AI Readiness Audit Audits start at $3,500

Know What to Fix Before You Invest in AI

Most teams do not have an AI problem. They have a data hygiene problem that AI will amplify. We audit your sources, definitions, pipelines, governance, and workflow fit so you know where AI can help now — and what foundation work has to come first.

Book an AI Readiness Audit Most AI readiness audits run $3,500-$7,500 depending on stack complexity and scope. Fixed fee, clear deliverables, and no generic strategy fluff.

Get a practical yes, not AI theater

  • See whether your data is clean enough for scoring, automation, and copilots
  • Identify the source, metric, and governance gaps most likely to break trust
  • Prioritize the 20% of fixes that unlock useful AI use cases first
  • Leave with a roadmap your team can execute with or without us

This is for you if...

  • Leadership wants AI use cases and you need to separate signal from hype
  • 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 data quality
  • You already have clean, governed, trusted data and just need implementation hands

What We Assess

Source Reliability

Schema drift, duplicates, null patterns, latency, and ownership across your core systems

Metric & Model Trust

Business definitions, dbt tests, transformation quality, documentation, and lineage

Workflow Fit

Which AI use cases fit your operating cadence, tools, and decision points right now

Roadmap & Priorities

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

How It Works

1

Inventory

We review your core systems, warehouse models, reporting layer, and the AI use cases your team is considering.

2

Assess

We inspect data quality, testing coverage, business definitions, ownership, and delivery workflows to find the trust gaps.

3

Prioritize

We separate must-fix foundation issues from nice-to-have cleanup and identify the best first AI opportunities.

4

Roadmap

You get a concise plan covering what to fix first, where AI can help now, and what should wait until the foundation is stronger.

Need a different route?

See where AI readiness fits in the bigger services map

If the real issue turns out to be attribution, revenue trust, warehouse reliability, or activation rather than AI specifically, use the services hub to choose the better top-level path.

See all services
Get the framework we use to assess AI readiness before the tooling conversation

Not ready to book yet?

Get the framework we use to assess AI readiness 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

Go Deeper

Read the CRM-focused readiness guide if the likely blocker is dirty lifecycle, owner, and opportunity data rather than abstract AI strategy.

Read the CRM readiness guide

Related Reading

Common questions before booking this audit

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.

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 clarity before you buy another AI tool?

We will tell you honestly whether you are ready for scoring, automation, or copilots — and what to fix first if you are not.

Book a Discovery Call
Book a Discovery Call