RSM just published its 2026 Middle Market AI Survey. The headline number is striking: 97% of respondents said they are satisfied with AI’s performance in delivering business value.
That is an almost perfect satisfaction score for any enterprise technology.
Here is the other number. A separate survey from Kaufman Rossin, published the same month, found that 73% of mid-market manufacturers are still in the testing phase. Not one of them has reached full, company-wide AI deployment.
Both numbers are accurate. Neither one contradicts the other. That is precisely the problem.
Satisfaction and scale are not the same thing.
The RSM data explains the disconnect, if you read past the headline. When respondents were asked why they were satisfied, the top three answers were: having a clear strategy (41%), choosing the right technology (40%), and having sufficient infrastructure (38%). These are inputs. They describe what organizations built before they deployed. They do not describe what AI has done to the business.
The survey makes this explicit. Only 36% of respondents said they have AI fully embedded across core processes. Among organizations that ran AI pilots in the prior two years, about half described those pilots as delivering moderate or limited success. The leading reasons: data quality issues (53%) and integration challenges (47%).
So the satisfaction is real. It is just measuring the wrong thing.
Most mid-market organizations are satisfied with a foundation they built. They are not yet measuring whether that foundation is producing results at scale. There is nothing wrong with the foundation, provided you understand what you are standing on.
The infrastructure problem is not a technology problem.
The Kaufman Rossin data is useful here because manufacturing is the most data-intensive sector in the mid-market, and it is also the furthest from operational AI deployment. The reason is not a lack of investment appetite. 91% of the manufacturers surveyed plan to increase AI spending. The reason is that only 27% of those companies have a data warehouse or data lake in place. Across the broader mid-market, that figure is 60%. Manufacturers are starting from a deeper hole.
You can buy excellent AI tooling and spend it all on systems that cannot yet receive it.
This is the version of the AI adoption story that rarely gets told, because it is harder to report than the satisfaction number and less useful for selling software. But it is the version that actually describes where most mid-market companies are today.
What this means if you run a mid-market company.
Pilot satisfaction is a reasonable signal that you chose a capable technology and identified a use case where it works. It is not a signal that AI is embedded in how your business operates. Those are different claims, and confusing them leads to underinvestment in the infrastructure work that actually closes the gap.
The RSM report quotes one of their AI practice leaders directly: “The challenge is how to operationalize AI beyond isolated wins. That means prioritizing issues, figuring out how AI can solve them, and then executing on those ideas.”
That sentence describes a sequenced capability-building problem, not a technology selection problem. The companies that move from pilot to production in the next 24 months will not get there because they found better AI. They will get there because they fixed the data infrastructure that was preventing scale, built the governance layer that makes AI trustworthy enough for operational decisions, and changed the operating model to embed AI in how work actually gets done.
The 97% satisfaction rate will become a meaningful number when it starts measuring that.
Sources: RSM Middle Market AI Survey 2026 | Kaufman Rossin / Automation World: AI Arrived on the Factory Floor Before the Foundation Did
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