Practical notes from the engineers who build agents, copilots and AI products.
A fixed-price AI quote should name the workflows in scope, the accuracy and latency thresholds it will be measured against, the first-year running cost, who owns the code and prompts, and an explicit exclusions list. A quote missing any of these is a number, not a commitment.
AI strategy consulting costs roughly $3,250 to $10,500, or ₹2,00,000 to ₹6,80,000, for a scoped two-to-four-week engagement. Eazyware's AI Product Strategy work starts at $4,250 or ₹2,80,000. Big-four style programmes run far higher because you are paying for people-weeks rather than a decision.
You implement AI strategy consulting in five phases: frame the problem, assess data and systems, rank use cases by value and feasibility, design the target architecture with an evaluation plan, then commit to one build. Each phase ends in a written decision gate rather than a status update.
An AI strategy consulting RFP should include the business problem in numbers, the systems and data the work will touch, the decision you need at the end, named deliverables, acceptance criteria, a fixed fee and date, IP terms and the people who will do the work.
AI strategy consulting takes two to four weeks at Eazyware, or ten working days for a narrower discovery sprint. Four weeks covers several candidate use cases and a roadmap. Anything longer usually means the scope grew, not that the work is harder. Access delays are the commonest cause of slippage.
AI strategy consulting fails for five reasons: an unfalsifiable brief, an assessment that never touched real data, a roadmap with no owner, a plan that ignores permissions and security, and a recommendation nobody funds. None of them is model quality, and each has a specific preventive decision.