BUSINESS NEWS 5 - The World Management

18 August

SAP’s New Industry AI Portfolio Tackles the Hardest Challenges Faced by Enterprises

Despite rapid advancements in frontier AI models, many enterprise problems remain hard to solve. The challenge goes beyond just accessing better AI-generated suggestions.

It’s about making consequential decisions inside complex processes that require a deep industry specific context of data, regulations, human workflows, and processes.

For example, tasks like deploying and coordinating thousands of field technicians to restore energy grids after a storm and reduce unplanned downtimes or like keeping critically important production lines running and service levels high despite global supply chain disruptions involve many difficult decisions.

“These problems are incredibly hard to solve,” says Dominik Metzger, president of Industry AI at SAP, because solving them demands immense organizational change, especially in highly regulated industries.

Frontier LLMs are not enough

There is a growing realization among business leaders that solving these challenges requires more than just a powerful AI language model. In these situations, AI must do more than generate suggestions. It needs to act on insights and context to support the execution of business processes. This requires agentic AI that can work with trusted business data, apply industry-specific knowledge, and take actions in ways that are reliable, explainable, and useful to the people closest to the work.

AI is most valuable when it understands the context in which decisions are made. Manufacturers need to balance demand, production capacity, supplier risk, and quality requirements. Energy companies need to manage assets, safety, sustainability, and regulatory obligations. Life sciences companies need to innovate while meeting strict compliance expectations.

To help solve the most complex problems facing large enterprise customers, SAP is bringing together 50 years of deep industry expertise, leading AI engineering know-how, and customer-facing forward-deployed delivery capabilities in one organization to combine forward-deployed engineering with strong productization capabilities.

This enables SAP to move beyond custom AI solutions and build, productize, and scale end-to-end AI transformations for industry-specific business problems.

Think of it this way: the Autonomous Enterprise is SAP’s strategic direction, SAP Business AI Platform is its foundation, and Industry AI acts as a highly focused customer transformation offering, solving industry-specific challenges for individual customers to generate substantial business value.

What makes Industry AI different

The Industry AI portfolio is built on three differentiators: first, more than 50 years of SAP’s industry and process expertise across 26 industries; second, the richness of SAP customers’ data footprint and ontologies in an existing system of record; and third, a dedicated forward-deployed engineering (FDE) workforce to solve problems that do not have off-the-shelf answers, customer by customer.

Forward-deployed engineering embeds AI specialists, such as data scientists and AI builders, directly with customers to solve high-value, industry-specific problems, rather than relying solely on packaged software. Metzger explains that forward-deployed engineering involves “getting obsessed with the problems of our customers” and immersing SAP’s agentic AI developers in the challenges these customers face. “Working directly with customers, we will build, deploy, and scale Industry AI applications to deliver tangible business value for our customers,” he says.

Lessons learned by building these tailored solutions with selected customers will be productized as a standardized platform offering for many more customers to deploy and use. This model allows fast scaling and delivery, while also building up SAP’s platform and solution portfolio of high-value agentic solutions that are close to customer needs and current industry priorities.

Getting up close with customers

SAP’s decision to establish Industry AI as a focused business offering reflects the conviction that true value from agentic AI is created in close collaboration with the industry experts who face specific business challenges every day. This proximity is the fastest way to identify the most critical problems, develop and test practical AI solutions, and refine them based on real-world experience. Most importantly, it allows to deliver tangible business value and prove the impact of AI in practice.

This approach helps reduce the gap between a promising idea and a solution. On the one hand, teams can move more quickly from identifying a need to deploying a solution that delivers measurable value. It also ensures that what is built reflects real-world needs rather than assumptions made far from the customer environment.

For SAP’s enterprise customers, the promise of the Industry AI offering is not simply smarter software. It is a more practical path to the Autonomous Enterprise. Instead of asking teams to adapt to generic tools, Industry AI can help bring deep intelligence into the processes people already use and the decisions they already make, while staying connected to the business data, controls, and applications that keep organizations running.

