As we enter 2026, volatility and uncertainty have accelerated rather than eased, which puts additional pressure on global supply chains. At the same time, we are hearing from so many of our customers that technology is no longer just part of the supply chain story, but the solution to some of its toughest challenges. From geopolitical uncertainty to rising customer expectations, supply chain leaders are facing mounting pressure to deliver resilience, agility, and sustainability. The good news is that innovations like agentic AI and advanced analytics are no longer theoretical; they’re transforming workflows today, at scale.

The past few years have taught us that disruption is the new normal. Whether it’s global conflicts, raw material shortages, or sudden demand spikes, supply chains need to pivot faster than ever. That’s why this year, the conversation isn’t about incremental improvements—it’s about reimagining processes with intelligent technologies that anticipate, adapt, and act autonomously.

From complexity to clarity: how agentic AI changes the game

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

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

These aren’t distant possibilities—they’re real scenarios already piloted by SAP Supply Chain customers. AI isn’t replacing people; its amplifying human decision-making, freeing teams to focus on strategy rather than firefighting.

Why this matters: analyst rankings tell the story

SAP solutions underpin strategies that earned recognition in major 2025 analyst reports, including:

• IDC MarketScape for Multi-Enterprise Supply Chain Commerce Networks*: SAP was named a Leader for enabling real-time collaboration and orchestration across global ecosystems with SAP Business Network.
• IDC MarketScape for AI-Enabled Enterprise Asset Management Applications**: SAP was positioned as a Leader for innovations like predictive maintenance and agentic AI through Joule.
• Gartner Magic Quadrant for Cloud ERP for Product-Centric Enterprises***: SAP was recognized as a Leader for integrating planning, manufacturing, and logistics with advanced analytics and AI.

These accolades aren’t just badges of honor. They validate the trust our customers and partners place in SAP and the impact we deliver together. They also reinforce a critical truth: supply chain excellence is now a boardroom priority.

Sustainability: from obligation to advantage

Sustainability isn’t just a compliance checkbox; it’s a competitive edge. More than 25% of global emissions are already taxed or regulated by trading systems. Circularity and carbon accountability have become core KPIs for supply chain leaders because responsible practices deliver measurable benefits. Meeting environmental, social, and governance (ESG) standards lowers regulatory and reputational risk, while optimizing logistics for lower emissions often translates into fewer miles traveled and reduced fuel costs. At the same time, customers and investors increasingly favor brands with transparent sustainability metrics, making it a powerful differentiator in the market.

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

Looking ahead: our 2026 roadmaps

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

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

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

Together, these areas form the backbone of the innovations we are delivering this year, with a clear aim of helping customers move from reactive operations to intelligent, proactive orchestration. But technology alone isn’t enough. The real magic happens when we collaborate with our customers and our partners, turning complexity into opportunity.

The takeaway: 2026 is about action at scale

The supply chain landscape is evolving faster than ever. Agentic AI, sustainability, and intelligent automation aren’t optional, but essential. Companies that embrace these technologies to truly evolve how they operate and take even complex decisions will lead in resilience, efficiency, and responsibility. Those that hesitate risk falling behind in a world where adaptability is the ultimate competitive advantage.

Don’t wait for disruption to force your hand. Build the capabilities now that will carry you through uncertainty and position you for growth. Learn more about SAP’s AI-powered solutions here.

Dominik Metzger is president and chief product officer of SAP Supply Chain Management.

*IDC MarketScape: Worldwide Multi-Enterprise Supply Chain Commerce Network 2025 Vendor Assessment, November 2025, IDC #US53010225
**IDC MarketScape: Worldwide AI-Enabled Asset-Intensive Enterprise Asset Management Applications 2025–2026 Vendor Assessment, December 2025, IDC ID # US52977525
***IDC Magic Quadrant for Cloud ERP for Product-Centric Enterprises, October 2025, IDC ID# G00826212

Sustainability rarely took center stage at Davos this year. Instead, it quietly delivered by playing an implicit and influential role in most conversations throughout the week.

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

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

1. AI is a sustainability enabler with responsibilities

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

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

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

For many organizations, the focus shifted towards how responsible AI can support sustainability objectives while remaining aligned with enterprise governance and financial oversight.

2. Water is key to societal and economic stability

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

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

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

• Early-stage innovators were selected at Davos to boost water resilience across infrastructure, industry, and agriculture systems. 
• Public-private collaboration initiatives were launched to accelerate water finance and investment ahead of the 2026 UN Water Conference. 
• Research and alliance work was directed at bridging the €6.5 trillion global water infrastructure gap, and commitments were made to mobilize private capital and improve water resilience strategies. 

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

What can business leaders take away?

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

Sustainability is increasingly understood as financial risk

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

The World Economic Forum’s Global Risks Report 2026, released shortly before the meeting, reinforced this view by ranking environmental risks (including extreme weather and biodiversity loss) and critical changes to Earth systems among the most severe long-term global threats. The same report also highlighted that adverse outcomes from artificial intelligence are rising sharply in long-horizon risk rankings, reflecting growing concern about both technological and environmental disruption. 

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

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

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

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

The focus is shifting from ambition to execution

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

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

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

Integration decides whether sustainability insights lead to action

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

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

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

SAP’s ERP-centric, AI-enabled approach connects business and sustainability data to help give full visibility across a company’s value chain, enabling it to align business objectives with sustainability priorities across areas like material choice, efficient transport and distribution, improved asset performance, and reduced carbon impact.

