AI-ready data diagnostic Two diagnostic paths: $5K-$7.5K entry, $12K-$20K flagship

AI-Ready Data Diagnostic

Find the marketing, ad, and product metrics your AI answers wrong today. Most teams do not have an AI problem first. They have marketing data, ad platform, product-event, CRM, and revenue-definition problems that AI will amplify. You leave with one number: the % of your board, revenue, and marketing-performance questions an AI would answer wrong today against your current data — plus the repair sequence that makes the first useful workflow safe.

Book the AI-Ready Data Diagnostic Start with a focused $5K-$7.5K diagnostic when you need a fast read, or use the 2-4 week AI-Ready Data Diagnostic when you need the board-ready wrong-answer rate, live demo, and build roadmap. The full flagship diagnostic fee credits toward a committed build.

Leave with the wrong-answer rate

  • Quantify the share of board, revenue, and marketing-performance questions AI would answer wrong today
  • See which CRM, ad, product, warehouse, and semantic-layer gaps create confident bad answers
  • Classify which use cases can suggest, assist, route, or act — and which should wait
  • Prioritize the 20% of metric-certification and source-trust fixes that unlock useful AI workflows first

This is for you if...

  • Leadership wants AI use cases and you need to separate signal from hype
  • CRM duplicates, lifecycle drift, or stale account fields would make the workflow risky
  • Your dashboards still disagree and nobody can define the same metric twice
  • You have warehouse data, but testing, documentation, or governance is weak
  • You want to avoid buying another AI tool before the inputs are trustworthy

This isn't the right fit if...

  • You want a generic AI strategy deck with no data review
  • You need a custom LLM app built immediately regardless of CRM or warehouse quality
  • You want AI to act autonomously before anyone has checked field ownership, exceptions, and rollback paths
  • You already have clean, governed, trusted data and just need implementation hands

What does the AI-Ready Data Diagnostic include?

CRM Data Hygiene

Duplicate records, lead-to-account linkage, lifecycle stage drift, stale firmographics, and fields nobody owns

Source Reliability

Schema drift, null patterns, sync latency, warehouse lineage, and ownership across your core systems

Governed Semantic Layer & Metric Certification

Marketing, ad, product, and revenue metric definitions, dbt tests, transformation quality, documentation, and whether each metric is safe to expose to AI answers

Workflow Risk Level

Whether each AI workflow should only suggest, assist with human review, route work, or act automatically

Exception Handling

What happens when the output is missing, stale, contested, or wrong — including the human review and rollback path

Roadmap & Priorities

A sequenced plan for CRM hygiene fixes, governance work, and fast-win AI pilots

Signature demo

See the wrong answer before it becomes an operating rule

The diagnostic includes a live side-by-side demo: the same GTM question asked against raw, loosely governed data and against a certified metric path. The point is not to embarrass the AI. It is to show exactly where the answer becomes unsafe before a team builds a workflow around it.

Question tested

What was net revenue retention last quarter?

Raw text-to-SQL answer

NRR was 108%

The query used opportunity amount, missed expansion timing, ignored churn adjustments, and treated a stale customer-status field as current. It sounds precise because the data model let it be precise in the wrong way.

Governed semantic-layer answer

NRR was 96%

The certified metric path uses finance-recognized renewal, expansion, contraction, and churn rules, with account hierarchy and close-date logic documented before the answer is trusted.

This is one evidence point behind the wrong-answer rate: repeat the test across the board, revenue, and marketing-performance questions leadership already trusts. If leadership sees the demo, the repair work stops looking like governance theater and starts looking like risk removal.

How the AI-Ready Data Diagnostic works for SaaS teams

1

Inventory the stack

List the CRM objects, ad platforms, product-event sources, warehouse/dbt models, reporting layer, and AI workflows the business wants to put into production.

2

Inspect trust breaks

Check CRM hygiene, source reliability, semantic-layer coverage, model tests, metric ownership, workflow risk, exception paths, and whether rollback is realistic.

3

Classify each use case

Decide whether each workflow should suggest, assist with human review, route work, automate action, or wait until the trust layer is repaired.

4

Sequence the repairs

Turn the audit into a short roadmap: what can ship now, what needs Data Foundation work first, and what would be AI theater if forced.

Need a different route?

When the AI-Ready Data Diagnostic is the right path versus Data Foundation

Use the AI-Ready Data Diagnostic when the business is already pushing for copilots, scoring, routing, or automation and you need a practical yes, no, or not-yet answer on the metrics those tools would trust. If the review exposes broken source reliability, warehouse logic, or missing system-of-record ownership across the business, Data Foundation is usually the better first move. If the data is trusted but the question is how to push it into CRM, lifecycle, or product workflows, Data Activation is usually the next path.

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Get the framework we use to assess AI-ready data before the tooling conversation

Not ready to book yet?

Get the framework we use to assess AI-ready data before the tooling conversation

Most AI failures are data hygiene failures in disguise. This framework shows how we evaluate source reliability, governance gaps, and workflow fit before recommending any AI investment so you do not automate a mess.

