Many organizations rolling out AI standardize on a single provider, because one contract is simpler to buy and govern. It also means the business inherits that model's blind spots and its price list, with no way to check either.
We put that to the test on a recent project. Over two weeks, AI tools wrote the code for a software release, and the finished revision then went to seven different AI models for independent review against the same requirements. Each produced a different list of issues. Several of the findings that mattered came from models outside the two market leaders. Had we relied on the market-leading tools alone, those problems would have shipped.
More models also meant more disagreement. A control that looked finished did nothing when clicked. Six models flagged it, and the seventh called it correct. Some plausible findings turned out to be wrong. Settling it took a process. We consolidated the reports into one list, reproduced or rejected every finding with evidence, and left the release decision to one person.
Use several models to find problems, and one accountable person to decide. Models are good at producing work and spotting issues. Deciding what the product should do, what risk is acceptable, and when the work is finished stays with the owner.
Start with one bounded workflow that can be checked. Name the owner who accepts the result. Define the data and actions the tools may use. Have the work reviewed by different models from the ones that produced it. Test the assembled result before release, and plan for runs that stall or fail. In our build, that final gate ran more than 4,900 automated tests on each of two platforms before the release shipped on Windows, macOS, and Linux. The method applies well beyond code, because the same tools draft documents, build slide decks and graphics, triage tickets, and reconcile invoices.
Using several models here is a deliberate choice, made for work where a second opinion matters. That is the opposite of the sprawl described in AI adoption is outpacing governance, where everyone tries every tool and nobody chooses. Designing the workflow so models can be swapped or combined also protects the budget: when one provider raises prices or falls behind, the work moves without starting over.
We help organizations implement AI this way: choosing the first workflow, selecting and combining models, setting up review and acceptance, and putting the controls around it. The result is faster work with an owner who can stand behind it.
Figures are taken from the project record.