As artificial intelligence moves beyond providing information and recommendations, enterprise software is becoming more capable of reasoning, making decisions, and taking action across business processes.

It’s a noticeable shift that could fundamentally change how enterprises operate. But according to Manish Sood, CEO of Reltio, an SAP company, and co-author of Agentic Intelligence, it also exposes a challenge companies have wrestled with for decades: fragmented, inconsistent, and poorly connected data.

“There is tremendous enthusiasm around agentic AI right now, and for good reason,” said Sood. “Almost every company is experimenting with it in some form or the other. But there is a very significant gap between ambition and underlying readiness.”

Research conducted by Harvard Business Review Analytic Services underscores that readiness gap: 94% of organizations surveyed are exploring or implementing agentic AI, while only 15% believe their data foundation is truly ready.

“An AI demo can tolerate a lot of imperfections and gaps in the data,” said Sood. “An AI agent operating inside a real business context cannot. The moment we give software the ability to make decisions or to take action, the quality, timeliness, and context of underlying data becomes much more consequential.”

AI highlights legacy data challenges

Sood believes fragmentation, trust, and governance are three recurring barriers when it comes to AI adoption. Because data remains scattered across hundreds or even thousands of enterprise systems, governance models built for traditional enterprise systems must evolve for an environment in which AI agents can initiate actions.

While these are familiar challenges, the speed and autonomy AI introduces is much different.

Historically, humans have compensated for fragmented systems via tapping into institutional knowledge. Employees know which spreadsheet to check, which colleague to call, or which exception to make. AI agents do not inherently possess that organizational context.

“If a human has incomplete information, we can often recognize it, ask another question, or find someone who knows the history,” said Sood. “In comparison, an autonomous agent may simply act.”

The question for enterprises therefore shifts from whether they can build an AI agent to whether they can trust the environment in which that agent operates.

From digital filing cabinets to connected intelligence

Many organizations still operate around what Sood refers to as the “filing cabinet” model. When businesses digitized, individual functions created their own systems for marketing, finance, sales, supply chain, service, and human resources. Moving those systems to the cloud did not necessarily eliminate the underlying silos.

“The intelligence age requires us to move from storing information to connecting that information,” he said.

This connected foundation supports what Sood and his co-author describe as “co-agency,” a new relationship between people and intelligent machines.

Historically, enterprise technology followed a relatively simple model: humans made decisions and machines executed them. AI changes that equation. Machines can contribute speed, scale, pattern recognition, and continuous operation, while people provide judgment, experience, accountability, and an understanding of nuance.

“If the human sees one version of the customer and the agent sees another, you don’t have co-agency; you have conflict,” said Sood.

Turning insight into action

These implications extend beyond technology architecture as agentic intelligence requires companies to rethink strategy, investment, workflows, governance, and the relationship between people and machines. It’s a notable shift that moves from insight to action.

“Insight by itself does not create economic value. Action does,” Sood shared.

For example, predicting that a piece of industrial equipment is likely to fail can provide valuable information. But an intelligent system capable of coordinating maintenance, locating the necessary parts, and scheduling service before the failure occurs can potentially transform that insight directly into a business outcome. In order for this to happen, however, trusted information needs to be available when and where decisions happen.

According to Sood, four responsibilities are necessary for organizations preparing for this environment:

1. Connect critical enterprise data.
2. Contextualize it with relationships, history, policies, and interactions.
3. Operationalize that trusted context at the point of decision.
4. Govern how AI agents act.

“Define where an agent can act, where a human needs to intervene, how decisions are traced, and how systems learn from mistakes,” Sood explained. “The goal isn’t simply to make more data available to AI. The goal is to make the enterprise understandable through AI.”

Building for the intelligence age

As AI models continue to improve, Sood believes competitive advantage will increasingly come from proprietary data, business context, workflows, processes, and feedback mechanisms surrounding them.

Trust will also become more consequential as organizations delegate greater authority to AI. As a result, companies will need confidence not only in what an agent knows, but where its information came from, how current it is, and what boundaries govern its actions.

“The companies that win won’t simply be the companies with the most sophisticated AI,” said Sood. “They will be the companies that create environments in which AI can understand the business, operate with trusted context, and act responsibly.

Within the global manufacturing industry, maintaining a competitive edge requires a delicate balance between driving internal efficiency and fostering strong external relationships. For Applied Materials, a leader in materials engineering solutions for the semiconductor industry, this challenge became the foundation for a strategic finance transformation program, with an SAP Taulia solution emerging as a key enabler.

The journey began in early 2019 with the launch of Agile Finance, an end-to-end transformation initiative designed to support the company’s aggressive growth trajectory, which included a goal to double in size. The initiative was built around three strategic pillars: enhancing the efficiency and effectiveness of the finance organization, promoting career fulfillment, and establishing a robust digital operating model. The impact was significant, with the finance function achieving approximately 35% productivity gains in its labor force.

The third pillar—the move to a digital operating model—is where the partnership with SAP Taulia began.

