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

Fragmentation is the hidden bottleneck behind 鈥渟low鈥� decisions

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

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

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

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

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

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

Most payroll errors aren鈥檛 human鈥攖hey鈥檙e structural

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

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

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

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

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

Fragmentation quietly erodes trust and limits AI adoption

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

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

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

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

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

The cost of standing still

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

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

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

The real risk isn鈥檛 innovation

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

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

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

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

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

*

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

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

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A community of changemakers

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

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

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

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

From environment to politics

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

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

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

Education as foundation for progress

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

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

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

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

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

Serving business and society

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

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

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

From AI insight to AI in execution

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

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

Orchestrating the supply chain end to end with AI

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

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

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

New AI agents redefining planning, service, and operations

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

Manufacturing

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

Assets & services

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

AI agents advancing logistics execution

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

Aligning workforce, logistics, and assets in real time

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

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

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

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

Regulatory readiness and what鈥檚 next

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

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

See it live at Hannover Messe 2026

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

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

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

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

Connected AI that works across HCM

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

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

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

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

Unified experiences that adapt to how work gets done听

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

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

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

Processes designed for clarity, accuracy, and compliance听

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

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

Skills governance听for sustainable growth听

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

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

A connected foundation for the future 

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

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

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

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

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

AI as an accelerator of readiness

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

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

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

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

When productivity accelerates, expectations follow

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

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

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

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

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

Rethinking the first step into work

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

1. Build foundational skill development intentionally.

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

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

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

3. Establish AI governance from day one.

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

4. Ensure equitable AI access across teams and managers.

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

The bottom line

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

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

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

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

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

The 11th cycle of the Hasso Plattner Founders鈥� Award saw an evolution of the award itself. With a refined structure and this year鈥檚 strong thematic focus on AI, the award now recognizes achievements in two categories: Emerging Ideas, honoring visionary concepts that explore new architectural directions and long-term opportunities for customers and the business, and Scaling Innovation, celebrating innovations already delivering proven impact at scale.

The jury received a total of 254 submissions from all over the globe, from which 41 finalists representing nine different countries were chosen. Six teams made it to the final round, highlighting the breadth of innovation across teams worldwide.

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The winning teams were formally recognized and celebrated during a red carpet award ceremony on March 26 at 麻豆原创 headquarters in Walldorf, Germany. Executive Board Members Christian Klein and Sebastian Steinhaeuser introduced the finalists and announced the winners in the Emerging Ideas and Scaling Innovation categories, respectively. 

Emerging Ideas winner: 麻豆原创 Cognitive Twin Enterprise

The Emerging Ideas category honors bold thinking and visionary concepts that explore the future of enterprise software. This year鈥檚 winner, 麻豆原创 Cognitive Twin Enterprise (麻豆原创 CTE), embodies this forward-looking spirit by presenting a new way of how organizations plan, simulate, and execute in an increasingly complex and fast-changing world.  

麻豆原创 CTE introduces the idea of an ever-learning, AI-powered intelligence layer built on a continuously updated model of the entire organization. It unifies data, simulation, and AI on 麻豆原创鈥檚 foundation to help deliver guided autonomy across functions, supporting the shift from keyboard-centric SaaS to governed decision-making and agent-led execution. 

Acting as a constant observer of the business landscape, 麻豆原创 CTE evaluates an organization鈥檚 position against anticipated trends and potential changes. It runs what-if simulations and provides governed recommendations on 麻豆原创 applications and data across finance, spend, supply chain, HR, and customer experience, with selective, low-risk auto-execution and human-in-the-loop control for high-risk steps. By doing so, it can transform ERP into an AI-native system of foresight and elevates workforce intelligence. 

The solution helps organizations move from reacting after events occur to proactively and continuously testing scenarios, anticipating risks, and evaluating options. This provides the information they need to make critical decisions, allowing them to anticipate what鈥檚 next, shape it, and execute in a single, connected environment.  

鈥淲inning the Hasso Plattner Founders’ Award validated 麻豆原创 Cognitive Twin Enterprise鈥檚 vision,鈥� Natalia Aksakova, Strategy & Portfolio at Global Finance and Administration, says on behalf of the team. 鈥淚t reinforced that we are on the right path and gives us the momentum to bring the next era of ERP to life faster, with the ambition to help define how organizations operate in the years ahead.鈥� 

Scaling Innovation winner: 麻豆原创 Document AI

The winner in the Scaling Innovation category demonstrates how breakthrough innovation becomes truly transformative when it is embedded into everyday business processes and adopted at global scale. The 麻豆原创 Document AI solution can fundamentally change how organizations process and understand the vast volume of documents that power daily operations.

Across industries, enterprises continue to grapple with the rapid growth of unstructured data. Invoices, purchase orders, contracts, shipping documents, and many other business records still require significant manual handling in many organizations, creating bottlenecks, delays, and avoidable errors. The 麻豆原创 Document AI team addressed this challenge by bringing intelligent document processing directly into core business applications, enabling customers to automate document workflows seamlessly and securely.

What sets this achievement apart is not only the technological innovation but the scale of real-world adoption. The solution has become deeply embedded across 麻豆原创鈥檚 portfolio and is used by tens of thousands of customers worldwide to process billions of documents. By integrating advanced AI capabilities directly into existing workflows, the team has made automation accessible without the need for complex integrations or specialized expertise. This approach enables organizations to accelerate business processes, reduce manual effort, and improve the quality and speed of decision-making.

The award recognizes the team鈥檚 ability to translate research excellence and engineering innovation into measurable business impact. Their work demonstrates how embedded AI can move beyond experimentation to become a trusted and reliable component of everyday enterprise operations. By operationalizing AI responsibly and at scale, the team has helped strengthen 麻豆原创鈥檚 position as a leader in enterprise automation and intelligent applications.

Equally important is the long-term perspective behind the innovation. The continued evolution of document understanding capabilities, combined with growing adoption across 麻豆原创鈥檚 platform, illustrates how scalable AI can serve as a foundation for future innovation. The recognition celebrates not only the impact already achieved but also the momentum created for the next generation of intelligent enterprise processes.

鈥淲inning this award is a tremendous honor for our team,鈥� Tobias Weller, chief product owner and team lead, says. 鈥淚t validates years of hard work, close collaboration, and a shared belief in the transformative potential of AI to accelerate essential business processes and capture true business value for our customers.鈥�

Celebrating innovation across the AI spectrum

The Hasso Plattner Founders鈥� Award has long celebrated the people and ideas that drive 麻豆原创 forward. By recognizing both scaled impact and visionary thinking, the award highlights how innovation thrives at every stage of the journey鈥攆rom early exploration to global adoption. It underscores the belief that long-term success depends on both delivering value today and continuously reimagining what is possible.

At the ceremony in Walldorf, employees around the world came together or joined virtually to celebrate the winning teams and the many contributors who helped bring their ideas to life. Their work reflects the creativity, dedication, and passion that define 麻豆原创鈥檚 culture of innovation. As the company continues to advance its AI-driven strategy, this year鈥檚 winners demonstrate how teams across the organization are turning ambition into reality鈥攈elping customers run better, adapt faster, and prepare for the future.

The winning teams will be given the opportunity to pitch their project to the Executive Board in 2026. The projects will be recognized in the permanent Founders鈥� Exhibits in Walldorf and Palo Alto. In addition, the members of the winning teams will receive a personalized trophy.

ANYbotics, a Swiss robotics company, has stepped into this space with a vision to shape a safer future for industrial inspection, one where robots operate as autonomous members of the inspection team, running inspection operations integrated into plant maintenance workflows.听

This vision is embodied in the company鈥檚 鈥淎NYmal鈥�: a four-legged inspection robot designed specifically for heavy industry.

Unlike general-purpose robotics platforms, ANYmal is engineered to operate in 鈥渂ig, dirty, dusty, and dangerous鈥� environments, says Nicole Zingg, director of Technology Partnerships at ANYbotics. Places where stairs, corrosion, heat, and unreliable connectivity are the norm, not the exception.

But hardware, Zingg says, is only one part of the puzzle that makes ANYmal indispensable to customers.

Inspection robotics is about data

鈥淲e build a hardware platform,鈥� Zingg explains, 鈥渂ut inspection robotics is really about data that is consistent and trustworthy.鈥�

ANYmal autonomously navigates industrial sites to collect data that goes beyond what a human can collect alone. Beyond just visual inspection, its sensors also collect multi-modal data, including thermal imaging, ultrasonic leak detection, gas concentration detection, acoustic anomaly detection, and more. The observations are fed into what ANYbotics calls 鈥渋nspection intelligence,鈥� which transforms the collected data into actionable operational insights. The result is higher uptime, longer asset lifecycles, and, most importantly, safer working conditions for humans.

ANYmal can make a huge impact on operations. One offshore wind customer, Zingg says, has used ANYmal to manage all inspections and has eliminated the need to send personnel to a remote platform for months. When human intervention was eventually required, ANYmal鈥檚 data from prior inspections made all the difference. The customer already knew exactly what was wrong, which expert to send, and what equipment to bring鈥攁voiding costly and risky trial-and-error site visits.

Yet for ANYbotics, delivering insights is not enough if those insights are not integrated in the software systems customers use.

鈥溌槎乖� is where ANYbotics needs to be native鈥�

Through extensive user research, ANYbotics discovered that many plant operators, maintenance managers, and field service teams already run their daily operations in 麻豆原创. Work orders, asset histories, performance trends, and decisions all flow through 麻豆原创 systems. 鈥淚f customers are using 麻豆原创, 麻豆原创 is where ANYbotics needs to be native,鈥� Zingg says.

Meanwhile, 麻豆原创鈥檚 Project Embodied AI was looking for robotics companies to partner with. The project focuses on extending the impact of 麻豆原创 Business AI into physical operations by enabling robots to autonomously perform complex tasks with an understanding of the broader business context.

It was clearly a perfect fit and has delivered advantages for both companies.

On the system side, a continuous, unbroken digital thread connects ANYbotics insights from industrial inspections to data in 麻豆原创 systems, helping inform key business and operational decisions across the organization.

For end users, embedding ANYmal directly into familiar 麻豆原创 workflows can also help ease adoption, since introducing robotics into already stretched industrial workforces can trigger anxiety. Concerns about job security, workflow disruption, and complexity are common, but embedding ANYmal directly into familiar 麻豆原创 workflows can help reduce that friction, Zingg explains.

Treating robots as part of the workforce

The first major integration point was听. Rather than sending only human technicians, customers can now dispatch work orders directly to ANYmal as they would to any other field team member. The robot then autonomously executes inspection tasks, gathers data, and reports the results directly back into a company鈥檚 麻豆原创 system.

From there, the integration expanded into asset-related scenarios and is now moving toward broader enablement via 麻豆原创 Business Technology Platform (麻豆原创 BTP), with the goal of allowing robot-generated data to land wherever customers need it in their 麻豆原创 landscape.

The ambition is not to force humans to adapt to robots, but for robots to adapt to human workflows. 鈥淎NYmal has to put data in the 麻豆原创 system, just like human team members,鈥� Zingg notes. ANYmal becomes another worker in the same operational system of record.

Project Embodied AI in practice

This combination of ANYbotics robotic technology with 麻豆原创 bridges the gap between physical operations and enterprise applications and tangibly reflects the goal of Project Embodied AI.

On the 麻豆原创 side, AI agents operate on ANYmal鈥檚 robotic systems to execute physical tasks, such as safety inspections.

On the ANYbotics side, ANYmal is a physical object that moves through space, perceives its environment, and acts within real-world constraints. ANYmal uses 麻豆原创 historic and time-series data to inform decisions while at the same time remaining fully autonomous even in environments with no connectivity.

It鈥檚 important to note, Zingg stresses, that ANYbotics has control over ANYmal鈥檚 behavior and inspection execution, while 麻豆原创 has control over the business context such as work orders, asset data, or operational priorities. It is the 麻豆原创 business context that informs how ANYmal鈥檚 insights are consumed and acted upon while ANYbotics controls ANYmal鈥檚 physical interactions.

Scaling safely and responsibly

Today, more than 200 ANYmal robots are already in productive use worldwide, with inspection deployments in heavy-industry environments that would otherwise require constant human exposure.

Safety remains central to ANYbotics. Each deployment includes extensive testing and an on-site field engineer who helps ANYmal learn and validate its environment and trains customer teams on safe operational procedures. While ANYmal is built to work independently, humans remain firmly in the loop.

A glimpse into the future

As industries face labor shortages and aging workforces, undocumented expertise can all too often be lost. With autonomous inspection robots such as ANYmal, this knowledge is captured and turned into programs that can run day in and day out across multiple sites. The captured data flows into 麻豆原创 to become organizational intelligence that survives any workforce turnover.  

ANYbotics鈥� partnership with 麻豆原创 shows that this combination of robotics and enterprise software is moving swiftly from the experimental stage to real-world implementation.

In the future, industrial inspection will be powered by AI, not as disembodied dashboards or isolated machines, but as an integrated intelligent system where physical robots and digital workflows in 麻豆原创 systems operate as one.

In that future, robots like ANYmal are no longer novelties. They are coworkers, albeit mechanical four-legged ones, quietly extending human capability into places humans were never meant to go. These robots, together with 麻豆原创, are shaping for a future where dirty, dangerous, and dusty industrial inspections are being transformed into business insights.

Top image courtesy of ANYbotics

SafetyGuard was created as part of a program at UTUM鈥檚 Digital Product School鈥攚here teams of selected students work on real-world challenges鈥攊n just 12 weeks by a student team, 鈥淢IDAS,鈥� and 麻豆原创 Research & Innovation. This project illustrates just how effective cross-location collaboration with the ecosystem can be in rapidly creating prototypes as a foundation for product development at 麻豆原创.

Embodied AI: a joint focus of innovation

The objective of this particular prototyping project was to identify use cases in which robotics and AI could be seamlessly integrated into business processes. Based on its research and on user studies, team MIDAS pinpointed the reliable detection and documentation of safety risks as one such use case.

