The anniversary offers an opportunity to look beyond any single year and consider how procurement has changed since the first year of this survey in 2022. What stands out is that the function has not followed a simple path from tactical to strategic. Its mandate has broadened, but so has the expectation to prove its value: control cost, anticipate risk, strengthen supply continuity, and turn technology investment into measurable outcomes.

Five takeaways from the research help show where procurement has been and where it may be heading next.

Takeaway 1: cost never left the job description鈥攂ut it鈥檚 now more important than ever

In 2022, procurement leaders were still operating in the shadow of the pandemic. Supply chain risk ranked as the leading organizational risk, while COVID-19 disruption was the top driver of digital transformation.

As conditions changed, procurement鈥檚 agenda expanded to resilience, visibility, supplier diversification, and digitalization. But cost never disappeared. In 2022, cost savings led the areas where respondents believed procurement could deliver greater value. In the 2026 study, 54% of executives identify cost savings and optimization as procurement鈥檚 primary contribution to the organization. The line of questioning is not directly comparable year over year, but the broader pattern reveals that a more strategic mandate has not lessened the importance of financial discipline.

That is not a retreat. Modern cost management requires understanding demand, supplier economics, working capital, risk, and the operational consequences of commercial decisions. Procurement鈥檚 cost mandate has become more sophisticated, not less important.

Takeaway 2: digital transformation has moved from funding to execution

One of the clearest changes is what now stands in the way of transformation.

In 2022, budget was the leading barrier to digital transformation at 30.2%. By 2026, it dropped to fourth on the list, as ease of deployment became the primary concern of 71.7% of executives, a category that did not exist as a response option in 2022. Budget still matters, but it is no longer the defining obstacle.

Procurement spent years making the case for technology investment. The conversation has moved past 鈥淐an we afford it?鈥� to 鈥淐an we actually implement it?鈥� Procurement leaders are now challenged to embed technology across real processes, data environments, and organizations.

The motivation for transformation has also shifted. In 2022, pandemic disruption was driving both preparation and investment. Today鈥檚 expectation of flat-to-cautious growth is more structurally normal, but the pressure to do more with less remains a constant. The question for today鈥檚 procurement teams is whether they can deploy new technology effectively enough to generate repeatable business value.

Takeaway 3: AI has raised both the opportunity and the standard

The latest findings make the scale of the AI shift unmistakable. Sixty percent of executives name digital transformation as procurement鈥檚 top strategic priority for the next 12 to 18 months, up from 38% in , with agentic AI emerging as the most sought-after near-term technology.

But enthusiasm is now accompanied by greater scrutiny. As AI enters category management, intake, sourcing, and other workflows, procurement leaders need to be clear about the business outcome being improved, the data required, and the controls governing how AI acts.

The five-year view shows how quickly expectations have advanced. In 2022, digital technology broadly led the category-management improvement agenda at 35.8%. By 2023 and 2024, priorities had grown more specific, centering on cost analysis, scenario planning, and data quality. By 2026, AI-driven and predictive insight is the dominant category-management priority, cited by 66.6% of respondents.

Technology is shifting from helping procurement professionals find information toward helping them interpret choices and execute defined tasks. As that happens, the value case must move with it鈥攆rom access to information toward measurable improvements in decisions and outcomes.

Takeaway 4: strategic influence is becoming more operational

For years, procurement leaders have talked about earning a 鈥渟eat at the table.鈥� But the research suggests that where procurement sits on the organizational chart may be becoming a less useful measure of its strategic influence.

In 2022, the COO was already the largest procurement reporting line at 33.5%, with the CFO close behind at 29.3%. By 2026, the COO share has risen to 44%, while the CFO share stands at 24.4%, and reporting lines to the CEO have declined from 18.8% to 15.7%.

This suggests that procurement鈥檚 strategic relevance is increasingly expressed through operational execution. Influence comes from improving decisions across cost, supply, risk, and technology鈥攏ot from where procurement appears on an organizational chart.

The results across recent years reinforce that shift. In 2023, 53% of executives agreed that procurement effectively collaborated with the rest of the organization to meet the company鈥檚 vision. That rose to 75% in 2024 and 90% in 2025, pointing to growing integration between procurement and the wider business.

That growing integration does not necessarily mean procurement has secured a permanent strategic role. In 2024, 70% of executives said procurement was actively involved in developing the wider organization鈥檚 digital transformation strategy, although only 18% strongly agreed. By 2026, executive confidence in procurement鈥檚 influence over digital transformation had fallen to 68%, down from 91% in 2025.

The contrast is telling: procurement is becoming more integrated with the business, but greater visibility does not automatically translate into sustained strategic influence. A seat at the table matters less than what procurement delivers once it is there.

Takeaway 5: the skills equation has changed

Technology is not the only thing procurement teams have had to reinvent. The capabilities required to use it effectively have changed just as quickly.

In 2022, skills gaps were relatively dispersed across technology (34.6%), category knowledge (32.0%), risk management (26.8%), and customer experience (25.6%). By 2025, AI proficiency and ethics had emerged as the leading skills priority at 68.2%, far ahead of more traditional procurement competencies at 34.1%.

But there is another part of the skills picture that deserves attention. In that same 2025 research, curiosity was prioritized by just 10% of respondents. That stands in contrast to what I continue to hear from procurement leaders, who increasingly describe curiosity as one of the skills that will separate the teams that thrive in the AI era from those that merely use the technology.

Traditional procurement expertise is not obsolete. If anything, more capable technology increases the value of professionals who understand categories, suppliers, markets, and organizational change. AI fluency on its own is not enough. As technology takes on more work, professionals need the curiosity to question outputs, challenge assumptions, explore what is changing in a market, and bring business context to the decisions technology helps inform.

The opportunity lies in combining deep commercial knowledge with greater AI fluency. The procurement professional of the next five years will need to know not only how to use new tools, but when to challenge their outputs, how to apply business context, and where human judgment still matters most.

The next chapter is about proof

Five years ago, procurement transformation was heavily shaped by external shocks. Today, organizations are making more deliberate choices about AI, analytics, talent, and operating models while being asked to deliver savings, manage volatility, and improve productivity simultaneously.

The next chapter is about forging a clear connection between strategic and cost value, and between human expertise and AI capability. Five years of research suggest procurement has earned a broader role. The next five will be about proving what it can do with it.

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

Payroll presents a unique paradox. It鈥檚 one of the business functions most suited to AI and automation because it鈥檚 highly structured, data-intensive, and compliance-driven. At the same time, it鈥檚 one of the most trust-sensitive functions in the enterprise because even small errors can have immediate consequences for employees’ financial well-being and confidence in their employer.

Payroll professionals often joke that nobody notices payroll when it goes right, and everyone notices when it goes wrong. Behind that observation is an important reality: every payroll run becomes a moment of trust between employer and employee. That trust has measurable consequences. According to new research* by 麻豆原创鈥檚 , 38% of employees worldwide have experienced a payroll error. These employees are significantly less likely to trust both their organization (13% lower trust) and their payroll function (17% lower trust). Trust declines even further with each additional payroll error experienced.

As National Payroll Week takes place across both the and the , we have an opportunity to recognize the payroll professionals who earn and maintain that trust every day, while reflecting on the transformation taking place across the payroll function itself.

