Many are actually buying integration projects.
If you removed every AI feature from the discussion, would your organization still have the data architecture necessary to support the business outcomes you expect?
Inc.'s recent survey of Inc. 5000 CEOs caught my attention for what it revealed beneath the AI headlines.
While 37% of CEOs expect to replace legacy systems with AI enabled tools, fully 50% reported needing additional investment to address software integration issues.
At the same time, easier integration ranked as the second biggest factor that would increase confidence in a major technology investment.
That tells me something important.
AI is no longer the hardest part.
Connecting AI to fragmented data, legacy systems, business processes, governance controls, reporting platforms, security models, and operational workflows is where many organizations will spend most of their time, attention, and money.
Boards are approving AI initiatives across every industry.
Many have not yet grasped that the enabling infrastructure often determines whether those investments create value or become another pilot searching for a business case.
The organizations that move fastest over the next few years may not be the ones with the most advanced models.
They may be the ones with the cleanest data.
The strongest integration capabilities.
The fewest barriers between information and action.
A reasonable counterargument is that modern cloud platforms, APIs, and software ecosystems have made integration dramatically easier than it was a decade ago.
That is true.
Yet the survey itself points to the remaining challenge.
If integration were largely solved, it would not rank near the top of CEO concerns, nor would half of respondents be allocating additional resources to address it.
Before approving your next AI investment, ask a different question.
Do we have an AI problem?
Or do we have a data and integration problem that AI is exposing?
Three actions I would recommend:
Inventory the systems, data sources, and business processes that an AI capability will actually depend on.
Budget for integration, governance, security, and data quality from day one rather than treating them as follow on work.
Measure readiness before measuring AI maturity. If information cannot move reliably across the enterprise today, adding AI rarely fixes the underlying issue.
When you look at your organization's AI roadmap, what percentage of the real work is AI and what percentage is integration?
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