Safety inspections are essential in many industries, but they are often time-consuming and error-prone. SafetyGuard could change that: leveraging embodied AI, it makes inspections faster, more efficient, and more reliable, without increasing the workload for employees.

Embodied AI鈥攖he integration of artificial intelligence into physical robot systems鈥攚ill be a focal point of investment and development work at 麻豆原创 going forward.

SafetyGuard combines two technologies: modular robotics and AI-powered autonomy. In this prototype, robots, such as drones and humanoid systems capable of inspecting work environments without human intervention, are equipped with a specialized AI model that is trained, for example, to detect where protective equipment is missing and to automatically document safety-related incidents. What鈥檚 more, the robots can multitask. While they are carrying out inspections, they can transport materials and monitor machinery鈥攁n approach that combines efficiency gains with increased safety levels.

鈥淪afetyGuard demonstrates just how effective our ecosystem approach is. In this project, an 麻豆原创 team in Potsdam and a group of students in Munich joined forces and very quickly built a prototype that will have a real impact on product development,鈥� Tobias Riasanow, head of Ecosystem Development at 麻豆原创 Labs Germany, says. 鈥淚t鈥檚 the perfect example of what 麻豆原创 understands by 鈥榚cosystem development.鈥欌��

The project is also strategic to 麻豆原创 in that SafetyGuard addresses real-life industry requirements and complements 麻豆原创鈥檚 solutions for environment, health, and safety management. The prototyping approach that led to SafetyGuard could therefore become part of the 麻豆原创 portfolio in the future to help further reduce the time it takes to get from an idea to a product.

Programs with impact

To date, 13 prototypes have been developed for 麻豆原创 under programs run by UTUM, all in close collaboration with product teams at 麻豆原创 and directly related to their respective road maps. The programs offered by UTUM include:

• Innovation Sprint: Here, students have just one week to work on prototypes for solving specific user challenges. The most recent sprint, The Future of Retail with 麻豆原创 Joule, produced five such prototypes, including solutions for intelligent inventory optimization, virtual shelf monitoring, and retail assistance systems.
• Digital Product School: This 12-week program for students is designed as a platform for developing executable software prototypes. One prototype created out of this program was Carbon Data Exchange, which is now part of 麻豆原创 Sustainability Control Tower. Another is an application that makes it easier for developers to access the extensive documentation for 麻豆原创 Field Service Management. Instead of wasting valuable time searching, they can simply type their questions into a chatbot and receive helpful answers, including code examples and direct links to the documentation they need.
• Multistakeholder projects: Additional prototypes have been created in longer-term programs such as Digital Hub Mobility and Circular Republic. Programs like these bring together experts from companies, startups, and research institutions over several months and allow 麻豆原创 to test innovations in real-world customer and partner environments at an early stage in their development.

Co-innovation gets results鈥攆aster

The collaboration between 麻豆原创 and UTUM shows how quickly ideas can lead to tangible results when students, researchers, and 麻豆原创 teams work together to apply scientific approaches to real-life industrial challenges. SafetyGuard is an example of how embodied AI innovations can be created and how different groups of experts can benefit from one another.

鈥淪afetyGuard demonstrates the extensibility of 麻豆原创鈥檚 approach to embodied AI. 麻豆原创鈥檚 embodied AI layer integrates the business context and triggers actions across the suite. SafetyGuard鈥檚 AI-native, manufacturer-agnostic design is scalable across different use cases and enables multitasking, allowing, for example, robots to process safety incidents while performing other tasks such as industrial asset inspections. SafetyGuard is a proof point for 麻豆原创 for the added value of using cognitive robotics in safety scenarios.鈥�

Adelya鈥疐atykhova and Dr.鈥乽kasz鈥疧strowski, Initiators and Supervisors of the student challenge

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

I鈥檓 pleased to announce that next鈥慻en 麻豆原创 Ariba is now available, marking the next phase in 麻豆原创鈥檚 journey to reimagine source鈥憈o鈥憄ay for the age of AI. This milestone represents the transition from announcement to execution, bringing a fundamentally rebuilt platform into customers鈥� hands.

This milestone comes alongside strong industry recognition. 麻豆原创 has been named a Leader in the 2026 Gartner庐 Magic Quadrant™ for Source鈥憈o鈥慞ay Suites, an acknowledgment that aligns with the delivery of next-gen 麻豆原创 Ariba and our continued focus on platform modernization and AI鈥慸riven innovation at enterprise scale.

Why rebuilding the foundation matters

As procurement leaders know, AI鈥檚 potential is widely recognized, but its impact has often been uneven. Confidence is high, yet results depend on more than algorithms alone. AI only delivers value when it is supported by the right data, processes, and platform architecture. That belief shaped our decision to rebuild 麻豆原创 Ariba from the ground up.

Independent analyst firm as 鈥渁 complete reengineering of the largest and most entrenched source鈥憈o鈥憄ay platform in the world,鈥� emphasizing that this move goes far beyond adding AI features to legacy systems. Instead, it establishes the architectural foundation required for AI to operate reliably and at scale.

This aligns with what we consistently hear from customers that sustainable impact requires modernization at the core.

An AI鈥憂ative source-to-pay platform built on 麻豆原创 Business Technology Platform

Next鈥慻en 麻豆原创 Ariba is built on (麻豆原创 BTP), providing a unified, real鈥憈ime data foundation across the source鈥憈o鈥憄ay lifecycle. This shift enables tighter integration with , improved extensibility, and faster innovation delivery.

By moving to 麻豆原创 BTP, 麻豆原创 Ariba can support open APIs, cross鈥憇uite data consistency, and the responsiveness required for intelligent, AI鈥慸riven procurement operations鈥攃apabilities that are increasingly expected of leading source鈥憈o鈥憄ay platforms but are difficult to achieve on legacy architectures.

Current next鈥慻en capabilities will continue to be delivered incrementally throughout 2026 and into 2027, giving customers flexibility to adopt innovation at a pace that aligns with their business priorities.

Embedded intelligence with Joule: moving from insight to action

A defining element of next鈥慻en 麻豆原创 Ariba is the deep integration of directly into procurement workflows. Rather than treating AI as an optional add鈥憃n, next鈥慻en 麻豆原创 Ariba can embed intelligence where work happens鈥攕upporting faster, more informed decisions while reducing friction across everyday processes.

Early capabilities include:

• A Bid Analysis Agent, which can automatically evaluate complex bid scenarios, including total cost considerations
• AI-assisted contract support to help automate routine inquiries, generate summaries, and provide instant access to contract details

These capabilities reflect a broader shift from systems that require constant manual input to platforms that actively support outcomes.

A more unified, intuitive procurement experience

Next鈥慻en 麻豆原创 Ariba also addresses long鈥憇tanding fragmentation across the source鈥憈o鈥憄ay lifecycle.

Key improvements include:

• 麻豆原创 Ariba Intake Management, now globally available, providing a single entry point for procurement requests
• A simplified 麻豆原创 Fiori鈥慴ased user experience, delivered through a central launchpad
• A modernized contract lifecycle, supported through integration with Icertis Contract Intelligence

Together, these improvements are designed to make procurement easier to engage with while working to ensure processes remain connected, compliant, and intelligent behind the scenes.

What this means for customers

For existing 麻豆原创 Ariba customers, next鈥慻en 麻豆原创 Ariba provides choice and continuity. Customers can:

• Transition to next鈥慻en 麻豆原创 Ariba on a voluntary basis.
• Access next鈥慻en capabilities without commercial implications.
• Run current and next鈥慻en environments in parallel during an active transition, reducing risk and disruption.

麻豆原创 is providing tools, services, and advance timelines to support a managed transition, allowing organizations to move forward with confidence rather than urgency.

The foundation for what comes next

Next鈥慻en 麻豆原创 Ariba is not an endpoint, it is the foundation for the future of procurement.

With an AI鈥憂ative architecture, embedded agentic intelligence, and a unified user experience, this new generation of 麻豆原创 Ariba helps organizations move beyond transactional efficiency toward smarter decisions, greater resilience, and measurable business outcomes.

As Ardent Partners observed, this rebuild has the potential to act as a catalyst鈥攏ot just for 麻豆原创 Ariba customers, but for the broader procurement technology landscape. By combining scale, data, and AI in a fundamentally new way, next鈥慻en 麻豆原创 Ariba is helping define what modern source鈥憈o鈥憄ay platforms can deliver.

With the solution now available, we look forward to partnering with customers as they move from vision to value. Together, we can shape the future of intelligent procurement.

Baber Farooq is senior vice president of Product Marketing for 麻豆原创 Ariba and 麻豆原创 Fieldglass.

A modern foundation for growth at global scale 

In an era defined by global volatility and ambitious growth targets, businesses require a system that doesn’t just record data but actively anticipates needs and simplifies complexity. 麻豆原创 Cloud ERP Private is evolving into a truly AI-enabled ERP, serving as the critical core foundation that can allow organizations to navigate the realities of global operations while maintaining total control over their footprint. 

To achieve this, innovations in FPS01 are strategically delivered across three key dimensions: AI, data, and applications. 

AI in action: from assistants to agents 

The shift toward an AI-enabled ERP is highlighted by two key advancements in FPS01: 

1. AI assistants and specialized agents: A standout in this release is the Change Record Management Agent for R&D. Previously a manual, high-friction process, this agent can now autonomously analyze change impacts and propose next steps, helping to free R&D teams to focus on innovation. 
2. Process embedded AI: 麻豆原创 is making the system more intuitive through Joule. Instead of navigating complex menus, users can now use conversational shortcuts, for example, to instantly search service contracts or extend expiring prices in sales, turning multi-minute tasks into five-second interactions. 

Looking at the road ahead, 麻豆原创 is building toward agent-to-agent collaboration, where specialized agents across functions like R&D and procurement “talk” to one another to resolve bottlenecks before they even reach a human user. FPS01 is a critical step toward that future. 

Data: industry-ready insights 

On the data front, 麻豆原创 is introducing specialized data products for key industries, like retail, and functional areas, such as asset management and services. These are not just tables; they are pre-configured, business-ready data sets that align with our 麻豆原创 Business Data Cloud (麻豆原创 BDC) roadmap. This helps ensure your data is “AI-ready,” allowing you to move from raw data to industry-specific insights with zero friction. 

Application: strengthening the global core 

On the application side, 麻豆原创 continues to deliver deep functional enhancements based on direct customer feedback to help ensure your business backbone remains agile. A key highlight is the new Multistage Intercompany Sales and Stock Transfer. Following our commitment at the RISE with 麻豆原创 moment in November, 麻豆原创 is further expanding the scope to cover two-entity transfers, enabling automated orchestration across multiple legal entities. This can ensure even the most complex global supply chains remain transparent and compliant. 

A full collection of deep-dive articles on the new FPS01 is available on . 

Looking ahead: your catalyst for transformation 

FPS01 reflects a core 麻豆原创 principle: innovation should be both a foundation for today and a catalyst for what鈥檚 next. With enterprise AI, industry-ready data, and a stronger application core, organizations can run smarter and transform at their own pace. 

To see these innovations in person, to experience the future of the autonomous enterprise. 

Maura Hameroff is chief marketing officer for 麻豆原创 Cloud ERP Private and RISE with 麻豆原创.

Today, we鈥檙e announcing that is now integrated with , working to deliver a unified experience, connected data, and integration with AI companion and to embed intelligent assistance directly into hiring workflows to help teams move faster, make better decisions, and deliver better candidate experiences. This integration, following 麻豆原创鈥檚 acquisition of SmartRecruiters in September 2025, establishes the foundation for a fully connected talent architecture, where hiring decisions, skills intelligence, and workforce planning can operate as one system.

Advancing intelligent hiring with SmartRecruiters for 麻豆原创 SuccessFactors

The solution can deliver a consistent, end-to-end hiring experience designed to meet the scale, speed, and intelligence requirements of modern organizations. By combining intuitive workflows with embedded AI, recruiters can eliminate repetitive administrative tasks and focus on higher value interactions, while candidates move through a streamlined, personalized journey from first touch to offer.

The integration with 麻豆原创 SuccessFactors HCM builds on these capabilities by connecting hiring processes to the full employee lifecycle and the broader business, helping to ensure that every hiring decision is grounded in real-time data and organizational context. Designed for scale, SmartRecruiters for 麻豆原创 SuccessFactors helps set the foundation for a complete intelligence layer across hiring and HR, with people, job, and organizational data flowing seamlessly between 麻豆原创 and SmartRecruiters.

Organizations can gain a more predictable hiring process with streamlined workflows, consistent tools, and shared real-time data across each hiring stage. They can expect:

• Single login: one entry point into 麻豆原创 SuccessFactors and SmartRecruiters for recruiters, hiring managers, and approvers
• Unified navigation: a seamless experience across 麻豆原创 SuccessFactors and SmartRecruiters to help reduce complexity and speed up adoption
• Aligned data: synchronized people, job, and organizational data that flows between systems, helping to ensure accuracy and consistency end to end

In practice, core organizational data like job families, cost centers, and locations can flow automatically from 麻豆原创 SuccessFactors into SmartRecruiters, helping to eliminate manual entry and inconsistencies. New roles open with these attributes already applied, and user management is just as smooth: recruiters, hiring managers, and approvers created in 麻豆原创 SuccessFactors appear in SmartRecruiters with the right permissions, helping to reduce errors and keep approval flows and reporting clean. As integration deepens, hiring becomes fully connected to core HR and workforce systems, creating a unified, trusted foundation for talent decisions across the enterprise.

With the enhanced benefits of SmartRecruiters for 麻豆原创 SuccessFactors, simple and flexible integration paths are now available for customers currently using the 麻豆原创 SuccessFactors Recruiting solution. 麻豆原创 will continue to honor all contracts, and customers will not be required to migrate.

Integration that activates enterprise鈥�ready AI

With 麻豆原创鈥檚 continued investment, SmartRecruiters for 麻豆原创 SuccessFactors is evolving quickly, bringing AI-driven innovation to every part of the hiring experience. High-volume hiring can become high-quality hiring through AI-assisted workflows, including intuitive applications, automated scheduling, intelligent matching, and streamlined interview feedback.