As payroll teams navigate growing complexity, they’re being asked to do more with less while maintaining the accuracy, compliance, and reliability employees depend on. Employees increasingly expect faster, more flexible payroll experiences, including more frequent pay cycles and greater visibility into earnings and deductions, while tax laws, wage rules, leave requirements, and reporting obligations continue to evolve across countries, states, and local jurisdictions. At the same time, today’s workforce is more dynamic than ever, with employees changing roles, locations, schedules, and compensation arrangements in ways that can create downstream payroll impacts. Managing these changes manually is becoming increasingly difficult and raising the risk of errors, delays, and compliance gaps, positioning payroll not simply as an administrative process, but as strategic infrastructure supporting employee experience, compliance, finance, and business operations.

This combination of rising complexity and rising expectations is driving the next evolution of payroll: autonomous payroll.

Scaling trust in an era of complexity

Autonomous payroll is designed to help organizations navigate growing payroll complexity while maintaining accuracy, compliance, and confidence at scale. By combining AI, intelligent automation, integrated data, and real-time payroll monitoring, organizations can move beyond reactive payroll operations and proactively identify issues before they affect employees.

The goal isn’t to remove people from payroll, but to enable them to focus on higher-value activities by reducing manual effort and increasing visibility. Our global research suggests that employees do not see AI and human involvement as mutually exclusive. In fact, nearly half (49%) say they would trust AI in payroll more if they still had access to a human when AI fails. Additionally, 43% of employees say that the ability to request a human review of AI-generated outcomes would increase their trust in the use of AI in payroll. Trust is built not only through accuracy and efficiency, but also through the confidence that employees can escalate concerns, seek clarification, and access human support when needed.

Emerging capabilities such as AI-powered payroll agents and continuous payroll are helping organizations move from processing payroll to actively managing it. These capabilities can assess payroll readiness, detect anomalies, validate changes, and monitor workforce events across HR, payroll, and time data. By identifying how changes in roles, locations, schedules, compensation, or leave may affect payroll outcomes, organizations can anticipate impacts and resolve exceptions before they become costly errors. The result is improved reliability, stronger compliance, and greater trust across the workforce.

The shift is significant. Rather than finding errors at the end of a payroll cycle, organizations can increasingly investigate and resolve them throughout the cycle. But technology alone isn’t enough. As payroll becomes more autonomous, organizations must ensure intelligent systems operate with appropriate oversight, transparency, and accountability.

Automation requires accountability

As AI becomes more embedded in payroll operations, the conversation shifts from adoption to accountability. Trust becomes paramount, raising important questions about where AI can operate autonomously, where human review adds value, and where human accountability must remain visible.

Our global research shows that employee comfort with AI varies by payroll task. Employees are most comfortable with AI supporting calculation-heavy activities such as payroll calculations and anomaly detection, while they prefer human involvement for tasks requiring judgment, investigation, or employee interaction. Final decisions on payroll disputes remain the area where employees most strongly prefer a human. These findings reinforce an important point: the goal is not to replace human expertise, but to apply AI where it adds the most value while preserving human oversight where trust and judgment matter most.

The most successful applications of AI in payroll won’t simply automate tasks. They’ll strengthen trust by improving consistency, reliability, transparency, and the overall payroll experience employees depend on.

In other words, the future of payroll isn’t human or AI. It’s human expertise amplified by AI.

Building the foundation for Autonomous HCM

The implications of autonomous payroll extend well beyond payroll operations.

At 麻豆原创, we believe autonomous payroll is a key enabler of Autonomous HCM. Trusted AI experiences depend on trusted data, connected processes, and embedded intelligence, and payroll plays a critical role in making that vision possible.

Every hiring decision, compensation adjustment, promotion, workforce change, or organizational initiative ultimately affects payroll. As a result, payroll remains one of the most trusted and comprehensive sources of workforce data across the enterprise.

Realizing the full potential of autonomous payroll requires a unified foundation across HR, payroll, and time data. When organizations establish a single source of truth, they can reduce complexity, improve payroll accuracy, strengthen compliance, and enable AI to deliver more meaningful insights and recommendations.

This foundation is essential because AI cannot solve payroll challenges if it鈥檚 simply layered on top of fragmented systems and disconnected processes. Instead, organizations need connected data and integrated workflows that allow intelligence to operate across the entire workforce lifecycle.

When payroll, HR, and time data come together on that foundation, organizations can create more intelligent workforce experiences, make better decisions, and increase organizational agility.

Looking ahead

National Payroll Week is an opportunity to celebrate the professionals who keep one of the most important business functions running every day. It’s also an opportunity to recognize how dramatically that function is evolving.

For decades, payroll was viewed primarily as an administrative necessity. Today, it is increasingly recognized as a strategic capability that influences employee experience, compliance, operational resilience, and organizational trust.

The organizations that lead in the years ahead won’t simply process payroll more efficiently. They’ll create payroll operations that are more intelligent, more connected, and better positioned to support both employees and the business.

As payroll continues its evolution from a transactional function to a strategic business capability, the opportunity is not simply to automate existing processes. It’s to build payroll operations that can scale trust, resilience, and confidence in an increasingly complex world.

Because when payroll works, it does more than deliver pay. It helps build confidence in the organization behind it.

Discover how helps organizations simplify payroll, reduce complexity, and accelerate their journey to autonomous payroll.

*Data from a global survey of 1,576 full-time employees in July 2026.

Generative AI is moving quickly from experimentation to essential infrastructure. Employees have woven it into routine daily tasks, and teams and applications are leaning on it more heavily every quarter. That growth comes with a new cost category finance wasn鈥檛 designed to handle: AI token consumption.

When AI is writing code and powering agents, token consumption can outpace traditional planning processes. Finance leaders suddenly find themselves in unfamiliar territory, needing to bring discipline to AI spending without becoming the function that kills adoption. Getting the balance right means treating tokens as an enterprise resource that must generate returns commensurate with its cost.

At 麻豆原创, we have been working through these questions firsthand. Here鈥檚 what we鈥檝e learned from building, testing, and adjusting that framework.

You can鈥檛 manage what you can鈥檛 see

Most finance leaders wouldn鈥檛 manage a major cost category from a single line item, yet that鈥檚 exactly where companies start with AI. Total spend matters, but it won鈥檛 tell you which teams, workloads, or usage patterns are driving that spend, or whether the consumption is actually producing useful outcomes.

Getting that visibility requires real collaboration across commercial, engineering, finance, and product teams. Finance brings the forecasting questions and accountability framework. Other functions bring the operational context that makes the numbers meaningful.

In our experience, repeated forecasting cycles generally improved our financial models as we layered in more operational detail around usage. The broader lesson: when a cost category is new and fast-moving, don鈥檛 wait for precision before acting. Start with enough transparency to make better decisions, then sharpen the model as patterns emerge.

Someone has to own it

Our most consequential insight was philosophical rather than financial. We learned that visibility alone isn鈥檛 enough and that consumption needs an owner.

A centralized AI budget makes early experimentation easy, but it also disconnects the people spending tokens from any financial accountability for them. As AI becomes more deeply embedded in business processes, that model breaks down.

This isn鈥檛 an argument for immediately charging back every LLM call with forensic accuracy. It鈥檚 an argument for managing AI consumption the same way companies manage other enterprise resources such as software, external services, and labor. Give decision-makers a clear picture of what their teams are consuming and what outcomes that consumption is expected to produce.

At 麻豆原创, allocating token costs to business areas has shifted the conversation from 鈥淗ow much are we spending?鈥� to 鈥淲hat are we getting for this?” and “Is this the right place to invest more?鈥� That鈥檚 a healthier conversation.

