This year’s SAP for Energy and Utilities Conference, held in Toulouse, France, marked a notable first: the inaugural presentation of the Energy and Utilities Innovation Award. Companies whose projects stood out were honored in the categories AI, Innovation, Transformation, Customer Experience, and Best Team.

AI as a key element

In the AI category, the Austrian energy company OMV prevailed with its SAP Business AI transformation project. Building on an existing SAP S/4HANA landscape, the transformation focused on intelligent automation, advanced analytics, and AI-driven decision-making in core business processes. A key factor for success was a step-by-step approach: instead of a hasty implementation, OMV first laid a solid foundation and introduced AI in a targeted manner into the user experience and business processes. The focus was on regulation, security, and continuous evaluation. The clear principle was to standardize, integrate, and establish a stable data foundation before it scaled more broadly. Early in the process, the company relied on Joule in SAP SuccessFactors solutions and SAP S/4HANA. In the next step, the transformation will continue so OMV can make even greater use of AI.

Intelligent water supply

With a data-driven platform for monitoring its water network, the Belgian water utility Farys was recognized by the jury in the Innovation category. With the help of SAP technology and AI, the utility can now deploy resources more efficiently and optimize its processes in a targeted way. Water leaks can be detected early before greater damage occurs, water quality issues can be predicted, and energy consumption can be reduced. The result: less downtime, lower repair costs, and a noticeably better quality of service. Having a unified solution also fosters cross-departmental collaboration by breaking down existing data silos. In the future, risk assessments for pipelines and facilities will be integrated, and data-driven decisions will help determine where repairs will have the greatest impact. With this strategy, Farys is preparing its infrastructure for the demands of climate change.

Achieving goals through teamwork

E.ON UK won the Best Team category with its SAP S/4HANA transformation project. With its group-wide project running from 2021 to 2027, the company aims to standardize processes and build a future-proof, scalable IT landscape. The central challenge was to consolidate various legacy systems in a highly regulated market. What sets this project apart is the close collaboration between the company, IT, and external partners. The company used workshops, road shows, and a targeted key user network to focus on team culture and cohesion from the very beginning. The result is not only a successful technical transformation, but above all a lived team culture—a key factor to the project’s success.

A new data foundation

In the Transformation category, Electrica Furnizare came out on top. The starting point was a fragmented system in which data was scattered and an overarching overview was missing. The company’s existing infrastructure was neither scalable nor able to respond flexibly to growing regulatory and customer requirements. With the introduction of SAP S/4HANA combined with SAP Business Technology Platform (SAP BTP), SAP Customer Experience solutions, and SAP Business AI, Electrica Furnizare achieved one of the largest transformations in the utility sector. The result: a single, reliable data source for all departments, end-to-end optimized processes, and a solid foundation that paves the way for future innovations and the full deployment of AI capabilities.

Better customer service through comprehensive modernization

Loudoun Water’s far-reaching modernization project won the Customer Experience category, with the company using SAP S/4HANA, SAP Service Cloud 2.0, and SAP SuccessFactors solutions to optimize its processes. With the introduction of SAP Service Cloud 2.0, Loudoun Water is the first utility company worldwide to take this step. The effort paid off: customer service is faster and more modern, work orders are better organized, billing and payments are managed more efficiently, and time tracking for employees has been simplified. The migration was carried out in a single, coordinated step, creating a future-proof, scalable foundation on which Loudoun Water can further expand its position as a technology leader in the industry.

Innovation as a driver of the energy transition

The winners of the first Energy and Utilities Innovation Award demonstrate the wide range of possibilities through which companies in the energy and utilities sector are actively shaping digital transformation. Whether AI-driven decision-making processes, intelligent infrastructure monitoring, or a consistent realignment of the IT landscape, all the projects have one thing in common: they rely on solid foundations, step-by-step implementation, and close collaboration among all stakeholders. Melanie Fiolka, go-to-market & community engagement lead for Utilities at SAP, summarizes it as follows: “The energy and utilities industry is undergoing profound change. The winners of the first Energy and Utilities Innovation Award demonstrate how vision is turned into tangible value through clear strategies, solid data foundations, and the adoption of AI.”