Beginning in 2026, Winston and 麻豆原创鈥檚 generative AI solution will work together as connected agents. Additionally, new protections such as fraud detection, enhanced consent management, and applicant data transferability will help embed trust in the hiring cycle, strengthening both system integrity and candidate confidence.

From talent acquisition to talent readiness

As organizations look beyond filling roles to building future-proof capabilities, intelligent hiring becomes just one part of a broader talent strategy. The power of integration delivered by SmartRecruiters for 麻豆原创 SuccessFactors extends beyond hiring, creating a connected, AI-enabled talent experience across the entire 麻豆原创 SuccessFactors HCM suite and, ultimately, . This is how organizations become skills-ready: hiring decisions tied to outcomes, and employees supported with clear paths to grow and contribute.

See SmartRecruiters for 麻豆原创 SuccessFactors in action:听Catch the听听of our March 5 webinar – 鈥淭he Future of Intelligent Hiring鈥� – to explore how 麻豆原创 is redefining hiring, talent orchestration, and long-term workforce strategy.

Lara Albert is chief marketing officer, 麻豆原创 SuccessFactors.
Rebecca Carr is CEO of SmartRecruiters, an 麻豆原创 company.

Complementing 麻豆原创鈥檚 established logistics portfolio supporting large-scale supply chain operations, 麻豆原创 Logistics Management brings innovative capabilities specifically tuned to the needs of smaller operations seeking efficiency, real-time visibility, and faster decision-making.

鈥淟ogistics is undergoing a fundamental transformation. Those who invest in strategic warehousing and logistics networks are investing in a future defined by responsiveness and relevance. It鈥檚 about being ready, connected, and purposeful,鈥� says Till Dengel, 麻豆原创鈥檚 global head of Product Marketing for Logistics.

Designed for local impact, powered for global reach 

While 麻豆原创鈥檚 best-in-class  continue to serve complex global logistics with extensive capabilities, 麻豆原创 Logistics Management now bridges the gap for local and satellite operations needing powerful, streamlined tools and controls without the complexity of large enterprise systems. Not only can 麻豆原创 Logistics Management elevate the performance of localized operations, but it also enables a connected network of satellite operations that can stay coordinated with global enterprise systems.   

The  solution offers: 

• Connected fulfilment excellence: By uniting warehousing and transportation capabilities, 麻豆原创 Logistics Management helps streamline order fulfillment by managing pick-pack-ship workflows while efficiently planning and coordinating the freight movement. The solution includes built-in collaboration with carriers through 麻豆原创 Business Network, connecting shippers directly with logistics providers for real-time updates that can minimize costly delays and keep shipments on track.  
• Tailored for smaller operations: 麻豆原创 Logistics Management is ideal for local branches, subsidiaries, and seasonal operations. It helps eliminate unnecessary complexity, giving smaller operations the powerful tools and connectivity they need without the overhead of large enterprise systems. 
• Seamless integration: Designed to work seamlessly with , 麻豆原创 Logistics Management can eliminate hidden interface costs and helps ensure compatibility with 麻豆原创鈥檚 existing transportation and warehouse management tools. Businesses can benefit from a smooth, cohesive ecosystem. 
• AI-driven and human-centric: Embedded AI empowers faster, smarter decision-making and workflows. can enable users to interact via natural language, helping to make involved logistics questions easy to handle right from the start. The solution鈥檚 mobile-first design helps ensure users can ramp-up quickly and access essential functions anytime, anywhere, for efficient workflows on the go. 
• Scalable SaaS solution: As a SaaS-native solution, 麻豆原创 Logistics Management can be up and running in days. It can scale effortlessly as the business grows, offering built-in analytics and dashboards that help provide actionable insights tailored to operations鈥攕mall or large. 

鈥淰isibility grants insight, connectivity empowers control, and agility provides the decisive edge,鈥� Dengel says. 鈥淭he future of logistics belongs to those who digitize every process, orchestrate every tier, and deliver on every promise.鈥� 

麻豆原创 Logistics Management helps empower businesses to connect and manage logistics operations efficiently, regardless of size. Whether running small local operations or orchestrating global distribution, 麻豆原创鈥檚 latest AI-powered innovation can equip businesses with the tools and capabilities to thrive in highly competitive markets by enabling them to be connected, agile, and growth-oriented. Learn more about . 

Oyku Ilgar is part of 麻豆原创 Supply Chain Thought Leadership & Awareness.

Since 2021, has been 麻豆原创鈥檚 solution for calculating EPR fees across markets. At that time, the number of jurisdictions with installed mandatory EPR was around 60. By 2030, that number is expected to grow to 200.

With an evolving regulatory landscape, 麻豆原创 has implemented new updates within the solution to help ensure that customers can navigate diverse reporting requirements, deadlines, and fees while increasing accuracy, reducing fees and exposure, and improving insights. Read on for updates about recent innovations and how the solution combines enterprise data with information about local and global regulations to help calculate obligations like the EU Packaging and Packaging Waste Regulation (PPWR).

Meet EPR regulations with a flexible, AI-driven, and configurable approach

To date, 麻豆原创 Responsible Design and Production has supported customers in meeting EPR requirements with out-of-box report categories that are pre-configured based on country. However, the new global regulatory landscape necessitates a more flexible approach.

Enterprises need solutions that enable them to:

• Understand obligations and quickly adapt to new or changing regulations.
• Prepare the data, consolidating from many sources.
• Prepare reports, which requires custom reporting logic based on specific business contexts; reuse of rules and fee structures across similar reporting schemes or producer responsibility organizations (PROs); and calculating EPR fees against shipments and printing the results on invoices to show customers the breakdown between product costs and indirect taxes.

To address these challenges, 麻豆原创 Responsible Design and Production introduced user-defined reports to help enable customers to design their own sustainability reports, tailoring them to match unique regulatory logic, business context, and compliance requirements.

With new innovations in 麻豆原创 Responsible Design and Production, users can:

• Structure reports according to the specific rules, PRO requirements, and fee models relevant to their business.
• Precisely narrow down to the packaging and transactional data that is being reported.
• Satisfy complex requirements with multiple dimensions used in report output structure.
• Integrate reports with external and third-party recyclability guidelines assessments.
• Report and comply with the increasing number of PROs that have incorporated eco-modulated ERP fees with eco-modulation grading capabilities.

AI capabilities enhance efficiency and accelerate readiness

The 麻豆原创 Sustainability portfolio can deliver AI-led operational transformation by capturing sustainability data at the source and embedding intelligence directly into core business processes, thereby enabling continuous improvement at scale. In 麻豆原创 Responsible Design and Production, two AI cases involving user-defined reports are in beta testing:

麻豆原创 Responsible Design and Production, AI-assisted user-defined report explanations

This capability uses AI to analyze and describe how developed extended producer responsibility rules contribute to the assessment results of selected product packaging. During the development of user-defined report categories, EPR report specialists may struggle to understand why a given packaging was categorized in a certain way or not categorized at all. The AI use case is intended to help resolve and simplify an error-prone process by which specialists manually recheck packaging data and reevaluate rules.

麻豆原创 Responsible Design and Production, AI-assisted rule creation for user-defined reports

This capability uses AI to translate from regulatory language to system rules, empowering packaging compliance managers to create and validate EPR reporting rules faster and with higher accuracy. The AI use case is intended to help reduce manual effort, avoid costly compliance errors, and accelerate report readiness across markets.

Prepare for future compliance and competitive advantage using AI

The EU PPWR is coming, and most companies aren鈥檛 ready. PPWR introduces significant obligations for all economic operators placing packaged goods on the EU market. Brand owners, importers, packaging manufacturers, distributors, and digital marketplaces are all impacted. To mitigate risk and ensure readiness, businesses must take immediate action to assess and adapt packaging materials, data systems, and regulatory reporting.

The detailed packaging data management offered by 麻豆原创 Responsible Design and Production will help enable organizations to efficiently handle PPWR compliance. With robust data capture and flexible reporting, companies can anticipate regulatory changes and maintain transparency throughout their packaging supply chain.

麻豆原创 Responsible Design and Production is working on document of compliance capability, recyclability assessments using real product shipment data, so you can assess which changes have the biggest impact. Stay tuned for future announcements from 麻豆原创 on this topic.

An ERP-centric framework for many business outcomes

By enabling organizations to build reports that align with their changing needs, 麻豆原创 Responsible Design and Production can ensure that compliance remains agile, scalable, and future-ready. The solution can connect design, production, and regulatory demands to enable organizations to minimize waste, optimize costs, and adhere to global sustainability standards.

Unlike standalone compliance tools, 麻豆原创 Responsible Design and Production operates within an ERP-centric framework, helping to ensure seamless integration with existing 麻豆原创 solutions for product master data and transaction data.

Traditional, manually intensive processes are not enough to keep pace with regulations and ensure competitive advantage by unlocking data insights. One 麻豆原创 Responsible Design and Production customer recently confirmed that using the solution was four times faster and less expensive than manual processes with a spreadsheet or using consultants.

With 麻豆原创 Responsible Design and Production, your organization can be empowered to respond swiftly and confidently to regulatory changes, working to ensure compliance while freeing your teams to focus on business impact that matters.

Listen to the recent to discover how businesses can address upcoming PPWR requirements with 麻豆原创 solutions.

Learn more about user-defined report capabilities or request a demo of 麻豆原创 Responsible Design and Production. Read these customer stories:

Gunther Rothermel is chief product officer for 麻豆原创 Sustainability.

Following the presentation of the 鈥淪caling Innovation鈥� category finalists, we now turn to 鈥淓merging Ideas,鈥� which honors visionary concepts at an earlier stage鈥攑rojects that explore new architectural directions, challenge established models, and open long-term strategic opportunities for 麻豆原创 and its customers. The winners will be announced during the award ceremony on March 26, 2026.

麻豆原创 Cognitive Twin Enterprise (CTE)

Modern enterprises are very effective at monitoring their business and analyzing vast amounts of data, yet many still see untapped potential in safely testing complex scenarios end to end and turning insights into cross鈥慺unctional, policy鈥慳ligned options before making mission鈥慶ritical decisions. 麻豆原创 Cognitive Twin Enterprise (麻豆原创 CTE) addresses this gap by creating an AI鈥憄owered digital brain built on a continuously updated model of the whole organization. It runs what鈥慽f simulations and provides governed recommendations on 麻豆原创 applications and data across finance, spend, supply chain, HR, and customer experience, with selective, low鈥憆isk auto鈥慹xecution and human鈥慽n鈥憈he鈥憀oop control for higher鈥憆isk steps.

The business case is compelling. Organizations that combine digital twins with agentic AI at scale report double鈥慸igit improvements in efficiency and cost, plus materially faster decision cycles. For a global industrial enterprise with approximately 鈧�40 billion in revenue, 麻豆原创 CTE is modeled to systematically prevent margin leakage, excess working capital, and audit exposure, delivering an estimated 鈧�229 million or more per year in hard impact and risk-adjusted cash benefit. By maintaining a continuously updated representation of the business, companies can test scenarios before execution and dramatically reduce the risk of costly mistakes.

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麻豆原创 CTE鈥檚 real differentiator is its enterprise鈥憌ide scope. It consolidates existing 麻豆原创 capabilities and builds on 麻豆原创 Signavio solutions, 麻豆原创 Business Data Cloud, and 麻豆原创 Knowledge Graph to maintain a shared semantic model of how the whole business runs. This cross鈥慸omain intelligence lets Joule and AI agents optimize complex trade鈥憃ffs鈥攕uch as cost versus service level versus carbon footprint versus operational risk鈥攁cross all functions, rather than pushing problems from one silo to another. At the same time, 麻豆原创 CTE provides a safe innovation environment: enterprises can trial new pricing strategies, network configurations, and workforce models in a production鈥慻rade twin before agents execute changes in live systems.

麻豆原创 CTE represents a strategic shift in how enterprises operate. It turns 麻豆原创鈥檚 deep process knowledge, rich transactional data, and mature governance tooling into a differentiated position in a cognitive twin market that analysts expect to accelerate from US$36 billion today to US$150 billion by 2032, with 30%-40% annual growth. As an extensible platform, 麻豆原创 CTE is designed to be the trusted operational brain for that future: new agents, scenarios, and data products plug into the same enterprise twin, allowing customers to expand autonomy and business impact over time without rebuilding their foundation.

鈥溌槎乖� CTE is more than an initiative: it鈥檚 our vision for a new era of connected intelligence. We鈥檙e bringing strategy, data, and execution into one continuous system of insight, so customers don鈥檛 just react to change鈥攖hey anticipate what鈥檚 next and shape it. That鈥檚 how we win and grow together,鈥� said Natalia Aksakova, Strategy & Portfolio at Global Finance and Administration.

麻豆原创 Signavio Transformation Advisor

Organizations planning business transformations face a persistent bottleneck: identifying the right challenges to focus on and creating actionable initiatives is slow, costly, and heavily dependent on expert consultants and detailed knowledge of the organization. This traditional approach delays decision-making and increases risk in fast-changing markets, with analysis often taking weeks or months to complete.

麻豆原创 Signavio Transformation Advisor reimagines this workflow by using AI to extract business challenges and create actionable recommendations to solve them in minutes. The solution identifies business challenges in uploaded reports or via text input and instantly generates recommendations linked to process insights and best practices to make them addressable. By combining advanced language models with the 麻豆原创 Signavio portfolio鈥榮 process knowledge, it enables users to achieve in minutes what previously required weeks of manual effort while keeping users in full control.

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Early results demonstrate significant impact. The tool cuts analysis time by up to 80%, enabling faster decision-making and reducing reliance on scarce consulting resources. Since launch, approximately 200 customers have tested the transformation advisor, validating its value across organizations at different maturity levels. The solution has proven valuable both for customer engagements and for internal use in preparing sales pitches.