Token spend is not a technology line item. It is a strategic and operational investment decision. The goal is to make AI spending intentional, not just cheap.

Cost per token is the wrong scorecard

A large AI bill draws attention, but optimizing purely on cost can lead to exactly the wrong decisions.

The more useful question is the relationship between consumption and business impact. An AI tool that meaningfully accelerates software development, reduces repetitive work, or improves customer service will carry real token costs. Cutting that usage simply because the line item is visible could destroy more value than it saves.

We saw this firsthand. After rolling out AI developer tools at 麻豆原创, we recorded a mid-double-digit percentage increase in pull-request merge rates, a clear signal that development work was moving faster. The consumption was worth it.

Finance needs a paired view: cost metrics alongside value metrics. Governance without that view risks optimizing for cost at the expense of value creation.

Guardrails should target waste, not adoption

As usage scales, controls become necessary, but the right controls are surgical, not sweeping. When we examined consumption patterns in detail, three root causes of disproportionate spend emerged: power-user and automated-agent concentration, model misalignment, and tool proliferation.

Those are the areas where guardrails earn their keep. Controls matter because they focus on the sources of avoidable spend rather than putting a blanket brake on usage. At 麻豆原创, our response centered on three levers: token capping to prevent runaway consumption, model routing to better match capability and cost to the task, and tool rationalization to eliminate redundancy and concentrate investment where utilization justified it. That work helped contain a triple-digit-million-dollar financial risk while keeping adoption moving forward.

The principle is simple: remove waste, preserve productive demand.

From cost control to value governance

AI isn鈥檛 going to pause for the next planning cycle. Its capabilities, usage patterns, and economics will keep shifting, and the governance model around it needs to keep pace.

The work is not complete. The next step is to embed these practices into regular planning and reporting, assign clearer ownership, improve allocation, and build forecasting capabilities that can anticipate where costs are heading before they arrive.

Companies that do this well will still have an AI bill to pay. But they will have the transparency and accountability to tell the difference between consumption that is creating value and consumption that isn’t and direct investment accordingly.

Lukas Deutsch is chief controlling officer at 麻豆原创.
David Imbert is chief marketing officer for 麻豆原创 Financial Management.

has long used frustration-free, quiz-style games to create these connection moments for people living with Alzheimer鈥檚 and dementia. Now, with help from 麻豆原创 and , the organization is exploring how AI can personalize its games at scale.

鈥淲hat we saw, as AI was coming in, was that we could take two or three more steps to really personalize the experience and trigger positive memories for each individual,鈥� Bruce Elliott, CEO and cofounder of Memory Lane Games, says. 鈥淏ut, as a six-person startup on the Isle of Man, a little island in the middle of the Irish Sea, it was a daunting task. We had a brilliant vision, but to bring it together we needed support.鈥�

Collaboration for good

Through the MovingWorlds鈥� platform, Memory Lane Games collaborated with 麻豆原创 and EY to move beyond generic reminiscence content, creating an AI prototype that is designed to help transform personal details, family photos, and life experiences into memory games.

This project was part of , a MovingWorlds cooperation between 麻豆原创, EY, and Microsoft that provides social enterprises鈥攖hat often have limited resources, complex technical needs, and a lack of access to partners鈥攚ith strategic coaching, pro-bono consulting, expert-led workshops, and access to a global partner ecosystem at no cost.

For employees at companies like 麻豆原创 and EY, these engagements are equally transformative. Real-world problem-solving with social enterprises is among the most effective forms of experiential learning, helping to develop skills that benefit both the individual and the organization.

Through this program, Memory Lane Games gained access to the technical support needed to explore how generative AI could deepen personalization in its games. To start, the project team鈥擶ade Tsai, global client technology architecture leader at EY, and 麻豆原创 developers Robin Baeurle and Michael Zadikowitsch鈥攍ooked at what the organization had done in the past and how it could be transformed by generative AI. The intersection of personalization and images was where the team landed.

Turning memories into personalized games

The foundation of the project team鈥檚 prototype is a persona profile that the AI can reference. A caregiver or loved one creates a profile for the person living with dementia with basic information like age bracket, gender, first language, places they鈥檝e lived, what they did for work, and cultural background as well as other details like past vacations, favorite foods, hobbies, pets, and more. The goal is to capture the culture of the individual, not personal data. Using that persona profile, the AI suggests topics and then generates games by pulling in open-source images and writing multiple-choice questions.

For example, a demo of the Memory Lane Games prototype showed the creation of a persona profile of a 75-to-80-year-old woman who lived in Savannah, Georgia, was an elementary school teacher for 35 years, vacationed on Hilton Head Island and in the Blue Ridge Mountains, had a tabby cat named Magnolia, and enjoys Motown and soul music, gardening, birdwatching, and cooking Southern classics like pecan pie and shrimp and grits. The AI prototype generated several games for her: Savannah鈥檚 Historic Squares, Blue Ridge Mountain Getaways, Savannah鈥檚 Southern Kitchen Favorites, and Magnolia the Cat and Backyard Birds.

鈥淎t the start, we didn鈥檛 know how to solve this problem of extracting metadata from images, or even how to collect images, where to find them, and how to create these games,鈥� Zadikowitsch says. 鈥淎nd then we started experimenting and trying things out鈥攇enerating questions and then finding images, which didn鈥檛 work well, then finding images and then generating questions, which worked better.鈥�

The prototype is currently in the testing stages. Once it is deployed, there is the possibility to expand game personalization with photos submitted by a caregiver or loved one.

Tsai explains that current AI models can identify objects in images, but directing them to extract metadata and EXIF data, which contains the exact date, timestamp, and GPS coordinates indicating when and where the photo was taken, can take it a step further.

The layers of information stored within a photo file can be a treasure trove for Memory Lane Games鈥� AI prototype. 鈥淲e can use that information as a hint to extrapolate what else we can pull from the surroundings that could help reshape that memory experience,鈥� Tsai says. 鈥淚t鈥檚 going beyond where that photo is taken.鈥�

AI with tangible human impact, not just productivity gains

While the AI prototype helps Memory Lane Games create personalized games more efficiently, its greater promise is human: helping people living with dementia and loved ones connect through joyful memories.

鈥淥ften in the context of AI, the typical audience is people working at other companies, not, for example, people with dementia,鈥� Baeurle says. 鈥淭here鈥檚 a lot of potential that goes to waste in not realizing that these audiences can also benefit from digital solutions and, specifically, AI-powered solutions.鈥�

For the project team, that human outcome was the guiding principle. 鈥淲hatever we build, it should spark joy,鈥� Baeurle adds.

鈥淐aregivers are really busy. Family members are really busy. Care staff in care homes are very busy. If we can take one photo from a family and a couple lines of text and create six to 10 questions that can really pull out all of those memories, trigger a positive memory, and start those wonderful stories鈥攖hat鈥檚 what [this collaboration] has been able to help us deliver,鈥� Elliott says.

AI makes it possible to create memory games not only about a city, but about a neighborhood, a local landmark, or another detail closely tied to someone鈥檚 life. The more personal the prompt, the more likely it is to encourage conversation and connection.

鈥淲e know that social isolation is one of the top modifiable risks for dementia in older age, so the more we can get people talking and those neurons firing, just by letting them talk about something they want to talk about, is powerful,鈥� Elliott says.