“The SAP Taulia Dynamic Discounting solution was introduced not merely as a cost-cutting measure, but as a strategic tool to transform and digitize the interaction with Applied’s extensive, global supplier base,” Junaid Ahmed, corporate VP, Finance at Applied Materials, says. “We understood that to reap the benefits of digitization, we had to ensure the suppliers were on board. It needed to be a win-win outcome.”

Unprecedented flexibility for suppliers

The program empowers suppliers—thousands of them worldwide—to self-select which approved invoices they wish to discount for early payment. This is not a continuous, all-or-nothing commitment but rather a decision made on an invoice-by-invoice basis. This flexibility allows suppliers to manage their working capital needs with greater precision, taking advantage of early payment during their own critical periods, such as quarter-end or year-end, to help meet their own financial targets.

The system also drastically improves transactional efficiency. Suppliers no longer have to call Applied to track invoice status, approval, or payment date. All this information is available 24/7 in the SAP Taulia solution, reducing resource allocation on both sides and ensuring both reap the benefits of moving to an integrated, digital system.

Strategic benefits for Applied Materials

For Applied, the program is a testament to its focus on balancing efficiency with strong supplier relationships. The philosophy is a “win-win” built on a crucial spread: Applied Materials, as a Fortune 500 company with strong cash flow, has a significantly lower cost of capital than many of its suppliers. By funding the discounts, Applied captures a return—the discount income—while offering its suppliers funding at a rate close to their cost of capital, but with greater convenience.

This relationship-focused approach is critical. Applied’s supplier account managers actively support the program because they recognize its mutual benefit, not viewing it as a finance mandate to push costs onto the supply base.

Furthermore, the “dynamic” nature of the discount rates is a powerful risk mitigation tool. Unlike fixed contractual discounts, the rates can be adjusted in response to global economic changes, such as shifts in interest rates. When interest rates rose after the pandemic, Applied was able to adjust the discount rates accordingly with minimal pushback, as the core proposition remains the valuable spread between the parties’ cost of capital.

The SAP Taulia Dynamic Discounting solution has been rolled out globally, giving all suppliers the opportunity to use it. This has been critical over the last 12 months as many businesses around the globe have been subject to new and often unexpected tariff costs impacting their margin and their liquidity.

“The flexibility of the solution means suppliers can access funds when they need them, which helps them navigate some of the economic uncertainty that many businesses are facing,” Dirk Holoubek, managing director, Finance Shared Services, explains. “2025 saw a 23% increase in usage of the discounts, reflecting the pressures that suppliers are feeling right now on their cash flow.” 

The solution’s capability to drive sophisticated analytics is also a major strategic asset. It helps provide insights into the different costs of capital between Applied and its supplier base. This data allows for targeted outreach and communication, ensuring that the offer of capital support is proactively extended to the suppliers that need it most.

The strategic value of the solution is further cemented by its ownership. The acquisition of Taulia by SAP brings several advantages.

“Trust is really important to both us and our suppliers,” Ahmed says. “For our suppliers to adopt a new solution, they need to know its technology they can rely on in the long term. Being part of SAP creates that assurance in the long-term future of the program.”

Looking forward, Applied Materials is already focused on the next stage of the transformation project: Agile Finance 3.0, which is focused on enabling the organization to become AI-first. The company is deploying a global, organization-wide AI assistant to drive personal productivity, but the strategic application of AI in the supplier management space is even more profound.

AI is expected to transform decision-making enablement by analyzing critical information and communicating effective options. In the future, AI will be able to proactively assess the specific needs and attributes of the supplier base, enabling Applied to address issues more quickly and resolve them earlier. The benefits are already tangible in e-invoicing: AI has made the solution more flexible and “human-like,” capable of reading minor changes in invoice format that would have previously caused electronic errors. This reduced rigidity and increased flexibility are directly contributing to the overall efficiency of the digital operating model.

By leveraging the SAP Taulia Dynamic Discounting solution, Applied Materials has not only digitized a process but also strategically transformed its financial operations, creating a system that is agile, resilient, and focused on maintaining mutually beneficial relationships with its global supplier ecosystem.

Cedric Bru is CEO of SAP Taulia.

Productivity, typically measured as output per hour worked, is the primary long-term driver of income growth and living standards. Both the U.S. and Europe have experienced slower productivity growth since the mid-2000s compared with earlier decades.

Now, however, many economists and policymakers view AI as a potential catalyst for reversing that slowdown. AI—especially the rise of generative AI and AI agents—is widely expected to shape the next phase of productivity growth in advanced economies, including those in the U.S. and Europe.

The key question for business leaders is not whether AI will matter, but how large the productivity gains will be, how quickly they will materialize, and which region will benefit most.

Productivity growth

The Organization for Economic Co-operation and Development (OECD) estimates that AI could raise annual labor productivity growth in advanced economies by roughly 0.4 to 1.3 percentage points, depending on adoption intensity and sector exposure. These gains would be meaningful because even an additional half percentage point of annual productivity growth compounds significantly over a decade.