Benefits

Examples of these benefits are easy to describe. An energy provider avoids costly downtime by identifying a likely spare part demand early and recommending relevant suppliers. A retailer adjusts inventory positions and trade promotions based on simulated scenarios and real demand signals before stockouts occur. A pharmaceutical manufacturer ensures the safe and reliable release of life-saving drugs through a highly precise, high-quality, and fully automated batch release process.

That matters because it can help organizations move faster, improve quality, and free employees to focus on higher-value work. It can also help companies turn industry knowledge into a lasting advantage, especially as AI becomes a larger part of how businesses operate.

For SAP, the formation of the Industry AI unit represents a strategic leap, combining industry-specific expertise, end-to-end offerings, and a value-based offering in a way that is clearly designed to differentiate SAP from competitors.

SAP’s differentiation from other AI platform providers and more traditional forward-deployed engineering companies is not simply about providing custom AI services: the focus is on turning industry-specific expertise into scalable, repeatable offerings that can be deployed across customers, creating a more sustainable and differentiated model for delivering AI value.

Specifically, the Industry AI offering is positioned as an all-in-one commercial package, including platform consumption and cloud services, solutions, forward-deployed engineering, and expert support, with pricing based on real customer business value and a single contract.

It is based on SAP’s unique deep industry-specific knowledge, business AI platform, and knowledge graph, enabling tailored processes for customers. In addition, SAP’s approach is grounded in customers’ business logic, decades of experience and enterprise grade governance, all implemented in standard products, differentiating it from some recently announced market offerings.

What is frontier AI?

Frontier AI refers to the most advanced artificial intelligence models that represent the cutting edge of AI capabilities at any given time. These highly capable foundation models, generally implemented as very large language models, push the boundaries of what is possible with AI technology. They are typically characterized by their massive scale, multimodal capabilities, and ability to perform a wide variety of complex tasks across different domains.

As of mid-2026, models widely regarded as sitting at the frontier include Anthropic’s Claude Opus 4.8, OpenAI’s GPT-5.5, Google DeepMind’s Gemini 3.1 Pro, xAI’s Grok 4.3, and open-weight challengers such as DeepSeek V4 and Alibaba’s Qwen3.7-Max.

What comes next

As the World Economic Forum has highlighted, successful AI scaling depends not only on the technology itself, but also on practical changes to how people work, how decisions are made, and how organizations govern new capabilities.

SAP solves industry problems that generic AI—even if super powerful—cannot. Ultimately it is about enterprise and business process transformation, not AI deployment only. This then can redefine how AI transforms industries, by moving beyond isolated use cases toward autonomous, end-to-end agentic execution.

14 August

BUSINESS NEWS

SAP Positioned as a Leader in the Inaugural Gartner® Magic Quadrant™ for Supply Chain Management Suites

by tempuser_3528357508 0 Comments

Building a resilient, future-ready supply chain is one of the most complex challenges organizations face today. For more than 50 years, SAP has helped organizations navigate supply chain volatility by connecting planning, execution, and business processes across the enterprise. We are proud to share that SAP has been positioned as a Leader in the inaugural Gartner® Magic Quadrant™ for Supply Chain Management Suites*, a recognition that, in our view, reflects not just where we are today, but the direction we are heading.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from SAP or at https://url.sap/h6cruh.

Supply chain leaders today operate in an environment where disruptions are constantly changing, customer expectations shift quickly, and the window for effective response keeps shrinking. Organizations need more than digitalization. They need platforms that connect people, processes, data, and business networks—and that turn that connectivity into faster, better decisions.

Our vision: Autonomous Supply Chain Management

The next frontier in supply chain is not just connectivity, it is decision intelligence. At SAP, the Autonomous Enterprise represents our vision for how organizations will run their businesses in the future, with data, business context, policy, and AI reasoning working together so that the right decisions happen faster, more consistently, and at scale, while human judgment remains central.

Autonomous Supply Chain Management is a practical step toward that vision, where people define goals and priorities, AI assistants orchestrate activity across domains, and agents execute the work within governed, end-to-end processes. Enterprises rely on fully autonomous agents to run supply chain processes and decisions within guardrails defined by the organization. Reaction times to disruptions compress from days to minutes. Data collection across organizational silos happens in near real time. Rather than manually collecting and interpreting data, planners receive auto-generated scenarios that are pre-validated and ready for a decision. That speed advantage is transformative.