The future of AI-enabled memory care

What this collaboration between Memory Lane Games, 麻豆原创, EY, and MovingWorlds showed is that AI has a place in memory care. 鈥淭he team demonstrated that AI can help create engaging [memory] games. I think this was the goal,鈥� Zadikowitsch says.

Tsai attended the United Nation鈥檚 International Telecommunication Union (ITU) this summer, after winning the 鈥淎I for Good for Entrepreneurship鈥� category in EY鈥檚 inaugural AI for Impact Challenge with this Memory Lane Games project.

The project team is confident that this is just the beginning for AI-enhanced memory care and has already come up with more use cases: an AI companion optimized for dementia that runs on speech-based interfaces and emotional and voice intelligence, adding more languages since dementia patients often revert to their first language, and using generative AI to create images of places for which no photos exist.

鈥淲e鈥檝e always taken a very simple yet scalable approach. And this [project] has taken both of those: kept it simple for the user but made it infinitely scalable. And I think that鈥檚 where the real magic of AI is,鈥� Elliott says. 鈥淎I is the key to us scaling, and we needed this collective help, this collaboration, in order to test those interesting hypotheses. And we鈥檝e seen positive early results.鈥�

For Memory Lane Games, that magic is not technology for technology鈥檚 sake. It is the possibility of helping more people reconnect with the stories, places, and people that make life feel familiar and spark joy.

MovingWorlds has supported more than 3,000 successful projects across 110 countries, unlocking more than $50 million in pro-bono consulting expertise for social enterprises. If you are a social enterprise in need of skilled support in AI or another line of business, for support on the MovingWorlds’ platform. 

NTT DATA is a $30+ billion global leader in AI, digital business and technology services, serving 75% of the Fortune Global 100.

The 麻豆原创 solutions will help NTT DATA replace multiple legacy HR systems with one unified platform for people data and processes, strengthening decision-making, employee experience and workforce planning. 麻豆原创 SuccessFactors solutions will serve as the system of record for people and talent data, working alongside NTT DATA’s existing employee service platform and specialist workforce planning tools. 麻豆原创 Business Data Cloud will connect this data with insights across other business functions.

鈥淭he initiative reflects NTT DATA’s view of talent as a strategic differentiator and AI as a capability that should be embedded across enterprise organizations,鈥� said Stijn Nauwelaerts, Chief People Officer, NTT DATA, Inc. 鈥淯ltimately, this is about creating an environment where our people feel empowered to do their best work, wherever they are in the world.鈥�

The deployment builds on a strategic partnership between 麻豆原创 and NTT DATA spanning more than 36 years, during which NTT DATA has collaborated as an 麻豆原创 platinum partner, global service partner and global reseller for 麻豆原创. In 2025, NTT DATA adopted 麻豆原创 Cloud ERP Private solutions to modernize its core systems.

麻豆原创 SuccessFactors solutions will now be deployed internally at NTT DATA over a 12-month period, with the company applying its own 麻豆原创 expertise to design and roll out the platform. This will create a single, authoritative source of HR data and processes, laying the foundation for faster, more consistent HR services across the organization. Leading its own implementation will also strengthen NTT DATA’s ability to guide clients through AI-driven HR transformation, with firsthand experience of the solutions it delivers.

鈥淣TT DATA is demonstrating how AI and cloud technology can redefine the employee experience,鈥� said Thomas Saueressig, Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE. 鈥淲ith a unified, intelligent HR platform, the company will unlock new levels of productivity and scale a people strategy that supports a connected workforce worldwide.鈥�

Visit the . Get 麻豆原创 news via  and .

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In an interview, 尝颈茅苍补谤诲 talks about the evolution of 麻豆原创’s proprietary programming language and ABAP platform, and discusses what the innovations announced at 麻豆原创 Sapphire will mean for customers.

Q: In our first conversation, you explained what ABAP is, what the future looks like, and why agentic AI will change the market. Let’s start from there and then dive deeper. How does ABAP interact with other development technologies in today’s 麻豆原创 landscapes?

Sonja 尝颈茅苍补谤诲: ABAP has been around for over 40 years and has been evolving ever since. With the era of agentic AI, we are now reaching the next stage of that evolution. The modern version of the development model is ABAP Cloud, which has been developed from the outset according to the guiding principles of openness and comprehensive support for the business logic.

We have robust, well-defined APIs that are interfaces to the outside world and offer a full environment with Open Data Protocol (OData) services, modern front ends, and an integration layer. This enables the development of modern web applications. This is in addition to 麻豆原创 Business Technology Platform (麻豆原创 BTP) extensions and an external system like 麻豆原创 Integration Suite, which can also be integrated.

We currently use Eclipse as a development environment. Starting in Q2 2026, ABAP development聽is聽also possible in Visual Studio Code, which is a milestone. The community was quite vocal in requesting this and it was an open door for us. Visual Studio Code is gaining ground and is currently the preferred IDE (integrated development environment) for many developers. Many AI extensions on the market being optimized for it first.

We have redesigned the architecture and are pursuing an open IDE strategy where we can also offer further IDEs for ABAP development. By opening it up, we now have a rich third-party ecosystem that we can leverage for AI, such as GitHub Copilot and Amazon Q.

In addition, we have published an ABAP MCP (Model Context Protocol) server that lets ABAP developers benefit from the entire AI tooling and ecosystem. This is a major, important step and an impressive example of interoperability. Furthermore, we can still rely on the strength of ABAP business logic. With this approach, we are combining the open IDE strategy with a high rate of innovation.

You mentioned the ABAP MCP server. What is that exactly?

尝颈茅苍补谤诲: MCP stands for Model Context Protocol and is an open standard for AI agents to interact with external tools and systems in a structured manner. It is a universal language that AI agents use to ask questions, trigger actions, and retrieve results.

The ABAP MCP server provides ABAP development capabilities based on this protocol. Any agent that supports MCP can interact with ABAP code and our ABAP systems in an intelligent, agent-driven approach. It is a new channel through which we can make our ABAP-specific capabilities available to the outside world.

The ABAP MCP server is an important building block for everything related to agentic AI and an important technical foundation for our new Custom Code Assistant for 麻豆原创 S/4HANA transformation. The ABAP MCP server for Eclipse is available as of Q2 2026.

What are the risks of using AI in 麻豆原创 enterprise systems and how do you address them?

尝颈茅苍补谤诲: The entire AI market is incredibly dynamic, fast-paced, and characterized by different interests. As such, we need to carefully examine which solutions are durable, robust, and trustworthy enough to run the world’s business processes. We want our solutions to remain secure, compliant, and true to everything 麻豆原创 stands for.

We must put our core mission at the heart of all decisions. ABAP platform is known for its ability to run large enterprise business. Our customers and partners trust this capability The AI solutions on the market do not qualify for this through their ability to build short-lived solutions, but instead by supporting our core capabilities. That鈥檚 why it helps to take a step back, look at the big picture, and make sustainable decisions, but also to remain open to revising past decisions in response to major shifts in the market.

The second point is the accuracy of the code. AI can generate seemingly plausible code that contains subtle errors. To counter this, we rely on a combination of human review and thorough agent testing, keeping humans in the loop at every critical step. AI agents handle the quality checks and validation, with developers making the judgement calls.

Another risk is the loss of business logic during the transformation. We want to help our customers migrate their legacy applications to modern solutions. We are developing custom code management agents for this purpose. The first was released in June. It is crucial for our customers and partners to retain their business logic during the transformation.

