"How do I know if my company is ready for AI"

"AI readiness depends on four factors: data accessibility (can you get clean data from your systems without a multi-month project?), process documentation (do you know how decisions are currently made?), technical foundation (do you have APIs, not just spreadsheets?), and leadership alignment (does the executive team agree on what problem AI should solve first?). Companies with $5M+ revenue, 3+ years of operational data, and at least one engineer who understands APIs are ready for Level 1 AI adoption. You don't need a data science team or perfect data — you need a specific problem and good-enough data."

Is Your Company Ready for AI? A Practical Readiness Assessment

I get asked “are we ready for AI?” at least twice a week. The honest answer is almost always the same: you’re ready for some AI applications right now, you’re not ready for others, and the gap between where you are and where you need to be is smaller than you think.

The problem is that most readiness assessments are designed by AI vendors who want to sell you a transformation program. They’ll tell you that you need a data lake, a machine learning team, a governance framework, and an 18-month roadmap before you can start. That’s nonsense. You need a specific problem and good-enough data.

The Four Readiness Dimensions

Dimension 1: Data accessibility. Not data quality — data accessibility. Can you get the data you need out of your systems in a usable format without a six-month data warehouse project? If your CRM has three years of customer interaction data and you can export it to a CSV, you’re ready for AI-powered customer insights. If that same data is locked in a legacy system with no API and no export capability, the AI project is actually a data infrastructure project wearing an AI costume.

The bar here is lower than most people assume. You don’t need a unified data lake. You need the specific data for the specific problem you’re solving, in a format that an API or script can access. Messy data is fine — AI tools are surprisingly good at handling inconsistency. Inaccessible data is the real blocker.

Dimension 2: Process documentation. AI augments decisions. Before it can augment a decision, you need to understand how that decision is currently being made. If your sales team qualifies leads based on criteria that live in the team leader’s head, AI can’t help — not because the technology isn’t capable, but because there’s no process to augment.

Map the decision before you automate it. What inputs go in? What criteria are applied? What outputs come out? If you can document that, AI can likely improve it. If you can’t, you have a process problem, not an AI readiness problem.

Dimension 3: Technical foundation. You need systems that can talk to each other. If your business runs on APIs and cloud services, you’re ready. If your business runs on spreadsheets emailed between departments, you have infrastructure work to do first — but that infrastructure work is valuable regardless of AI.

The minimum technical bar: at least one system with an API (your CRM, your ERP, your ticketing system), a cloud-hosted environment where you can run scripts or services, and someone on your team who can write or manage API integrations. That’s it. You don’t need Kubernetes. You don’t need a data engineering team. You need basic connectivity.

Dimension 4: Leadership alignment. This is where I see the most companies fail. The CEO wants AI for customer experience. The VP of Sales wants AI for lead scoring. The CTO wants AI for code generation. Everyone wants AI for something different, nobody has agreed on priorities, and the result is three half-finished AI projects that don’t deliver value.

Before you start any AI initiative, the leadership team needs to agree on one question: What is the single most valuable problem AI could solve for us in the next 90 days? Not the most impressive problem. Not the most technically interesting problem. The most valuable one.

The Quick Assessment

Answer these five questions:

Do you have at least one business process where you make the same type of decision repeatedly, using data you already collect? If yes, you have an AI use case.
Can you access that data through an API, database query, or structured export? If yes, you can implement AI without a major data project.
Do you have someone technical enough to manage an API integration? This doesn’t have to be a senior engineer — a competent developer or even a technically savvy operations person can handle most AI tool integrations.
Has your leadership team identified and agreed on a specific problem to solve? Not “we want to use AI” but “we want to reduce customer support response time by 40%.”
Can you measure the outcome? If you can’t measure whether AI improved the process, you’ll never know if the investment worked.

If you answered yes to all five, you’re ready. Start with Level 1 AI adoption — AI-assisted tools for your existing workflows — and expand from there.

What “Not Ready” Actually Means

If you answered no to questions 1-3, you have foundational work to do. But that work — documenting processes, making data accessible, building basic API infrastructure — is valuable independent of AI. It makes your business more efficient and more adaptable.

If you answered no to questions 4-5, you have a strategy problem, not a technology problem. Fix that first, because throwing AI tools at an unfocused organization just creates expensive confusion.