The award makes it clear that innovation in the industry is no longer the exception, but has become a strategic necessity.

WALLDORF — SAP SE (NYSE: SAP) today announced it has completed the acquisition of Prior Labs, the pioneer of Tabular Foundation Models (TFMs).

The acquisition will accelerate SAP’s success in TFMs that started with SAP-RPT-1 and bring one of the world’s leading TFM research teams into the SAP family. Prior Labs will continue to operate as an independent entity, with SAP committing to investing more than €1 billion over the next four years to scale it into a globally leading frontier AI lab for the structured data that underpins the world’s businesses.

For additional information about the acquisition, see the press release from May 2026. 

Visit the SAP News Center. Get SAP news via LinkedIn and Bluesky.

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For decades, enterprise transformation followed a familiar playbook. Digitize processes. Move to the cloud. Standardize operations. Then optimize them over time.

Those investments are critical. But they weren’t the finish line. They were the foundation. And today, they’re paying off in new ways as organizations adopt AI to drive measurable business outcomes.

But there’s another part of that foundation which is becoming just as important: data. The organizations best positioned to compound their value from AI are those that have invested in data foundations that give AI the trusted business context it needs to reason, recommend, and act.

Now, in conversations with customers across the Americas and around the world, I’m seeing a new pattern emerge. The discussion is shifting from where AI can be applied to what happens when intelligence becomes embedded into the core of how the business operates.

While every transformation is different, three common shifts keep coming up.

1. From AI use cases to intelligent business processes

The first wave of AI adoption focused on proving value. Organizations identified high-impact use cases, delivered measurable results, and built confidence that AI could make a difference.

That work isn’t finished. But increasingly, customers are asking how intelligence can become part of the business processes employees use every day. We’re already beginning to see what this looks like in practice.

For example, HR Path Brazil, a Brazilian company specializing in recruiting and managing talent for international firms, is using Joule embedded in SAP SuccessFactors HCM to automate routine HR interactions. It is helping employees find the information they need faster while allowing HR teams to focus on more strategic work. The company has reported a seven percent reduction in standard HR support cases and two hours of HR support workload eliminated each week, which quickly adds up. It’s one example of how embedded and connected AI is becoming part of how work gets done.

2. From measuring AI use to measuring the business outcomes it creates

One of the biggest changes I’m seeing is how organizations define success. ROI is becoming a given and that is reflected in the data, especially in Oxford Economics research out just this week. It showed that organizations investing in AI expect to see an average return of 21% this year but increasing to 38% in two years. And as agentic AI continues scaling, it is projected to deliver $17.6 million in returns, more than quadrupling last year’s estimates (US$4.3 million).

This is allowing organizations to focus more on business outcomes from their AI. They are asking questions like can we shorten cycle times? Can we improve decision-making? Can we free employees to spend more time creating value? Can we become more resilient and responsive as a business?

This is an important shift because it changes the conversation from implementing technology to improving how the business performs.

3. From systems of record to the new operating system for the enterprise

The third shift is the one I believe will have the greatest long-term impact. For decades, enterprise software primarily captured transactions, standardized processes, and automated routine work. Now it’s beginning to help organizations anticipate change, recommend actions, coordinate work across functions, and increasingly execute routine decisions with human oversight.

That’s why I believe the Autonomous Enterprise represents more than the next phase of automation. It represents a new operating model for business.

Instead of people spending time connecting information across finance, supply chain, procurement, HR, and customer operations before deciding what to do next, intelligent systems can increasingly provide context, surface recommendations, orchestrate work, and help teams execute.

People remain firmly in control. But they’re supported by enterprise software that is becoming an active participant in how the business operates and executes, not simply a system that records what already happened.

