Software as a service made sense when the software was the hard part. With AI, the hard part is your data, your knowledge, and your judgment. Rent the platform that runs on those, and you are renting access to your own advantage. Four risks are worth naming.
You give away your data.
Your matters, your playbooks, your outcomes: this is the raw material that makes AI useful inside a firm, and it is the one thing a vendor cannot buy. Feed it into someone else's platform and you have handed over the asset that differentiates you, often under terms that let the vendor learn from it. Which model runs matters far less than who owns the data that grounds it.
You lose control and visibility.
When the system is a black box you rent, you cannot see how it reached an answer, what it was trained on, or when it quietly changed. In a profession accountable for its work, that is a hard place to stand. You inherit the vendor's roadmap, the vendor's outages, and the vendor's definition of good enough.
The costs are unpredictable.
Per-seat pricing, usage metering, and annual increases turn a tool into a tax. As adoption grows, so does the bill, and the pricing power sits with the vendor, not with you. What looked cheap in the pilot compounds across the firm.
You risk commoditization.
If every firm rents the same platform, every firm produces the same output. The capability that was supposed to set you apart becomes table stakes, and the differentiation you paid for evaporates. Renting the common tool cannot produce an uncommon result.
Decide deliberately.
None of this means never buy. It means keeping the things that are actually yours, and treating the decision to hand them over as the strategic choice it is. Where it matters, prove a built alternative first, then choose with evidence instead of a sales deck.