacturers have invested in analytics and dashboards to improve visibility. But visibility alone does not prevent disruption. What鈥檚 required now is AI embedded directly into core business processes, where intelligence can analyze alerts, reason over business impact, and provide real-time solutions to resolve issues. With agentic AI, companies are now able to go a step further: automating the right actions for the best outcomes, with humans remaining in the loop wherever critical decisions are required.

麻豆原创 is operationalizing AI at industrial scale by embedding AI agents directly into supply chain and manufacturing workflows and contextualizing them with trusted enterprise and network data. Built on harmonized industrial, transactional, and network data, these agents can move beyond analysis to real鈥憈ime prediction and execution, working to deliver resilience, regulatory readiness, and measurable customer impact from day one. Creating tangible ROI is what matters most鈥攚hether by reducing unplanned downtimes, scrap, and rework, or by increasing quality and ultimately production output.

Orchestrating the supply chain end to end with AI

At the center of 麻豆原创鈥檚 focus at Hannover Messe is .

麻豆原创 helps manufacturers connect processes and data not only across internal teams, but also across company boundaries鈥攚ith suppliers, logistics partners, and service providers. By using AI agents to connect design, planning, procurement, manufacturing, logistics, service, and asset management鈥攁nd by integrating seamlessly with ERP and line-of-business systems鈥斅槎乖� helps break down silos that slow decision-making and increase operational risk.

This new agentic orchestration is powered and governed by a portfolio of intelligent applications that act, not just analyze, enabling faster, more coordinated responses without sacrificing quality, control, or growth.

New AI agents redefining planning, service, and operations

At Hannover Messe 2026, 麻豆原创 will showcase AI agents that help manufacturers and operators reduce time to value, stabilize operations, and improve service levels amid ongoing disruption. As a precursor to broader announcements planned for 麻豆原创 Sapphire, these agents demonstrate how agentic AI delivers practical benefits across all supply chain domains. Here are a few examples:

Manufacturing

• Production Master Data Agent helps automate and optimize the creation and maintenance of production master data. By leveraging the bill of materials, the agent can generate production routings鈥攊ncluding operations and work centers鈥攁nd help ensure components are correctly assigned across the production process. This helps reduce manual effort, accelerate production setup, and keep production data accurate as requirements change. General availability is planned for Q2 2026.
• Production Planning and Operations Agent enables planners to release production orders using natural language while automatically validating material availability, capacity, and scheduling constraints. Joule provides recommendations鈥攕uch as alternative components or rescheduling options鈥攖hat planners can review and approve, reducing manual work and keeping production aligned with real鈥憌orld conditions. General availability is planned for Q2 2026.

Assets & services

• Field Service Dispatcher Agent can improve service responsiveness and asset uptime by dispatching the right technician based on skills, location, asset condition, and priority鈥攄riving faster resolution and better workforce utilization. General availability is planned for Q2 2026.
• Alert Processing Agent can enrich operational alerts using past incidents, resolutions, and contextual signals and recommend clear, data鈥慸riven actions to help teams resolve issues faster and improve operational reliability. General availability is planned for Q3 2026.
• Asset Health Agent analyzes time鈥憇eries health indicators to assess and summarize the current and projected health of individual and multiple technical objects. By forecasting when assets are likely to become critical and alerting users in real time, the agent supports condition鈥慴ased maintenance and helps minimize downtime while ensuring asset availability. General availability is planned for Q3 2026.

AI agents advancing logistics execution

• Material Reservation Agent helps ensure materials are available when and where needed by automating reservation creation and maintenance based on business rules鈥攔educing delays, improving inventory accuracy, and optimizing working capital. General availability is planned for Q2 2026.
• Outbound Task Orchestration Agent can protect customer service levels by detecting and resolving picking and packing issues in real time, orchestrating corrective actions to support on鈥憈ime, accurate delivery. General availability is planned for Q2 2026.

Aligning workforce, logistics, and assets in real time

Operational resilience also depends on synchronizing people with all other resources as conditions change.

