A new report names the anxiety CIOs have been talking around
West Monroe published a report in July 2026 titled "Building the AI-Native Enterprise," based on proprietary research from more than 400 U.S. business leaders. Its central finding gives shape to something CIOs have been describing anecdotally all year: enterprise AI strategy is increasingly shaped by fear of getting locked into the wrong vendor. That fear is not abstract. IBM's Institute for Business Value found that seven in ten senior executives report difficulty switching away from their primary AI provider, a figure that puts vendor lock-in on par with cost and security as a top-tier concern.
Erik Brown, senior partner of technology and experience at West Monroe, put the stakes plainly in comments tied to the report's release: "Lock-in is super important to consider when it comes to the large AI vendors." That statement lands differently in 2026 than it would have a few years ago, when enterprise software lock-in was a familiar, manageable risk. AI lock-in is different in kind, because switching providers can mean retraining workflows, rebuilding prompt engineering investments, and in some cases starting agent development over from close to scratch.
Why AI lock-in bites harder than past software lock-in
Traditional enterprise software lock-in centered on data migration costs and contract terms, painful but well understood risks that procurement teams have managed for decades. AI vendor lock-in adds a layer those playbooks were not built for: the behavior of a model itself becomes something an organization builds process around. Prompts get tuned to a specific model's quirks, agent workflows get calibrated to a particular provider's latency and reliability patterns, and staff develop institutional knowledge specific to one vendor's tooling. None of that transfers cleanly when a company switches providers.
Brown's prescription is architectural: "We need that flexibility, we need the ability to experiment consistently across the board." That means building abstraction layers between business logic and the underlying model, so a workflow calibrated for one vendor's model can be redirected to another without a full rebuild. Few enterprises have made that investment so far, mostly because it adds engineering overhead to programs that are already under pressure to show results quickly, which is exactly the tradeoff the report is warning against.
The measurement problem sits underneath the lock-in problem
West Monroe's report also surfaces a related, arguably more fundamental gap: most organizations still lack a clear way to measure whether their AI investments are working. Brown frames it as a basic accountability question that most CIOs have not fully answered yet: "How do we really measure the efficacy and make sure we're using AI effectively." Without that measurement discipline, lock-in risk compounds, because an enterprise cannot easily justify switching vendors, or even evaluate whether switching would help, if it never established a clear baseline for what success looked like with the vendor it already has.
This connects to a broader theme running through 2026's enterprise AI coverage: spending has scaled faster than measurement frameworks have matured. Enterprises adopted AI tools quickly under competitive pressure, then found themselves without the KPI infrastructure needed to prove those tools were delivering value, let alone to make an informed case for switching providers if a competitor's model started outperforming their incumbent one. Finance teams that once demanded a clear ROI model before approving major software spend largely waived that discipline for AI purchases in 2024 and 2025, and West Monroe's report reads as an argument for reinstating it before the next budget cycle locks in another year of unmeasured spend.
Pushing back on the replacement narrative
Brown also uses the report to challenge a framing that has dominated public AI discourse this year, that AI is coming for knowledge workers' jobs wholesale. His view is more specific: "We are not getting replaced by AI, the way we work is going to drastically change." That distinction matters for how CIOs plan workforce strategy alongside technology strategy, because it points toward a need to redesign roles and workflows around AI collaboration rather than simply toward headcount reduction targets set independently of how the technology is actually being used.
That framing has practical implications for change management, an area many AI rollouts have treated as secondary to the technology deployment itself. If the work is changing rather than disappearing, training and process redesign become core parts of an AI program's critical path rather than optional add-ons, and organizations that skip that step are likely to see the trust and adoption problems showing up across other 2026 surveys of enterprise AI usage.
What CIOs should take from this report
The report's clearest actionable guidance is to build for multi-vendor flexibility before signing the next major AI contract, not after discovering a switching problem two years into a deployment. That means favoring architectures with abstraction layers between business logic and specific model providers, negotiating contract terms that anticipate the need to run parallel evaluations, and building internal measurement frameworks now rather than retrofitting them once a lock-in problem is already visible in the budget.
It also means treating vendor evaluation as a continuous process rather than a one-time procurement decision. With 70 percent of executives already reporting difficulty switching providers, the enterprises still early in their AI vendor relationships have the most leverage to build flexibility in from the start. Those further along face a harder, more expensive retrofit, which is exactly the position West Monroe's report is trying to help CIOs avoid before it becomes unavoidable.
The bigger picture for enterprise AI strategy
Taken together, West Monroe's findings describe an AI market maturing past its initial adoption rush into a phase where the operational discipline, measurement, flexibility, workforce redesign, matters as much as raw model capability. That is a healthy sign for the market overall, even if it means the easy wins of early AI adoption are giving way to harder, more structural decisions that require sustained executive attention rather than a single procurement sign-off.
For CIOs, the report's real value is naming a risk that has stayed implicit in AI strategy conversations all year without much explicit vocabulary attached to it. Lock-in, measurement, and workforce redesign have always been familiar categories of enterprise IT risk, and AI has raised the stakes on all three simultaneously, which is why West Monroe is arguing they deserve dedicated strategy rather than being treated as downstream concerns to handle after deployment.
