The AI Automation Readiness Playbook
A practical framework for deciding which processes to automate first, what each one will cost, and how to tell the difference between a project that will ship and one that will not.
Published 20 May 2026
What this covers
The playbook is the framework we use in our own readiness audits, written so you can run a first pass yourself before talking to anyone — including us.
Process selection. A scoring model across volume, rule consistency, data availability, error cost and current effort. Processes that score badly are not candidates, and knowing that early saves the most money of anything in this document.
Data readiness. What has to be true about your inputs before automation is viable — format consistency, access, ownership, and what the DPDP Act permits you to do with them.
Build, buy or leave alone. The three-way test. A meaningful share of automation candidates are better solved by a SaaS product or by fixing a broken handoff, and this section is deliberately blunt about that.
Cost modelling. Hours currently spent, current error rate, engineering effort, running inference cost and payback period. With a worked example you can adapt.
Governance. The minimum viable governance package for an AI system in India, and which parts have architectural consequences rather than documentation ones.
Failure modes. The eight ways these projects most commonly fail, and the early signals for each.
Who it is for
Heads of operations, engineering leaders and finance partners who need to build or challenge a business case. It assumes no machine learning background and does not require one.
What it is not
It is not a vendor comparison and it is not a maturity model. There is no five-stage journey diagram. It is a set of decisions with criteria attached.
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