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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsAs AI access becomes easier to buy, an enterprise is less likely to stand out simply because it uses a capable model. The more defensible advantage is the company’s ability to combine useful operational knowledge, redesigned workflows, expert judgment, trusted customer or ecosystem positions, and reliable execution. Which of those matters most depends on the business: no single AI “moat” is established as a universal winner.
Why access to AI is becoming a weaker differentiator
When competitors can obtain similar general-purpose AI capabilities, choosing a model is less likely to create a lasting lead by itself. The strategic question shifts from “Which model can we access?” to “What does our organization know, do, and improve with it that others cannot readily reproduce?”
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OpenAI’s 2026 B2B Signals analysis describes a difference in how its enterprise customers use AI. Firms at the 95th percentile of its usage measure generated 3.5 times as much intelligence per worker as typical firms, up from twice as much in April 2025. OpenAI uses generated tokens as a proxy for intelligence demanded; that figure is not a measure of business value. In the same analysis, message volume explained 36% of the measured usage gap, while the report attributed most of the difference to deeper and more complex use. Frontier firms also sent 16 times as many Codex messages per worker as typical firms—a measure of tool use, not proof of productivity or return on investment.
Those figures describe aggregated use of OpenAI products, not the whole enterprise market. They nevertheless illustrate a useful distinction: access is not the same as organizational capability. OpenAI summarizes it this way: “Typical firms are using AI to answer questions; frontier firms are using it to help execute complex work.”
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What can become an enterprise advantage?
McKinsey’s 2026 report, The symbiotic enterprise, argues that as foundation models commoditize, advantage may shift from access to ownership of proprietary intelligence. That does not mean a company wins by accumulating the most data. The more plausible advantage is a system that turns relevant, legally usable information and expertise into better decisions and execution, then learns from what happens.
| Potential source | What makes it distinctive | Question to test |
|---|---|---|
| Operational data and learning loops | Current, reliable information about the company’s own operations, customers, products, or supply networks, connected to feedback from real outcomes. | Does the information improve a recurring decision or task, and does the business capture results to improve future use? |
| Redesigned workflows | AI changes how work moves across a process rather than sitting beside an unchanged process as an optional assistant. | Has the end-to-end process, including handoffs and exception handling, been redesigned around the intended business result? |
| Expertise and organizational learning | People can interpret outputs, apply context, handle exceptions, and turn what they learn into better procedures or system behavior. | Can staff explain when to trust an output, when to challenge it, and how to improve the process? |
| Customer and ecosystem position | A trusted customer relationship, recurring workflow, useful integration, or coordination point that is difficult to replace. | Does the company provide a dependable, valuable part of a customer’s work—or is it an easily substituted feature? |
| Production execution | Systems that are reliable, secure, auditable, governable, reusable, and economical at real operating scale. | Can the organization run the use case safely and predictably, and measure its cost per business outcome? |
| Enabling inputs and dependencies | Access to the data, infrastructure, skills, and external services the use case requires, with an understanding of dependence on providers. | What happens if a provider changes pricing, availability, policy, or technical direction? |
These sources can reinforce one another. For example, a company may capture operational outcomes, have experts interpret them, and use that learning to improve a redesigned process. Merely possessing a large dataset, buying an agent, or labeling a tool “proprietary” does not establish such a cycle.
Why changing the work matters more than adding another tool
AI can help an individual complete a task faster without changing the overall economics of a business process. More substantial gains may require moving beyond individual assistance to cross-functional automation or a redesigned operating model: deciding which work an agent can handle, where a person must review or decide, and how exceptions return to the right team.
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McKinsey’s 2026 survey analysis found that respondents whose organizations redesigned workflows were 5.3 times more likely to report enterprise value capture than respondents whose workflows were unchanged: 32% versus 6%. Respondents reporting high AI fluency in leadership teams were 3.9 times more likely to report value capture than those reporting low fluency: 35% versus 9%. These are survey associations, not causal estimates. They do not show that redesign or leadership fluency alone produced the reported outcomes.
The practical test is whether saved effort is redirected to work that matters and whether the process performs better as a whole. A faster first draft, for instance, may have little enterprise effect if review, approval, or customer response remains the bottleneck. Measure the process outcome—such as quality, cycle time, service, or cost per completed transaction—rather than counting AI interactions as a proxy for value.
Why human judgment remains part of the advantage
AI can search, summarize, classify, and surface patterns, but those capabilities do not by themselves establish which signal matters, whether an action is appropriate, or how to handle an unusual case. Kearney’s 2026 AI Trends Report emphasizes the role of domain expertise in interpreting data and acting in context. Vel Dhinagaravel, CEO of Beroe, puts the distinction this way: “Technology gives us the reach to see everything, but not the judgment to know what matters.”