Lastly, security and data protection are central topics. AI models always need context to be effective. In the enterprise environment, this context can contain sensitive business data. That鈥檚 why we are taking a very careful approach: ABAP AI services only operate within established 麻豆原创 compliance and trust frameworks, for example. Customers always have control over what is shared and what isn鈥檛.

Let鈥檚 talk about 麻豆原创 Sapphire in 2026. What innovations from your area were presented?

尝颈茅苍补谤诲: 麻豆原创 Sapphire is also a very important conference for . In Q2 2026, we published the first release of the ABAP development tools for Visual Studio Code, initially in the ABAP Cloud scope including 麻豆原创 Fiori app development and with integration of GitHub Copilot and Amazon Q as AI solutions. Another goal is to support classic ABAP development in Visual Studio Code; additional ABAP object types will follow throughout the year. Also, as of Q2 2026, the ABAP MCP server for Eclipse and Visual Studio Code are generally available to connect third-party solutions, particularly AI-specific third-party tools, to the ABAP system.

We also delivered the first Custom Code Assistant in Q2 2026. The feedback is promising. This enables us to automate and accelerate code migration significantly. In this approach, all communication between legacy migration tools and new agents will take place through an engagement layer. Our overall migration strategy, which we presented at 麻豆原创 Sapphire, comprises seven different agent families across all phases of a migration project, from planning to execution.

Smaller customers and those with older system versions often found it difficult to access AI tools. What is changing here?

尝颈茅苍补谤诲: The barriers to entry are getting much lower. We are switching from users-per-month billing to a usage-based model. Billing will be according to actual usage, based on 鈥淎I units鈥� with individual prices. This will help make it easier to use our AI solutions. Customers will be able to better plan how much they want to spend on AI solutions.

In addition, as of Q2 2026, we introduced a side-by-side service that enables the use of all AI solutions, regardless of release. The availability of ABAP AI will be expanded to include 麻豆原创 S/4HANA Cloud Private Edition for all releases from 2021 and later. This enables us to support most of our customers with AI capabilities, even those that have older releases.

How do ABAP and AI agents fit into 麻豆原创鈥檚 longer-term product vision?

尝颈茅苍补谤诲: We are following the overall 麻豆原创 strategy, of course. It was apparent at 麻豆原创 Sapphire that agentic AI is the new theme. In the past, we had Infrastructure as a Service, Platform as a Service, and Software as a Service. Agentic AI solutions are now being added.

I like to use three concentric circles to describe our vision: ABAP Cloud is at the center, as the modern, clean-core variant of our language for long-term stability. The second circle is the ABAP AI layer: developer tools for code explanation, code generation, and ghost texting. The outer circle is comprised of AI agents, a network of specialized agents for complex and multi-step tasks such as transformation, migration, and quality validation. They can also take on development tasks, however, significantly reducing the required effort. This can free up time for decisions regarding business logic and architecture, as well as for verifying quality.

The circles coexist and reinforce each other. The fundamental orientation of the platform and the core of the solutions remain stable. Agents and AI skills complement this in the best possible way.

What milestones should customers and partners pay attention to?

尝颈茅苍补谤诲: There are four main milestones. The development environment for Visual Studio Code and the MCP server were released in Q2 2026. This enables the development of AI agents in an open IDE ecosystem. We released the first agent for the custom code migration strategy in Q2 2026, along with the extension of ABAP AI to include 麻豆原创 S/4HANA Cloud Private Edition release 2021 and later. A second agent for clean core transformation is planned for Q3 2026. And we will continue to develop multi-agent orchestration and add more agents to the portfolio in the course of 2026.

Do you have any other takeaways to share?

尝颈茅苍补谤诲: Development should be seen as an opportunity. It鈥檚 all about development and collaboration. ABAP and developers remain a strong team. AI will not change anything here. ABAP has a long track record of successful reinvention. AI is just the next chapter, taking over routine tasks and creating freedom for what creates value: knowing your business, making architectural decisions, providing high quality, and meeting security and compliance requirements.

Customers and developers can trust 麻豆原创鈥檚 AI to be implemented reliably: with a clear road map and high quality, security, and compliance standards that are critical for enterprise systems. We work closely with the community and our customers and partners. That is a key success factor.

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Danish wholesaler Lemvigh-M眉ller transformed manual document handling with 麻豆原创 Business AI.

The company automatically processes incoming business documents, including orders, delivery notes, and invoices received as PDFs and emails, reducing manual work and helping teams focus on execution rather than paperwork. Discover how a 200-hour AI project became a scalable foundation for broader business process automation. Read the article.

For Lemvigh-M眉ller, an efficient supply chain isn’t a nice-to-have鈥攊t’s the business model. “Our company is low margin, and we are living from a very efficient supply chain,” says Frederik Aakerlund, CIO of Lemvigh-M眉ller. “We need to cut costs wherever we can, and we need to make sure our customers get our products as quickly as possible.”

Not every business partner connects via EDI (Electronic Data Interchange), the standard for exchanging business documents directly between IT systems. For Lemvigh-M眉ller, that means a steady stream of orders, delivery notes, and invoices arriving as PDFs and emails鈥攄ocuments that, until recently, had to be read and entered manually.

“Today we are receiving so many PDF files and emails that we don’t have the time to read them,” Aakerlund explains. “Basically, we don’t update our system, or we don’t find the deviations from what we expect, quickly enough.”

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Letting AI read the mail

To close that gap, the team built a use case for receiving documents from business partners that can’t be exchanged via EDI and having them read automatically by 麻豆原创 AI engines.

“This is exactly where AI is helping us,” Aakerlund says. “It’s reading 10 20-page documents in a few seconds, updating our system, and there’s no person involved.”

Behind the scenes, the solution combines a mix of 麻豆原创 Business AI Platform, AI components, and 麻豆原创 Fiori apps, integrated with Lemvigh-M眉ller’s core 麻豆原创 system鈥斅槎乖� Cloud ERP Private, which the company adopted two years ago.

The shift in daily work is tangible. “Our users, instead of reading a lot of emails, are just working in a dashboard, finding the things they need to work on,” Aakerlund says. “We’re living the in a small part of our business.”
What started as a single use case has since become a template. “We’ve kind of made it a template for receiving business documents like orders, delivery notes, invoices, and so on,” Aakerlund notes. “Whenever we can’t get them digitally, we read them via this new system. It works for all kinds of PDFs and emails we receive from our business partners.”

Aakerlund’s advice to other companies considering AI projects: don’t start big.

“A good piece of advice could be to find the pockets of inefficiency in your company and apply AI there, instead of going for some really, really big project,” he says. The first version of the use case took just 200 hours over 10 weeks to build. “It turned out to be a reusable architecture, with reusable templates for a lot of other business processes.”

That is the leadership challenge procurement now faces: accelerating the pace and improving the quality of work while maintaining accountability across every supplier interaction and enterprise transaction. That challenge is especially significant because procurement teams are also being asked to control costs, manage risk, strengthen resilience, and support broader digital transformation efforts.

The urgency is real. In research from the 2026 Economist Enterprise report titled聽“,” sponsored by 麻豆原创, 56% of executives identified AI strategy as the main catalyst for procurement鈥檚 digital agenda. The study, which covered 2,648 C-suite leaders, also recorded lower confidence in the function鈥檚 ability to translate technology investments into consistently better outcomes.

