Core question
Should we standardize on one AI platform or use several?
Short answer
Standardize on a small sanctioned set rather than on one tool or on twelve, and avoid long contracts while the market is unsettled. Committing to a single provider for years right now is a bet on a landscape that has not settled. Letting every team choose freely is the problem you are trying to solve.
This is a genuinely difficult question and anyone who answers it with confidence is oversimplifying.
The case for standardizing is strong. Different teams using different tools for the same work produce different answers to the same question, and nobody can tell which is right. Costs multiply quietly across subscriptions nobody tracks. And the security exposure of an uncounted set of systems is its own problem.
The case for caution about long commitments is also strong. The landscape is moving month to month, and picking today's winner for a multi-year term is a bet on a market that has not settled. Some technology leaders are handling this by deliberately refusing long-term AI contracts and staying flexible, on the reasoning that it is hard to know which providers will end up on top.
For a mid-sized company, the practical position is usually in between. Standardize on a small sanctioned set rather than one tool or twelve. Be clear about which work is done where. Keep your own data and your own records in a form you can move. And when you sign anything, know what leaving costs.