Data Readiness for AI

Data readiness for AI, assessed in two weeks.

AI does not fail on models. It fails on definitions. Your pilot demonstrated well and will not deploy. Before spending more on the model, find out whether the data underneath it can carry the use case at all — and exactly what would have to change.

Two weeks, fixed scope, assessed against one named use case
Entity resolution, definition conflicts and quality baseline quantified
Findings a data team can act on, written for an executive audience
Honest verdict — including when the answer is that you are ready

Why pilots stall

Six reasons an AI programme stops at the pilot.

None of them is the model. Each is a data foundation question that was reasonable to defer during a proof of concept and becomes blocking at deployment.

The pilot ran on a curated extract

Someone who understood the data prepared it by hand. Production runs against the estate as it actually is, and nobody has quantified the difference.

Two departments define the same term differently

Ask finance, operations and sales how many active customers you have and you will get three numbers, each correct within its own definition. A model trained across them produces confident, unusable answers.

The same entity appears several times

Without resolved entities, any per-customer or per-supplier output is computed on fragments. The model is not wrong; the input was never whole.

Nobody owns the definitions

Analytics tolerated ambiguity for years because a human sat between the number and the decision. Automation removes that person, and the caveat has nowhere left to live.

Quality has never been measured

Without a baseline for completeness and accuracy on the fields the model depends on, there is no way to know whether output problems are model problems.

The governance answer is "we will do it after"

Deploying first and governing later means the definitional ambiguity gets embedded in an action rather than a report — and becomes far more expensive to unwind.

Worked example

One use case, traced to its data.

The assessment starts from the decision the AI is meant to make and works down to the records it reads. The example below was written for this page to show the method; it is not drawn from a client engagement.

01
The use case
A churn model has been built and validated. The business wants it to choose which customers account managers call each week.
02
The entities it depends on
Customer, contract, product and support case — and the attributes the model actually reads from each.
03
What the assessment finds
Billing and the CRM define an active customer differently. The same customer exists under several identifiers across billing, CRM and support. Contract end dates are missing on older contracts.
04
The verdict
Not ready. Resolve customer identity and agree one definition of active before retraining; backfill contract dates in parallel. Each fix is sequenced with an effort estimate and an owner.

The engagement

Two weeks. One use case. A clear verdict.

Assessed against a specific, named AI initiative rather than in the abstract — because readiness is meaningless without saying readiness for what.

Duration

2 weeks

Investment

₹3L – ₹5L · $3,500 – $6,000

What you receive

Use case data dependency map
Critical entity and attribute inventory
Entity resolution assessment
Definition conflict analysis across functions
Data quality baseline on dependent fields
Governance and ownership gaps
Go / no-go verdict with reasoning
Sequenced remediation plan with effort estimates
Executive readout

Request an assessment

If the assessment concludes your foundations are adequate and the blocker lies elsewhere, we will say so plainly. That is a useful outcome and you keep the analysis either way.

How it compares

Data readiness versus a broad AI readiness assessment.

Both have a place. A broad assessment tells you where the organization stands; this one tells you whether one use case can ship.

	Data readiness for AI	Broad AI readiness assessment
Scope	One named AI use case	The whole organization
Focus	Definitions, entity resolution and quality on the fields the use case reads	Strategy, talent, technology, governance and data, each at a high level
Output	A go / no-go verdict and a sequenced remediation plan	A maturity score and a roadmap
Duration and fee	Two weeks, fixed fee published below	Often several weeks, usually quoted after scoping

The dependency

Every layer depends on the one beneath it.

AI sits at the top of a stack most organizations have never assessed as a whole. A weakness low down surfaces as an inexplicable failure high up.

01AI & automationWithout it: Confident answers nobody can act on

02AnalyticsWithout it: Two departments, two numbers, no decision

03Master dataWithout it: The same customer counted three times

04Data qualityWithout it: Match rules operating on noise

05GovernanceWithout it: Nobody accountable when a definition changes

06Integration & platformWithout it: Data that cannot reach the systems that need it

AI without trusted data produces unreliable intelligence — confidently, at scale, and faster than anyone can check it. That is why the foundation work is not a delay to the AI programme. It is the AI programme.

See how we build that foundation →Profile your data with DataInsight Accelerator →

Questions

Data readiness for AI: common questions.

What is data readiness for AI?

Whether the data a specific AI use case depends on is defined consistently, resolved to the right entities, of measured quality and owned by someone. Readiness only means something against a named use case.

How is this different from an AI readiness assessment?

A broad AI readiness assessment scores the whole organization across strategy, talent, technology and data. This assessment goes deep on one use case and the data it reads, and ends in a go / no-go verdict with a remediation plan.

How long does it take and what does it cost?

Two weeks, fixed scope. The fee is ₹3L to ₹5L (US$3,500 to $6,000), confirmed in writing before work starts.

What do you need from us?

One named use case, access to the data it depends on or a representative extract, and time with the people who own the definitions — usually the use-case owner, a data engineer and two or three business owners.

What if our data turns out to be ready?

We say so. If the blocker lies elsewhere — the model, the process or adoption — the report says that plainly, and you keep the analysis either way.

Does this apply to generative AI and AI agents?

Yes. Retrieval and agent systems read and act on entity data and business definitions just as predictive models do, often with less human review between output and action. The assessment covers the entities and definitions the system will read or act on.

Find out before you spend more on the model.

Name the use case and tell us where it stalled. We will tell you whether two weeks of assessment would answer the question — and if it would not, we will say that instead.

Data readiness for AI, assessed in two weeks.

Request an assessment

Six reasons an AI programme stops at the pilot.

The pilot ran on a curated extract

Two departments define the same term differently

The same entity appears several times

Nobody owns the definitions

Quality has never been measured

The governance answer is "we will do it after"

One use case, traced to its data.

The use case

The entities it depends on

What the assessment finds

The verdict

Two weeks. One use case. A clear verdict.

What you receive

Data readiness versus a broad AI readiness assessment.

Every layer depends on the one beneath it.

Data readiness for AI: common questions.

What is data readiness for AI?

How is this different from an AI readiness assessment?

How long does it take and what does it cost?

What do you need from us?

What if our data turns out to be ready?

Does this apply to generative AI and AI agents?

Find out before you spend more on the model.

Request an assessment