AI readiness assessment template, with a section on licensable data assets

An AI readiness assessment template scores a company on strategy, data foundations, technology, people and governance, then turns the gaps into a roadmap. This version adds a sixth section, data assets, which asks whether the company's historical records could be licensed to AI developers, so consultants can spot clients worth an optional SourceX fit screen.

What this AI readiness assessment template covers

This template scores a client on the five dimensions most readiness frameworks share (strategy, data foundations, technology, people and governance) and adds a sixth, data assets, that asks whether the company's historical records could be licensed to AI developers. Standard frameworks treat data only as fuel for the client's own projects. Section 6 treats it as something the company owns that others may pay to use.

It is built for a diagnostic of a few weeks: kickoff, stakeholder interviews, a systems walkthrough with IT, then a readout. The data asset questions fit inside the systems walkthrough you already run, so they add little time to the engagement.

Why add the section now? AI developers are moving from chat models toward agents that carry out multi-step work, and teaching agents needs records of how real work gets done: tickets and their resolutions, approvals, project histories, decisions with outcomes. Those records live inside companies, not on the public web. The US Copyright Office's report on generative AI training, released as a pre-publication version in May 2025, notes that model performance depends heavily on data quality and discusses licensing as one way developers obtain training material.

How to score the five core dimensions

Score each dimension from 1 to 4 using the evidence column, not the client's self-assessment. Write the evidence next to each score; a readout built on interview notes and system settings holds up better than one built on impressions.

Dimension	1: Ad hoc	2: Emerging	3: Defined	4: Embedded	Evidence to collect
Strategy and use cases	No named use cases	A list with no owners	Prioritized use cases with owners and budgets	AI goals tied to the operating plan and tracked	Leadership interviews, budget lines
Data foundations	Data trapped in individual tools	Some exports, no catalog	Key systems documented with named owners	Governed data with quality checks and lineage	System list, data owner names
Technology	No policy on AI tools	Individual, unmanaged tool use	Approved platforms with access controls	AI built into core workflows	Tool inventory, access settings
People and skills	No training	Pockets of enthusiasts	Role-based training plan	AI work in job descriptions and reviews	Training records, interviews
Governance and risk	No policy	Draft acceptable-use policy	Policy, review process and vendor checks	Monitored, audited and updated	Policies, vendor assessments

Averages hide the bottleneck. Report the lowest-scoring dimension first, because that is the one that will stall the roadmap.

Section 6: the data asset questions

Ask these during the systems walkthrough, ideally with the CFO or CIO in the room. You are asking about the shape of the records, never their contents, and you do not open, export or copy anything.

Breadth and depth

How many business systems hold work history today: email, Slack or Teams, CRM, finance, ticketing, engineering, project management, document storage? Strong companies often run 10-15 or more.
How many years does each system go back, and where do retired systems live (archives, backups, old tenants)?
Is any system scheduled for migration, consolidation or shutdown in the next 12 months?

Outcomes and structure

Do records show what happened next: tickets resolved or escalated, deals won or lost, projects delivered late or on time, approvals granted or refused?
Can one piece of work be followed across systems, for example from a CRM opportunity to the contract, the project plan and the support tickets?

Rights and sensitivity

Did the company create these records, or do client contracts say the work product belongs to clients?
What do the privacy policy, customer terms and employee notices say about using data for other purposes?
Is the material mainly consumer personal data or patient health information? If so, mark it red for licensing.
Has any of this data already been licensed for AI training?

Sponsor and baseline

Is it a US company that reached 50+ full-time employees at peak (contractors excluded), with several years of documented operations?
Would the owner, CEO or CFO consider a one-time payment for a license that is exclusive for AI training over an agreed term, with the company keeping ownership?

The privacy question matters more than it looks. FTC staff have written that a company's promises not to use customer data for undisclosed purposes, such as training models, are enforceable wherever they appear: privacy policies, terms of service or marketing materials. Record what the documents say; leave the interpretation to the client's counsel. This is general information, not legal, tax or financial advice.

How to read the data asset section

Apply the green-floor rule: size, rights and sponsor must all be green before breadth and depth matter.

Result	What it means	Next action in your readout
Size, rights and sponsor green, plus 3 or more breadth and outcome items green	The records may be licensable	Recommend an optional SourceX fit screen as a roadmap item
Size and sponsor green, rights unclear	Possible, but contracts decide	Recommend counsel review client contracts and privacy commitments first
A system retiring within 12 months	Archive at risk	Recommend preserving a full export before shutdown, whatever the licensing decision
Below the size baseline, or mainly consumer or health data	Not a licensing candidate now	Note it and keep the finding for internal AI work
Already licensed for AI training	Likely conflicts with an exclusive license	Note it; no introduction

Before raising it with the client, you can run the company fit checker, a preliminary, non-binding screen that needs no contact details. The SourceX explainer on what a data audit is shows what happens once a company decides to look deeper.