However, the OECD and other economists stress that outcomes depend heavily on complementary investments in digital infrastructure, workforce training, and organizational change, rather than on technology alone.

Between 1995 and 2019, U.S. labor productivity grew at 2.1% annually compared to one percent in Europe. This disparity arose in part because companies in the U.S. invested more aggressively in information, communications, and technology while those in Europe were constrained more by regulatory and other factors.

Expectations for AI-driven productivity gains remain generally stronger in the U.S. than in Europe. Goldman Sachs suggests that widespread adoption of generative AI could raise U.S. labor productivity growth by around one to 1.5 percentage points per year.

Several structural factors support this view. The U.S. has a deep technology ecosystem, global leadership in AI research and venture capital, and a large, digitally intensive services sector, including finance, professional services, and IT, where generative AI tools can be rapidly deployed.

Agentic AI

In both Europe and the U.S., AI agents represent a particularly important development. Unlike earlier automation tools that handled isolated tasks, AI agents—like Joule Agents from SAP—are designed to plan, reason, and execute multi-step workflows. For example, an agent might manage customer service tickets, draft responses, query databases, escalate issues, and update systems—all with limited intervention.

In knowledge-based industries, this kind of workflow automation could significantly raise output per worker. But rather than replacing entire occupations, AI agents may reduce time spent on repetitive administrative and “long-tail” tasks, enabling workers to focus on higher-value analysis, strategy, and interpersonal activities.

Despite stories about failed corporate AI projects, which can typically involve bolt-on or stand-alone AI pilots rather than a more integrated, holistic approach, recent evidence from the U.S. suggests that productivity gains are already emerging in some sectors. For example, financial institutions have reported significant efficiency improvements in back-office operations through AI deployment.

Similarly, experimental studies in professional services show that generative AI can increase output quality and speed, particularly for less experienced workers, effectively narrowing skill gaps within teams.

European outlook

The outlook for productivity gains in Europe from AI is more mixed. According to a recent International Monetary Fund (IMF) report the medium-term gain in productivity from the AI alone would vary considerably across countries, and for Europe as a whole would be rather modest: about 1.1 percent cumulatively over five years.

But with pro-growth reforms, the IMF suggests that much bigger gains are possible over the longer run. Like the OECD, the IMF emphasizes that regulatory frameworks, labor market structures, and the pace of technology diffusion will strongly influence outcomes.

Several structural differences shape Europe’s trajectory and the size of what has been called the “AI growth dividend.” First, AI adoption among small and midsize enterprises (SMEs), which form a larger share of the European economy than in the U.S., tends to be slower. Second, Europe’s digital market remains more fragmented across national boundaries, languages, and regulatory systems, which can complicate scaling technology platforms. Third, the European Union has taken a more precautionary regulatory approach to AI governance. While this may reduce certain risks, it could also dampen short-term productivity gains if compliance burdens slow deployment.

Europe’s strengths

That said, Europe has strengths. It leads in advanced manufacturing and industrial engineering, sectors where AI-driven optimization, robotics, and predictive maintenance can raise capital productivity. In these areas, AI agents embedded in industrial systems could significantly enhance supply chain efficiency and reduce downtime.

In addition, as SAP executives have pointed out, Europe has an enormous repository of structured business and manufacturing data, which is essential for reliable and effective AI systems as well as trust in AI Agents.

If AI adoption accelerates in manufacturing and energy systems and if European companies seize the opportunity to build advanced AI agents and apps using their business data, Europe could see much more robust medium-term productivity gains. As an example, SAP’s internal use of AI tools has already significantly improved its own developer productivity.

Labor flexibility

A critical factor in both the U.S. and Europe is labor market adjustment. Historically, the U.S. labor market has demonstrated greater flexibility, with higher rates of job switching and occupational mobility. This flexibility may facilitate faster reallocation of workers into AI-complementary roles, amplifying productivity gains, though this could be offset by more effective existing workforce retraining.

As the Bank for International Settlements (BIS) has noted, AI’s productivity effects are unlikely to be automatic. Productivity gains from AI depend on complementary investments in skills, management practices, and digital infrastructure. The BIS warns that without these, AI tools may produce only marginal efficiency improvements.

The historical lesson from past general-purpose technologies, such as electricity and IT, is that productivity surges occur only after organizations redesign processes to exploit new capabilities and take a holistic rather than piecemeal approach toward implementation.

No AI bubble

While some investors have expressed concerns about an AI bubble, total AI spending in the U.S. is still below one percent of GDP. Joseph Briggs, senior global economist at Goldman Sachs, notes that this is well below historical infrastructure cycles. For comparison historical infrastructure investments such as IT spending, railroads and canals typically represented between two and five percent of GDP.

Like these previous investment waves AI, particularly agentic AI, is likely to generate significant productivity growth and a corresponding boost to GDP in those regions and sectors that seize the AI opportunity.