How should a technical leader respond when a customer asks to add AI to an existing application?
The answer requires structure and clear thinking.
1. First, clarify the Actual Problem
Never assume that AI is the right solution. I would start by understanding the business objective. Many requests framed as AI needs turn out to be workflow issues, reporting gaps, or rule based automation opportunities. Accurate problem definition prevents unnecessary complexity.
2. Evaluate Data/App Readiness
AI depends completely on data quality. Assess what data exists, how clean it is, and whether privacy or compliance concerns limit its use. If data foundations are weak, that becomes a critical part of the project plan.
3. Identify the Correct AI Capability / Use Case.
If AI is still appropriate, determine the specific capability that matches the problem. For example. Prediction or classification, Natural language processing, Recommendation engines, Vision based models etc.
Selecting the correct approach ensures the solution remains focused on measurable value.
4. Plan the Integration Strategy
Any AI component must fit the existing architecture. Consider decisions such as cloud versus on premise execution, real time versus batch processing, security controls, model versioning, and monitoring. Architectural alignment prevents operational issues later.
5. Assess Risks and Constraints
AI introduces uncertainty. Evaluate accuracy expectations, acceptable errors, model drift, latency, cost, and potential failure modes. Build guardrails that protect users and the business.
6. Build a Proof of Concept
Before committing to a full solution, run a small POC using real/test data. This validates assumptions, exposes hidden constraints, and demonstrates clear value. If the POC succeeds, scale in controlled phases.
7. Ensure Long Term Operability
AI is not a one time integration. It requires continuous monitoring, retraining, cost oversight, and transparent governance. A sustainable operational plan is essential.
A structured approach ensures that adding AI enhances the application rather than becoming an expensive experiment.
