It is often assumed that AI will diminish the importance of traditional enterprise software. If employees can use AI agents to access information, complete tasks, and interact with multiple systems, the underlying applications may become less significant.
However, healthcare appears to be moving in the opposite direction.
Recent research from Bain & Company and KLAS Research shows that nearly 95% of surveyed providers and payers rank software and digital technology among their top five strategic priorities, with most anticipating increased spending. However, investment standards are rising.
Organizations with formal return thresholds most commonly expect a return of 3.0 to 3.9 times the original investment. Health systems are focusing spending on areas directly linked to operating performance, such as revenue cycle management, clinical workflow optimization, and AI-enabled documentation.
This is significant as AI is moving beyond the experimentation phase.
CFOs and operating leaders will increasingly require evidence that AI investments improve revenue, reduce costs, increase capacity, or enhance productivity. A successful pilot alone is no longer sufficient.
A key finding is how AI may affect incumbent platforms.
Nearly 80% of acute provider respondents believe generative AI will either increase switching costs or leave them unchanged. The research also indicates that Epic customers prefer EHR-native solutions for functions closely tied to clinical workflows.
This challenges the assumption that AI will automatically commoditize underlying applications.
An EHR is more than an interface; it manages clinical data, workflows, permissions, orders, documentation, and integrations. If the same platform also provides the AI layer for these functions, the vendor relationship may strengthen rather than weaken.
AI may, in fact, reinforce the advantage of incumbent vendors.
That does not mean health systems should automatically buy whatever their existing platform vendor offers. Best-of-breed vendors will continue to win where they solve important problems materially better.
But the burden of proof should be higher.
Every additional AI vendor creates another integration, security review, data flow, identity relationship, support model, and governance process. Those costs grow as AI moves from generating content to taking action.
A vendor may enter through ambient documentation, then expand into coding, clinical intelligence, and workflow automation. Over time, a point solution can become part of the enterprise architecture without anyone explicitly deciding that it should.
That is the real CIO issue.
Health systems need to decide where best-of-breed performance creates enough value to justify added complexity, and where consolidation creates more enterprise value than marginal product differentiation.
They also need to think harder about exit costs. Replacing an AI platform may eventually mean rebuilding workflows, integrations, governance, and employee behavior—not just migrating software.
The choice is not between innovation and consolidation.
It is about being deliberate before architecture gets created one purchase at a time.
AI may change how clinicians and employees interact with technology, but the platforms controlling the data and workflows underneath may become even more important.
For CIOs, the question is no longer which AI product performs best.
It is which vendors deserve to become strategic platforms—and how much authority the organization is willing to give them.
This is the version I’d use for Forbes: fast opening, one argument, minimal setup, and no repeated platform-consolidation points.