AI strategy for growing businesses
Buy, build or partner for AI: a decision framework
Choose the route that fits the strategic value, risk, capability and pace of the business problem.
Begin with strategic distinctiveness
Decide whether the capability is ordinary infrastructure, an enabling process or part of how the business competes. This matters because buy build or partner AI decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Do not custom-build an undifferentiated job merely because the technology is interesting. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Assess process maturity
A standard product fits a stable common workflow more easily than a process full of local exceptions and unclear ownership. This matters because buy build or partner AI decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Fixing the process may create more value than selecting a more configurable platform. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Test lifecycle capability
Consider product ownership, data, engineering, security, monitoring, user support and continual improvement after release. This matters because buy build or partner AI decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. The ability to create a prototype is weak evidence that the business should own a production system. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Compare pace with reversibility
Buying may accelerate launch while creating contractual and data dependencies; building may preserve control while increasing time and execution risk. This matters because buy build or partner AI decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Choose a first step that creates evidence without locking the business into the final architecture too early. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
Keep the business brief independent
Partners should answer a defined problem rather than shape the requirement around the solution they already sell. This matters because buy build or partner AI decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. Retain ownership of outcomes, acceptance criteria, commercial assumptions and the decision to stop. Record the choice, the evidence still required and the person who will return with it. Keep the mechanism proportionate: the purpose is better judgement and follow-through, not additional ceremony.
Make the recommendation conditional
State why the preferred route wins, what would change the decision and which risks require active mitigation. This matters because buy build or partner AI decisions rarely fail through a lack of possible technology. They fail when the business problem, operating context and responsibility for the outcome remain implicit. Bring evidence from the people doing the work, the systems supporting it and the leaders accountable for the result. Test assumptions about time, behaviour, data quality and implementation effort before treating them as facts. A good decision framework allows the business to adapt without reopening every settled question. Record the choice, the evidence still required and the person who will return with it. Bring specialist judgement into the decision where required while retaining business ownership of the outcome.
What to carry into the work
- Do not custom-build an undifferentiated job merely because the technology is interesting.
- Fixing the process may create more value than selecting a more configurable platform.
- The ability to create a prototype is weak evidence that the business should own a production system.
- Choose a first step that creates evidence without locking the business into the final architecture too early.