Readout language for the data asset finding

Put the finding in the readout as one slide or one paragraph, framed as an option the client controls.

If the client wants to proceed, the introduction email builder drafts the follow-up, and the list of introduction email subject lines helps it get opened.

How to adapt the template to the engagement

Engagement type	Where Section 6 fits	Who answers it
Standalone AI readiness diagnostic	After governance, before the roadmap	CIO or head of IT, then the CFO
AI assistant rollout	During the content and permissions review	IT lead and records owner
ERP or CRM replacement	Before data migration scoping	Project sponsor and system owners
Sponsor-backed portfolio diagnostic	Alongside the value creation review	Portfolio CFO and operating partner
Exit readiness work	With data room preparation	CFO and deal counsel

When to raise it during the engagement

Kickoff: list data assets in the scope document so the section is expected, not a surprise.
Systems walkthrough: ask the breadth, depth and outcome questions with IT present.
Sponsor interview: ask the rights and baseline questions with the CEO or CFO.
Readout: present the green-floor result on one slide, next to the five maturity scores.
Roadmap: if the client agrees, add the fit screen as a dated item with a named owner.

What never goes into the assessment

Samples, exports or screenshots of records. You describe systems; the company keeps its data.
A value for the records, a price range or the name of any potential buyer.
A promise that the company qualifies, or a payment timeline.
Your referral reward as a typed number. If you are a SourceX partner, say plainly that you may earn a referral reward paid from SourceX's fee, never deducted from what the client receives, and check your firm's conflict and independence policies before recommending a provider that could pay you.

Partners earn 25% of the eligible platform fees SourceX actually collects from the referred company's licensing deals, capped at $100,000 per referred company. Rewards become payable only after the buyer pays and SourceX receives its fee, so no reward is guaranteed. Owners weighing an offer can work through the data license term sheet checklist with their counsel.

Next step

Add Section 6 to your next readiness engagement. When a client passes the green-floor rule and wants to proceed, register as a partner so you can send a referral link, and see the referral page for management consultants for other points in an engagement where introductions come up.

Step 1Share your linkSend your personal link to a company you know.
Step 2Company appliesThe company applies itself at /apply.
Step 3Buyer selects and paysThe buyer selects and pays for the data and SourceX receives its fee.
Step 4You get your rewardYour share of SourceX fees becomes payable.

Common questions

What are the main sections of an AI readiness assessment?

Most assessments cover five dimensions: strategy and use cases, data foundations, technology, people and skills, and governance and risk. Each is scored on a maturity scale using evidence from interviews and system reviews, and the lowest score usually sets the roadmap's first priority. This template adds a sixth dimension, data assets, which asks whether the company's historical records could be licensed to AI developers.

Does the consultant need to see client records to complete the data asset section?

No. The section asks about the shape of the records: which systems exist, how many years each covers, whether outcomes are captured and who holds the rights. Answers come from interviews and system settings. Consultants never open, export or describe confidential records, and if the client later pursues a license, SourceX works directly with the company under redaction rules agreed before any work begins.

Can a company license its records and still use them for its own AI projects?

The company keeps ownership and keeps using its records to run the business. Licenses arranged through SourceX typically give AI-training exclusivity for a set term, so how the company's own AI work is treated is a point to settle in the license terms before signing. The company agrees price and terms first, and nothing is binding until it signs.

Which clients score poorly on the data asset section?

Clients that fall below 50+ full-time employees at peak with contractors excluded, whose records mainly belong to their own clients, whose data is mostly consumer or patient information, who have deleted archives or cancelled tools without exports, or who already licensed the data for AI training. These clients can still score well on the other five dimensions and benefit from the rest of the roadmap.

Should the data asset finding appear in the final readout if the client is not a fit?

Yes, briefly. Knowing which systems hold the longest history and which are about to be retired is useful for any AI roadmap, whatever the licensing answer. Record the result of the green-floor rule, recommend preserving full exports before any shutdown, and keep the licensing option off the roadmap until the size, rights and sponsor conditions are met.

Free resources

Earnout scenario calculator — Probability-weighted earnout value and its present value.
Profit margin calculator — Profit and margin across three scenarios.
Client opportunity brief generator — An editable intro email, summary and checklist.
All free tools · MCP resource center

By SourceX Partnerships Team · Published 2026-10-09 · Updated 2026-10-09

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