AI Strategy Consultant

CBAP® certified — not just AI-enthusiastic

Most AI consultants come from a technology background. Vikrant combines CBAP® certified requirements rigour with AI strategy — meaning every AI recommendation is grounded in what the business actually needs, not what is technically impressive.

Problem-first, not tool-first

Engagements start with understanding the business problem, not identifying which AI tool to buy. This prevents the most common AI failure mode: deploying impressive technology that doesn't solve the actual bottleneck.

Vendor-neutral evaluation

No vendor affiliations, no platform kickbacks. Build-vs-buy analysis is conducted using structured evaluation criteria — not which vendor offered the best partner margin.

Regulatory-aware by default

AI strategy work for healthcare and fintech clients integrates compliance requirements from the start — not as a post-implementation afterthought.

What does an AI strategy consultant actually do?

An AI strategy consultant helps organisations identify where AI creates measurable business value, assesses their readiness to adopt it, selects and validates use cases against data and feasibility constraints, evaluates build-vs-buy options, and produces a phased implementation roadmap. The goal is a grounded, executable plan — not a theoretical deck about AI potential.

How is an AI strategy consultant different from a data scientist?

A data scientist builds AI models. An AI strategy consultant defines the problems that need solving and determines whether AI is the right solution, what data is required, and how the organisation needs to change to adopt it successfully. Most successful AI programmes need both — the consultant ensures the data scientist is solving the right problem.

How long does an AI strategy engagement take?

An AI Readiness Audit takes 2 weeks. A full AI Strategy Sprint — covering opportunity mapping, vendor evaluation, and requirements documentation — takes 4 weeks. Ongoing transformation retainers run 3+ months.

What makes a good AI use case to start with?

The best AI starting points combine: clear, measurable business value; data that already exists and is reasonably clean; a process that is currently manual and repetitive; and a low cost of AI error. AI Readiness Assessments score use cases across all four dimensions so the team can choose the highest-confidence starting point rather than the most exciting one.

How do I know if my company is ready for AI?

AI readiness depends on five factors: data quality and availability, process documentation maturity, technical infrastructure, team AI literacy, and executive alignment. Organisations rarely score well across all five — the AI Readiness Assessment identifies which gaps to close before investing in AI development.