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

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

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

Monica Molesag is global head of Sustainability Communications at SAP.

Supply chain transformation today is a balancing act between ambition and execution. Leaders are being asked to deliver growth, improve resilience, adopt AI, and meet rising customer and regulatory expectations, all while keeping operations running.

At NASCES25, I moderated a roundtable with supply chain and transformation leaders from Toyota, L’Oréal, Owens Corning, Ecolab, American Airlines, and Bayer. Across industries, one theme was clear: transformation succeeds when it is grounded in clarity, centered on people, and focused on customer impact.

If you can’t name the problem, don’t buy the technology

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

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

AI is only as smart as the network behind it

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

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

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

Transformation doesn’t fail on technology, it fails on trust

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

Clear communication, local champions, and ongoing engagement help build trust and momentum. Leaders emphasized the importance of reinforcing that technology is designed to elevate roles by removing manual work and enabling employees to focus on higher-value activities, instead of replacing them. Being honest about what is working, what isn’t, and when to pivot builds credibility and keeps teams engaged over the long term.

Customer-centric supply chains don’t happen by accident

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

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

Customer collaboration is also playing a larger role in transformation. Sharing data and aligning processes across networks creates shared value and unlocks differentiated services. Progress must be communicated in terms customers care about—not internal metrics, but outcomes that improve service, speed, and transparency.

What the best transformation leaders do differently

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

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

Turning ambition into measurable impact

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

SAP Business Network plays a critical role in this journey by helping organizations connect processes, partners, and data across the value chain to improve visibility, resilience, and decision-making at scale. And beyond the network, SAP’s supply chain management (SCM) portfolio helps orchestrate people, processes, and technology end-to-end—enabling leaders to move from intent to impact with integrated planning, execution, and insights that can drive real operational transformation.

To hear more directly from the leaders shaping this future, I encourage you to watch the full NASCES25 roundtable discussion and continue the conversation on how supply chains can lead meaningful transformation.

Keith Baranowski is global head of Sales for SAP Business Network.

As enterprises move deeper into large-scale AI adoption, the conversation is shifting from experimentation to impact. Leaders are looking for outcomes they can trust, decisions that are consistent, and experiences that truly work for customers.

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

1. Relevance beats raw intelligence in customer decisions

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

2. Scaling complexity without losing control

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

3. Differentiation that compounds over time

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

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

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

5. A cross-industry shift toward embedded intelligence

These principles extend well beyond dispute management. In manufacturing and supply chains, customer-specific AI supports fulfillment exceptions and service-level disputes. In financial services, it enables complaint handling aligned with regulatory frameworks. In healthcare, it supports decisions grounded in institutional protocols and patient journeys. In retail and services, it drives relevance by learning customer preferences, brand rules, and operational constraints. Industry observers increasingly note that AI’s next phase of growth will be driven by intelligence embedded into customer-facing processes—not stand-alone tools. According to SAP’s “Value of AI” report with Oxford Economics, the majority of businesses expect AI to become central to business processes, decision-making, and customer offerings by 2030, with only 3% saying otherwise.

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

Sindhu Gangadharan is head of Customer Innovation Services and managing director of SAP Labs India.

The clear message for retailers attending National Retail Federation’s 2026 Big Show in New York last week was that they need to urgently address the challenge brought about by the rapid adoption of generative AI tools by consumers and update their back-office and data systems if they are to thrive in the agentic commerce era.

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

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

“Agentic commerce—shopping powered by AI agents acting on our behalf—represents a seismic shift in the marketplace,” McKinsey, the strategic management consultancy, noted in a report published late last year. “It moves us toward a world in which AI anticipates consumer needs, navigates shopping options, negotiates deals, and executes transactions, all in alignment with human intent yet acting independently via multistep chains of actions enabled by reasoning models.”

This, as speakers and panelists at the NRF conference acknowledged, isn’t just an evolution of e-commerce; it’s a rethinking of shopping itself, in which the boundaries between platforms, services, and experiences give way to an integrated, intent-driven flow through highly personalized consumer journeys that deliver a fast, frictionless outcome.

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

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

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

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

SAP Customer Experience has also unveiled a Digital Service Agent recently that can be combined with the Shopping Agent, creating one conversational AI that can handle the entire journey from product discovery and transaction to post-sales support.

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

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

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

As McKinsey noted in a separate report published to coincide with NRF, “while most retail merchandising teams have invested in automation tools and experimented with AI, 71% of merchants say that AI merchandising tools have had limited to no effect on their business so far.”

“The challenge,” McKinsey said, “often lies less in the technology than in how it’s integrated and used. Systems remain fragmented, data is too messy to use to deliver useful recommendations, and adoption is uneven: 61% of respondents say that their organization isn’t at all or is only slightly prepared to scale AI across merchandising.”

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

“Retailers are operating in an environment defined by volatility—tariffs, margin pressure, supply chain disruption, and customers that expect real-time, hyper-personalized experiences everywhere,” Bechtold said during a discussion with Gymshark, the workout apparel retailer. “At the same time, boards and investors are asking a tougher question than ever before: what outcomes are we actually getting?”

“The challenge,” he said, “isn’t a lack of innovation. In fact, most retailers have plenty of tools, pilots, and point solutions. The real issue is that disconnected technology doesn’t translate into resilient growth. That’s why the conversation is shifting. It’s no longer about isolated AI use cases or shiny new features. It’s about whether AI and data are embedded across the business—connecting supply chains, finance, merchandising, and customer engagement—in ways leaders can trust.”

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