  • How we separate real AI opportunities from vendor-driven urgency
  • The data quality and governance checkpoints we run before any AI recommendation
  • A practical way to frame AI readiness for leadership without overselling or stalling
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What This Makes Possible

B2B SaaS Sales Team

AI lead scoring increased sales efficiency 40%

The warehouse data was solid, so we built a lead scoring model on top of it. Product-qualified scores synced to the CRM and sales efficiency jumped 40% in the first quarter.

Read case study

Mid-Market SaaS Data Team

Pipeline reliability went from constant firefighting to 99%+ uptime

Brittle pipelines and missing governance made AI a non-starter. We stabilized the foundation with automated testing, documentation, and ownership patterns — uptime hit 99%+ and the team could finally trust what they shipped.

Read case study

Not ready to book yet?

Start with the AI readiness tools

Use these worksheets when the business is pushing for AI and the team needs to decide what can safely suggest, assist, route, or automate.

Open worksheet group

AI readiness and CRM hygiene

Readiness checks and CRM reliability tools for separating safe AI workflows from not-yet automation.

Go deeper on AI-ready data diagnostic decisions

Read the CRM-focused readiness guide if the likely blocker is duplicate records, lifecycle drift, stale account fields, or weak opportunity linkage. If the review shows broader source trust or modeling reliability problems, move into Data Foundation. If the data is trusted enough and the next problem is pushing it into real workflows, use Data Activation instead of forcing a one-off AI pilot.

Read the CRM readiness guide

Related Reading

Common questions before booking the diagnostic

When is the AI-Ready Data Diagnostic the right next move?

Use the audit when leadership already wants a copilot, scoring workflow, routing change, or automation push and you need a practical answer on whether the current data, definitions, and workflow can support it. It is the right move when the question is not “should we care about AI?” but “can this use case survive real operating use without creating a bigger trust problem?”

What does CRM data hygiene have to do with AI readiness?

CRM data hygiene determines whether the workflow is acting on the right account, lifecycle stage, owner, opportunity, and field definition. Duplicate records, stale firmographics, inconsistent stages, and unowned fields become more dangerous when AI starts scoring, routing, or recommending action from them.

Can we use AI workflows if our CRM data is messy?

Sometimes, but the workflow needs a lower authority level. Messy CRM data may be acceptable for suggestion or assistive review. It is not acceptable for autonomous routing, customer communication, compensation-sensitive scoring, or any workflow where a bad field creates real operating damage.

Is this an AI strategy project or a data audit?

It is a readiness audit for one or more real workflows. We look at the data, the CRM and warehouse trust layer, the workflow owner, the decision the AI would influence, and the operating risk. You get practical next steps, not a generic AI strategy deck.

What does the AI-Ready Data Diagnostic include?

A useful diagnostic includes CRM hygiene, ad and product source reliability, warehouse/dbt model trust, governed semantic-layer coverage, metric ownership, workflow-risk classification, exception handling, human review, rollback paths, and a clear decision on whether the workflow should suggest, assist, route, or act. If it skips those operating checks, it is usually an AI strategy deck wearing an audit label.

How long does the flagship diagnostic take?

The flagship AI-Ready Data Diagnostic usually takes two to four weeks, depending on how many workflows, CRM objects, source systems, ad/product sources, and warehouse models need review. If the team needs a lighter first pass, we can scope a lower-friction diagnostic around the most urgent question.

Will you tell us not to do AI yet if that is the right answer?

Yes. The point of the audit is to separate useful near-term AI use cases from expensive theater. If the inputs, governance, or workflow fit are not there, we will say that directly.

What kinds of AI initiatives does this evaluate?

Typical examples include lead scoring, lifecycle automation, support copilots, internal analytics assistants, and workflow recommendations that depend on trusted source data and clear business definitions.

Do you review the warehouse and modeling layer, or just the tools?

We review both. Tool selection without source reliability, tested models, documentation, and ownership is how teams end up amplifying bad data with a more expensive interface.

When is Data Foundation the better next step instead?

Data Foundation is usually the better first move when the bigger problem is upstream source reliability, brittle warehouse logic, weak testing, or missing system-of-record ownership beyond one AI use case. If the audit shows the trust layer is too shaky for automation with teeth, we will point you there directly.

Is this still an AI readiness audit?

Yes, but the useful version of AI readiness is not a generic maturity score. This is an AI readiness audit for your actual marketing, ad, product, CRM, and revenue data. The deliverable is the wrong-answer rate, the live evidence behind it, and the repair sequence that makes the next AI workflow safe enough to trust.

What happens after the audit?

You leave with a sequenced roadmap. Sometimes that means a fast-win AI pilot. Other times it means cleaning up definitions, governance, or delivery workflows first through a narrower foundation engagement.

Need one number before AI gets more authority?

We will show which marketing, ad, product, and revenue questions AI would answer wrong today — and what to fix first before those answers shape real work.

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