The innovation lies in bridging strategic business challenges and operational processes in a way no existing tool does. It automatically identifies organizational pain points and links them to targeted process flows, best practices, and improvement opportunities within the 麻豆原创 Signavio ecosystem. This seamless integration empowers leaders to move from insight to action in just a few clicks, aligning transformation initiatives with company strategy.

The team embraced a proactive and entrepreneurial mindset: it started with a pure technical proof of concept then moved to a prototype for internal demonstrations, general accessibility and testing, and ultimately a releasable feature. The team demonstrated both transparency and customer focus by responding early to pull from go-to-market and sales teams while clearly stating tool limitations at each stage.

“The real fun in developing such a solution lies in seeing your idea and your knowledge grow at the same time and getting a clear pull from the market early on. The best customer sessions were those where the tool was improved live during the interview. That combined is a clear signal that we are on the right track,鈥� said Alex Cramer, product manager at 麻豆原创 Signavio Next.

AURA (Asset Understanding & Reliability AI)

Field engineers maintaining critical infrastructure face a frustrating reality: reporting asset faults requires completing complex forms on mobile devices, scrolling through endless dropdowns and codes. At one heavy equipment and infrastructure customer, 300 users report 400 to 1,000 asset faults monthly through 麻豆原创 S/4HANA, but the process is slow, manual, and error prone. A single classification mistake can send the wrong maintenance crew and delay urgent fixes.

AURA (Asset Understanding & Reliability AI) revolutionizes this workflow by combining 麻豆原创 HANA Cloud vector engine, 麻豆原创 AI Core, and generative AI into a single intelligent solution. Instead of completing eight or more complex form fields, engineers simply upload a photo of the fault; review an AI-generated report automatically populated with asset type, location, and recommended classification; and confirm submission鈥攁ll within seconds.

The technology uses embedded text, semantic search, and geospatial data to analyze both images and historical fault reports. AURA cross-references similar cases in the knowledge base, suggests the most accurate fault category, and learns from user corrections over time. 麻豆原创 Cloud Application Programming Model provides the secure foundation, 麻豆原创 HANA geospatial content supports asset location intelligence, and AI models process text and images using 麻豆原创 HANA Cloud vector engine for similarity matching.

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Results demonstrate substantial operational impact. AURA delivers 80% faster fault reporting, fewer data entry errors and misclassifications, and improved response times. For the customer, this translates to safer infrastructure, reduced operational costs, and a future-ready foundation for predictive maintenance. The response validated the approach: the customer loved the proof of concept and agreed to proceed with AURA as an official project.

Beyond defect detection, AURA lays the groundwork for scalable AI asset intelligence. Future phases include building a knowledge graph to link asset relationships, a data product integrated into 麻豆原创 Business Data Cloud for advanced reporting, and a self-learning model that continuously improves accuracy. This creates a repeatable, cost-efficient framework adaptable across industries.

The solution embeds responsible AI principles from inception. The model uses customer-specific historical data to prevent bias, includes human review before submission, and explicitly handles uncertainty to avoid hallucinations. It ensures transparency and compliance with 麻豆原创’s responsible AI framework while empowering human decision-makers.

鈥淲e believe the future of AI is not replacing people, but elevating them,鈥� said Ruth Peng, AI specialist from 麻豆原创 HANA ANZ. 鈥淎URA equips every engineer in the field, from junior to expert, with the confidence to perform at their best.鈥�

The first category, “Scaling Innovation,” honors project teams that excel in innovation, align with 麻豆原创’s strategic priorities, and demonstrate significant and sustainable impact on the company. The winners will be announced during the award ceremony on March 26, 2026.

麻豆原创 Joule for Developers, ABAP AI capabilities

As organizations worldwide accelerate their transition to 麻豆原创 S/4HANA, one reality remains unchanged: ABAP continues to power the core of 麻豆原创鈥檚 technology and the mission鈥慶ritical business processes that run on it. At the same time, developers working with this backbone technology have long lacked the modern AI鈥慸riven tooling available in other programming ecosystems鈥攁n issue that becomes even more pressing during large鈥憇cale transformation projects.

This gap is precisely what 麻豆原创 Joule for Developers, ABAP AI capabilities addresses. As part of the broader Joule generative AI portfolio, it brings AI capabilities tailored for ABAP development directly into the hands of development teams. Its mission: to modernize the developer experience of writing, understanding, and maintaining ABAP code and to dramatically accelerate innovation at enterprise scale.

麻豆原创 Joule for Developers, ABAP AI capabilities is natively integrated into the ABAP development tools. It supports developers in a hybrid approach that utilizes a large language model fine-tuned by 麻豆原创 data scientists on millions of lines of ABAP code, along with commercial models augmented with context derived from decades of 麻豆原创 expertise. This equips developers with specialized capabilities covering everyday tasks like predictive code completion, unit test generation, real-time explanation, and chat-based assistance鈥攕ignificantly boosting productivity and developer satisfaction.

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Complementing this, ABAP AI for custom code migration redefines how organizations approach the complex task of revamping legacy custom code from 麻豆原创 ERP Central Component to 麻豆原创 S/4HANA. What once required weeks of manual analysis can now be accomplished in hours, with AI explaining legacy logic, highlighting needed adaptations, and generating migration proposals. Integrated into the Custom Code Migration app on 麻豆原创 Business Technology Platform (麻豆原创 BTP), it empowers project managers and consultants to better scope work packages and plan code migration project timelines with far greater accuracy.

麻豆原创 Joule for Developers, ABAP AI capabilities already serve thousands of developers across more than 280 customers, 470 partners, and 6,500 internal ABAP developers, with adoption growing rapidly inside 麻豆原创 and across the ecosystem. Evolving from a 2023 proof鈥憃f鈥慶oncept to enterprise availability in 2025, the project stands as a testament to what cross鈥憃rganizational collaboration between ABAP, AI, and 麻豆原创 S/4HANA teams can achieve鈥攂ringing innovation to one of 麻豆原创鈥檚 most essential developer communities.

Team lead Jasmin Gruschke, AI architect and project expert, describes the extraordinary team spirit: 鈥淯nited by a shared vision and customer dedication, we poured our collective energy and dedication into bringing an extraordinary idea to life, demonstrating that, together, we can turn visionary concepts into remarkable realities.鈥�

麻豆原创 Document AI

Modern enterprises face a growing obstacle in an increasingly data鈥慽ntense world, reflected in the rapid proliferation of unstructured business documents. From invoices and purchase orders to contracts and shipping papers, companies are drowning in information that demands time鈥慶onsuming manual processing. 麻豆原创 Document AI tackles this challenge head鈥憃n by transforming the way enterprises extract, process, and act on document鈥慴ased data.

Today, more than 30,000 customers rely on the solution to process billions of documents, embedded seamlessly across 麻豆原创鈥檚 core applications. 麻豆原创 Document AI delivers enterprise鈥慻rade automation without costly integrations or extensive model training, enabling businesses to accelerate workflows, reduce errors, and improve decision鈥憁aking at scale. Real鈥憌orld customer data shows the tangible impact: automated document processing powered by 麻豆原创 Document AI generates an estimated 鈧�2.6 billion in annual business value.

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麻豆原创 Document AI is now natively integrated into 32 business processes across 麻豆原创 S/4HANA, 麻豆原创 Business Network, 麻豆原创 Concur solutions, 麻豆原创 Fieldglass solutions, 麻豆原创 SuccessFactors solutions, the 麻豆原创 Customer Experience portfolio, and 麻豆原创 BTP, with dozens more use cases in development. This deep embedding of 鈥渆veryday AI鈥� into products that are already in use by customers is a key driver of adoption across 麻豆原创鈥檚 global installed base.

The technology behind the solution sets new industry benchmarks. 麻豆原创 was among the early innovators in schema-based zero-shot document processing, an approach that enables AI systems to understand complex documents without task-specific retraining, and which is now widely adopted across the AI ecosystem. Even before the rise of large language models, 麻豆原创 researchers advanced the field with award-winning and trend-setting papers such as CharGrid, BERTgrid, and Charmer, AI methods designed to help computers understand documents.

The team continues to innovate with AI that learns instantly from user feedback, 鈥渟ees鈥� and interprets documents visually, and understands content well enough to take intelligent actions on it. Their next generation of generative AI models now support over 110 languages. Building on these innovations, platform usage on 麻豆原创 BTP for custom document automation has increased 285-fold since 2020, underscoring how developers worldwide are leveraging this technology to streamline business processes. Next, 麻豆原创 Document AI will launch reusable tools that empower AI agents to handle complex document workflows across industries. As unstructured data and diverse document types become central to business processes, demand for smarter, faster, and more adaptable document understanding solutions has never been higher.

Tobias Weller, chief product owner and team lead, states: 鈥淲e built 麻豆原创 Document AI to deliver measurable business value at global scale, securely, responsibly, and embedded in everyday processes, demonstrating 麻豆原创鈥檚 ability to operationalize AI at massive scale.鈥�

麻豆原创 SuccessFactors Learning: GenAI Content Generation

With AI reshaping work at unprecedented speed, organizations face a dual challenge: mastering new skills and managing overwhelming amounts of information. 麻豆原创 SuccessFactors Learning: GenAI Content Generation is a game-changing capability designed to streamline, accelerate, and scale how learning content is produced across the enterprise.

The new capability leverages advanced large language models (LLMs) to convert simple prompts or uploaded files into complete, compliant learning experiences in minutes. What previously required days, weeks, or even months, can now be accomplished almost instantly. The system generates course outlines, quizzes, interactive elements, summaries, and assessments鈥攁ll tailored to the user鈥檚 input and organizational context.

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A key innovation lies in its multi鈥慙LM orchestration, enabling dynamic selection and combination of specialized models. This ensures high accuracy, domain relevance, and enterprise鈥慻rade content governance. Real-time multilingual translation allows learning teams to launch global courses simultaneously, while AI-based skill extraction automatically aligns content with workforce development strategies.

Early validation shows organizations can produce learning content five times faster, dramatically reducing costs and enabling teams to respond more quickly to shifting skill demands. Subject-matter experts throughout the organization can now create content and share knowledge more effectively鈥攚ithout requiring instructional design expertise. By transforming knowledge into structured, scalable learning experiences, the capability helps organizations strengthen agility, boost employee engagement, and ensure continuous upskilling across the enterprise.

Team lead Neha Dhawan, principal product manager, 麻豆原创 SuccessFactors Learning, describes the impact of the project: 鈥淲e鈥檙e not just building technology, we鈥檙e building possibilities鈥攆or admins to move faster, for managers to better support their teams, and for learners to experience content that feels personal and meaningful. If we can make learning more accessible and inspiring, then we鈥檝e created something that truly matters.鈥�

According to industry , auto executives expect AI to boost product value by 22% and digital service value by 37% within three years. As vehicle portfolios expand鈥攅lectric, hybrid, software-defined, and increasingly customized鈥攖he operational complexity for automakers and suppliers has risen sharply. Nowhere is this more evident than in service parts management and new product introduction (NPI).

Service parts planners sit at the intersection of engineering, supply chain, manufacturing, and customer service. Their task is deceptively simple: ensure the right parts are available at the right time and place across a vehicle鈥檚 lifecycle. In reality, they grapple with fragmented data, limited inventory visibility, unpredictable demand signals, and compressed timelines鈥攅specially as new models and components are introduced at unprecedented speed. High data quality, tight orchestration across systems, and rapid decision-making are no longer nice to have, they are business-critical.

This is where becomes transformative. Instead of treating NPI as a linear, manual, and reactive process, AI agents can fundamentally reimagine how service parts planning is executed. By embedding AI directly into the planning workflow, service parts planners are supported鈥攏ot replaced鈥攂y intelligent systems that operate with full contextual awareness. These AI agents can monitor real-time data across inventories, supplier readiness, historical demand patterns, external risk factors, and engineering changes, as well as orchestrate the NPI process end to end.

In practice, this means planners move from firefighting to foresight. AI agents can automate sequential NPI steps, flag potential bottlenecks before they materialize, and dynamically adjust plans as conditions change. A single, unified dashboard provides transparency across the entire process, while built-in what-if simulations allow planners to test scenarios鈥攕upplier delays, demand spikes, geopolitical disruptions鈥攂efore decisions are locked in. Crucially, humans remain firmly in control. AI augments judgment, improves speed, and enhances confidence, rather than operating in a silo.

Platforms like 麻豆原创 Business Technology Platform (麻豆原创 BTP), combined with Joule and the agent builder capability in Joule Studio, can enable this multi-agent approach at enterprise scale. By integrating AI seamlessly with core business processes, automakers can ensure that intelligence flows across functions, rather than being trapped in silos. The result is not just automation, but orchestration鈥攚here systems, data, and people work in concert.

The impact is tangible. Automakers can significantly reduce planning cycle times and improve time-to-market for new products. Planning risk is lowered through continuous what-if analysis that incorporates both internal and external variables. Service readiness improves, ensuring that customers experience continuity and reliability even as product complexity increases. At an ecosystem level, this translates into greater resilience, lower costs, and higher customer satisfaction.

More broadly, this use case points to a shift in how we should think about customer-specific AI in automotive. The future will not be defined solely by smarter vehicles, but by smarter enterprises鈥攚here AI agents support decision-making across the value chain, from product inception to end-of-life service. In an industry under pressure to innovate faster, operate leaner, and remain sustainable, AI-driven operations are fast becoming a competitive necessity. The automotive ecosystem is evolving. Those who embrace AI not just as a technology but as a new operating model will be best positioned to lead it.

Sindhu Gangadharan is head of Customer Innovation Services and managing director for 麻豆原创 Labs India.

Naturipe has been an 麻豆原创 customer since 2008, and while initially it was not very proactive with software upgrades, the company鈥檚 philosophy has changed over time. Today, Carol McMillan, Naturipe鈥檚 senior IT director, emphasizes the importance of upgrading and upskilling the team regularly.

“There’s actually more of a risk not upgrading the product and not upskilling your team than there is being more aggressive with that timeline,” she says. Since migrating to 麻豆原创 S/4HANA in 2018, the company has undergone three upgrades, including .