What those findings suggest is that the next phase of AI adoption is not about access to technology. It is about building the governance, accountability, and data foundations needed to turn potential into measurable business value. The practical question is where to start.

Start with the outcome, not the technology

AI programs often begin with finding the best possible tool for a problem. Procurement leaders should reverse that sequence and focus on the desired outcome.

The first questions they should ask are, 鈥淲hat outcomes would impact the broader business, and what decision or workflow needs to improve?鈥� A sourcing team may need to shorten event preparation. A category manager may need earlier warning of price or supply changes. A purchasing organization may want to reduce off-contract buying. Each objective carries different data requirements, risk levels, and measures of success.

Defining the outcome first forces the question early, before deployment choices narrow your options. Leaders can specify which actions AI may complete, which recommendations require review, and which decisions must remain under human control. They can also set escalation rules for exceptions involving sensitive data, high-value commitments, supplier concentration, or regulatory obligations.

This turns governance from a final approval step into part of the operating design.

Build a connected data foundation

AI cannot provide dependable guidance when supplier records, contract terms, spend information, and risk signals are fragmented across systems. More importantly, an agent cannot safely execute work without the context that accompanies this information. Procurement needs unified data governance that covers common definitions, data ownership, access controls, and traceable sources. Without it, AI operates on assumptions rather than facts.

Perfection is not a realistic prerequisite, and waiting for it will stall progress. But organizations should be explicit about uncertainty. When information is incomplete, the system should surface that limitation or route the matter to a person rather than present an assumption as fact. As the Economist Enterprise research highlights, fragmented data remains one of the most significant barriers to realizing AI鈥檚 potential in procurement, and it is a barrier that governance can address.

Apply human oversight where it matters most

The right balance between human and AI involvement varies depending on the procurement activity. Routine, rules-based work can support greater automation, while strategic supplier decisions require a different standard.

The Economist Enterprise research shows that fewer than one in 10 respondents would give AI the lead across most procurement choices within three years. By contrast, 46% expect the technology to assist with tactical work, while people retain authority over strategic matters.

That balance reflects something procurement practitioners understand from experience. Data can indicate that a supplier offers favorable terms or strong performance. It cannot tell you whether that supplier will collaborate during a disruption, bring you new ideas before they to a competitor, or treat your business as a priority when capacity is tight. Those judgments depend on relationships, commercial context, and years of accumulated experience that no system fully captures.

As organizations adopt similar tools and draw from increasingly comparable data, the real source of differentiation will be how procurement leaders interpret those outputs and apply judgment.

Human review should therefore be concentrated where the consequences are greatest, not added indiscriminately to every automated step. Clear thresholds can protect control without recreating the delays AI is intended to remove.

Measure value and risk together

Responsible adoption will not scale through policy alone. Employees need to understand how AI changes their work, when to challenge an output, and who is accountable for the final decision. Procurement, finance, IT, legal, and operations also need a shared view of ownership before something goes wrong rather than after.

Metrics to determine success and failure should be defined before deployment. Cycle-time reduction, contract compliance, spend under management, user adoption, supplier performance, and risk response can all show whether a use case is working. These measures should be paired with indicators such as exception rates, human overrides, data-quality failures, and control breaches.

This matters because AI can produce visible efficiency without improving the decisions that matter most. In the same research, many executives reported that AI has yet to meaningfully improve procurement decision-making quality despite growing investment in the technology. That gap is the real opportunity.

Procurement leaders who link AI to specific outcomes, build connected data, assign clear decision rights, and prepare their teams to work alongside the technology will move beyond isolated automation toward something more durable. The organizations that get this right will not simply be the ones that automate the fastest. They will be the ones where AI amplifies judgement, relationships, and experience that procurement professionals have always brought to the table, and where accountability for the decisions that matter most remains firmly in human hands. 

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

It reinvented circus arts and became a world leader in live entertainment, performing for more than 365 million spectators in 90 countries. Today, the organization operates 38 shows in cities around the world, supported by over 4,000 touring artists and staff from more than 80 countries.

Each touring show functions like a moving profit center. A production may operate in Mexico City, then move to London, then Paris鈥攂ringing with it new companies, employees, assets, tax requirements, local regulations, merchandising operations, and artists from dozens of nationalities.

As Philippe Lalumi猫re, vice president of Information Technology at Cirque du Soleil, explains, 鈥淧eople underestimate the complexity of running a circus.鈥�

Orchestrating an autonomous accounts payable process

This is especially true for the company鈥檚 accounts payable (AP) department. Every year, more than 70,000 invoices flow through Cirque du Soleil, non-stop, 24/7. Around 40% of vendor inquiries are standard requests for invoice status. But answering those questions was far from simple. AP specialists had to search across systems, review invoice histories, understand payment status, determine the cause of delays, and manually respond.

The work was repetitive, time-consuming, and emotionally draining. 鈥淔rom a morale point of view, receiving e-mails from suppliers, some of them a bit impolite because they鈥檙e asking, 鈥榃hen are we going to get paid?鈥欌�攊t鈥檚 not a fun job,鈥� Lalumi猫re says.

Finding the right problem to solve鈥攑utting AI into action

As an 麻豆原创 customer for more than 25 years, Cirque du Soleil knows firsthand how to leverage 麻豆原创 technology not only to run its operations, but also to reinvent them.

Cirque du Soleil, known聽as聽an early adopter and leader in digital transformation, was approached by 麻豆原创聽AppHaus聽with a question: how could 麻豆原创 generative AI technology be used to improve your business processes?

The answer emerged through collaborative workshops involving 麻豆原创, Cirque du Soleil’s IT team, and business users across its departments. Accounts payable quickly rose to the top due to its lean structure, high volume of interactions, and clear automation potential. And the choice aligned with the broader business evolution鈥擜P automation is one of the most widely adopted AI use cases across industries.

With a clear opportunity identified, Cirque du Soleil and 麻豆原创 moved from ideation to execution, developing an AI-powered solution that automated AP processes and improved responsiveness.

AI enters stage right鈥攆rom vision to reality

The workshops led to something more than an automation project鈥攖hey led to Genato, a multilingual AI agent that now works alongside the AP team.

The polyglot agent scans the AP inbox, analyzes sentiment, identifies urgency, extracts invoice numbers from messages and attachments, connects to 麻豆原创 data to retrieve invoice status, and drafts a response for human review. If it cannot find an invoice or resolve an inquiry, it flags the issue.

鈥淭rust in the quality of the answers coming from the agent was an initial concern,鈥� Lalumi猫re says. 鈥淏ut, the team quickly realized that Genato鈥檚 information was spot on.鈥�

Together, Genato and the AP team now serve Cirque du Soleil’s diverse global supplier network more efficiently.

Lalumi猫re, however, is clear about one thing: 鈥淵es, AI is a very powerful tool, but it鈥檚 not pixie dust. Sprinkling AI into processes is not going to solve everything. There is work involved.鈥�

That work included designing聽appropriate connectors聽and integrating with the company鈥檚 麻豆原创 and AP systems using . This enterprise AI foundation聽can bring together capabilities and technologies (think 麻豆原创聽Business Data Cloud, Business Transformation Management solutions, and 麻豆原创 Business AI), unifying AI, data, process context, and governance, so customers can build, integrate, scale, and run AI that delivers business impact聽while working to ensure the solution is sustainable and cost-efficient.