But the value goes beyond speed. Organizations learn from past decisions and harmonize decision-making across the enterprise, leading to consistently better outcomes over time. AI delivers the greatest value when it is embedded where work actually happens, grounded in deeply integrated processes and trusted data.

Our approach

A truly integrated supply chain platform does more than connect data—it connects decisions. That is the principle that guides how we have built the SAP supply chain management suite, and it is what we believe sets SAP apart.

Our breadth across execution, transactions, business networks, finance, and AI is unique. No other vendor brings together operational systems, trusted business data, and intelligence and orchestration in a single, deeply integrated suite. That breadth matters because supply chain decisions do not happen in isolation—a sourcing decision affects manufacturing capacity, which affects logistics, which affects financial commitments. When those systems are connected, decisions improve across the board.

We organize our platform around three layers. The first is operational systems, including applications such as SAP Ariba, SAP Integrated Business Planning, SAP Digital Manufacturing, SAP Asset Performance Management, SAP Transportation Management, SAP Extended Warehouse Management, and SAP Business Network. These are where supply chain work happens. The second is trusted business data, anchored by SAP Business Data Cloud, which aggregates data from SAP and third-party sources to help give organizations a unified, real-time view across the enterprise. The third is intelligence and orchestration, delivered through SAP Business AI and Joule, our AI engagement layer, which brings together agentic AI, embedded analytics, and network-enabled execution to help organizations move from reactive problem-solving to proactive decision-making.

This architecture helps organizations standardize processes across domains, reduce dependence on fragmented point solutions, and improve coordination from sourcing through final delivery—linking operational, financial, and network outcomes in real time.

Putting innovation into practice

We see demand for capabilities that bring together operational data, business context, and AI-powered insights, and we are actively investing to meet that need. New Joule Assistants are being embedded across planning, manufacturing, logistics, asset management, and supplier collaboration, helping teams act on changes faster and reduce time spent on manual coordination.

Alongside these assistants, we are delivering purpose-built AI agents across supply chain processes, designed to take guided action within defined business guardrails while keeping people firmly in control. These capabilities are becoming available in phases through 2026, aligning with customers’ existing SAP landscapes.

Delivering real results for customers

Ultimately, in our opinion, recognition like this is only meaningful when it reflects real impact for the organizations we serve. Takeda Pharmaceuticals International AG, a global leader in R&D-based biopharmaceuticals, offers a concrete example of what this looks like in practice. Using SAP’s supply chain planning assistant, Takeda has moved from manual root-cause analysis to AI-detected diagnostics and AI-suggested remediation, with automated performance tracking that enables a self-healing supply chain capability.

“AI represents the key enabler for Takeda’s transformation for the future,” said Rebecca Kaufmann, senior vice president and head of Enterprise Platforms at Takeda Pharmaceuticals. “We are combining the SAP industry and technology knowledge with our organizational real-life experience. The supply chain planning assistant will provide us with AI-detected root causes and AI-suggested remediation and also automated performance tracking resulting in a self-healing capability.”

Looking ahead

Supply chains don’t become autonomous overnight. This evolution happens workflow by workflow, expanding automation where it delivers real value, while keeping people firmly in control. Supply chain teams are increasingly looking to spend less time monitoring and firefighting and more time shaping decisions, managing trade-offs, and building resilience.

We are grateful to the many customers, partners, analysts, and SAP teams whose collaboration and insights help shape our products and strategy. While we are honored by this recognition, we view it as an important milestone for us rather than a destination. We remain committed to ongoing innovation that helps organizations transform their supply chains into strategic sources of competitive advantage—and we thank our customers for their trust in that journey.

Devesh Mishra is GM and chief product officer for SAP Supply Chain Management.

*Gartner, Magic Quadrant for Supply Chain Management Suites, Jan Snoeckx, Balaji Abbabatulla, Christian Titze, August 11, 2026.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

14 August

BUSINESS NEWS

SAP Order Management Services Named a Leader in IDC MarketScape: Worldwide AI-Enabled Order Orchestration and Fulfillment Applications for Retail and B2C 2026 Vendor Assessment

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IDC has named SAP a Leader in the IDC MarketScape: Worldwide AI-Enabled Order Orchestration and Fulfillment Applications for Retail and B2C 2026 Vendor Assessment.

