How to Optimize AI for the Entire Enterprise, Not Just the Individual

There’s a classic Harvard case study, where a rowing coach selects his best eight rowers for the top team and his bottom eight rowers for the junior team. Contrary to what you would expect, the top team, with the fastest and strongest rowers, consistently lost to the junior team.

The top team’s rowers focused entirely on maximizing individual power. If the boat slowed, they rowed harder in isolation, disrupting the oars’ synchronized rhythm and creating water drag. Meanwhile, the junior rowers knew they were individually weaker, so they rowed in harmony.

Enterprises have faced countless variations of this problem: implementing systems that maximize productivity at the individual or team level but actively hinder the wider enterprise. Many organizations are experiencing something similar with AI today.

AI and sub-optimization

Sub-optimization is a systemic failure that occurs when the performance of a specific part of a system is maximized, inadvertently hampering the performance of the entire system. There are three intertwined themes: intensity, context, and prediction, which, taken together, explain how AI can sub-optimize an organization by making individuals and local systems stronger while straining the broader organization.

Research reinforces this disconnect between individual or even company-wide AI adoption and the value it delivers. McKinsey’s State of AI report shows near-universal enterprise AI adoption: 88% of respondents report regular AI use in at least one business function, but only 39% report an earnings before interest and taxes (EBIT) impact from AI at the enterprise level. Even worse: only six percent of companies can be categorized as high performers that already capture significant organization-wide value from AI.

What makes it so hard to move from AI adoption to measurable value capture? I believe there are three themes that influence a company’s ability to benefit its entire organization.

Intensity

AI tools often don’t reduce work; they intensify it. A study from Berkeley found that employees who heavily use AI worked faster, took on a broader range of tasks, and worked longer hours, often without being asked. So, what appears to be higher productivity in the short run is actually silent workload creep and mounting pressure as employees manage new AI workflows and do more with less. Another study found that the most mentally taxing form of AI engagement was oversight; AI tools that require direct monitoring increased feelings of being overwhelmed by the volume of information at work.

It is easy to see why AI can feel intense: tasks that once required days can now be prompted into existence almost immediately. People start more things; they do more analysis and write more memos. However, like an eight-lane highway that suddenly narrows to a single-lane toll booth, individuals must still consume all this output. This bottleneck only compounds at the organizational level, as all employees produce more than ever, leaving both individuals and the organization as a whole struggling to keep up. Creation has scaled. Absorption has not.

The solution isn’t necessarily to use less AI, but to change where and how AI shows up. AI should understand user intent and surface the insights needed to answer the question, rather than generating static assets or requiring you to switch between different apps and systems.

If your question creates more things, it’s not helping absorption. SAP’s answer is Joule Work, a central workspace across SAP and non-SAP systems that uses AI agents to handle tasks.

Ask, “Which stores run out of 65‑inch TVs in the next 72 hours, and where is stock I can move?” It will pull data across systems and orchestrate agents to act on the user’s behalf. In this case, SAP’s answer is autonomous action combined with a highly individual user experience for that specific situation, not more assets to be absorbed. If employees can avoid juggling systems and consuming assets, they can spend more time exercising judgment on actions that matter. This is how AI can alleviate intensity.

Context

Most AI systems understand the world, but not the enterprise in which they operate. There is a difference between a system of record—transactions, master data, process logic—and tacit knowledge—emails, chats, unwritten rules. And enterprises run on both. If AI only sees the system of record, its answers might be technically correct but contextually wrong because they don’t reflect the organization’s lived practice.

Even the most ostensibly basic questions require company context. Asking “Which suppliers can I source coconuts from?” requires knowledge of an organization’s process landscape across procurement, supply chain, compliance, finance, and other domains. This type of enterprise knowledge is usually scattered across process models, policies, chats, spreadsheets, and applications, so it’s tough to maintain. And even if they find it, agents cannot turn it into action without procedural knowledge of the involved people—the unwritten rules, decisions, and steps—that make a process executable.

SAP Company Memory preview continuously captures institutional knowledge and makes it usable for both people and agents. It turns written inputs, chat inputs, process knowledge, policy guidance, and application logic into reusable building blocks that AI agents can consume. Blocks are captured once, governed centrally, and reused across the company. So when someone asks Joule Work about coconuts, the answer is driven by the company’s memory and reflects actual rules the process owners agreed upon—for example: “Only source from Brazil; others require formal exception approval.”

SAP Company Memory is not a one‑time implementation; it’s continuous. In this way, company knowledge behaves like infrastructure, ensuring agents act contextually, not just correctly, as policies and teams change.

Prediction

Business decisions are fundamentally prediction problems that rely on structured data. Most organizations use LLMs, which are great at unstructured data like text but for architectural reasons not so great at working with and generating the structured numerical data that underpins good predictions. Delay prediction, forecasting, anomaly detection, stock optimization, and credit risk are everyday operating questions that depend on structured, tabular data and forward-looking judgment.

Asking LLMs for reliable forecasts on enterprise tables is simply the wrong tool for the job. At the same time, traditional custom machine learning approaches are too slow for many real-time questions: after extracting data, sending it to specialists, and waiting weeks, the question often has changed by the time the answer comes back. This combination means predictive capabilities are either restricted to specialists or rendered inaccurate by generic LLMs; in either case, the organization’s decision-making is weakened.

Reliable forecasting and risk assessment should be a system property, not an individual hack. SAP-RPT-1.5 and TabPFN 3 are models that excel with tabular data and will integrate with Joule Work and SAP Business Data Cloud for forward-looking questions directly on live tables.

SAP-RPT-1.5 for SAP data and TabPFN 3 for any tabular data are specialized prediction engines for structured data. They enable decision-makers working in the core systems to ask, “Should I reroute volume? What’s the probability of on‑time delivery? What’s the cost delta across scenarios?” and get answers grounded in real enterprise data.

Availability across the organization eliminates specialist bottlenecks and better equips the enterprise to handle uncertainty through prediction. This enables informed top-level decisions that really move the needle for a company.

AI for the benefit of the whole organization

AI has already proven it can make people more capable, but that does not automatically help the wider organization. AI shouldn’t be about optimizing isolated tasks; it should be about reshaping how work, knowledge, and decisions flow through the company.

Joule Work, SAP Company Memory, and SAP-RPT-1.5/TabPFN 3 are great examples of how SAP designs system-level capabilities. They offer a unified engagement layer, a living institutional memory, and a prediction engine for structured business data that elevate AI from individual-level hacks into a collective benefit for the enterprise.

This is AI that bridges the individual-to-institutional value gap, moving from simply getting AI into the company to generating value throughout the company.

Florian Kunzke is global director of AI Strategy at SAP.