What executives should do
The following five actions are dynamic capabilities you need to build – routines organizations need to run continuously as the floor rises.
1 – Sort your project portfolio by model year, not just ROI.
Ensure tasks are routed to the right model. Commodity initiatives get bought cheaply and deployed broadly. Frontier initiatives get funded selectively, with real evaluation attached.
In practice: At the next capital review, add one column to the project list – the expected date each capability hits the commodity floor. Anything under 18 months gets bought, not built.
2 – Avoid long vendor lock-ins for capabilities that are falling in price.
If a frontier lab can only charge premium prices for a limited time, discounts in exchange for a long commitment or rights to your data are their way of buying insurance against commodification with your money. Price declines should accrue to you, not to your vendor’s margin. Be wary of labs’ attempts to shift from selling tokens to selling work. Agents, seats, and workflow integrations are little more than attempts to convert a depreciating asset – the model – into durable ones: distribution and switching costs.
In practice: Cap contracts for commodity capability at 12 months, and make exportability of prompts, workflows, and evaluation data a condition of signature.
3 – Treat proprietary data as a balance-sheet asset.
Govern it carefully and know exactly what you’re giving away and what you are getting for it. The companies that signed broad data-sharing terms for early access will find they traded their only durable moat for an 18-month head start.
In practice: Commission an inventory of the data the organization holds that no competitor can replicate, who currently has contractual access to it, and what was received in return. Few executive teams can answer the third question.
4 – Build evaluation capability in-house.
In every scenario, the ability to determine whether a model is actually better for your specific use is scarce and undersupplied. It’s the closest thing to a no-regrets investment on the list. Its most developed form is a standing instrument rather than a series of studies: a live process wired so that agents run in shadow alongside people, both are scored against real outcomes, and each decision is promoted toward autonomy or demoted back as the evidence shifts.
In practice: Assign a small standing team to run every candidate model against a fixed set of the organization’s real tasks – not vendor benchmarks – before any purchase or renewal, and budget for it as infrastructure, like security, not as a one-off study.
5 – Build governance, accountability, and trust capabilities.
Evaluation tells you what the models can do. Governance decides what they may do, and who answers when one is wrong. The two boundaries move on different schedules: a capability can be ready long before regulators, customers, or your own professionals will accept it. Treat that acceptance as something you build: a named owner for every autonomous decision, clear rules for pulling authority back the moment the evidence turns, and records good enough to defend a decision to a regulator or a court.
In the third scenario, this is the binding constraint. In the other two, it’s what lets you go faster than rivals without betting the franchise. The commodity floor also arrives with a jurisdiction question: the cheapest capable models are increasingly Chinese – permissively licensed and self-hostable, but with hosted services that can place your data under foreign data law, and without the compliance attestations regulated industries require. Cheap is not the same as deployable. A model cannot sign an audit, hold a license, or be sued for malpractice, so in regulated markets the labs must sell through incumbents rather than around them. That is leverage you should use.
In practice: Before adopting any low-cost model, ask where the data goes and who signs the compliance attestation. If the answers are “abroad” and “nobody,” self-host or pay more.
The implementation process is summarized in Figure 1.