The recognition reflects SAP’s continued commitment to innovation in order management and the confidence customers place in SAP Order Management Services to run some of the world’s most complex, high-volume order operations.

Every order is an opportunity to keep a customer promise, earn lasting loyalty, and increase profitability. However, as customer expectations rise and supply chains grow more distributed, order management has become a complicated, siloed process. This is where SAP Order Management Services comes in and turns order management into enterprise execution intelligence.

SAP Order Management Services empowers businesses to run orders with intelligence, connecting demand, inventory, fulfillment, and financials in real time. With its cloud-native, API-first, composable architecture, the solution helps organizations to maintain operational efficiency and adapt to the changing business needs continuously.

Seamless, cohesive order management processes

Siloed, fragmented data and workflows are bottlenecks to delivering on customer promises. Organizations need connected order management processes throughout the entire order life cycle. SAP Order Management Services unifies data and processes into a single, cohesive experience with an intuitive interface that enables teams to monitor order processes, resolve exceptions, and tailor business flows to fit the way each organization operates.

From order capture to inventory visibility, fulfillment execution, and exception handling, every step of the order life cycle needs to be connected in real time. With SAP Order Management Services, every team member involved in order management can access a single source of truth, reducing errors and accelerating data-driven decision-making.

In addition, the Order Management Assistant empowers businesses with AI-driven actions and recommendations across the order life cycle. The assistant surfaces potential risks and gaps before they become customer issues and recommends next-best actions. Team members interact with the assistant in natural language, turning real-time order data into faster business decisions.

Composable order management

SAP Order Management Services integrates with SAP S/4HANA, SAP Customer Experience (SAP CX) solutions, the broader SAP ecosystem and partner solutions, connecting orders, inventory, pricing, and financials across the enterprise. Moreover, its cloud-native, API-first architecture enables businesses to easily adapt and expand new order channels, fulfillment nodes, and business rules as needed.

This modular approach gives organizations the flexibility to support a wide range of business models, from B2C to complex B2B, and to evolve their order management capabilities incrementally as the business changes.

Looking ahead

Being recognized as a Leader by IDC reflects the continued innovation of SAP Order Management Services. SAP is investing in expanded order management capabilities and deeper AI innovation to help businesses stay ahead of customer expectations and drive profitable growth.

To learn more, read the IDC MarketScape excerpt and explore the capabilities SAP Order Management Services provides.

Emilie Fournelle is head of Product Management for SAP Order Management Services at SAP.

13 August

BUSINESS NEWS

Service-Led Growth Starts with the Business Model

by cyrus@ascelade.com 0 Comments

Organizations often look at AI, automation, or new service tools as the starting point for service-led growth. In reality, service-led growth starts with a business model. Technology can help scale a service business, but it does not define how value is created, delivered, or monetized.

Service-led growth is one of the seven macro trends within Autonomous CX, SAP’s vision introduced at SAP Sapphire in 2026. At its core is a simple principle: every customer promise must be backed by the operational reality needed to deliver it.

Recent SAP research highlights a growing gap between customer expectations and the operational reality of service delivery. Disconnected handoffs, fragmented information, and pressure to adopt AI make service a business-model question, not just a technology one.

A practitioner’s perspective

More than twenty years ago, I was part of a global service organization that wanted to move beyond viewing service as a cost of doing business.

At the time, our primary objective was cost recovery. Service was necessary to support the product business, but it was not really seen as a business in its own right.

We started changing that by introducing new professional services and finding ways to monetize expertise we already had. Remote device monitoring, for example, allowed us to organize support across time zones and offer profitable after-hours services to customers who depended on continuous operations.

A few years later, I was asked a more fundamental question: could the service organization survive as an independent business?

To explore that question, we used an early version of what later became widely known as the business model canvas. We looked beyond service operations and examined the complete picture: our value proposition, customer segments, channels, activities, resources, costs, and revenue streams.

