Been seeing a stat going around that around 95% of IT leaders name data integration as their number one barrier to AI adoption, not the AI models themselves, the plumbing underneath.

That tracks with what I’ve seen anecdotally: a lot of AI pilots (lead scoring, forecasting, customer health scoring) get built on top of a CRM or ERP that has duplicate records, stale fields, or data that’s only half-synced with whatever other system holds the rest of the picture. The AI ends up confidently wrong because the input was never clean or complete in the first place.

Curious if others have seen this play out.

Has an AI pilot at your company stalled or underdelivered because the underlying data wasn’t in good enough shape to feed it? What ended up being the actual blocker: data quality, systems not talking to each other, something else entirely? And did anyone go back and fix the data/integration layer first before trying again, or is that too big an ask when leadership wants an AI win now?