Where we go from here

The organizations creating the greatest long-term advantage won’t just be the ones deploying the largest number of AI use cases. They’ll be the ones that use those early successes to rethink how work gets done across the enterprise.

The journey to the Autonomous Enterprise won’t happen overnight, and it won’t replace the need for strong leadership, governance, or talented people. If anything, those become even more important.

We’ll likely look back on today’s AI projects much the same way we now look back on the early days of cloud transformation; not as the destination, but as the foundation for a fundamentally new way of operating and innovating continuously.

The true winners from this shift will be the ones who continuously become more intelligent, more adaptive, and ultimately more autonomous.

Jan Gilg is a member of the Extended Board of SAP SE and global president of Customer Success & Americas.

Each day, businesses invest in new software tools: marketing platforms, commerce engines, service systems, and sales technology. They are told that assembling the right combination will unlock their digital transformation journey and finally deliver that personalized and seamless experience they wish to provide to their customers.

But what if more or better software is not necessarily the answer? Each solution solves a real problem for a specific team. But collectively, they create a consequence no one planned for: every new tool builds its own data world, without a common language or context across them.

In a 2026 study by Oxford Economics, only 25% of respondents described their customer experience (CX) technology environment as fully harmonized and integrated. Twenty-nine percent remain highly fragmented. Organizations with siloed CX tech are more likely to face an inability to connect customer needs to actionable data insights. Fifty-eight percent reported this challenge, compared to 47% among those with harmonized environments.

More than what tools businesses choose to add to their CX landscape, how they connect and interact with each other becomes even more important.

A unified data strategy sounds straightforward in principle. In practice, most businesses find that the obstacle is not ambition but rather execution. Every integration decision made without a clear data architecture becomes a future campaign mired in manual reconciliation, a customer journey that breaks at the handoff, or a personalization promise the disparate data sources cannot support. Implementation without a validated strategy creates new fragmentation inside the solution meant to eliminate the old. And without a structured way to pressure-test decisions before any commitment is made, even well-resourced organizations find themselves repeating the same cycle: invest, integrate, fragment, repeat.

The real barrier is not budget or technology

A Forrester study of more than 1,000 senior executives found that data quality and integration issues are cited more than any other factor as the top cause of delayed or derailed transformation projects (39%), ahead of budget, technology maturity, and talent. The same study found that 56% of respondents struggle with poor data quality; 55% face persistent data silos. This is not for lack of investment in technology, but because the connections between systems were not designed or maintained effectively.

What bridges that gap is not another platform, it is the expertise to think through data connectivity decisions before they are made and the ongoing guidance to ensure those decisions compound into measurable gains over time. The Advanced Success Plan for SAP Customer Experience solutions provides that guidance along every step of the journey.

Define what success actually looks like

The most common reason data unification projects fall short of expectations is not technical failure, it is a failure to define and measure what success looks like for the entire business before the work begins.

The value management session from the Advanced Success Plan for SAP Customer Experience establishes that definition at the outset: What does a fully unified data strategy actually enable? It means running the next marketing campaign without manual data reconciliation, and presenting an AI readiness road map without caveats. Stakeholders will know, at every checkpoint, whether the investment is moving the business forward, not just moving the project forward.

Design the strategy before building the integrations

A robust data strategy starts with good design. Product guidance from the Advanced Success Plan for SAP Customer Experience covers available out-of-the-box integrations, common usage scenarios, and pitfalls and how to avoid them—all delivered in a live remote session by an SAP expert who can answer questions in real time. The data strategy can be conceptualized and pressure-tested before any budget or technical commitments are made.

Validate every critical decision with expert guidance

With the technical assistance and functional assistance from the Advanced Success Plan for SAP Customer Experience, businesses have continuous access to expert guidance at every critical decision point. Beyond resolving immediate questions, the ongoing access also shares insight on how SAP thinks through problems, strengthening in-house expertise with every interaction. The result is an organization that makes better decisions not just now but for the future.

