·6 min read
What Actually Happens in an AI Readiness Audit
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"AI Readiness Audit" sounds like one of those consulting terms designed to be vague enough to mean anything. I get it. So let me tell you exactly what happens, step by step, when you work with us on one.
No jargon. No mystery. Just a clear walkthrough of the process, what we deliver, and how it helps you make better decisions about where (and whether) to invest in AI.
Here's the scenario I see constantly: a business owner reads about AI, gets excited, hires a developer or buys a tool, and six weeks later has spent $15,000 on something that doesn't quite solve the problem they had.
The issue is almost never the technology. It's that they skipped the step where you figure out which problem to solve first, and whether AI is actually the right tool for it.
An audit is that step. Think of it like getting a structural inspection before renovating a house. You could skip it and start knocking down walls, but you'll make much better decisions if you understand what you're working with first.
Our audit costs $2,500 and takes two weeks. Here's what those two weeks look like.
We start with a 90-minute kickoff call. This isn't a sales pitch — it's structured discovery. We want to understand:
After the kickoff, we schedule 30-minute conversations with 2-4 key team members. These are the people doing the actual work — customer service reps, operations managers, salespeople. They know where the real bottlenecks are, and their perspective is often very different from leadership's.
Based on those conversations, we map your core workflows. Not in exhaustive detail — we're looking for the 4-6 processes that consume the most time, involve the most repetitive work, or create the most friction.
For each process, we document:
We also assess your data landscape. What systems are you using? What data do you have? What format is it in? Is it clean enough for AI to work with? This isn't glamorous work, but it's where most AI projects succeed or fail.
By the end of week one, we have a clear picture of your operations. We identify every potential AI application we can see — usually somewhere between 8 and 15 opportunities, depending on the complexity of your business.
Not all of these are worth pursuing. That's what week two is for.
This is where we do the actual analysis. For each opportunity we identified, we score it across four dimensions:
Impact — How much time, money, or quality improvement would this create? We estimate conservatively, using the data from your team's actual workflows.
Feasibility — How technically complex is this? Does the technology exist and is it mature? Do you have the data to support it?
Risk — What could go wrong? What are the dependencies? How would it affect your team?
Speed to Value — How quickly could you see results? We favor projects that can show meaningful improvement within 30-60 days.
Each opportunity gets scored on a simple 1-5 scale across these dimensions. This isn't subjective guessing — it's based on the process mapping and data assessment from week one, combined with our experience implementing similar solutions.
Based on the scores, we build a phased implementation roadmap. This typically looks like:
Phase 1 (Quick Wins): 1-2 high-impact, low-complexity projects that can be implemented in 2-4 weeks. These build momentum and demonstrate value.
Phase 2 (Core Improvements): 2-3 medium-complexity projects that require more setup but deliver significant returns.
Phase 3 (Strategic Bets): Longer-term opportunities that might require more data, more integration, or more organizational change.
For each recommended project, we include estimated implementation cost, expected time to value, recommended tools or approaches, and what internal resources you'd need.
You receive a written report covering everything above. It's typically 12-15 pages, and every page is actionable. No filler sections about "the state of AI" — just your business, your data, your opportunities.
Then we do a 60-minute strategy call to walk through everything together. We discuss the findings, answer questions, pressure-test assumptions, and help you decide where to start.
Concretely, you get:
Some clients take the roadmap and implement it themselves or with their existing development team. That's a perfectly good outcome — the audit gave them clarity on where to focus.
Some clients hire us to implement the first phase. We're transparent about pricing for that upfront, and there's never pressure to continue. The audit is designed to be valuable on its own, regardless of what you do next.
And occasionally, we tell clients that AI isn't the right move for them right now. If the audit reveals that the fundamentals aren't in place — undefined processes, insufficient data, misaligned expectations — we say so. I'd rather be honest and earn trust than sell implementation work that won't deliver results.
I'll let you do the math. If the audit identifies even one automation that saves your team 5 hours per week, that's roughly 250 hours per year. At an average loaded cost of $40/hour, that's $10,000 annually — a 4x return on the audit investment alone, before you've even implemented anything.
Most audits identify multiple opportunities at that scale or larger. The question isn't whether you can afford the audit. It's whether you can afford to keep guessing.
If you're curious whether this makes sense for your business, reach out. We'll have a 15-minute conversation and I'll tell you honestly whether an audit would be worth your time.