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When competitors can access similar AI tools, the advantage is less likely to come from the tool itself. It is more likely to come from combining AI with distinctive data and expertise, redesigning work around it, and building teams that can measure, improve, and scale the results. Those capabilities can support an edge; current evidence does not show that any one of them guarantees durable competitive advantage.
Why AI access alone is unlikely to set a company apart
As widely available AI tools become easier to adopt, using one for common tasks can become a baseline capability rather than a differentiator. Berkeley California Management Review’s October 2024 analysis, Competitive Advantage in the Age of AI, argues that companies should focus on a small number of industry-specific, company-defining capabilities instead of treating general-purpose AI adoption as the strategy.
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The strategic question is not simply whether a company uses AI. It is whether it can apply AI to a valuable problem in a way that fits its customers, operations, expertise, and information better than alternatives can. A competitor may be able to license a similar model; recreating an effective combination of trusted data, specialized judgment, well-designed processes, and organizational learning can be harder. That is a strategic argument, not proof that the combination will remain exclusive or produce superior results in every market.
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Distinctive problems and domain expertise
Generic tools are available to many organizations. The opportunity is to apply them to specific customer pain points or industry tasks where a company understands the context, exceptions, and cost of mistakes. Domain expertise helps teams choose useful problems, interpret outputs, and decide when a result needs review. Berkeley’s analysis emphasizes industry-specific applications and full workflow reinvention; it does not establish that a particular application will outperform competitors across sectors.
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Usable data, not just data ownership
Proprietary information may make an AI application more relevant to a company’s products, customers, or operations, but possession alone is not a capability. The information must be fit for the intended use and accessible where the work happens; disconnected systems can prevent teams from using it consistently.
In IBM Institute for Business Value’s 2025 study, 72% of the 2,000 CEOs surveyed viewed proprietary data as key to unlocking generative AI value, and 68% viewed an integrated, enterprise-wide data architecture as critical for cross-functional collaboration. The survey was conducted with Oxford Economics from February through April 2025 across 33 countries and 24 industries. These are executive views, not measured proof that data ownership or integration by itself creates an edge. The same survey found that 50% of respondents said the pace of recent investment had left their organization with disconnected, piecemeal technology.
Workflows redesigned end to end
Adding an AI assistant to one step may save time, but broader value can require changing how work moves from input to decision to outcome. That can mean clarifying which tasks AI handles, where people make judgments, how information passes between teams, and what happens when the system is uncertain or wrong.
McKinsey’s 2025 global AI survey found that respondents it classified as AI high performers were more likely to report fundamental workflow redesign. The high-performer segment was about 6% of respondents and required both reporting AI-attributed EBIT impact of at least 5% and reporting significant value from AI use. These are self-reported survey categories and associations, not a universal performance benchmark or evidence that workflow redesign alone caused the reported impact. McKinsey also found that meaningful enterprise-wide bottom-line impact remained rare.
People, ownership, and the ability to learn
AI-enabled work needs people who understand the tools, the business process, and the consequences of errors. Role-specific training, clear leadership ownership, cross-functional participation, human validation where appropriate, and feedback loops help teams adapt a workflow as they learn what works. A pilot owned only by a technical team may not have the authority or operating context to change a process across the business.
Wharton School and GBK Collective’s 2025 AI Adoption Report tracks surveyed enterprise leaders across three waves from 2023 to 2025. In its 2025 wave, 82% said they used generative AI at least weekly and 46% said they used it daily. These adoption levels make the distinction between access and execution especially important: frequency of use is not itself evidence of business impact.
Value measured in outcomes
Teams need to define what success means before scaling: for example, a change in cycle time, quality, customer experience, revenue, or cost. Adoption counts and prompts generated can help describe activity, but they do not establish that a business objective was achieved. A useful measurement plan also accounts for review effort, rework, and failure costs rather than counting only apparent time saved.
The Wharton/GBK report says 72% of its surveyed enterprise leaders formally measured generative AI ROI, and three out of four reported positive returns on generative AI investments. IBM’s separate 2025 CEO survey found that 25% of surveyed CEOs said their AI initiatives had delivered expected ROI over the prior few years, while 16% said initiatives had scaled enterprise-wide. The populations, survey questions, and measures differ, so these figures should not be read as a direct contradiction or comparison.
How to distinguish adoption from a stronger position
Use the following questions to assess whether an AI effort is building a business capability or merely adding another tool:
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| Dimension | Adoption without much differentiation | Capability that could support differentiation |
|---|---|---|
| Use case | AI assists with an isolated task. | The organization redesigns an end-to-end workflow around a defined customer or operating problem. |
| Distinctiveness | The application is broadly available and easy for competitors to reproduce. | The application draws on industry context, company expertise, or a specific customer need. |
| Data | Relevant information is fragmented, difficult to access, or not suited to the task. | Useful information is integrated into the process and governed for its intended use. |
| Organization | A siloed pilot has unclear ownership and limited user feedback. | Business, technical, and operational teams share responsibility, with leadership sponsorship and appropriate training. |
| Value | Success is defined mainly by usage or adoption. | Results are assessed against a baseline and business outcomes, including quality and costs of review or rework. |
The second column describes a stronger strategic hypothesis, not a guaranteed moat. Competitors may learn, buy comparable capabilities, or solve the same problem differently. The practical test is whether a capability delivers valuable results, can be improved from experience, and remains relevant as the market changes.
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- Start with a consequential problem. Identify a customer or operating need, the people affected, and the outcome that would count as improvement. Do not begin with a model and search for a use afterward.
- Map the whole workflow. Document the current steps, handoffs, decisions, exceptions, and sources of delay. Decide where AI could assist and where human judgment or approval remains necessary.
- Check information and process readiness. Determine what data the use case needs, whether it is reliable and accessible, and how the AI-enabled step will connect to existing work. Resolve gaps before assuming that more data or another tool will solve them.
- Give a cross-functional team ownership. Include the people who perform or manage the work, relevant technical and data specialists, and a leader accountable for the intended result. Train users for their roles and establish a way to report failures and improvements.
- Set a baseline and test proportionately. Choose measures tied to the problem, such as time to complete, error or rework rates, service quality, or cost. Include the resources needed for human review. Compare the AI-supported process with the existing one under clearly stated conditions.
- Scale only what holds up in operation. Check whether benefits persist beyond a small pilot, whether the workflow works across relevant teams or cases, and whether data, oversight, and support can keep pace. Expand, revise, or stop based on the results.
What the available evidence can—and cannot—show
Survey results, reported usage, and case examples can point to promising practices, but they answer different questions. IBM and Wharton report what their surveyed executives or enterprise leaders said. McKinsey’s high-performer analysis compares a self-reported segment with other respondents. OpenAI’s 2025 enterprise report uses de-identified, aggregated usage data from its enterprise customers alongside survey data; it is informative about that ecosystem, not a representative census of organizations or AI systems.
In OpenAI’s report, users engaging across roughly seven task types reported five times more time saved than users engaging across roughly four. This is an association in matched usage and survey data, not an independent causal finding that expanding task use will multiply savings for another organization.
Gartner’s 2025 CEO survey describes executive intentions and reported beliefs about operating models, new revenue, and operational AI. Intentions are useful context for where leaders expect change, but they are not evidence that those plans produced revenue or productivity gains.
Across these sources, no causal result isolates the effect of proprietary data, workflow redesign, training, leadership, or another single capability on durable competitive advantage across industries. Treat them as capabilities associated with or argued to support value capture. Whether they create an advantage depends on the specific business problem, execution, and outcomes a company can demonstrate.
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