The impact was evident almost immediately. Generative AI began prioritizing urgent supplier requests, retrieving invoice information, drafting responses, and translating communications automatically, delivering measurable improvements across the AP organization:

• 97.92% improvement in handling priority requests
• 25% reduction in AP backlog
• 25% faster response times for non-urgent inquiries
• Improved vendor satisfaction and employee morale

The impact on employees cannot be overstated. By removing repetitive research and emotionally charged supplier follow-up from the team鈥檚 workload, Genato has become more than a tool. 鈥淲e now include Genato as a virtual team member,鈥� Lalumi猫re explains, 鈥淚t鈥檚 a paradigm shift.鈥�

That adoption is the clearest sign of success. Lalumi猫re recalls that after the prototype moved into production, one of the end users made her feelings clear: 鈥淣o, no, no, you cannot take it away from me! There is no way I鈥檓 going to live without it now.鈥� Today, she is one of the solution鈥檚 biggest ambassadors.

Collectively, the solution demonstrates how AI can be introduced into a focused business process, quickly earn user trust, and create a blueprint for broader enterprise innovation.

As Lalumi猫re puts it, 鈥淵ou鈥痗ould say we鈥檙e an 麻豆原创 shop.鈥�

The next act of AI innovation

For Cirque du Soleil, accounts payable is only the beginning.

Lalumi猫re believes that 鈥淎I is a huge and beautiful tool, but you still need human judgment to prioritize use cases and move forward.鈥� He also sees AI evolving across three layers: personal productivity with solutions such as Joule; AI applied to business processes, such as Genato; and, eventually, AI embedded in the audience experience itself. 鈥淚 think we鈥檙e at the dawn of a new era of circus arts,鈥� he says.

His advice to others beginning their AI journey is simple: start small, involve users early, and be willing to experiment. 鈥淭ry a small proof of concept and be ready to throw them away,鈥� Lalumi猫re says. 鈥淭he AI train is moving, and you have to hop on.鈥�

For a company built on reinvention, transforming accounts payable may seem far removed from the spotlight, but for Lalumi猫re, the principle is the same: innovation happens when people are willing to rethink what’s possible. Today, that mindset is improving back-office operations. Tomorrow, it may help redefine the audience’s experience.

Sid Misra is CMO of 麻豆原创 Business AI Platform Technology Foundation.
Top image courtesy of Cirque du Soleil.

Between technical deployment and actual adoption, there is often a gap. What is needed goes beyond tools鈥攊t requires enablement, communication, and organizational change management that puts people first. Experiences from 麻豆原创 customers show how this can succeed.

Global transformation, local anchoring as a guiding principle

Dulaan Punsag-Odefey cites a number that immediately makes the challenge tangible: 265. That is how many key users serve as change ambassadors at Hapag-Lloyd, distributed across six regions worldwide. The shipping company is one of the five largest in the world and is in the midst of an 麻豆原创 S/4HANA Finance transformation. The “Fast Forward” program affects 4,000 麻豆原创 users in more than 140 countries.

Punsag-Odefey, organizational change management (OCM) lead at Hapag-Lloyd, explains: “Change management is anchored in our program as a strategic enabler. Not as an add-on, but as a core element.”

In practice, this means the 265 ambassadors translate global standards into local language and local practice. Controllers are expected to evolve into business partners who no longer just produce Excel spreadsheets but deliver decision-ready recommendations for sales and operations. The message to the workforce is “It will be different, but better.”

How a federal agency demonstrates effective change management

That transformation can succeed without following the textbook is demonstrated by the Bundesanstalt f眉r Post und Telekommunikation (BAnst PT), Germany’s Federal Agency for Post and Telecommunications. The agency implemented 麻豆原创 S/4HANA in just one year: greenfield, public cloud, 1,000 affected users, go-live on January 1, 2026. In parallel, the organization moved to a new administration building with a new-work concept.

Simone Kunze, specialist in the 麻豆原创 Service Center at BAnst PT, knows the phrases that come up in every organization: “Is there an official directive for this?” or “Standard won’t work for us.” What helped was honest communication and direct moderation within business units instead of token feedback sessions.

A fit-to-standard approach replaced legacy custom solutions. Key users were developed from project experts who had already built depth through workshops, user stories, and test cases. The investment in support paid off: after go-live, dozens of thank you e-mails came in regarding the new 麻豆原创 travel expense management and Fiori apps, and survey response rates exceeded 70%.

Thilo Menges from the Medical University of Lusatia (MuL) takes this one step further. His project has a unique starting point: with 3.6 billion euros in funding, an entirely new organization is being built from scratch, including 麻豆原创 technologies. Menges makes a point that many organizations do not state this clearly: “For me, change management is an investment protection measure. This is an organizational project, and people need support in change processes.”

Change management in his project accounts for 4.3% of the total budget鈥攖he largest single line item in the 麻豆原创 contract.

麻豆原创鈥檚 change management framework with its six dimensions, which MuL follows, is integrated into the 麻豆原创 Activate methodology. Particularly important are early assessments, target-group-specific communication, and the identification of trusted multipliers rather than a blanket approach.

What was also highlighted: learning does not end at go-live. Tools like WalkMe enable context-sensitive support in the flow of work, especially for infrequent processes. Enabling the organization to independently maintain and evolve these systems is critical for sustained success.

The organizations mentioned above are supported by change management consultants from 麻豆原创. More information is available .

From shadow AI to structured integration at KIT

While BAnst PT and Hapag-Lloyd are primarily transforming 麻豆原创 system landscapes, the Karlsruhe Institute of Technology (KIT) faces a different question: how do you get 25,000 students and 10,000 employees to use AI responsibly, instead of each person experimenting on their own in the shadows?

In 18 months, KIT made the journey from uncontrolled AI usage to an AI toolbox with governance rules. Rather than issuing bans, KIT focuses on enablement.

Andreas Sexauer from the Center for Technology-Enhanced Learning at KIT describes the approach as follows: a mandatory qualification module covers foundational knowledge and legal aspects before students and faculty gain access to the AI toolbox. In parallel, use case workshops run across departments, from leadership teams to the legal department. After the teaching rollout in April 2025, 31 didactic chatbots were created in the first seven days. Faculty configure them directly in the learning management system for their respective courses.

KIT also takes a pragmatic approach to costs: after initially providing free access, a budget cap per person per month was introduced.

What research confirms

Whether shipping company, federal agency, or university, all customers report similar patterns. Prof. Dr. Renate Osterchrist from the Munich University of Applied Sciences provides the scientific foundation: she analyzed 119 studies on the effectiveness of change interventions, examining six intervention fields: communication, support, involvement, reinforcement, social influence, and coercion. Key effectiveness factors in change include:

• Dialogue formats are more effective than one-way communication.
• Coaching for managers improves not only their leadership capability but also measurably enhances implementation competence. Coaching and peer exchange are also very beneficial for employees.
• The dimension of coercion had been under-researched until now: clarity in messaging about what behavior is expected proves helpful. Manipulation and political maneuvering, on the other hand, reduce commitment.
• The frequently cited claim that 70% of all change projects fail is not supported by current data.
• The statement “Honestly, I have never experienced a change where there was too much communication” further underscores the important role of communication.

When AI agents enter the picture, change becomes even more critical

What happens when not only new systems are introduced but AI agents take over parts of the work? This is precisely the question that arose when Joule Studio 2.0 was demonstrated live at the forum. With this solution, 麻豆原创 customers can create AI agents that access their business context: 麻豆原创 Knowledge Graph, process models, and domain knowledge. The agents are code-based and transparent. Developers describe the desired outcome in natural language; Joule Studio can generate the specification and executable code. Both no-code and pro-code approaches are possible.