Measure whether the strategy is delivering

Adoption and innovation checkpoints conducted on a quarterly or semi-annual basis bring the measurement back to where it started: the business outcomes defined at the outset. Do campaigns run without manual reconciliation? Is the AI use case performing against its stated goals? A clear throughline from the value management success KPIs to the adoption and innovation checkpoints proves the benefits of the investment.

Where cycles and silos break

The cycle of invest, integrate, fragment, repeat is not inevitable. It is the predictable result of making technical decisions without an anchoring business imperative, and integration decisions without strategic expert guidance. Organizations that break the cycle do not necessarily have better technology than their competitors—they have better judgment about how to use it.

The Advanced Success Plan for SAP Customer Experience exists for exactly that reason: to put proactive and prescriptive guidance at every decision point where that judgment matters most. When data strategy is designed before it is built, validated before it is committed, and measured against real business outcomes, the technology investment already made starts working harder.

The stack was never the problem. When the thinking behind the technology finally matches its ambition, the personalized, seamless experience becomes a reality.

Tara Tracey, global product owner for the Advanced Success Plan for SAP Customer Experience.
Ella De Torres, product manager for the Advanced Success Plan for SAP Customer Experience.

A new study by SAP and Oxford Economics has revealed businesses around the world are increasingly driving positive return on investment (ROI) from AI, even as challenges continue to accrue.

While the amount the average global business spends on AI increased slightly to US$28 million this year, the level of ROI from that investment has spiked. Globally, companies expect to drive ROI of 21% this year (US$6.3 million), up from 16% last year. That ROI is expected to grow to 38% in two years’ time (US$15.9 million).

Agentic AI is central to those ROI expectations. In the next two years, average ROI from agentic AI is expected to reach US$17.6 million, more than quadrupling from last year’s estimates (US$4.3 million).

These insights have been revealed in new global research, Value of AI Report 2026, which surveyed 2,600 business leaders across 13 countries.*

Commenting on the research, SAP Chief AI Strategy Officer Sean Kask noted, “AI has moved from experiment to execution, and that’s beginning to show real returns. But there’s still a long way to go. Because AI that lacks context—whether that’s processes, data, or governance—at best creates activity without outcomes and at worst creates risk.”

AI inching closer to enterprise maturity

While global investment in AI increased slightly from US$26.7 milion in 2025, there were significant changes in key markets. Investment increased significantly in Brazil, UK, Australia, and Germany, while leading markets like China and India saw funding decreases.

Today, almost a third of all tasks (30%) in the average business are supported by AI, a figure expected to increase to 48% in two years. Yet, while strategic investment in AI has almost doubled year-on-year to 17%, piecemeal approaches remain by far the most prevalent (41%).

Some of this may be a leadership problem. Under a half of companies have a dedicated AI leader responsible for AI adoption (46%), clear frameworks about AI development (52%), or even training on AI capabilities and risks (41%).

Yet, despite those challenges, 69% of businesses are satisfied with their current AI ROI, even though more than two-thirds are not convinced AI is achieving its full potential.

Some of this optimism is due to agentic AI, since over eight in 10 (83%) businesses say agentic AI has moderate to very high potential to transform their organization. Yet, it is still early days for the technology, with only three percent of businesses saying they are fully prepared for agentic AI, while the majority say they are either partially prepared or not prepared at all.

Global businesses meeting key AI challenges

Organizations are facing a range of challenges achieving ROI from AI, including data, workforce, and governance issues.

Data quality remains the biggest challenge for global organizations. The number of businesses that say they are data ready for AI dropped from last year, with 73% of companies revealing challenges with incomplete data. And that is impacting day-to-day work, with 79% of businesses experiencing rework, delays, or backlogs due to low quality AI outputs.