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鈥淲e migrated to RISE with 麻豆原创, and we are looking at how we use those tools to optimize our supply chain and to create a better user experience for our workforce. Our workforce is constantly changing, and technology needs to change with the workforce,鈥� McMillan says.

She says it鈥檚 also important to stay at the forefront of technology in order to solve business problems efficiently, and notes that technological advancements have been crucial in managing the company’s exponential growth: “You can’t have linear growth in technology when you have exponential industry growth.”

McMillan says the Naturipe team uses 麻豆原创鈥檚 technology to prioritize its people and customers, improve workplace happiness, and give users a better experience. 鈥淲e use technology to make sure that we’re running as optimally as possible,鈥� she says. 鈥淲e look at process improvements and try to be at the forefront of technology. So, keeping up with the upgrades on a regular basis helps us be prepared for when business problems need to be solved, that way we already have the technology there to be able to solve them.鈥�

Delivering the freshest, highest-quality products to customers 365 days a year requires constant agility鈥攅specially in a category influenced by weather variability and the complexity of a highly time-sensitive supply chain. 鈥淏erries have a very short shelf life, so getting the best quality to our customers as quickly as possible is essential,鈥� she says.

To support this commitment, Naturipe Farms has invested in advanced logistics and supply chain technologies that enhance speed, visibility, and decision-making. These investments help ensure timely delivery of fresh products while also supporting employee satisfaction and work-life balance through wellness programs and flexible work arrangements.

Looking ahead, Naturipe wants to further enhance the user experience with more powerful tools and learn how to make better use of all the tools that come with RISE with 麻豆原创.  McMillan also identifies the potential of AI and the adoption of tools like Joule to improve Naturipe鈥檚 operations. “We really want to make the best use of AI and to give our users the best experience possible,” she says.

Her advice for other organizations in the agricultural industry includes the importance of putting people first, using technology to drive strategic growth, and not being a prisoner to risk. “It’s just as risky to not upgrade your technology as it is to have an aggressive timeline and have the tools that you need in order to drive that exponential growth,” she says.

The past few years have taught us that disruption is the new normal. Whether it鈥檚 global conflicts, raw material shortages, or sudden demand spikes, supply chains need to pivot faster than ever. That鈥檚 why this year, the conversation isn鈥檛 about incremental improvements鈥攊t鈥檚 about reimagining processes with intelligent technologies that anticipate, adapt, and act autonomously.

From complexity to clarity: how agentic AI changes the game

Agentic AI is reshaping supply chains, and we鈥檙e already seeing real value in practice:

• Supplier onboarding in hours, not weeks: Companies can drive substantial efficiencies by collaborating more deeply with their suppliers, such as material or logistics providers. AI agents autonomously validate supplier credentials, check compliance, and integrate them into your network, cutting onboarding time by up to 50%.
• Predictive maintenance and service that prevents downtime: Instead of reacting to failures, AI agents monitor equipment health and trigger proactive service, reducing unplanned outages by 30%.
• Autonomous disruption handling: When short-term disruptions or opportunities arise to demand or supply levels, AI agents evaluate events and alerts, model scenarios, and drive action while keeping humans in the loop. If critical inventory needs to be shifted for example, agents place orders automatically, optimizing stock levels and reducing lead times by 25%.

These aren鈥檛 distant possibilities鈥攖hey鈥檙e real scenarios already piloted by 麻豆原创 Supply Chain customers. AI isn鈥檛 replacing people; its amplifying human decision-making, freeing teams to focus on strategy rather than firefighting.

Why this matters: analyst rankings tell the story

麻豆原创 solutions underpin strategies that earned recognition in major 2025 analyst reports, including:

• IDC MarketScape for Multi-Enterprise Supply Chain Commerce Networks*: 麻豆原创 was named a Leader for enabling real-time collaboration and orchestration across global ecosystems with 麻豆原创 Business Network.
• **: 麻豆原创 was positioned as a Leader for innovations like predictive maintenance and agentic AI through Joule.
• ***: 麻豆原创 was recognized as a Leader for integrating planning, manufacturing, and logistics with advanced analytics and AI.

These accolades aren鈥檛 just badges of honor. They validate the trust our customers and partners place in 麻豆原创 and the impact we deliver together. They also reinforce a critical truth: supply chain excellence is now a boardroom priority.

Sustainability: from obligation to advantage

Sustainability isn鈥檛 just a compliance checkbox; it鈥檚 a competitive edge. More than 25% of global emissions are already taxed or regulated by trading systems. Circularity and carbon accountability have become core KPIs for supply chain leaders because responsible practices deliver measurable benefits. Meeting environmental, social, and governance (ESG) standards lowers regulatory and reputational risk, while optimizing logistics for lower emissions often translates into fewer miles traveled and reduced fuel costs. At the same time, customers and investors increasingly favor brands with transparent sustainability metrics, making it a powerful differentiator in the market.

麻豆原创 solutions help companies measure emissions, enable ESG compliance, and embed sustainability data deep into operational decision-making for procurement, logistics, dispatching, and planning. This turns sustainability into a lever for growth rather than a reporting exercise. In fact, companies recognized in Gartner鈥檚 rankings often cite sustainability as a driver of resilience and profitability. When businesses can prove carbon accountability and circularity, they鈥檙e not just meeting regulations, they鈥檙e building trust and unlocking new market opportunities.

Looking ahead: our 2026 roadmaps

In 2026, our priorities center on enabling supply chains that are more intelligent, more connected, and more resilient. We are deepening our investment in agentic AI to support end-to-end value streams such as integrated business planning, sales and operations execution, digital manufacturing, and logistics execution. The goal is to bring AI directly into processes where decisions are made so planning becomes more predictive and execution becomes more automated. Over time, organizations will entirely redesign workflows and decision-making processes for the true step-change in agentic AI.

We are also advancing our capabilities in supply chain orchestration. As global supply chains operate increasingly across networks, companies need a coordinated layer that unifies planning, procurement, manufacturing, and logistics with partner ecosystems. This year, we will continue strengthening how our solutions identify risks in the n-tier network of complex supply chains by synchronizing data, prescribing decisions and actions across the enterprise and the broader network, and helping customers manage disruptions end鈥憈o鈥慹nd with greater speed and clarity.

Finally, we remain focused on data excellence. Reliable, harmonized data is essential for AI-driven decisions and for orchestrating the supply chain. In 2026, we are continuing to enhance master data consistency, improve network-wide data quality, and support AI鈥憆eady data models that help ensure our customers can trust and operationalize their insights at scale.

Together, these areas form the backbone of the innovations we are delivering this year, with a clear aim of helping customers move from reactive operations to intelligent, proactive orchestration. But technology alone isn鈥檛 enough. The real magic happens when we collaborate with our customers and our partners, turning complexity into opportunity.

The takeaway: 2026 is about action at scale

The supply chain landscape is evolving faster than ever. Agentic AI, sustainability, and intelligent automation aren鈥檛 optional, but essential. Companies that embrace these technologies to truly evolve how they operate and take even complex decisions will lead in resilience, efficiency, and responsibility. Those that hesitate risk falling behind in a world where adaptability is the ultimate competitive advantage.

Don鈥檛 wait for disruption to force your hand. Build the capabilities now that will carry you through uncertainty and position you for growth. Learn more about 麻豆原创鈥檚 AI-powered solutions .

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

*, November 2025, IDC #US53010225
**, December 2025, IDC ID # US52977525
***, October 2025, IDC ID# G00826212

The major topics of geopolitical risk, artificial intelligence, and economic uncertainty consistently circled back to environmental exposure and long-term resilience, pointing to a broader shift: sustainability is becoming less of a separate agenda item and more an underlying consideration in enterprise risk and strategy.

For leaders looking to shape the next phase of business, two major and consequential themes emerged.

1. AI is a sustainability enabler with responsibilities

Artificial intelligence was central to many Davos discussions this year, including those touching on sustainability. The focus was less on experimentation and more on how AI is already influencing operational and strategic decisions.

In several sessions, leaders pointed to practical applications where AI, combined with sustainability and operational data, is helping organizations to reduce waste, improve resource efficiency, and better anticipate environmental risks.

At the same time, there was no lack of recognition that AI brings new challenges. Its growing energy and water requirements, along with questions around governance, transparency, and equity, featured prominently in discussions. Leaders emphasized the fact that AI鈥檚 sustainability value depends heavily on how well it is integrated into existing business systems and decision-making processes, rather than deployed as a standalone technology. This was also underscored by broader analysis showing that emerging regulatory frameworks are struggling to keep pace with AI鈥檚 environmental footprint and governance needs. 

For many organizations, the focus shifted towards while remaining aligned with enterprise governance and financial oversight.

2. Water is key to societal and economic stability

One of the most prominent sustainability topics at Davos 2026 was water. Across both formal and informal sessions, leaders discussed water and ocean health as a foundational element to stable societies, economies, and business continuity.

Much of the conversation focused on the growing gap between economic dependence on water and the level of investment dedicated to protecting and managing water systems. With a significant share of global GDP in the coming decades, participants highlighted the operational and financial implications for supply chains, production facilities, and communities. According to the , 31% of global GDP could be located in regions of high water stress by 2050, underscoring the urgency of rethinking water investment and risk. 

To this end, new collaborative initiatives were announced during the week, including efforts aimed at integrating water considerations more directly into corporate strategies and strengthening ocean stewardship across industries. For example:

• were selected at Davos to boost water resilience across infrastructure, industry, and agriculture systems. 
• were launched to accelerate water finance and investment ahead of the 2026 UN Water Conference. 
• was directed at bridging the 鈧�6.5 trillion global water infrastructure gap, and commitments were made to mobilize private capital and improve water resilience strategies. 

These discussions signaled a move away from viewing water solely through a sustainability reporting lens and toward understanding it as a material risk and resilience issue for businesses.

What can business leaders take away?

While AI and water dominated the headlines at this annual meeting, sustainability quietly permeated most strategy meetings, with three takeaways arising as directional signals for leaders looking to build resilience into their business:

Sustainability is increasingly understood as financial risk

One of the clearest signals from Davos was the extent to which sustainability risks are now discussed in financial terms.

The World Economic Forum鈥檚 , released shortly before the meeting, reinforced this view by ranking environmental risks (including extreme weather and biodiversity loss) and critical changes to Earth systems among the most severe long-term global threats. The same report also highlighted that adverse outcomes from artificial intelligence are rising sharply in long-horizon risk rankings, reflecting growing concern about both technological and environmental disruption. 

While geopolitical and economic issues dominated short-term attention at the annual meeting, environmental risks were consistently framed as persistent factors shaping long-term planning and resilience strategies.

Furthermore, the role of the CFO is also evolving to meet sustainability requirements, including reporting non-financial KPIs, managing plastic and carbon taxes, steering the business, and aligning business decisions with carbon and environmental cost trade-offs. and management solutions can provide the capabilities needed to address CFO sustainability priorities.

As 麻豆原创 Chief Sustainability & Commercial Officer Sophia Mendelsohn noted during the week,听“Sustainability remains firmly planted in both the Davos agenda and the minds of the CEO and CFO. The reality of climate change persists鈥攂oth its risks and opportunities, and they are already showing up on the balance sheet.“

For many executives, this framing reflects how sustainability considerations are increasingly influencing investment decisions, insurance strategies, and assessments of long-term enterprise value.

The focus is shifting from ambition to execution

Davos discussions also underscored a growing emphasis on execution. While sustainability remains firmly planted in the C-suite agenda, many leaders acknowledged a gap between ambition and implementation.

Despite years of commitments and target-setting, fewer than one in five companies have implemented climate adaptation and mitigation measures at scale. This is that helps explain why sustainability strategies are now evaluated more closely through the lens of financial feasibility, operational readiness, and data credibility.

In an environment where sustainability investments compete with other priorities, including AI and digital infrastructure, leaders emphasized the need for clear business cases and measurable outcomes. Sustainability initiatives that can demonstrate value creation and risk reduction are more likely to secure long-term support.

Integration decides whether sustainability insights lead to action

Data availability is no longer the primary challenge for most organizations. The tools to measure emissions, water use, climate exposure, and supplier impacts are widely accessible. What remains difficult is turning that information into decisions.

Across Davos, there was broad agreement that sustainability data needs to be integrated into core business systems for planning, procurement, asset management, and finance. When sustainability information sits outside these systems, it tends to inform reporting rather than operational or strategic action. When it is embedded, it can support more forward-looking decisions around resilience, investment, and supply chain design.

This shift toward integration reflects a broader understanding that sustainability efforts are most effective when they are aligned with how the business already operates.

connects business and sustainability data to help give full visibility across a company鈥檚 value chain, enabling it to align business objectives with sustainability priorities across areas like material choice, efficient transport and distribution, improved asset performance, and reduced carbon impact.

Davos 2026 clearly reflected a maturing phase of the sustainability conversation, one that is less about visibility and increasingly about how organizations can confidently prepare for the decade ahead.

For business leaders shaping sustainability strategies, there is a pressing need to make plans financially grounded, operationally integrated, and supported by reliable data.

Enterprise systems play an important role in this transition. When sustainability information is connected across business functions, leaders gain clearer insight into risk and opportunity, supporting more resilient and informed decision-making.

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

At , I moderated a roundtable with supply chain and transformation leaders from Toyota, L鈥橭r茅al, Owens Corning, Ecolab, American Airlines, and Bayer. Across industries, one theme was clear: transformation succeeds when it is grounded in clarity, centered on people, and focused on customer impact.

If you can鈥檛 name the problem, don鈥檛 buy the technology

The most effective transformations begin with a clear articulation of the problem to be solved, the value at stake, and the outcomes expected. Too often, organizations chase new technology simply because it is available or gaining attention, not because it is tied to a specific business need. However, technologies like AI only deliver value when applied to well-defined business challenges.

Tying initiatives to measurable outcomes helps secure leadership alignment and supports change management. Clarity creates focus and helps teams understand why transformation matters. At its foundation, successful transformation rests on three pillars: people, process, and technology, all powered by high-quality data.

