Start with the business problem
AI creates value when it removes cost, risk or delay from a process people already care about. Begin by listing the decisions and tasks that are slow, repetitive or error-prone, and what they cost today.
- Pick processes with clear inputs, measurable outcomes and enough volume to matter
- Avoid starting with a model or tool and looking for a problem to fit
Check your data readiness
Every AI use case depends on data that is available, accurate and allowed to be used. Review where the data lives, who owns it, how clean it is and what consent or regulation applies.
- Map sources, owners and access rules
- Measure quality on a sample before committing to a build
- Plan how personal data will be protected or masked
Prioritise a first use case
Score candidate use cases on value, feasibility and risk. The best first project is valuable enough to fund, small enough to prove in weeks and low-risk enough to put in front of users quickly.
Prove value with a pilot
Agree success criteria before you build — accuracy, time saved, cost per transaction, user satisfaction — then run a pilot on real data alongside the current process. Keep a person in the loop for decisions that matter.
Design for production
Moving from pilot to production means security, integration, monitoring and cost control. Plan role-based access, audit logs, model and prompt versioning, and a way to roll back if quality drops.
Govern and scale
Set simple policies for acceptable AI use, approvals and oversight, aligned with frameworks such as the NIST AI RMF, ISO/IEC 42001 and India’s DPDP Act. Then add use cases one at a time, reusing the platform you have built.