Similarly, businesses are managing the workforce impacts of AI. Almost eight in 10 businesses (78%) are either unsure or agree their company upskilling is not keeping up with the evolution of AI tools. And just one percent of respondents said AI will have no impact on their workforce planning. Meanwhile shadow AI use is increasing year-on-year, with 69% saying it happens at least occasionally.

“The next step in achieving value will be to integrate AI deeply with contextual data and processes,” Kask said. “But businesses across the world must understand AI often provides value that is harder to measure than expected, and risk that moves faster than most governance can keep up with. Businesses are quickly discovering that AI governance plays a foundational role in unlocking the value from AI.”

Governance is a critical obstacle in the way of enterprise AI value. Just 12% of businesses say either their skills or their processes and frameworks are fully ready to govern AI effectively.

These issues may be exacerbated in an agentic future. Today, 38% of companies do not have a human-in-the-loop process for agentic workflows, 37% don’t have permission and access controls for agents, and only 44% have a registry of the agents in their business. This is critical, given more than two-thirds of businesses (69%) either agree or are unconvinced if they are deploying agents quicker than they can govern them.

Future of value from AI is the Autonomous Enterprise

“Realizing real value from AI is not going to be easy because it demands a new approach,” Kask concluded. “Businesses large and small will need to connect AI to the data and processes that run their organizations, and make sure it has the context and governance to drive trusted results. That’s what we call the Autonomous Enterprise. This isn’t a technical change; it’s a human one. Because you can only achieve real value if agents, processes, and people work as one.”

*Australia, Brazil, Canada, China, France, Germany, Italy, India, Japan, Singapore, Thailand, United Kingdom, and United States.

In an environment shaped by constant change, workforce planning is no longer defined by predictable hiring cycles or seasonal demand. Shifting market conditions, evolving customer expectations, and persistent skills shortages mean that the line between permanent and temporary labor has all but broken down.

External talent, including contractors, consultants, and project-based specialists, is becoming a core component of how work gets done rather than a stopgap solution.

Leading organizations are responding by treating external talent less as a short-term fix and more as a standing part of workforce strategy. With 74% of employers worldwide reporting difficulty finding the skilled talent they need in 2025, workforce planning has become less about filling roles in sequence and more about maintaining access to critical capabilities. This shift moves organizations to a workforce model that can respond quickly, scale efficiently, and align with long-term business priorities.

End of “temporary” talent

External workers have historically been brought in to meet short-term needs, helping fill gaps during peak periods or support one-off projects. While that approach still exists, ongoing volatility has proven that this is no longer sufficient.

Demand signals change quickly, transformation is continuous, and new skill requirements emerge faster than internal teams can adapt.

In this context, external talent provides a clear advantage. It gives companies access to specialized expertise on demand, helps accelerate innovation, and supports operations without overextending internal resources. It also allows leaders to rethink workforce composition to better balance stability with adaptability.

This shift mirrors the recent shifts seen in procurement and supply chain functions, where visibility and cross-functional integration have become drivers of long-term success. Workforce strategy is moving in a similar direction.

You can’t manage what you can’t see

As organizations expand their use of external talent, visibility remains essential. Many companies still manage contingent labor in disconnected ways, which makes it harder to understand where talent is deployed, what it costs, and how effectively it is being used. Without that visibility, workforce decisions remain reactive.

When organizations can see how external talent is deployed across business units, geographies, and projects, they can plan with greater confidence. This level of insight also supports stronger governance by improving compliance, supplier performance, and consistency from sourcing to offboarding.

In practice, organizations that invest in visibility often see measurable improvements in efficiency, productivity, and decision-making speed. More importantly, they begin to treat external labor as a strategic lever rather than a cost center.

From reactive hiring to predictive planning

Visibility is essential, but the real opportunity lies in turning workforce data into actionable insight.

