Free resources on Dr. Hitesh Sarda

AI Readiness Assessment

Mon, 01 Jan 0001 00:00:00 +0000

Most readiness assessments score your infrastructure and your “data maturity” in the abstract, and most companies come out as “partly ready”, which tells you nothing about what to do on Monday.

This one asks about a single AI use case you are thinking of funding. It has 25 questions in five areas, each scored 0, 1 or 2, and it ends with what to do at each score: fix something first, run a small pilot, or scale.

AI Use Case Prioritization Matrix

Mon, 01 Jan 0001 00:00:00 +0000

Every leadership team has a list of AI ideas, and the usual way to rank them is value against feasibility. That ranking keeps producing pilots that impress in the demo and stall afterwards.

This matrix adds the criteria that decide whether AI pays off: how cheap the output is to check, what a mistake costs, whether the work is hard or merely messy, whether a smaller model would do, who owns the outcome, and what it costs at production volume.

AI Agent Governance Framework

Mon, 01 Jan 0001 00:00:00 +0000

Most AI governance documents are policy essays. This is a template: tables and checklists you fill in, short enough to adopt in a week and specific enough to argue with.

It is organized around four ideas I keep returning to. Limit an agent by what it can damage. Match its autonomy to how cheap a mistake is to catch and undo. Give it an owner, a scope and a review, as you would a new hire. And be able to say afterwards what it did.

AI Vendor Evaluation Checklist

Mon, 01 Jan 0001 00:00:00 +0000

A standard vendor questionnaire asks about certifications and uptime. Those matter, and they are not where AI purchases usually go wrong.

This checklist is written from the buyer’s side. It covers the risks I write about: pricing that rests on subsidized compute, metering you cannot audit, a demo that measures the vendor’s expert, a supplier who may ship your product, and what happens to your secrets when the vendor is breached.