With , skills, certifications, availability, and labor rules are aligned with real-time operational demand so workforce plans can adjust automatically as production changes.

In logistics, , together with the new solution, helps organizations reduce transportation costs, accelerate warehouse execution, and improve delivery performance. Using conversational interaction with Joule, order managers can prioritize fulfillment while automatically accounting for availability and scheduling constraints.

Asset and quality operations also benefit from embedded intelligence. AI-assisted anomaly detection and alert processing in helps teams identify risks earlier, prioritize actions, and reduce unplanned downtime. In parallel, 麻豆原创 Document AI can automate the , improving throughput, data quality, and compliance at scale.

Regulatory readiness and what鈥檚 next

As regulatory requirements tighten, 麻豆原创 is expanding support for Digital Product Passports as part of , aligned with the EU鈥檚 Ecodesign for Sustainable Products Regulation (ESPR). These capabilities help manufacturers create ESPR鈥憆eady product records capturing environmental impact, material composition, repairability, and recyclability data. General availability is planned for Q2 2026.

Expanded 麻豆原创 Business Network capabilities also deliver built鈥慽n e鈥慽nvoicing compliance and data-residency support, enabling secure partner collaboration, synchronized logistics, and improved delivery performance across global networks.

See it live at Hannover Messe 2026

Taken together, these innovations reflect a shift from reactive management to intelligent execution鈥攚here AI is embedded directly into the processes that keep manufacturing and supply chains running today while laying the foundation for the next wave of innovation that will be unveiled at 麻豆原创 Sapphire.

Visit 麻豆原创 at booth F08 in Hall 15 at Hannover Messe 2026, April 20鈥�24, to see how AI-infused orchestration, embedded AI agents, and end鈥憈o鈥慹nd supply chain applications are redefining manufacturing.

Dominik Metzger is president and chief product officer for 麻豆原创 Supply Chain Management.

With the听听1H鈥�2026 release,听we鈥檙e听deepening those connections across the HR lifecycle. This release focuses on听four听core priorities:听connected,听suite-wide听AI; unified experiences that adapt to how organizations work;听processes designed for clarity, accuracy, and compliance; and stronger foundations for skills and long-term growth.听Together, these innovations help organizations听anticipate听needs earlier, reduce friction in daily work, and move forward with greater confidence.听

Connected AI that works across HCM

AI in HR delivers the greatest impact when it works continuously across the entire workforce lifecycle鈥攏ot as isolated features, but as connected capabilities that share context and insight.

The 1H鈥�2026 release expands听suite-wide听agentic AI听across 麻豆原创 SuccessFactors solutions, helping听employees听get clearer answers, act sooner, and keep听work moving听across roles and responsibilities.听A connected network of听听now supports areas such as recruiting, workforce administration, payroll, learning, performance, and talent development鈥攚orking together behind the scenes to help听anticipate听next steps and surface relevant guidance.

This release also introduces a growing听workforce knowledge network, bringing trusted external expertise and research directly into the flow of work through Joule.听Teams can now access expert-backed global employment guidance and听research-driven听insights without leaving their workflows鈥攕upporting听faster, more听confident decisions.

To听further听support learning in the flow of work,听intelligent Q&A in听听now helps employees find information more easily. AI听can deliver instant,听context-aware听responses drawn directly from an organization鈥檚 learning content,听along with relevant links and resources,听so employees can get answers quickly without searching through courses or documentation.听

Unified experiences that adapt to how work gets done听

As HR听tasks听become more embedded in听day-to-day work, experiences need to feel intuitive, connected, and responsive听wherever work happens. In the 1H 2026 release,听麻豆原创 SuccessFactors solutions continue to unify experiences across the suite, giving employees, managers, and HR teams what they need听in听the moment.听

• Connected recruiting and onboarding:听Native integration between听 solutions, , and听听can bring AI-enabled听recruiting, core HR, and onboarding together into a single, continuous experience, helping hiring teams move faster while听maintaining听consistency from candidate through new hire.听