AI is playing an increasingly important role in this transformation. By analyzing hiring patterns, project pipelines, and market signals, AI can help organizations anticipate future talent needs instead of reacting to them. This is especially valuable in environments where workforce decisions need to balance cost, speed, and quality. For example, organizations can use AI to:

• Anticipate external talent needs tied to major initiatives, such as ERP rollouts or expansion projects, before staffing gaps affect delivery
• Identify where external specialists can help address immediate skill gaps while longer-term hiring continues
• Analyze market signals and workforce composition to help guide insourcing vs outsourcing strategies
• Flag bottlenecks and make corrections in onboarding, approvals, or assignment start times that delay productivity and increase costs

As organizations look to make external talent a more strategic part of workforce planning, technology becomes increasingly important. SAP Fieldglass helps organizations gain greater visibility into their external workforce, connect talent data across the enterprise, and use AI-driven insights to make more informed staffing decisions.

By bringing together workforce planning, services procurement, and external talent management, organizations can better anticipate skill needs, improve agility, and align workforce investments with business priorities.

More connected approach to talent

One of the most important shifts underway is how organizations think about workforce composition. Rather than treating external and internal talent as separate categories, forward-looking companies are managing both as part of a single ecosystem.

This integrated approach offers several advantages. First, it more closely aligns with business goals. Leaders can allocate resources based on outcomes rather than employment type, ensuring the right skills are applied where they create the most value. Second, it improves agility. When workforce models are designed to flex continuously, organizations can quickly respond to changing conditions without disrupting operations. Third, it enhances the employee experience for both internal teams and external contributors by streamlining processes and making them more efficient.

Technology plays a key role in enabling this shift and provides organizations with the tools to manage external talent alongside internal workforce data, improving visibility and supporting more data-driven decision-making. Solutions such as SAP Fieldglass help organizations bring greater transparency, consistency, and insight to how external talent is sourced, managed, and aligned to business needs. While no single solution defines success, the ability to connect data, processes, and insights is increasingly important.

Building resilience in an always-on economy

Business no longer moves in predictable cycles. Demand shifts quickly, priorities evolve in real time, and skills gaps can emerge faster than traditional hiring models can address. In that environment, resilience depends on staying adaptable while keeping work moving.

That is why external talent is becoming a more strategic part of workforce planning. With finding skilled talent becoming increasing more difficult, many organizations are looking for ways to maintain access to specialized capabilities as business needs shift. External talent can help teams move faster, bring in targeted expertise, and sustain progress on critical initiatives without overextending the core workforce.

For many organizations, this reflects a broader change in mindset. External talent has moved closer to the center of workforce strategy, especially in areas where speed, specialization, and adaptability matter most. How well organizations plan for and manage that talent will shape their ability to execute, compete, and grow.

Amber Roth is vice president of GTM for SAP Fieldglass.

Swarovski has followed its cloud transformation from 2023 with a global go-live of SAP Cloud ERP Private after choosing an exciting brownfield approach for the rollout. With its migration, the luxury brand is laying the foundation for using AI and for reaching its strategic targets by 2030.

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

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

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

25,000 tests with 600 participants

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

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

Simplification and standardization ensure consistent data

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

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

Cloud technology is not an end in itself

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

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

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

It’s not an IT project, it’s a business transformation

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

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

And the implementation is showing the luxury goods manufacturer the way to the future because everyone involved in the process didn’t just consider it to be an IT project but, above all, a business transformation from day one. One that involved hundreds of experts from different fields and that enjoyed full management support from the beginning.

AI-driven demand forecasts optimize warehouse stocks

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

Swarovski doesn’t use artificial intelligence as a standalone technology, but instead as an integrated capability that complements business processes across all functions. The company is already using AI for demand forecasting, for example, and then uses the results to optimize warehouse stock levels across regions, with the aim of improving the customer experience.

And the AI agent factory initiative enables the development of AI agents that link SAP Cloud ERP Private data with data from non-SAP systems, always with the objective of “automating repetitive tasks, supporting decision-making, and boosting productivity along the entire value chain,” Sonderegger emphasizes.

Top image courtesy of Swarovski