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When competitors can access similar AI models, the advantage is less likely to come from the model alone. It is more likely to come from company-specific knowledge, redesigned workflows, trusted customer and ecosystem positions, and the ability to operate AI systems reliably and economically. None is a guaranteed moat: the best combination depends on the business and its access to data, expertise, and infrastructure.
Why access to AI is becoming a weaker differentiator
A widely available capability can still be valuable, but it is harder to use as a lasting point of distinction when competitors can obtain comparable tools. McKinsey describes the strategic shift as moving “from access to intelligence toward ownership of proprietary intelligence” in The symbiotic enterprise (2026). In practice, that means asking what a company can build around a model that rivals cannot readily reproduce—not just which model it licenses.
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OpenAI’s 2026 B2B Signals analysis offers evidence of a gap in how its enterprise customers use AI, rather than proof of a market-wide performance gap. It reports that firms at the 95th percentile of its usage measure generated 3.5 times as much intelligence per worker as typical firms, up from 2 times in April 2025. OpenAI uses generated tokens as a proxy for intelligence demanded, not as a measure of business value. It attributes 36% of the measured usage gap to message volume, with the larger share associated with deeper and more complex use. Frontier firms also sent 16 times as many Codex messages per worker as typical firms; that figure measures use of the tool, not productivity or return on investment.
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OpenAI summarizes the usage difference this way: “Typical firms are using AI to answer questions; frontier firms are using it to help execute complex work.” The distinction is useful, but usage alone does not show whether that work creates lasting economic value.
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What can create an advantage beyond the model?
Potential advantages are complementary. Proprietary operational knowledge can make AI outputs more relevant; redesigned processes can put those outputs to work; experienced staff can handle judgment and exceptions; and strong execution can make the system dependable at scale.
| Potential advantage | How it could compound | What must be true | What can undermine it |
|---|---|---|---|
| Company-specific data and operational learning | Real transactions, decisions, customer interactions, and outcomes can provide context and feedback for improving future work. | The information is accurate, current, legally usable, relevant to operations, and captured in a form the system can use. | A large dataset without quality, rights, context, or a feedback loop may be difficult to turn into better decisions. |
| Redesigned workflows | AI can change how work moves across teams, rather than merely speeding up one task inside an unchanged process. | The workflow is important to business outcomes, responsibilities are clear, and saved effort is redirected toward useful work. | Adding an assistant to a fragmented process may leave delays, handoffs, and accountability problems intact. |
| Expertise and organizational learning | Domain experts can interpret outputs, resolve exceptions, and help turn repeated lessons into better practices. | Staff can challenge and improve system output, and the organization can preserve know-how instead of leaving it isolated with individuals. | Automation without the right expertise can produce plausible answers that miss context or mishandle edge cases. |
| Customer and ecosystem position | Trusted relationships, recurring workflows, and useful integrations may make a company a valuable point of coordination. | Customers continue to rely on the company for a meaningful service or interaction that is difficult to replace. | As agents reduce search and coordination frictions, some existing customer touchpoints may become easier to bypass. |
| Production execution | Reliable, reusable systems can help a company deploy and improve multiple AI-enabled processes. | Architecture, evaluation, security, governance, accountability, and operating costs are managed as part of delivery. | Fragile integrations, weak controls, or unmonitored costs can make a promising use case unsuitable for sustained operation. |
Why proprietary intelligence is about more than data volume
Data may help differentiate a company when it captures information competitors cannot easily obtain and connects that information to real decisions. McKinsey points to proprietary data, specialized agent skills, and learning loops based on execution as possible sources of differentiation. The proposed mechanism is cumulative: a system supports work, the work produces outcomes and exceptions, and the organization captures those lessons to improve what happens next.
That cycle depends on the quality and usability of the information, not merely on how much a company holds. Data must be relevant to the task, sufficiently reliable, and legally available for the intended use. It also needs a path into the operating process and a way for people to correct or contextualize results. Without those conditions, possession of a dataset does not by itself establish an advantage.
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Kearney’s AI Trends Report 2026 makes a related argument in procurement and supply-chain settings: widely available information may be less differentiating than curated knowledge about specific markets and supply networks, interpreted by people who understand the domain. Vel Dhinagaravel, CEO of Beroe, is quoted in the report saying, “Technology gives us the reach to see everything, but not the judgment to know what matters.” This is an industry perspective, not proof that a particular data strategy will outperform competitors.