AI is only as smart as the network behind it

As supply chains become more complex, multi-tier visibility has moved from a nice-to-have to a requirement. Many of the biggest risks today, including regulatory exposure, disruptions, and cost volatility, originate deep in the supply chain, often beyond direct supplier relationships.

AI has emerged as a powerful enabler, but only when supported by trusted, shared data. More than half of the data needed to drive intelligent supply chains sits outside an organization鈥檚 four walls. Without visibility across trading partners, AI鈥檚 impact is limited.

Standardization also surfaced as a recurring theme during our roundtable conversation. While one-size-fits-all approaches rarely work, excessive customization slows progress and undermines scale. Leading organizations are finding a balance by standardizing where it drives speed and consistency, while allowing flexibility where it creates meaningful value.

Transformation doesn鈥檛 fail on technology, it fails on trust

Technology may enable transformation, but people determine whether it succeeds. Change management remains a critical factor, and even well-designed initiatives can stall if employees do not understand the purpose or feel threatened by the change.

Clear communication, local champions, and ongoing engagement help build trust and momentum. Leaders emphasized the importance of reinforcing that technology is designed to elevate roles by removing manual work and enabling employees to focus on higher-value activities, instead of replacing them. Being honest about what is working, what isn鈥檛, and when to pivot builds credibility and keeps teams engaged over the long term.

Customer-centric supply chains don鈥檛 happen by accident

A significant shift underway is the move from inward-facing efficiency to explicit customer-centricity. Many supply chain organizations historically assumed their work benefited customers without clearly articulating how.

Today, customer impact is a unifying principle. Whether serving consumers, passengers, farmers, or B2B customers, supply chains are increasingly recognized as drivers of experience, reliability, and brand trust.

Customer collaboration is also playing a larger role in transformation. Sharing data and aligning processes across networks creates shared value and unlocks differentiated services. Progress must be communicated in terms customers care about鈥攏ot internal metrics, but outcomes that improve service, speed, and transparency.

What the best transformation leaders do differently

Coming out of our conversation, the strongest transformation leaders shared a few defining traits:

• They pivot quickly when something isn鈥檛 working. Rather than protecting sunk costs or rigid plans, they recognize when assumptions change and adjust course. Speed and adaptability often matter more than perfection.
• They invest in people as intentionally as they invest in platforms. Adoption requires time, training, and leadership attention. Workforce readiness is treated as a core deliverable, not a side effort.
• They celebrate progress. Visible milestones help sustain momentum and reinforce positive behavior throughout a long transformation journey.
• They hold themselves accountable for real value creation. Success metrics are defined early and tracked consistently, whether financial, operational, or customer driven.

Turning ambition into measurable impact

What stood out most from my conversation at NASCES25 was the shared recognition that transformation is no longer optional. Supply chain leaders are embracing bold ambitions, people-centered change, and customer-focused innovation to turn intent into measurable impact.

plays a critical role in this journey by helping organizations connect processes, partners, and data across the value chain to improve visibility, resilience, and decision-making at scale. And beyond the network, helps orchestrate people, processes, and technology end-to-end鈥攅nabling leaders to move from intent to impact with integrated planning, execution, and insights that can drive real operational transformation.

To hear more directly from the leaders shaping this future, I encourage you to watch the and continue the conversation on how supply chains can lead meaningful transformation.

Keith Baranowski is global head of Sales for 麻豆原创 Business Network.

In 2026, AI earns its place when it is anchored in the realities of the business, shaped by enterprise data, processes, and lived customer interactions. Customer-specific AI brings intelligence directly into day-to-day operations, helping teams navigate complexity and support better decisions at scale while keeping human judgment firmly at the center. This is the shift shaping the next phase of AI adoption, moving from generic tools to intelligence that understands the business and grows stronger with every customer interaction.

1. Relevance beats raw intelligence in customer decisions

As AI becomes more central to customer-facing decisions, accuracy and relevance become non-negotiable. Generic models often lack the contextual understanding needed to interpret nuanced, exception-heavy customer scenarios. Customer-specific AI, trained on enterprise data, can recognize patterns unique to the organization鈥攕uch as recurring dispute types, resolution bottlenecks, or region-specific service behaviors. According to 麻豆原创鈥檚 “Value of AI” report in collaboration with Oxford Economics, 36% of businesses say AI has already helped them address customer-related challenges, including improving customer engagement. This impact is strongest when intelligence reflects how customers actually interact with the business, rather than abstract assumptions.

2. Scaling complexity without losing control

Customer-specific AI proves most powerful where customer processes scale faster than manual intervention can keep up with. Returns, exchanges, dispute resolution, claims handling, and service exceptions span multiple systems, rules, and decision paths. AI that understands enterprise context can scale these processes without compromising consistency, governance, or accountability鈥攅nabling organizations to handle growing volumes while maintaining predictable outcomes and service quality.

3. Differentiation that compounds over time

Unlike generic AI capabilities that are broadly accessible, customer-specific AI is shaped by proprietary data, policies, and institutional knowledge. Over time, this creates intelligence that becomes deeply aligned with how the business operates鈥攁nd increasingly difficult for competitors to replicate. The more the system learns from real customer interactions, the more it compounds as a durable source of differentiation.

4. Where customer-specific AI proves its value, from theory to practice

The impact of customer-specific AI is most visible in high-volume, exception-driven environments. A large European manufacturing and consumer goods organization illustrates this well through its approach to dispute, returns, and exchanges management. Operating across regions and product lines, the company faced long resolution cycles, inconsistent outcomes, and heavy manual effort. By deploying AI trained on its own historical disputes, order data, pricing rules, and resolution workflows, the organization embedded intelligence directly into its processes. Incoming claims were automatically classified, relevant documentation was surfaced, and resolution recommendations were generated based on prior outcomes and policies. Cases were routed efficiently, reducing back-and-forth and manual effort. Crucially, the system evolved with policy changes and customer behavior鈥攁ugmenting human decision-making rather than replacing it. The result was a faster, more consistent, and scalable approach to managing customer disputes.

5. A cross-industry shift toward embedded intelligence

These principles extend well beyond dispute management. In manufacturing and supply chains, customer-specific AI supports fulfillment exceptions and service-level disputes. In financial services, it enables complaint handling aligned with regulatory frameworks. In healthcare, it supports decisions grounded in institutional protocols and patient journeys. In retail and services, it drives relevance by learning customer preferences, brand rules, and operational constraints. Industry observers increasingly note that AI鈥檚 next phase of growth will be driven by intelligence embedded into customer-facing processes鈥攏ot stand-alone tools. According to 麻豆原创鈥檚 “Value of AI” report with Oxford Economics, the majority of businesses expect AI to become central to business processes, decision-making, and customer offerings by 2030, with only 3% saying otherwise.

In 2026, enterprises will judge AI less by novelty and more by its ability to deliver consistent customer and business outcomes. Customer-specific AI sits at the center of this shift because it weaves intelligence directly into how organizations operate and serve customers. This next stage of AI is not about removing human judgment鈥攊t is about strengthening it. By absorbing complexity and surfacing context-aware insights, customer-specific AI enables faster responses, greater consistency, and confident scaling of customer-centric decision-making. In an increasingly complex and customer-driven landscape, the true edge will belong to enterprises that invest in intelligence that genuinely understands their business.

Sindhu Gangadharan is head of Customer Innovation Services and managing director of 麻豆原创 Labs India.

Building resilience and control across every category of spend

Organizations are under increasing pressure to manage costs, improve agility, and drive measurable outcomes. 麻豆原创鈥檚 connected and intelligence-driven source-to-pay suite is designed to help customers meet these challenges head on.

麻豆原创 Ariba solutions can deliver broad and deep functionality across the full source-to-pay lifecycle, spanning sourcing, contracting, procurement, invoicing, and supplier management. With the industry鈥檚 largest supplier network, 麻豆原创 enables buyers and suppliers to collaborate with confidence, consistency, and scale.

麻豆原创鈥檚 investment priorities: platform modernization and agentic AI innovation

Rather than reflecting external judgments, 麻豆原创鈥檚 strategic focus is centered on advancing its platform foundation, AI capabilities, and user experience to help customers operate with greater intelligence and agility. Our long鈥憈erm investments concentrate on three core areas:

Modernizing the platform for the future

A platform update arriving in 2026 will complete the modernization of 麻豆原创鈥檚 technical architecture. This modernized foundation can deliver greater extensibility, improved performance, and faster delivery of innovation, particularly in agentic and generative AI.

Built as an AI-native architecture, the next-generation platform can embed intelligence directly into workflows to help anticipate needs, guide decision-making, and automate actions across the entire source-to-pay process. This positions 麻豆原创 to deliver the first truly AI-native source-to-pay suite built for the future of procurement.

Expanding intelligence with Joule

plays a central role in bringing intelligence and insight to every stage of the source-to-pay process. Joule鈥檚 advanced AI agents can help automate tasks, support decision-making, enhance compliance, and unlock new productivity across sourcing, procurement, and supplier collaboration.

Reimagining the user experience

麻豆原创 is delivering an updated, consistent UI/UX across 麻豆原创 applications. For procurement teams, this means smoother navigation, modernized interfaces, and enriched contextual intelligence, including enhanced supplier 360 profiles and strengthened collaboration capabilities.

Strengthening global scale and operational flexibility

麻豆原创 continues to demonstrate industry-leading global scale, supporting high-volume transactions and diverse compliance requirements across regions and industries. With multiple cloud deployment options across major hyperscalers and a robust portfolio of security and regulatory certifications, including FedRAMP, customers can operate confidently wherever they do business.

Connected solutions across 麻豆原创 Business Network and the intelligent suite

麻豆原创 Business Network remains the largest supplier network in the source-to-pay market, spanning more than 190 countries. 麻豆原创鈥檚 broader spend ecosystem鈥攊ncluding innovations such as , , and 鈥攅nables organizations to unify data, intelligence, and processes across sourcing, procurement, invoicing, and spend management.

Importantly, 麻豆原创鈥檚 capabilities extend well beyond traditional source-to-pay. Through connected solutions covering travel and expense, contingent workforce management, external labor, and additional spend categories, 麻豆原创 provides a truly comprehensive spend management platform that can deliver visibility and control across the full spectrum of enterprise spend.

As organizations increasingly operate in heterogeneous application landscapes, 麻豆原创 helps deliver openness, security, and extensibility so customers can maintain cohesive and connected processes throughout 麻豆原创 and non-麻豆原创 environments.

Customer impact: outcomes that scale

Customers across industries and regions continue to demonstrate what鈥檚 possible with 麻豆原创鈥檚 source-to-pay solutions. Organizations report meaningful improvements in areas such as compliance, cost optimization, supplier collaboration, operational efficiency, and workforce productivity鈥攆rom managing millions of invoices to running global sourcing initiatives to scaling AI-powered automation across distributed operations.

麻豆原创 remains deeply committed to helping procurement and finance organizations navigate complexity with confidence. Our investments in platform modernization, agentic AI, user experience, and cross-suite integration are all grounded in a single mission: to help customers achieve sustainable, long-lasting impact.

We’re grateful for this recognition and energized by the opportunity to deliver even greater value in the years ahead.

. Read the full Gartner Magic Quadrant for Source-to-Pay Suites report .

Fang Chang is EVP and chief product officer for 麻豆原创 Procurement and External Workforce Solutions.
Baber Farooq is senior vice president and head of Market Strategy for 麻豆原创 Procurement and External Workforce solutions.

*Gartner Magic Quadrant for Source-to-Pay Suites, January 21, 2026 – ID G00833291, by Micky Keck, Kaitlynn Sommers, Lynne Phelan, Magnus Bergfors, Alex Brady

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates.

Agentic AI was everywhere at NRF, emblazoned across the booths of technology exhibitors and the focus of many of the daily conference sessions. The message was simple: retailers face a major upheaval as consumers switch from traditional browser-based search to AI-enabled product discovery.

Consumers are rapidly adopting AI agents to help them find, compare, and, increasingly, buy products鈥攖his while many brands are still optimizing for search engines and are quietly disappearing from the models driving the next generation of product discovery.

鈥淎gentic commerce鈥攕hopping powered by AI agents acting on our behalf鈥攔epresents a seismic shift in the marketplace,鈥� McKinsey, the strategic management consultancy, noted in a . 鈥淚t moves us toward a world in which AI anticipates consumer needs, navigates shopping options, negotiates deals, and executes transactions, all in alignment with human intent yet acting independently via multistep chains of actions enabled by reasoning models.鈥�

This, as speakers and panelists at the NRF conference acknowledged, isn鈥檛 just an evolution of e-commerce; it鈥檚 a rethinking of shopping itself, in which the boundaries between platforms, services, and experiences give way to an integrated, intent-driven flow through highly personalized consumer journeys that deliver a fast, frictionless outcome.

As the McKinsey report noted, the stakes are high. By 2030, the U.S. B2C retail market alone could see up to US$1 trillion in orchestrated revenue from agentic commerce, with global projections reaching as high as $3 trillion to $5 trillion.

This means all the participants in the retail chain, from brands and retailers to logistics and payment service providers, will need to adapt to the new paradigm and successfully navigate the challenges of trust, risk, and innovation.

To help retailers address the immediate challenges posed by the shift to agentic commerce, 麻豆原创 argues that three steps are necessary: first, restructuring web-page product data to be machine-readable; second, adding semantic summaries for LLM reasoning; and third, tagging products by the problems they solve, not just their attributes.

麻豆原创 announced a series of AI-enhanced retail innovations at NRF 2026, including a new storefront model context protocol (MCP) server that enables retailers to make their digital storefronts intelligible to AI and the new AI-native Retail Intelligence solution in 麻豆原创 Business Data Cloud that leverages data from across 麻豆原创 software and third-party systems to help provide accurate demand planning, improved forecast accuracy, and lower inventory costs to drive more seamless omnichannel engagements.

