THE STATE OF AI
Platform Fit:
Questions that reveal whether a tool will work in production
By the time a team is seriously evaluating AI tooling, the key question should not be ‘Can it do the things we need?’ The right question is 'Can we adapt our Creative Operation so the technology can be used reliably within a new, repeatable workflow'.
Answering that question usually requires more than a tool trial. It means understanding whether your ways of working are ready. If briefs are ambiguous, if ownership is unclear, or if review cycles are not designed for rapid iteration, AI will often amplify the challenges rather than remove them. The same is true for production planning: teams need to decide what should be generated, what should be captured, and how to design workflows that combine both without slowing delivery.
When we evaluate tools, we look for practical signs that they will hold up in production. What needs to change in ways of working without disrupting day-to-day delivery? Do outputs remain editable in the formats the team uses, and does that matter for your operating model? Can review and approvals happen smoothly without creating parallel processes? Do exports behave consistently across the channels we deliver to, without repeated manual correction?
Governance is equally practical. As AI becomes routine, permissions, traceability, and safe use patterns matter more. We also treat operational dependencies as part of the evaluation, including service availability and throughput expectations, because delivery plans should not rely on ideal conditions.
This is why separating evaluation from purchasing is so valuable. A structured pilot is often more informative than vendor comparisons, especially when it includes the operational realities: real assets, real deadlines, real review cycles, and the people who will actually work in the workflow. That is where fit becomes visible.