Looking back, that exercise taught me something that is still relevant today.

Service as a revenue driver is not a new idea. And it does not start with technology. It starts with a business model.

What do we mean by service-led revenue?

“Service as a revenue driver” is often used as if it means one thing. In reality, service can contribute to revenue in several different ways.

First, it can protect revenue. A customer whose issue is resolved quickly and professionally is more likely to renew, continue buying, and remain loyal.

Second, it can influence revenue. Service professionals often understand a customer’s operational reality better than anyone else. They may identify a need for additional services, training, upgrades, or new solutions, creating opportunities for commercial teams.

Third, it can generate revenue directly through premium support, professional services, subscriptions, remote monitoring, advisory services, or outcome-based offerings.

These ambitions are related, but they are not the same. Each requires different processes, skills, measures, and sometimes different operating models.

Not every service interaction should become a sales conversation. But every service organization should understand whether it is expected to protect, influence, or directly generate revenue.

Technology creates possibilities, not the business model

Technology has always played an important role in service innovation.

Remote monitoring reduced the need for on-site visits. Connected solutions made global support models possible. Customer and service platforms improved access to account, contract, equipment, and interaction data.

Today, Autonomous CX capabilities can expand these possibilities even further. Joule, embedded AI agents in SAP Service Cloud, and AI-assisted opportunity detection in SAP Sales Cloud can classify and route cases, summarize interactions, surface relevant knowledge, identify patterns, detect revenue opportunities, and increasingly automate routine requests. All within the context of a connected service and sales operating model.

Yet adoption does not equal usage, and usage does not equal value. Technology does not answer the most important questions:

What value are customers willing to pay for?
Which customers should we serve?
How will the service be sold, delivered, and measured?
Can it be delivered consistently, profitably, and adopted by employees and customers?

Customers are already drawing their own conclusions. In 2026, 81% believe AI in service is deployed primarily to save money, not to improve service. Seventy-nine percent still strongly prefer human support. These perceptions are not only a trust problem, they point to a business model problem. When the technology decision precedes the value decision, customers notice.

From ambition to execution

This is where many service-led growth initiatives struggle.

Ambition may be clear in the boardroom, while the organization underneath continues to operate as before. Service is still measured primarily on cost and case closure. Sales and service pursue different objectives. Customer information remains fragmented. Opportunities identified by service disappear during handovers. Employees are expected to adopt new behaviors without understanding why.

Turning service into a measurable revenue driver therefore requires more than enabling a new feature or deploying a new technology. It requires alignment between business objectives, processes, people, data, and technology.

The Advanced Success Plan version for SAP Customer Experience solutions is an expert-led engagement model that helps translate Autonomous CX into an executable plan. It does not replace business strategy or determine which services should be brought to market. Instead, it helps connect a chosen ambition to the processes, capabilities, and governance needed to make service-led growth measurable and repeatable.

1. Clarify the value intent

The first step is to define what service-led growth actually means for the organization.

Is the priority to improve retention? Increase renewals? Create opportunities through service interactions? Launch paid services? Improve profitability?

Through the value management expert session within the Advanced Success Plan for SAP Customer Experience solutions, stakeholders can align on business priorities, value drivers, and success measures.

The objective is not to create a long list of KPIs, but a shared understanding of the outcomes that matter most.

2. Connect the end-to-end process

Once the ambition is clear, the next question is how value will actually be created.

If a service professional identifies an opportunity, what happens next? Who owns the follow-up? Is the customer experience consistent from the initial interaction through fulfilment and invoicing?

Business process best practices and expert guidance can help identify gaps in ownership, handovers, and process alignment across service, sales, commerce, and supporting operations.

This matters beyond operational efficiency. SAP research shows that 45% of revenue leaders cite improving collaboration and handoffs across marketing, sales, and service as a current priority, and 39% are actively pursuing expansion, cross-selling, and upselling within existing accounts. Service teams often have valuable customer context, but the real question is whether the process exists to act on it.

3. Close capability and adoption gaps

Organizations can then assess which capabilities are needed to support the process. This could involve better access to customer information, improved knowledge management, analytics, automation, AI-supported recommendations, or opportunity management capabilities. Targeted expert services within the Advanced Success Plan for SAP Customer Experience solutions help connect SAP CX capabilities to desired business outcomes.