• Tailored experiences,听built faster:听The new听extensibility wizard听can provide guided, step-by-step support for creating custom extensions on听听(麻豆原创 BTP) directly within听麻豆原创听SuccessFactors solutions, making it easier to adapt experiences to unique business needs while preserving governance.听
• Simpler, clearer employee moments:听A redesigned, configurable 401(k) experience听in听听for U.S.听employees helps simplify enrollment and management by clearly explaining employer contributions and guiding deferrals and beneficiary setup, helping employees make informed decisions with confidence.听

Processes designed for clarity, accuracy, and compliance听

In the 1H鈥�2026 release, 麻豆原创 SuccessFactors introduces new capabilities that help organizations bring greater clarity and rigor to pay practices.

With听pay听transparency insights听in the , organizations can analyze compensation patterns and potential pay gaps, supporting transparent, data-driven pay practices in-line with evolving regulatory expectations,听including in the EU.听

Skills governance听for sustainable growth听

Preparing for听what鈥檚听next requires trusted, consistent skills data that organizations can rely on across HR, talent, and workforce planning.

In the 1H鈥�2026 release,听we are听strengthening听the听听with enhanced听skills governance, providing a centralized interface to help manage skills, apply governance standards, and ensure alignment across 麻豆原创 SuccessFactors solutions and partner applications. This helps organizations improve听skills听data quality, maintain consistency at scale, and make more confident,听skills-based听decisions.听

A connected foundation for the future 

This听release听reinforces听麻豆原创鈥檚 continued focus on an intelligent, connected HCM听foundation鈥攐ne designed to evolve with your organization and support confident decisions at every stage of work. By bringing together data, AI, and experiences across the HR lifecycle, these听enhancements help organizations reduce friction today while听laying听the groundwork for听tomorrow.

To explore what鈥檚 included in this release, check out the or watch the overview .

Bianka Woelke is group vice president and head of Application Product Management for 麻豆原创 SuccessFactors.

But new research from 麻豆原创 and Wakefield* suggests a different reality is emerging. AI isn鈥檛 making early talent irrelevant. Instead, it鈥檚 accelerating how quickly they become productive, reshaping the earliest stages of work, and raising expectations far earlier in the employee lifecycle.

According to the findings, 88% of CHROs say AI is making early-career talent role-ready faster. This acceleration raises the stakes on both sides. While organizations benefit from faster productivity and earlier impact, early鈥慶areer employees are entering roles with heightened expectations and fewer traditional learning buffers鈥攆orcing leaders to rethink how success is defined and supported from day one.

AI as an accelerator of readiness

Entry-level roles have long relied on repetitive, lower-stakes tasks that helped new employees learn how work gets done. Today, AI automates much of that foundational execution.

This shift is increasingly common: 79% of surveyed CHROs report that their early-career talent receives enterprise AI tools within their first month on the job. Additionally, 87% expect new hires to be comfortable with AI on day one or learn the tools immediately after joining.

With AI absorbing traditional tasks, early-career talent is stepping into meaningful work sooner鈥攁nd CHROs are already seeing the impact, with 56% reporting improved confidence and 55% citing increased productivity among those using AI.

This acceleration reflects themes we first explored in the 2025 麻豆原创 SuccessFactors Future of Work Predictions , where we examined how AI might reshape entry鈥憀evel roles. As foundational tasks continue to be absorbed by AI, the question becomes not whether early鈥慶areer roles will exist, but how organizations can redesign them to build capability in new ways.

When productivity accelerates, expectations follow

As early talent ramps faster, the expectations placed on them are rising just as quickly. Several structural factors are contributing to this shift: organizations are hiring fewer early-career talent, and those who do join are expected to take on more complex work earlier in their tenure. Our upcoming research from our makes this clear, as one research participant summarized, 鈥淓ntry level roles used to be focused on mundane tasks鈥攚hat should they do now? They bring an incredibly unique perspective; we want to hire early talent to challenge our norms and help us find better ways of working.鈥�

But with AI removing the mundane work, it may also remove many of the gradual, hands-on learning moments that once helped new hires build experience over time.

