01 / Market choice
Position the workflow, not just the model.
A broad technical capability can support very different businesses. Compare the task, buyer and alternative before choosing the language of your offer.
Read about positioning ↗Strategy consulting for AI companies
A capable model is not a strategy.
The harder questions sit around it: who needs the product, what delivery costs, and which evidence makes a buying decision possible.
Explore the decisions ↘
Five connected
decisions.
These guides examine the choices a consulting brief should make explicit. Start where uncertainty is blocking a decision, rather than treating every question as equally urgent.
02 / Delivery economics
Follow the cost of useful work.
Token charges are only part of the picture. Retrieval, retries, human review and service commitments can change which customers and workloads make sense.
Read about economics ↗
03 / Product evidence
Ask what a good result actually means.
An impressive demonstration does not establish reliable workflow performance. Evaluation needs a defined task, representative inputs and a view of failure consequences.
Read about evaluation ↗
04 / Enterprise buying
Make the purchase easier to assess.
The user, budget holder and security reviewer may want different evidence. A coherent buying process connects those questions without promising away real constraints.
Read about enterprise sales ↗
05 / Growth choices
Expand what is repeatable.
More demand can reveal a product advantage or expose a services bottleneck. Compare segment expansion, platform breadth and delivery capacity before committing.
Read about scaling ↗A useful brief starts
with a decision.
“We need an AI strategy” leaves too much open. A more useful starting point identifies the choice, the alternatives and the evidence that would change the answer. That might mean narrowing a target workflow, revising a pilot offer or deciding whether a deployment model can support the intended margin.
Bring the disagreement as well as the data. A strategy discussion becomes more concrete when commercial ambition, model behaviour and operational limits can be examined together.
Discuss an AI strategy question ↗