At the same time, employees need the right enablement, incentives, and confidence to adopt new ways of working. Without that, even the best-designed process remains a PowerPoint slide.

The cost of getting this wrong is real. Employees asked to adopt new behaviors without understanding why, or without the tools to support them, do not persist. When experienced people leave, they take institutional knowledge and customer relationships with them. Enablement is not a training exercise; it is a retention and continuity investment.

4. Measure, learn, and improve

Service-led growth is not delivered through a single project.

Recent research highlights several recurring priorities for service leaders: efficiency and time to resolution (48%), collaboration and handoffs across marketing, sales, and service (46%), first-contact resolution (40%), and AI-driven predictive insights (33%). These figures provide useful context, but the measures that matter for service-led growth depend on the value intent defined at the start. Progress should therefore be tracked against the agreed outcomes, whether these involve retention, renewals, service-generated opportunities, paid-service revenue, or profitability.

Ongoing governance and engagement planning helps organizations review progress, address gaps, and scale successful approaches over time.

Service-led growth is a business-model choice

Moving from cost recovery to service-led growth is not simply a matter of asking service employees to sell more. It is a business-model choice with implications for the value proposition, customer experience, processes, organization, skills, technology, and measures of success.

Twenty years ago, remote monitoring changed what service organizations could deliver. Today, AI is expanding those possibilities again. But the underlying challenge has not changed. Technology changes possibilities. Business models determine how that value is captured.

First, decide where service should create value. Then, build the operating model required to deliver that value consistently and profitably.

The Advanced Success Plan for SAP Customer Experience solutions can support that journey by helping organizations connect business outcomes with the processes, capabilities, adoption, and governance needed to turn ambition into measurable results.

Raf Dille is product manager for the Advanced Success Plan for SAP Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for SAP Customer Experience.

12 August

BUSINESS NEWS

How a Small AI Use Case Is Automating Document Processing in the Supply Chain of Lemvigh-Müller

by tempuser_3412383652 0 Comments

Lemvigh-Müller, a 180-year-old Danish wholesaler of industrial building material, technical, and steel products, has built an AI use case that reads incoming business documents—automatically and within seconds.

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

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

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

11 August

BUSINESS NEWS

AI’s Dual Role in Procurement Transformation

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Artificial intelligence is changing procurement at two speeds. It can compress work that once took days into minutes, helping teams analyze spend, review contracts, identify supplier concerns, and guide employees toward compliant purchases. At the same time, every new AI-enabled action creates another vulnerability where poor data or weak oversight affects a business decision.

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

The urgency is real. In research from the 2026 Economist Enterprise report titled “Procurement at a crossroads: from optimism to realism,” sponsored by SAP, 56% of executives identified AI strategy as the main catalyst for procurement’s digital agenda. The study, which covered 2,648 C-suite leaders, also recorded lower confidence in the function’s ability to translate technology investments into consistently better outcomes.

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

Start with the outcome, not the technology

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

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

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

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

Build a connected data foundation

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

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

Apply human oversight where it matters most

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

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

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

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

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

Measure value and risk together

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

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

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

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

Gordon Donovan is vice president of Research for Procurement and External Workforce at SAP.

08 August

BUSINESS NEWS

Innovating with AI Because Reinvention Is in Cirque du Soleil’s DNA

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For audiences, Cirque du Soleil is about wonder: gravity-defying performers, breathtaking costumes, immersive music, and moments that feel almost impossible. But behind every performance is something just as remarkable: a highly complex global business operation.

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

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

As Philippe Lalumière, vice president of Information Technology at Cirque du Soleil, explains, “People underestimate the complexity of running a circus.”

Orchestrating an autonomous accounts payable process

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

The work was repetitive, time-consuming, and emotionally draining. “From a morale point of view, receiving e-mails from suppliers, some of them a bit impolite because they’re asking, ‘When are we going to get paid?’—it’s not a fun job,” Lalumière says.