麻豆原创 Customer Experience has also unveiled a recently that can be combined with the听, creating one conversational AI that can handle the entire journey from product discovery and transaction to post-sales support.

These moves reflect a recognition that that LLMs have become a legitimate shopping channel, and that product discovery is moving from search engines to AI recommendations.

This shift challenges years of SEO and brand building. To stay relevant, 麻豆原创 believes retailers must take an AI-first approach and have strong, connected data that helps agents understand products, predict demand, and respond quickly. Without this strong data foundation, brands will be at risk because if customers get poor recommendations and errors in pricing, trust can disappear fast.

Although some early agentic AI adopters in the retail sector are already seeing the benefits of agentic commerce, many global retailers are still ill-prepared for the holistic transformation they need to succeed in this new retail environment.

As McKinsey noted in a separate , 鈥渨hile most retail merchandising teams have invested in automation tools听and experimented with AI, 71% of merchants say that AI merchandising tools have had limited to no effect on their business so far.鈥�

鈥淭he challenge,鈥� McKinsey said, 鈥渙ften lies less in the technology than in how it鈥檚 integrated and used. Systems remain fragmented, data is too messy to use to deliver useful recommendations, and adoption is uneven: 61% of respondents say that their organization isn鈥檛 at all or is only slightly prepared to scale AI across merchandising.鈥�

Onstage at NRF, Andre Bechtold, president for 麻豆原创 Industries & Experience, also emphasized that retailers should prepare now for agentic commerce and noted that simply “bolting on” AI tools to existing systems is not enough.

鈥淩etailers are operating in an environment defined by volatility鈥攖ariffs, margin pressure, supply chain disruption, and customers that expect real-time, hyper-personalized experiences everywhere,鈥� Bechtold said during a discussion with Gymshark, the workout apparel retailer. 鈥淎t the same time, boards and investors are asking a tougher question than ever before: what outcomes are we actually getting?鈥�

鈥淭he challenge,鈥� he said, 鈥渋sn鈥檛 a lack of innovation. In fact, most retailers have plenty of tools, pilots, and point solutions. The real issue is that disconnected technology doesn鈥檛 translate into resilient growth. That鈥檚 why the conversation is shifting. It鈥檚 no longer about isolated AI use cases or shiny new features. It鈥檚 about whether AI and data are embedded across the business鈥攃onnecting supply chains, finance, merchandising, and customer engagement鈥攊n ways leaders can trust.鈥�

Echoing the same point, Thomas Saueressig, member of the Executive Board of 麻豆原创 SE, Customer Services & Delivery, commenting in a this week about a PwC survey of global CEOs that found that companies rarely achieve lower costs or higher sales through the use of AI, emphasized that AI only contributes value when consistently embedded in business processes. 听鈥淎s long as AI runs alongside the core business as an isolated project, the effects remain limited,鈥� he said.

Our vision for agentic commerce is bold. In , we showcase a future where humans and AI agents collaborate to drive intelligent recommendations, proactive operations, efficient business processes, and deeper customer relationships. While this vision points forward, 麻豆原创鈥檚 focus is firmly grounded in helping retailers take practical steps today. This isn鈥檛 about flashy demos of a distant future鈥攊t鈥檚 about building the foundation now for how consumers will buy and retailers will sell in the years ahead.

Unlike traditional AI systems that respond to prompts, agentic systems act on intent. They learn from preferences, make proactive recommendations, and can complete transactions on a shopper鈥檚 behalf. These agents are increasingly becoming the starting point of the buying journey, reshaping how brands compete for visibility, trust, and loyalty.

This evolution introduces both opportunity and risk. As AI agents mediate more interactions between brands and consumers, retailers must rethink how they capture intent, transact with agents, and deliver post-purchase experiences that reinforce trust.

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Discovery is moving from search to assistants

Historically, product discovery revolved around search engines, marketplaces, and brand-owned storefronts. That model is shifting quickly. Answer engines and AI shopping agents are becoming new entry points for commerce鈥攐ften before a shopper ever visits a retailer鈥檚 site.

Like marketplaces before them, AI agents introduce a new layer between brands and customers. The difference is speed and autonomy. Agents don鈥檛 just surface options; they reason, decide, and act.

For retailers, success is no longer about ranking on a page. It鈥檚 about ensuring products are visible, understandable, and trusted by machines that influence purchase decisions on behalf of humans.

At NRF, 麻豆原创 expanded its agentic commerce vision with the announcement of the storefront MCP server for 麻豆原创 Commerce Cloud, planned for Q2 availability. The storefront model context protocol (MCP) server can enable channel-less commerce by allowing businesses to safely and reliably engage with multiple AI agents鈥攚hether embedded in a retailer鈥檚 own experiences or originating from third-party assistants like ChatGPT or Perplexity.

The storefront MCP server helps merchants surface products and can enable buying across channels for both people and machines. It鈥檚 the first of many steps 麻豆原创 is taking to help customers fully participate in agentic commerce by supporting MCP, ACP, UCP, and other emerging agentic protocols.

Product content becomes the currency of visibility

In an agent-driven world, product content is no longer just marketing鈥攊t鈥檚 operational infrastructure. AI agents cannot recommend what they cannot interpret. Every attribute, image, specification, availability signal, and proof point directly impacts whether a product is surfaced, compared, or selected.

This is where generative engine optimization (GEO) is evolving. Optimization must now serve two audiences: humans and machines. Product data must be structured, consistent, and enriched, so AI agents can confidently represent it to shoppers.

The in helps transform how merchants manage product data at scale. It can clean catalogs, enrich attributes, standardize details, fill gaps, and support multilingual content using real-time data. The agent can scale to catalogs with more than 10 million items, helping teams improve content 70% faster, increase data completeness by 5%, and reduce maintenance effort by 63%.

With AI-ready product data as its foundation, retailers can better match shopper intent, optimize merchandising by channel, and improve pricing and delivery decisions with precision.

Payments must evolve for autonomous commerce

As buying journeys fragment across devices, channels, and agents, payments must become more flexible and nearly invisible. Consumers expect to pay how and when they choose, including through agent-initiated transactions.

New payment rails like FedNow, RTP, and stablecoins are enabling faster, lower-cost transactions, while wallets and bank-based payments continue to converge. Networks such as Visa and Mastercard are already preparing for autonomous commerce by allowing consumers to set spending limits and controls for AI agents.

For retailers, the priority is delivering frictionless, secure payment experiences that integrate seamlessly into agent workflows.

The can enable this flexibility through a no-code, low-code approach. Its headless, extensible architecture helps support diverse payment methods, ensure compliance through automatic updates, and integrate natively with 麻豆原创 Commerce Cloud鈥攚orking to give retailers agility without sacrificing control or scalability.

Returns become a strategic intelligence engine

Returns are one of retail鈥檚 biggest challenges. According to IHL Group, global returns have surpassed US$1.9 trillion and are growing faster than sales. What was once a cost center is now a strategic differentiator.

The next phase of returns management is defined by intelligence. AI enables 鈥渒eep, reject, or return鈥� decisioning based on loyalty history, behavioral signals, margin impact, and lifetime value. Returns data becomes a feedback loop that improves forecasting, product quality, and merchandising decisions.

Complete, connected data is essential. 麻豆原创 can deliver this through native integration between 麻豆原创 ERP and 麻豆原创 Commerce Cloud, creating a single source of truth across inventory, costs, and transactions. found that organizations using both platforms achieved up to 80% lower TCO, up to 90% productivity gains, and 105%鈥�245% revenue uplift from hyper-personalized experiences.

can extend this foundation across the full returns journey, helping to orchestrate centralized rules, guided returns, real-time inventory visibility, and faster refunds鈥攖urning returns into a loyalty-building growth lever rather than a revenue drain.

Commerce is detaching from the storefront

As predicted at the end of 2025, AI agents are taking on more shopping tasks, pushing commerce beyond traditional storefronts. A shopper may simply state an intent and let an agent handle research, selection, and checkout.

Discoverability now depends on structured, trustworthy signals鈥攔eviews, ratings, social proof, and consistent data that agents rely on to evaluate quality and brand credibility.

Retailers must move beyond transactional efficiency to deliver connected, personalized experiences across every touchpoint. Loyalty programs must reward engagement, not just purchases. Inventory visibility, accurate delivery promises, and proactive issue resolution become table stakes.

can enable retailers to design adaptive loyalty strategies for this new environment, personalizing rewards and offers based on real-time behavior鈥攚hether purchases happen through traditional channels or AI agents. These insights can then feed transactional agents, helping to improve relevance and outcomes across the journey.

Operational reliability remains critical. 麻豆原创 Order Management Services help unify order, inventory, fulfillment, and POS data, while agentic innovations like the Order Reliability Agent can proactively resolve fulfillment issues before they impact customers.

Trust is the core retail responsibility

As agentic systems influence more of commerce, trust becomes the most valuable asset retailers can protect. Consumers must trust that their data, preferences, and payments are secure and governed responsibly.

Retailers and commerce providers increasingly act as AI trust custodians, balancing intelligence with deterministic constraints and governance. On-site AI can scale associate expertise and personalization while preserving brand integrity and customer confidence.

Commerce is becoming an ecosystem of intelligent interactions鈥攚here discovery, payments, fulfillment, and returns are connected by agents acting on behalf of shoppers and businesses alike.

The winners will be those who align product intelligence, flexible payments, data-driven returns, and trust across every touchpoint. Agentic AI can make commerce more personal, efficient, and scalable鈥攂ut only for those who build the right foundations today.

To learn more about how 麻豆原创 Commerce Cloud is powering AI-driven commerce, visit .

Kollen Glynn is global head of 麻豆原创 Commerce Cloud for 麻豆原创 Customer Experience.

That鈥檚 why the latest recognition from two of the industry鈥檚 most respected research firms鈥擜rdent Partners and Staffing Industry Analysts (SIA)鈥攎eans so much to us at 麻豆原创. Their findings reinforce what we see every day: isn鈥檛 just keeping up with the industry, we鈥檙e setting the standard for innovation, execution, and customer value.

Market leadership confirmed by independent analysts

named 麻豆原创 Fieldglass a 鈥淢arket Leader,鈥� a distinction reserved for providers with universal strengths and top-tier execution. The evaluation looked at every aspect of our portfolio, from supporting the full scope of managing the external workforce鈥攔equisitions, candidates, projects, direct sourcing, SOW/services procurement, integrations, and AI鈥攖o our execution ability, client success, references, talent ecosystem, product vision, and future of work readiness.

We鈥檙e especially proud to be recognized as an 鈥淓lite Performer鈥� in four critical categories: AI Innovation, SOW and Services Procurement, Direct Sourcing, and Total Talent Management. Ardent Partners highlights 麻豆原创 Fieldglass鈥� deep integration with 麻豆原创 SuccessFactors solutions, 麻豆原创 Ariba solutions, 麻豆原创 Build Work Zone, and the AI solution Joule, positioning it as a strategic hub for managing external talent and optimizing services procurement. Joule is transforming how users interact with the portfolio, offering conversational, data-driven guidance for requisition creation, SOW intake, job description design, and workflow acceleration.

also confirms 麻豆原创 Fieldglass鈥� global leadership. The portfolio operates in more than 180 countries, supports 22 languages, and enables invoicing in 118 countries, making it the largest footprint in the industry. This extensive reach is further underscored by consistently strong customer retention, reflecting the deep trust and satisfaction 麻豆原创 Fieldglass enjoys among global enterprises.

Customer-validated leadership

Recognition from leading analyst firms is important, but what matters most is the experience of our customers. 麻豆原创 Fieldglass has also been ranked as a category leader by , a respected platform for verified customer reviews. TrustRadius rankings are based on direct feedback from real users, making it a powerful complement to analyst evaluations. While analyst reports validate market position and strategic capabilities, TrustRadius highlights the day-to-day value customers experience, such as ease of use, depth of functionality, and overall satisfaction. This dual endorsement, from both industry experts and actual practitioners, reinforces 麻豆原创 Fieldglass鈥� commitment to delivering solutions that can meet business needs and exceed customer expectations.

Innovation at the core: AI, automation, and analytics

Innovation is at the heart of 麻豆原创 Fieldglass鈥� value proposition. The portfolio鈥檚 embedded AI solution, Joule, helps users initiate job requisitions, design and translate job descriptions, and accelerate approvals through a conversational interface. Rate benchmarking helps ensure customers stay competitive with market rates. Document AI can automate structured SOW creation, while machine learning and skills ontology models can power advanced candidate matching and workforce planning.

SIA notes that 麻豆原创 Fieldglass鈥� 鈥淎I-first鈥� product strategy is embedding intelligence into every workflow, emphasizing seamless data extraction, intuitive user experiences, and impactful use cases across applications. The business intelligence suite delivers real-time and historical analytics, benchmarking tools, and persona-based dashboards, empowering organizations to make data-driven decisions at every stage of the talent lifecycle.

Comprehensive capabilities for a modern workforce

What distinguishes 麻豆原创 Fieldglass is its wide-ranging and comprehensive set of features. The portfolio allows users to effectively manage all kinds of external labor expenses鈥攚hether that’s contingent labor, services procurement, independent contractors, high-volume workers, or field services. It offers streamlined onboarding, advanced and customizable tools for global compliance, AI-driven workflows, and robust skills extraction, helping to ensure depth and efficiency in every area. Additionally, its compliance and audit capabilities, mobile accessibility, and dashboards supporting diversity, equity, and inclusion help create a holistic solution.

SIA鈥檚 research, based on 23 in-depth VMS surveys and 36 customer assessments, found 麻豆原创 Fieldglass excels in supplier management, time/expense/billing, SOW capabilities, candidate sourcing, technical functionality, and customer perceptions. Customers cite the portfolio鈥檚 innovation, influence over roadmap evolution, and ability to scale with their programs. The open API framework, pre-built integrations, and strong MSP partnerships create a connected ecosystem that can support even the most complex workforce strategies.

