Start with the business decision
An AI strategy should identify the work that needs to improve, the people responsible for it, and the evidence that would justify a change. Choosing a model is one part of that decision. Data availability, workflow design, implementation cost, and the consequences of errors also matter.
We help leaders frame concrete use cases, distinguish a demonstration from an operational requirement, and decide what to evaluate before expanding adoption. The right starting point may be a bounded internal workflow, a model comparison, or a review of an existing deployment.
Board-level AI strategy and oversight
Boards need a clear account of where AI is used, which decisions it influences, and who remains accountable for the outcome. They also need to understand what the organization has measured and which assumptions remain untested.
Our board advisory work focuses on connecting AI capabilities with enterprise strategy and oversight. Topics include adoption priorities, model and vendor choices, evaluation requirements, operating constraints, and the review of results. We describe technical tradeoffs in terms of their effect on the proposed use.
Model selection and evaluation
A general benchmark score does not tell an enterprise how a model will perform on its own documents, tools, users, and failure cases. A useful comparison starts with representative tasks and explicit criteria for acceptable output.
Evaluation can examine task completion, factual support, consistency, tool use, latency, inference cost, and handling of cases that require human review. For agents, the actions performed should be checked alongside the answer reported.
We consider whether an existing model is sufficient, whether a specialized model is warranted, and whether the available evidence supports deployment. Fine-tuning should address an identified limitation, not replace measurement of the baseline.
Our approach to specialized AI models ↗Enterprise adoption and workflow design
Adoption changes how people complete work. A useful implementation defines where the model receives information, what it can act on, how its output is reviewed, and how a failure is escalated. These decisions should be made before a pilot is treated as a production system.
We advise on staged adoption: select a workflow, establish a baseline, test an AI-assisted version, review the results, and decide whether to expand, revise, or stop. Measurement should include the cost of reviewing and correcting output, as well as any reduction in task time.
Safety research applied to practical questions
Our AI safety work informs the questions we ask about model behavior, reporting, and tool use. Through DynoLab, we investigate agent actions and compare reports with independent execution evidence.
That perspective is relevant when a system can run commands, access services, or delegate work. A workflow needs more than a plausible final answer: it needs appropriate permissions, observable actions, evaluation criteria, and an owner who can respond when the system behaves unexpectedly.
Read about our AI safety research ↗What to bring to an advisory conversation
A useful first discussion includes the business objective, the workflow or use case, the current approach, and the constraints that matter. If a pilot already exists, its task examples, evaluation results, and unresolved failures are useful starting material.
Canivel’s work covers AI strategy and board advisory, model selection and evaluation, and enterprise transformation. Contact us to discuss the decision your organization is facing and where research or hands-on model development can inform it.