Every Box Ticked. Project Still Dead. Great Spreadsheet, Though.

Vadim Govzich

Digital Transformation Advisor, Azati

Quick answer: The RFP most likely to guarantee a failed AI project is the one that specifies the solution in loving detail and says nothing about the data. Every requirement gets met. The project still dies. But hey, flawless spreadsheet. Gartner backs the pattern: it expects 60% of AI projects without AI-ready data to be abandoned through 2026. AI outcomes live in your data, not your feature list. The best brief I’ve read ended with one line: "now tell us where you think we’re wrong." Start with a data and AI-readiness assessment, not a fixed spec.

Speccing the solution while ignoring the data is how projects die on time and under budget

Every box green. Wrong foundation.

Ask vendors to argue with you

The strongest briefs invite a fight over the premise; the weakest just collect polite compliance.

Data readiness belongs in the RFP, not the post-mortem

Quality, availability, and governance decide the result long before the model shows up – the work behind Data Engineering and AI Integration.

Case in point

Read the case study

Azati’s oilfield reservoir-analytics platform replaced a mess of fragmented calculations and cut floating-point error 1000x, the win was the data-and-computation foundation, not a shinier model.

FAQ

Why do detailed AI RFPs still fail?

They nail the solution and stay silent on data quality and availability – the exact thing AI lives or dies on. Gartner ties 60% of abandonments to missing AI-ready data.

What makes a better AI RFP?

One that scopes the data and dares the vendor to challenge your assumptions. We open with an AI-readiness diagnostic.

Writing an AI RFP?

Have us stress-test it first

Adapted from a LinkedIn post by Vadim Govzich, Digital Transformation Advisor at Azati – read the original. Stat: Gartner, February 2025.