Memory hook: Start with the business constraint and a measurable hypothesis.
Must remember
Define the strategic objective and the decision/process to improve. Describe the current baseline, target users, pain point and measurable outcome before selecting technology. Link AI initiatives to customer value, operational efficiency, risk reduction or a new capability rather than an abstract desire to use AI.
Prioritize a portfolio using potential value, feasibility, data readiness, adoption difficulty, time to evidence and risk. A high-value idea with unavailable lawful data may be less actionable than a modest well-supported use case. Balance learning experiments with dependable near-term improvements and longer-term differentiators.
Competitive advantage may come from trusted proprietary data, workflow integration, distribution, user experience and organizational learning, not merely access to a model competitors can also buy. Assess substitution, dependency and vendor lock-in. Interoperable architecture and clear exit/data-portability terms can preserve options.
Set a hypothesis with a test and decision threshold: for example, reduce average handling time while holding verified resolution and customer satisfaction above an agreed level. A pilot needs a sponsor, process owner, representative users and a scale/stop decision. Do not keep an experiment alive indefinitely because it produces impressive demos.
Choose under exam pressure
| Requirement | Choice and reason |
|---|---|
| Many proposed AI ideas | Score business value, feasibility, data readiness and risk. |
| Generic common capability needed quickly | Evaluate a suitable purchased solution. |
| Unique workflow/data creates differentiation | Consider targeted adaptation with clear ownership. |
Traps
- Technology novelty is not a business case.
- Being first does not guarantee a defensible advantage.
Active recall
1. What should precede vendor selection?
A defined problem, success measures and constraints.
2. Why use a portfolio?
To balance value, risk, timing and learning across initiatives.
3. What can make an AI capability defensible?
Trusted data, integration, customer relationships and an effective operating model.
4. What should a pilot decide?
Whether to scale, change or stop based on evidence.
5. Why define an exit strategy?
To manage provider dependence, data portability and continuity.