Customer experience and ecosystem strength

Customer experience is another area where 麻豆原创 Fieldglass shines. SIA鈥檚 customer perception data shows high marks for platform stability, ease of use, integration quality, adaptability, and support. Customers appreciate the portfolio鈥檚 innovation, the ability to influence roadmap evolution, and its capacity to scale with their programs.

Both Ardent Partners and SIA emphasize 麻豆原创 Fieldglass鈥� commitment to continuous innovation. The 鈥淎I-first鈥� and 鈥渟uite-first鈥� strategies focus on embedding intelligence into every workflow and delivering seamless, end-to-end business processes across the 麻豆原创 ecosystem. Key areas of ongoing investment include skills-based hiring, AI, enhanced analytics, and high-volume workforces for SOW management.

A portfolio for the future of work

The latest findings from Ardent Partners and SIA reaffirm 麻豆原创 Fieldglass鈥� leadership, vision, and relentless focus on customer value. Combined with customer-driven recognition from TrustRadius, 麻豆原创 Fieldglass stands as a proven leader in delivering solutions that can empower organizations to unlock the full potential of their extended workforce鈥攖oday and for the future of work.

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Amber Roth vice president of Global Presales & Strategy for 麻豆原创 Fieldglass.

Heartland Dental is the largest dental support organization in the United States, with over 3,000 supported doctors in more than 1,900 supported practices across 39 states and Washington, D.C. But beyond numbers, what sets Heartland apart is its tight knit community and people culture. As Jerome shared, “The first thing I associate with Heartland is community; we鈥檙e doctor-led. In our support role, we don鈥檛 tell dentists how to practice. Our role is to make their lives easier鈥攅nabling dentists to concentrate on patient care.”

Making lives easier is a vision Heartland shares not only for its supported dentists but for its own operations. The company’s digital journey began “backwards” starting in 2018 with 麻豆原创 Business Technology Platform (麻豆原创 BTP), instead of with ERP, to resolve disparate data from all over. Once 麻豆原创 BTP was established and adopted by the organization, it then began to incorporate .

What makes this journey truly stand out is how Heartland is using technology to serve people, supported through seamless integration with tools like 麻豆原创 Concur solutions, embedded听AI鈥攕uch as smart invoice management鈥攁nd embedded analytics. These features are freeing up time and resources so teams can focus on what matters most: supporting doctors and improving patient care.

The team has rolled out听麻豆原创 Build Work Zone across its supported practices and is investing in听AI tools like 听to help employees access information faster, automate repetitive tasks, and focus on what really matters鈥攑atient care and experiences. Jerome explained, “Just like we take the administrative burden off our supported doctors, 麻豆原创 takes the tech burden off us, so we can focus on supporting doctors and their teams.鈥�

Heartland’s next big milestone is going live with 麻豆原创 S/4HANA Cloud Public Edition听and taking AI a step further to automation.

Heartland Dental鈥檚 story shows that with the strategic adoption of technological innovations, it鈥檚 possible to build a future-ready healthcare support organization grounded in people and purpose.

Chris Putvinski is a communications specialist at 麻豆原创.

Since the launch, has empowered organizations to streamline operations for increased efficiency, reduced manual workloads, and untangled multi-channel complexity. With this approach, businesses can deliver on customer promises with seamless customer experience. This momentum has also been recognized in the market, as 麻豆原创 Order Management Services was named a Leader in by IHL Group for its robust capabilities and enterprise readiness.

, a leading German home improvement retailer, is already seeing the benefits. With 麻豆原创 Order Management Services, Hornbach connects digital and physical stores with full visibility into day-to-day transactions, providing omnichannel retail experience at scale to its customers.

However, the retail landscape is evolving continuously. While profitable growth is critical to businesses, earning and sustaining customer loyalty now is becoming more important. Ahead of the curve, 麻豆原创 has heavily invested in expanding capabilities in the 麻豆原创 Order Management Services bundle to help retailers deliver on customer promises with intelligence, scalability, and adaptability, leading to boosts in customer loyalty.

At NRF 2026, 麻豆原创 is unveiling new and enhanced capabilities that power retailers to not only operate more efficiently but also achieve loyalty-led growth through every order.

AI in 麻豆原创 Order Management Services

Joule in 麻豆原创 Order Management Services: 麻豆原创鈥檚 AI copilot, Joule, is now available in 麻豆原创 Order Management Services. Access order-related data, analysis, and insights through conversations in natural language and visual display.

Order Reliability Agent: Accelerate operational efficiency with the Order Reliability Agent in 麻豆原创 Order Management Services. Proactively mitigate and resolve any potential issues and gaps, such as stock discrepancies or process bottlenecks, to help ensure every order is fulfilled seamlessly and to boost customer loyalty.

AI-assisted copy generation and translations: Create promotional copy in seconds and translate it into any language with AI assistance, helping to reduce manual workload and accelerate time-to-market.

UI enhancements

Workflow-optimized UI: The enhanced and unified UI in 麻豆原创 Order Management Services can deliver a consistent user experience across order, inventory, and fulfillment operations. Teams can now work faster, reduce training time, and maintain full visibility across every step of the order lifecycle.

Watch the 麻豆原创 Order Management Services  to get a closer look at the AI capabilities in action. Visit the 麻豆原创 booth at NRF 2026, January 11 鈥� 13, to learn more about 麻豆原创 Order Management Services and catch an in-person demo.

Emilie Fournelle is head of Product Management for 麻豆原创 Order Management Services at 麻豆原创.

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BITZER, a听global leader in heat and cooling technologies, partnered with NEURA Robotics and 麻豆原创 to showcase the potential of cognitive robots powered by embodied AI from 麻豆原创 to transform and streamline warehouse operation.

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In an interview, BSH Head of Governance, Methods, and Systems Heiko Schletz explained how the company is reshaping finance and why its move to the cloud is a critical ingredient to a successful AI-enhanced future.

Technology follows vision

Represented in more than 50 countries, BSH manufactures home appliances in 39 factories worldwide. Schletz鈥檚 team oversees group controlling and is responsible for ensuring that financial data flows smoothly from its core systems, such as ERP, all the way up to the consolidated group level. His team manages how financial data is structured and integrated across all global entities, ensuring it can be used effectively for company-wide reporting and decision-making.

BSH is working to bring accounting and controlling together into one integrated process, supported by real-time data and analytics. One principle is guiding this transformation: technology follows vision, not the other way round. As part of its transformation journey, BSH is embracing change by testing new technologies to support its vision.

To simplify reporting efforts, for example, , 麻豆原创鈥檚 next-generation data management platform that can unify and govern all 麻豆原创 data and seamlessly connect with third-party data.

A recent use case automatically connected accounting balances, controlling P&L data and market metrics in 麻豆原创 Datasphere and delivering consolidated reports without spreadsheets and manual effort. 鈥淭his shows where the journey is going鈥攋oining sources, bringing them together,鈥� Schletz says.

Breaking down silos to empower AI

BSH鈥檚 long-term goal in the financial area is to get rid of silos between accounting, controlling, and treasury. Schletz envisions a parallel ledger architecture that supports both鈥攍egal entity and consolidated group views鈥攅nabling advanced analytics such as value-driver trees. By moving to 麻豆原创 S/4HANA Cloud Private Edition, integrated with 麻豆原创 Datasphere and 麻豆原创 Analytics Cloud, BSH aims to create a single source of truth for finance spanning from subsidiary ledgers to group-level consolidation.

Schletz is convinced that with a cloud-based, synchronized toolset, his finance team can deliver the latest figures for decision-making faster and with less manual consolidation. 鈥溌槎乖粹�檚 AI evolution is running in the direction we also want to go,鈥� Schletz explains. 鈥淭he technology meets our vision and that鈥檚 why it鈥檚 a perfect fit.鈥�

The company has relied on 麻豆原创 solutions for decades, starting with 麻豆原创 R/3 and now running 麻豆原创 S/4HANA, 麻豆原创 Business Warehouse, and 麻豆原创 Analytics Cloud. The next milestone is cloud migration: RISE with 麻豆原创. 鈥淚n the next two years, we go into the cloud,鈥� Schletz says. 鈥淲e want a synchronized toolset that gives us a holistic view.鈥�

To get the most out of analytics and AI functionalities, BSH is currently consolidating and simplifying its comprehensive business application landscape. The company鈥檚 target is to move from six separate ERP solutions to one global 麻豆原创 S/4HANA environment that covers all subsidiaries and geographies.

How to prepare an 麻豆原创 S/4HANA transformation

Schletz鈥檚 advice for other organizations exploring the RISE with 麻豆原创 journey is to start with a clear vision. 鈥淚f you don鈥檛 begin with a concept that combines accounting and controlling, don鈥檛 start with 麻豆原创 S/4HANA,鈥� he says, noting that a finance transformation is not an isolated IT project鈥攊t requires alignment across logistics, sales, and customer service. 鈥淭he 麻豆原创 S/4HANA conversion is a cross-functional adventure,鈥� he adds.

BSH鈥檚 journey is ongoing, but the direction is set. Cloud migration via RISE with 麻豆原创, integrated data, and a finance function designed for insight rather than manual effort. 鈥淲e want the machine to do what it does best, so people can focus on creating value,鈥� Schletz concludes.

Economist Impact鈥檚 new report, “,” sponsored by 麻豆原创, reveals how CFOs are shifting from stewards of financial accuracy to architects of business resilience, digital innovation, and long-term value. Based on input of 480 CFOs globally, the report highlights widening responsibilities, rising risk pressures, and an urgent need to adopt AI with both speed and discipline.

To lead effectively through volatility, today鈥檚 CFO agenda demands operational agility, intelligent automation, and a reimagined approach to workforce development. Here鈥檚 how today鈥檚 finance leaders are adapting.

The expanding CFO mandate

Gone are the days when CFOs focused solely on financial planning and reporting. Today, their influence extends far beyond traditional finance boundaries. Nearly 90% of CFOs report they are more involved in digital transformation and risk management than three years ago. Two-thirds are actively shaping sustainability and ESG strategies.

This evolution reflects a broader truth. CFOs are now central to decisions that impact customers, products, and talent. They are expected to anticipate disruption, mitigate risk, and enable agility鈥攁ll while safeguarding profitability.

Macroeconomic, geopolitical, and technological shifts are pushing CFOs deeper into operational decision-making. As one CFO quoted in the report explained, finance leaders today must 鈥渨ear multiple hats鈥� and develop a deep understanding of business fundamentals, processes, and controls to guide transformation effectively.

With expectations rising and responsibilities converging, the next challenge is clear: aligning these expanded priorities with the capabilities required to execute them.

A sharper risk radar in an uncertain world

CFOs are on the front lines of uncertainty with increasing pressure to keep risks and costs from ballooning. In fact, more than 80% of CFOs reported that they are now more involved in risk management and compliance, with 34% significantly so.

Yet, it is not higher costs that worry CFOs most, it is unpredictability. Inflation, shifting trade rules, and increased interest rates make capital allocation more challenging, with only 37% feeling confident about meeting liquidity targets, compared with nearly 90% for revenue goals.

In response, CFOs are doubling down on what they can control. AI-enabled scenario planning is enabling faster, more sophisticated modeling, while real-time operational signals are being translated into forward-looking risk indicators. Flexibility has also become essential, from upgrading systems for adaptable production to renegotiating vendor contracts with shorter, more variable terms.

Ultimately, the mandate is clear: build organizations that can absorb shocks, respond in real time, and maintain strategic momentum despite uncertainty.

CFOs at the center of AI adoption

Digital transformation has become a core responsibility of the CFO鈥檚 role, with nearly nine in ten reporting increased involvement鈥攎uch of it centered on AI. Finance leaders cite especially strong potential in compliance, where generative AI can parse complex regulations, track rule changes, and automate updates to internal systems.

But several challenges stand in the way of scaling AI鈥檚 impact:

Talent: the biggest barrier to AI acceleration

More than 60% of CFOs cited upskilling and hiring digitally fluent talent as top challenges, with fragmented systems and limited real-time data access adding further friction. As a result, CFOs are strengthening both team skills and data quality, recognizing that AI can only scale when people know how to use it and the data behind it is trusted.

The ROI paradox

CFOs must deliver quick wins from AI even though its most meaningful gains in forecasting, innovation, and growth take longer to materialize. To resolve this tension, leading CFOs are setting clear performance benchmarks, directing AI toward revenue-driving use cases, and coordinating across the business to scale capabilities that unlock sustained value.

Designing the workforce for an AI future

While AI is reshaping work, rising concerns about workforce displacement remain a real challenge for finance teams. However, nearly seven in ten CFOs see AI as a tool to augment human capability, prompting a rethink of roles, skills, and hiring decisions. Leading CFOs are redesigning early-career roles, investing in digital and analytical skills, and building blended teams that pair human judgment with AI-driven insight to strengthen the leadership pipeline.

Taken together, these shifts signal a broader evolution: finance is moving from a function rooted in historical reporting to one defined by predictive insight, real-time decision support, and enterprise-wide capability building.

CFOs who balance rapid efficiency with long-term investment in data, skills, and new ways of working will turn AI into a sustainable competitive advantage rather than a short-lived productivity boost.

Looking ahead: the new CFO playbook

Economist Impact鈥檚 research shows that the modern CFO shapes how organizations navigate risk, adopt AI, and build the workforce capabilities required for continuous transformation.

This shift demands a new playbook that unlocks capacity through automation, strengthens cross-functional alignment, builds flexibility into systems and supply chains, and reimagines finance career paths for a digital-first future. As one interviewee noted, 鈥淭he modern CFO is not just the guardian of value but the architect of future value.鈥� That future will belong to leaders who pair disciplined cost and risk management with bold investment in data, skills, and AI-driven insight.

With 麻豆原创鈥檚 financial management solutions, finance leaders can unify data, processes, and intelligence to meet the expanding demands of the role. As the expectations placed on finance continue to grow, 麻豆原创 remains committed to empowering CFOs with the clarity and confidence needed to lead through uncertainty and shape a more resilient future.

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David Imbert is head of Product Marketing for Finance at 麻豆原创.