In a procurement or supply-chain setting, broad market information may be widely available; curated knowledge of a particular supply network and the expertise to interpret it can be more useful. That is an industry perspective in Kearney’s report, not proof that a particular data strategy will outperform in every sector. The broader lesson is to treat expert review, exception handling, and the capture of decisions as parts of the system—not temporary steps to remove before understanding their value.
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Kearney’s report also quotes Suketu Gandhi, co-leader of global strategic operations at Kearney, saying that function-level ROI across procurement, planning, and supply chain often exceeds $100 million. This is an attributed report statement, not a typical or guaranteed outcome for enterprises. Gandhi’s accompanying framing is: “Continuous competitiveness is becoming the measure of how prepared an organization is for the future.”
How customer and ecosystem positions may matter
McKinsey argues that AI agents could reduce search, coordination, and switching frictions. If that happens, customer access and ecosystem roles may become more strategically important: a company embedded in a recurring workflow, with useful real-time context and dependable integrations, may be harder to replace than one offering a stand-alone AI feature.
This is a forward-looking strategic argument, not evidence that any particular firm already controls the interfaces through which agents will discover, compare, or purchase products. For a business assessing its position, the relevant questions are whether customers depend on its service for meaningful work, whether integrations make that work easier, and whether the relationship remains valuable if an agent mediates more of the interaction.
What execution requires—and what it can cost
A promising demonstration is not yet a durable capability. Production use brings work in architecture, engineering, orchestration, governance, security, and operating economics. McKinsey highlights the need to decide which actions belong to people or agents, build reusable skills and evaluation systems, coordinate workflows reliably, and monitor costs such as inference and cost per transaction.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →- Define accountability: Specify who owns a decision, what an agent may do, and when a person must review, approve, or intervene.
- Test the whole process: Evaluate quality, reliability, and exception handling across handoffs, not only the model’s response to an isolated prompt.
- Build for reuse: Where appropriate, create shared components, evaluation methods, and operating practices rather than rebuilding every use case from scratch.
- Track economics: Monitor costs against the completed business outcome, including the work required for review, integration, and ongoing operation.
- Plan for dependencies: Assess exposure to external model, cloud, or orchestration providers, including their pricing, availability, policies, and technical road maps.
These requirements make execution quality a possible source of advantage, but not a free one. A strategy that depends on external infrastructure may remain exposed to changes outside the company’s control. The OECD’s 2025 policy paper also cautions that AI’s competitive effects vary with sector exposure, company size and capabilities, and access to enabling inputs.
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Why there is no universal AI moat
The OECD finds that AI may lower some entry barriers, substitute for labor, reduce minimum efficient scale, and support innovation and product differentiation. It also points to concerns around data access and restrictive models. These forces can pull in different directions: a tool may help a smaller company compete while a larger or better-positioned firm benefits from stronger data, skills, or infrastructure access.
So the likely source of advantage differs by industry and business. A company with rich operational feedback may have a different opportunity from one whose bottleneck is expert judgment, customer access, or the ability to deploy secure systems. The evidence supports testing these mechanisms in context; it does not establish one universally durable AI moat or identify a guaranteed winner.
How to assess your company’s next advantage
- Choose a business outcome: Start with a meaningful customer or operating result, not a model feature or an adoption target.
- Map the work as it operates now: Identify the decisions, handoffs, delays, exceptions, and controls that shape that outcome.
- Find the company-specific input: Determine whether useful operational knowledge, specialist expertise, customer context, or ecosystem access could improve the process—and whether it is current and legally usable.
- Redesign the process: Define what changes, what remains a human responsibility, how exceptions are handled, and where outcomes will be captured for learning.
- Measure production performance: Compare the redesigned process with its baseline using business measures such as quality, cycle time, service, or cost per completed transaction. Track reliability and the cost of operating it as well.
- Review dependence and repeatability: Check whether the system can withstand provider changes and whether successful practices can be reused without ignoring differences between use cases.
When comparing candidate opportunities, favor the ones where a company-specific input, a meaningful workflow change, and a measurable business outcome fit together. If an advantage depends only on a widely available model feature, it may be easier for competitors to match. If it depends on learning and execution embedded in the business, it may be harder to copy—but it still needs to be demonstrated with the company’s own results.
What reported business outcomes can—and cannot—show
OpenAI’s 2025 report relays a BCG comparison in which AI leaders reportedly had 1.7 times revenue growth, 3.6 times total shareholder return, and 1.6 times EBIT margin over a three-year period. These figures are a secondary account of BCG’s work, as reported by OpenAI; they should not be treated as proof that AI caused the differences or as a forecast for another company.
Across the evidence, usage measures show how some organizations engage with AI, surveys show associations between reported practices and reported value, and consulting analysis offers strategic interpretations and examples. None settles the long-run causal effect of a particular implementation. For an enterprise decision, business-specific measurement remains essential.
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