The discussions that followed amongst 麻豆原创 customers and partners made clear that the change management described above will be essential here. When agents take over tasks, roles change, responsibilities shift, and the way humans and machines collaborate is transformed.

A full documentation of the 麻豆原创 Learning and Adoption Forum 2026, including videos, slides, and a chatbot, is .

Thomas Jenewein is business development manager for AI, Transformation, & Adoption Services at 麻豆原创.

Picture this: You are an IT manager at a mid-sized enterprise running 麻豆原创. A critical issue surfaces midweek. Your team scrambles to fix the problem, searching through documentation, reviewing the multiple support channels available, and monitoring the situation to ensure the issue does not snowball further. A business-down situation is stressful enough without having to navigate a complex support experience. With 麻豆原创鈥檚 Support Accreditation you can access a free enablement course designed to get you all the help you need. Through its structured, hands-on learning experience, this course helps equip customers, partners, and consultants to confidently get the most out of 麻豆原创鈥檚 support.

With the rapid growth of AI over the past few years, the support landscape of 2026 looks nothing like it did five years ago. Today’s support landscape includes AI-powered assistants, predictive capabilities, intelligent recommendations, and real-time engagement channels. The latest release of the Support Accreditation course can prepare learners to take full advantage of these innovations and more.

What鈥檚 new

After gathering insights from more than 40,000 learners, collaborating with internal support experts, and grounding every decision in real user feedback, 麻豆原创 has redesigned Support Accreditation from the ground up. What does the 2026 release of Support Accreditation offer customers, partners, and consultants? Upon completion, learners earn a digital accreditation badge that validates their expertise and skills in using self-service tools, accessing AI-powered support solutions, navigating support channels with clarity, enabling focused and high-quality support interactions, collaborating effectively, and maximizing the value of 麻豆原创 Enterprise Support. If you have heard about Joule in 麻豆原创 for Me, intelligent search, preventive support, incident solution matching, channel recommenders, or predictors for products, product functions, and priority鈥攖o name a few AI-driven features鈥攜ou can now explore these topics further. 

This release combines how people learn best with how support is evolving into a single, reimagined experience. Based on learner feedback, the latest release of Support Accreditation delivers:

• Human-centered learning
• Shorter, more focused learning units
• Content focused on real-world outcomes

The real benefits

Support Accreditation in 2026 isn’t just about earning the badge鈥攊t鈥檚 a credential that validates your ability to work effectively with 麻豆原创鈥檚 comprehensive support offerings. The accreditation can also equip you to: 

• Resolve issues faster by using intelligent self-service tools, AI-powered recommendations, and proven best practices to cut resolution times and queues.
• Increase self-sufficiency by reducing dependencies on traditional support interactions through 麻豆原创’s ecosystem of knowledge, diagnostics, automation, and digital capabilities.
• Improve support interactions by learning how to create higher-quality cases, communicate more effectively, and use the right channels at the right time.
• Stay ahead through continuous learning and be ready to take advantage of new capabilities.

How to get started

The Support Accreditation 2026 course is available now through 麻豆原创’s learning platform free of charge, on-demand, self-paced, and completed in around 60 minutes.

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

The company鈥檚 history goes back more than 100 years, and what began as a small textile shop in Aarhus, Denmark, is now an international retailer with 鈧�12 billion in revenue.

麻豆原创 has supported Salling Group for over 20 years and is central to its operations, said Alan Jensen, CIO and executive vice president at Salling Group. Recently, the company has modernized its ERP system to 麻豆原创 S/4HANA Cloud via RISE with 麻豆原创.

With this cloud-based infrastructure in place, the retailer is ready to begin its AI transformation.

Improving everyday life

Salling Group鈥檚 reason for introducing AI is threefold: improve customer experience, simplify for employees, and boost operational efficiency. 鈥淲e want to make everyday life better for our customers by having the right product for the right price every time they need it,鈥� Jensen said. 鈥淲e also want to make every day better for our employees, so the tools and systems they work with are intuitive and easy to use.鈥� This aligns with the company鈥檚 purpose to improve everyday life for customers, colleagues, and the communities it is a part of.

The company views AI as a key enabler, focusing on how to turn AI into real business value for customers, employees, and the company overall. One such area where AI will have real impact on the retailer is logistics, Jensen said. Currently, Salling Group uses in its 29 distribution centers. The application helps manage high volumes of goods and run sustainable, risk-resilient operations via digitalized warehouse processes in the cloud. For Salling Group, this means on-time delivery to stores and efficient supply chain operations.

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What鈥檚 next

Salling Group is using 麻豆原创 solutions and AI to modernize its retail operations end-to-end, with a clear emphasis on customer experience, employee productivity, and supply chain excellence鈥攚hile ensuring new technologies deliver tangible business outcomes.

When it comes to 麻豆原创, Jensen is looking forward to seeing how the Autonomous Enterprise will further Salling Group鈥檚 success with AI.

鈥淭he Autonomous Enterprise looks very exciting,鈥� Jensen said. 鈥淭here is definitely a lot in how we can improve the way we run our business every day, so we need to be curious and see how we can use it.鈥�

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聽麻豆原创 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.

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麻豆原创’s Laura Marwood speaks with Casey West, a developer advocate at Google Cloud, about the realities of building mission鈥慶ritical AI applications, how organizations should think about AI adoption, and how the 麻豆原创鈥揋oogle Cloud collaboration has evolved over the years.

It’s about responsible, thoughtful AI adoption 鈥� where guardrails, deep problem understanding, and strategic collaboration matter far more than speed or hype.

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.听聽

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聽(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聽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聽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聽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聽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聽.听
• Join the聽, a 90-minute onboarding designed to help you get up and running quickly.听
• Run the聽聽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聽聽and join our聽.听
• Subscribe to the monthly聽聽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聽the number of applicants per early talent job opening doubling since 2021.听A聽high volume of candidates聽are聽submitting聽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鈥檚聽a painful聽scenario for everyone involved. Fragile and unsustainable. And the聽implications聽will be聽profound for enterprises that聽don鈥檛聽act quickly.听聽

HR leaders voice concerns  

Playing out the current聽trajectory聽to its natural conclusion, what happens when all the聽fresh talent eventually dries up? Already,聽HR leaders聽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聽global head of early talent聽programs at a high-tech organization聽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聽will聽prove expensive to remedy. Organizations will struggle to build company capabilities,聽retain聽knowledge, and develop future leaders.听聽

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,聽there鈥檚聽an opportunity for HR leaders to be more intentional聽and strategic, to reimagine聽their approach聽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聽approach must evolve.听The nature of work is changing rapidly,聽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聽work that聽meaningfully addresses聽business challenges.听聽

HR has the chance to reshape entry-level positions,聽to provide support, guidance, and tools to enable聽junior staff聽to contribute聽in聽more聽impactful聽ways. This may involve them聽working聽with proper guidance聽to support聽more critical聽projects, interacting with customers,聽and聽even聽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,聽technology聽can help聽employers聽see beyond the聽noise of聽AI聽applications聽and rediscover the聽meaningful聽signals聽they聽need to create candidate shortlists and strengthen hiring decisions. Meanwhile, technology can also help to maintain engagement with other high-potential聽candidates聽who applied鈥攆or when the next opportunities arise.听聽

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.