Finding the right problem to solve—putting AI into action

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

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

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

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

AI enters stage right—from vision to reality

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

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

“Trust in the quality of the answers coming from the agent was an initial concern,” Lalumière says. “But, the team quickly realized that Genato’s information was spot on.”

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

Lalumière, however, is clear about one thing: “Yes, AI is a very powerful tool, but it’s not pixie dust. Sprinkling AI into processes is not going to solve everything. There is work involved.”

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

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

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

The impact on employees cannot be overstated. By removing repetitive research and emotionally charged supplier follow-up from the team’s workload, Genato has become more than a tool. “We now include Genato as a virtual team member,” Lalumière explains, “It’s a paradigm shift.”

That adoption is the clearest sign of success. Lalumière recalls that after the prototype moved into production, one of the end users made her feelings clear: “No, no, no, you cannot take it away from me! There is no way I’m going to live without it now.” Today, she is one of the solution’s biggest ambassadors.

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

As Lalumière puts it, “You could say we’re an SAP shop.”

The next act of AI innovation

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

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

His advice to others beginning their AI journey is simple: start small, involve users early, and be willing to experiment. “Try a small proof of concept and be ready to throw them away,” Lalumière says. “The AI train is moving, and you have to hop on.”

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

The full episode

Learn more about how Cirque du Soleil has transformed its AP process using AI to improve vendor relations, staff morale, and overall department productivity:

Thought leadership podcast: Lalumière sat down with Thulium CEO Tamara McCleary to discuss the shifts in the business and in the world that inspired Cirque du Soleil’s AI vision and application, and how others can learn from their journey.
Practitioners’ video: Lalumière shares technical insights and advice with Timo Elliott, VP and global innovation evangelist at SAP, about Cirque du Soleil’s AI applications, user experiences, and how the combination of both is essential to Cirque du Soleil’s automation journey.

Explore the on-demand series.

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

07 August

BUSINESS NEWS

AI Adoption and SAP Transformation: What Customers Report from Practice

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Many organizations are currently undergoing an SAP S/4HANA transformation, and some are already investing in AI technology. But how do new technologies actually work in day-to-day operations?

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

Global transformation, local anchoring as a guiding principle

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

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

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

How a federal agency demonstrates effective change management

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

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

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

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

Change management in his project accounts for 4.3% of the total budget—the largest single line item in the SAP contract.

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

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

The organizations mentioned above are supported by change management consultants from SAP. More information is available here.

From shadow AI to structured integration at KIT

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

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

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

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

Three fields of action

Across all examples, three patterns emerge:

1. Enablement before, during, and after deployment: Key users, business leads, and other stakeholders must be involved early. They are the change multipliers.

2. Local ownership matters: Global standards work when local teams take responsibility. This applies to shipping companies operating in 140 countries just as much as to federal agencies.

3. Embed and support AI in a structured way: Qualification, governance, and business context are more effective than generic tools. With multi-agent systems, we are still at the beginning.

What research confirms

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

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

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

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

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

A full documentation of the SAP Learning and Adoption Forum 2026, including videos, slides, and a chatbot, is available on SAP Community.

Thomas Jenewein is business development manager for AI, Transformation, & Adoption Services at SAP.

06 August

BUSINESS NEWS

Support Accreditation: SAP’s Enablement Course to Unlock Faster, Smarter, AI-Powered Customer Support

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What if you, as an SAP consultant, could resolve issues faster, work more independently, and stay ahead of AI-driven innovation—all in just over 60 minutes of learning?

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

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

What’s new

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

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

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

The real benefits

Support Accreditation in 2026 isn’t just about earning the badge—it’s a credential that validates your ability to work effectively with SAP’s comprehensive support offerings. The accreditation can also equip you to:

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

How to get started

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

Access the enablement course for smarter, faster, AI-powered support from SAP.

Stefan Steinle is executive vice president and head of Global Customer Support at SAP.

05 August

BUSINESS NEWS

How Salling Group Uses SAP and AI to Improve Everyday Retail

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Salling Group is northern Europe’s largest retail group, serving 15 million customers each week in its more than 2,100 stores across Denmark, Germany, Poland, Estonia, Latvia, and Lithuania.

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

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

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

Improving everyday life

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

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