Why workflow redesign matters more than adding another tool
An isolated assistant can help an employee complete a task, but a company-wide advantage may require changing the process around that task. McKinsey’s 2026 survey analysis distinguishes enabling individual work, automating cross-functional processes, and reinventing roles or operating models. The more consequential question is whether a company has changed how work is assigned, reviewed, handed off, and improved—not simply whether employees have access to an AI feature.
In McKinsey’s survey, respondents whose organizations had redesigned workflows were 5.3 times as likely to report enterprise value capture as respondents whose workflows remained unchanged: 32% versus 6%. Respondents reporting high AI fluency in leadership teams were 3.9 times as likely to report value capture as 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.
A practical test is to trace one important process from beginning to end. Identify where information enters, where decisions are made, where work stalls, and who is accountable when an AI-generated result is wrong or incomplete. Then determine whether AI changes the flow of work or simply adds another step. Time saved matters only if the organization can put that capacity toward a worthwhile priority.
How customer and ecosystem positions may change
AI agents may reduce the effort involved in finding options, comparing them, and coordinating transactions. McKinsey argues that this could increase the importance of customer relationships, recurring workflows, and ecosystem positions. A company that supplies useful real-time context through a dependable integration may be more difficult to displace than one whose only distinction is access to a general-purpose model.
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This is a strategic possibility, not evidence that a particular firm already controls the interfaces through which agents discover or purchase products. Companies should examine where they are genuinely useful in a customer’s process and whether that position depends on durable trust, service, or coordination—not assume that simply adding an AI interface creates customer lock-in.
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Turning a promising use case into a production capability involves more than selecting a model. McKinsey highlights architecture, industrialization, orchestration, governance, security, and AI economics as areas requiring attention. Companies must decide which actions agents may take, which decisions remain with people, how outputs are evaluated, how workflows are coordinated, and how risks and costs are monitored.
Cost needs to be measured in the context of the work being performed. Inference and other operating expenses can affect the economics of a transaction or process, so usage growth is not automatically a sign of better returns. Reusable components and evaluation systems may make deployment more consistent, but only if they meet the requirements of the business and its controls.
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Why there is no universal AI moat
The OECD’s 2025 analysis cautions against assuming that one asset will confer an enduring advantage in every sector. AI may lower some entry barriers, substitute for labor, reduce minimum efficient scale, and enable innovation or product differentiation. At the same time, access to data and restrictions around models can shape who is able to compete. The effects depend on sector exposure, company size and capabilities, and access to enabling inputs.
As a result, the same technology can help one company scale a differentiated service and help a competitor enter a market with fewer resources. A data-intensive business with deep operational records faces different opportunities from a firm whose work is less digitized. A company with strong domain expertise may be well placed to interpret outputs, while another may first need to improve its processes and data access. The relevant question is not “What is the AI moat?” but “Which advantage can this company build, and what conditions would make it durable?”
How leaders can assess their own position
Use these questions to test a proposed AI advantage before treating it as a strategic asset:
- Data: Is the information distinctive, accurate, current, legally usable, and tied to actual operations, customers, or supply networks?
- Feedback: Does execution produce useful learning that the organization captures and applies to future work?
- Workflow: Is AI embedded in a business-critical process that has been redesigned end to end, or is it an isolated tool within an unchanged process?
- Expertise: Can employees interpret results, handle exceptions, and improve the process as conditions change?
- Customer position: Does the company own a trusted interaction, recurring workflow, or coordination role that remains valuable as AI lowers search and switching frictions?
- Production readiness: Are reliability, evaluation, security, accountability, governance, and operating economics addressed?
- Dependencies: What would happen if a provider changed its price, availability, product direction, or policies?
Choose a process where the business outcome can be measured, establish a baseline, and track both the result and the cost of operating the system. That makes it possible to distinguish genuine improvement from higher usage or faster activity alone. Kearney’s report quotes Suketu Gandhi, co-leader of global strategic operations at Kearney, saying that “Continuous competitiveness is becoming the measure of how prepared an organization is for the future.” For an enterprise, that means treating AI advantage as a capability to build and test over time, not as a one-time model purchase.
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