Boards should own the rules of AI work, not just pay for access

As AI agents begin to carry out work rather than simply generate answers, we believe boards should make a clear distinction: buying access to intelligence is not the same as owning an AI capability. For an established company, the strategic asset should be the way that intelligence works inside the business. That is what we mean by owning the harness.

The harness is the framework that lets agents work alongside people. It defines their roles, the rules they follow, the information they can see, the actions they may take and the approvals they need. It includes checks on their work and shared knowledge. In our view, the model is the smallest part of the capability a board needs to understand and control.

We would expect two companies using the same model to achieve very different results if one has clear responsibilities, reliable knowledge and useful checks, while the other does not. A model cannot decide what a company should permit or accept. We believe those decisions belong to the company, expressed through a harness it understands and controls.

That is why we recommend starting with the work, not the technology. Map the processes. Identify where agents could contribute most, where handovers matter and where mistakes would be costly. Agree what improvement means and establish a baseline before changing anything. Otherwise, we risk celebrating activity without knowing whether the business is better served.

For a board, the measures should make business sense. Depending on the process, we might examine accuracy, consistency, turnaround or the amount of correction required. The important point is to agree the test before seeing the result. We believe an attractive demonstration should be an invitation to investigate, not a substitute for evidence from the company's own work.

Data boundaries should come before price comparisons. Some data must stay in-house where the company's obligations and policies require it. The company's own policies and lawyers must set those rules, not a supplier's preferred architecture. We recommend making those boundaries explicit so that model choices follow them, rather than forcing awkward exceptions afterwards.

Within those boundaries, our principle is straightforward: route each task to the cheapest model that performs it well enough against the agreed tests. Running routine, private work on the company's own machines deserves evaluation, not automatic approval or rejection. Test that option on the company's own examples, including its operating demands. A benchmark alone is not a reason to buy hardware.

Owning the capability does not mean building every component or refusing external services. We mean keeping the accounts, rules, agents and knowledge in the company's hands, with the freedom to use several model providers. We would make the ability to change providers a design requirement, rather than something to negotiate after dependence has developed.

Because AI changes so frequently, we believe the harness also needs a routine for evaluating new models and tools. Candidates should face the company's own tests for quality, permissions, data handling and cost. The company should decide what to adopt and what to leave alone. Newness is not, in our view, a sufficient reason to alter a working process.

That control needs an internal team able to run, question and improve the harness without depending on outside help. At DevMojos, we build agentic harnesses with companies, working with their own people. We sell expertise and accompaniment, not software, hardware or licences. Our aim is to help the company develop capability it can understand, govern and keep improving itself.

For directors and executive teams, we believe this is an ownership decision before it is a purchasing decision. The ambition should be practical: useful work, clear accountability and freedom to choose as the technology develops. We believe the companies that will get most from AI are the ones that own how it works, not just what it costs.