With these rising expectations, it鈥檚 easy to see how the cognitive load of entry level roles could increase substantially. CHROs report heightened performance pressure and increased mental effort as new hires try to keep pace with AI-accelerated work. Some researchers refer to this dynamic as 鈥�,鈥� the cognitive strain that comes from managing rapid, AI-driven workflow.

Together, these shifts create several risks for both employees and organizations:

• Shadow AI use rises: 56%of CHROs say early-career talent turns to unsanctioned AI tools when formal guidance is unclear. This behavior may reflect entry-level hires trying to keep pace rather than intentionally breaking policy.
• Inconsistent enablement creates talent risk: 44% of CHROs say uneven access to AI tools increases attrition risk, especially for early talent who may feel unable to live up to new performance expectations without tools to automate routine tasks.
• Foundational skills may erode: Even as AI boosts productivity, 38%of leaders worry early-career talent are not building long-term skills like communication, critical thinking, judgment, and collaboration. That concern is echoed in qualitative feedback from HR leaders as well. As one noted, 鈥淲e鈥檝e observed gaps in professionalism in business settings for entry鈥憀evel talent, from collaboration and stakeholder management [to] ownership and accountability.鈥�

Rethinking the first step into work

As traditional early鈥慶areer learning pathways narrow, organizations must now redesign how those learning moments happen. Our research points to several areas where HR leaders can intentionally strengthen the early-career ramp:

1. Build foundational skill development intentionally.

As repetitive tasks disappear, organizations have the opportunity to deliberately create new ways for early talent to build communication, collaboration, critical thinking, and decision-making skills. This can include structured, project-based experiences, clearer decision-making frameworks, and more frequent coaching that focuses on judgement and prioritization, not just task completion.

2. Design entry-level roles around higher-value work.

Early-career employees are capable of contributing more strategically when roles are designed with the right balance of scope and support. Redesigning entry鈥憀evel positions to include clear ownership鈥攕upported by explicit expectations, mentoring, and well鈥慸efined guidance for decisions and escalation鈥攈elps early鈥慶areer talent build confidence while managing risk.

3. Establish AI governance from day one.

Without clear guidance, early talent may struggle to understand how to use AI responsibly. Introducing AI expectations during onboarding, reinforcing role-specific best practices, and normalizing manager-led conversations about AI use can reduce shadow AI and build trust in new technologies early on.

4. Ensure equitable AI access across teams and managers.

As expectations rise, uneven access to AI tools can quietly increase workload pressure and stress for early-career employees. Providing consistent access, training, and enablement helps ensure new hires are equipped to meet accelerated demands without increasing burnout or attrition.

The bottom line

AI isn鈥檛 eliminating early-career talent from the workforce; it鈥檚 reshaping the path they take to become effective and increasing the value of the work they contribute. While entry-level roles may be fewer, expectations for impact are higher, placing greater importance on pairing AI fluency with strong human skills. For new graduates, developing both will not only help them land a job but also enable them to contribute quickly and build lasting capabilities.

When early鈥慶areer talent becomes productive sooner, companies can move faster, innovate earlier, and operate more efficiently, but only if that speed is matched with structure, coaching, and intentional development. Organizations that navigate this transition successfully will ensure early talent doesn鈥檛 just ramp up faster, but also builds the judgment, collaboration, and critical鈥憈hinking skills that AI can鈥檛 replace.

To stay on top of more upcoming research on the impact of AI on entry-level roles, visit our .

Lara Albert is chief marketing officer for 麻豆原创 SuccessFactors.

*The 麻豆原创 AI Talent Survey was conducted by Wakefield Research (www.wakefieldresearch.com) among 100 US CHROs (or CPO equivalent) at organizations with a minimum annual revenue of $500m where employees are using AI-enabled tools in their day-to-day responsibilities, between February 19th and March 2nd, 2026, using an email invitation and an online survey.