How to Evaluate a Business's AI Readiness Before You Acquire It

Most acquirers know how to evaluate financials, customers, and operations. Very few know how to evaluate a target's technology — and even fewer think about what that technology could become after the deal closes.

That's a missed opportunity. A business's AI readiness tells you two things: how much operational leverage you can create post-acquisition, and how much risk is hiding in outdated systems. Both directly impact your return.

Here's a practical framework for evaluating AI readiness during due diligence.

What "AI Readiness" Actually Means

AI readiness isn't about whether a business already uses artificial intelligence. Almost none of the businesses changing hands in the lower middle market do. It's about whether the business has the raw ingredients — data, processes, and infrastructure — that would allow you to deploy AI and automation after you take over.

A business with clean customer data, documented workflows, and modern software is far easier to automate than one running on paper invoices and the owner's memory. That difference can be worth hundreds of thousands of dollars in operational savings over a 3-5 year hold.

The Five Areas to Evaluate

1. Data Infrastructure

This is the foundation. Without usable data, nothing else matters.

What to look for:

• What CRM, ERP, or database systems does the business use?
• Is customer and transaction data stored digitally, or is it in spreadsheets, paper files, or someone's head?
• How far back does the data go? Is it consistent?
• Can you export data from their systems, or is it locked in proprietary software?

Red flags: No CRM. Customer information stored in personal email accounts. Financial data that only exists in QuickBooks with no backup. Key operational knowledge that lives exclusively with the owner or one employee.

Green flags: Modern cloud-based tools (even simple ones). Clean customer records going back 2+ years. Data that can be exported in standard formats.

2. Process Documentation

AI automates processes. If the processes aren't documented, you'll spend your first 6 months just figuring out how the business actually works before you can improve anything.

What to look for:

• Are core workflows documented anywhere? SOPs, checklists, training materials?
• Can employees describe their daily processes, or is everything ad hoc?
• How many steps in a typical workflow require human judgment vs. rote execution?

Red flags: No SOPs. Tribal knowledge everywhere. "Ask Mike, he knows how to do that." Processes that vary depending on who's working that day.

Green flags: Even basic documentation — a Google Doc with steps, a checklist taped to the wall. Consistency in how work gets done across employees.

3. Software Stack

The existing software stack tells you how much you'll need to rip out and replace vs. build on top of.

What to look for:

• What software does the business use for its core operations? When was it last updated?
• Are systems integrated with each other, or do employees manually transfer data between tools?
• Are there any custom-built tools or integrations?
• What are the annual software costs?

Red flags: Legacy on-premise software that hasn't been updated in 5+ years. No integrations between systems. Custom software built by a contractor who's no longer available. Software that requires a specific version of Windows to run.

Green flags: Cloud-based SaaS tools with APIs. Integrations via Zapier or similar platforms (even basic ones show the business thinks about workflow). Modern accounting software.

4. Communication and Customer Interaction Patterns

The way a business communicates with customers and handles inquiries is one of the highest-leverage areas for AI automation.

What to look for:

• How do customers reach the business? Phone, email, web forms, walk-ins?
• What percentage of customer inquiries are repetitive (scheduling, pricing, status checks)?
• Is there a ticketing or tracking system for customer requests?
• How much time do employees spend on communication vs. core service delivery?

Red flags: All communication happens through one person's cell phone. No tracking of customer inquiries. No email templates or standard responses.

Green flags: Centralized inbox or phone system. High volume of repetitive inquiries (these are perfect for AI). Some form of template or script for common interactions.

5. Team Technical Competence

You don't need engineers on staff. But you do need people who can work with modern tools.

What to look for:

• What's the team's comfort level with new software?
• Has the business adopted any new tools in the past 2 years?
• Is there anyone on the team who naturally gravitates toward process improvement or technology?

Red flags: The team actively resists new tools. The last software change was 10 years ago. No one on the team uses a computer for their primary job function.

Green flags: Team members who've suggested or adopted tools on their own. Recent software transitions that went smoothly. At least one person who's comfortable learning new systems.

How to Score It

I use a simple 1-5 scoring framework across these five areas:

• 1 = No foundation. Paper-based, no data, no documentation. Major investment required.
• 2 = Minimal. Basic digital tools in place but poorly used. Significant gaps.
• 3 = Adequate. Standard software stack, some documentation, usable data. Typical for most small businesses.
• 4 = Strong. Modern tools, clean data, documented processes. Ready for automation with modest investment.
• 5 = Exceptional. Integrated cloud systems, rich data history, well-documented workflows. AI deployment can begin immediately.

Most businesses in the $1M-$10M revenue range score a 2 or 3. That's fine — it's expected. What matters is whether the path from their current state to a 4 is realistic within your hold period and budget.

What This Means for Valuation

AI readiness should factor into your post-acquisition value creation plan, not necessarily the price you pay. A business scoring 2 out of 5 isn't worth less because of its technology — it's worth more to a buyer who knows how to close that gap.

The opportunity is in the delta. If you can take a business from manual operations to automated workflows, you're reducing labor costs, improving margins, and building a more scalable (and therefore more valuable) business for your eventual exit.

Where technology does affect price: if the business depends on legacy systems that need immediate replacement, that's a real cost you should factor into your model. A $200K software migration in year one affects your return.

Practical Diligence Steps

Here's what I actually do during a technical assessment:

1. Request a software inventory. Every tool the business pays for, including cost and what it's used for.
2. Shadow a day of operations. Watch how employees actually do their work. The gap between what management says and what actually happens is where the real insights are.
3. Export test data. Ask for a sample data export from their core systems. If they can't produce one, that tells you something.
4. Interview 2-3 employees. Ask them what's frustrating about their daily workflow. They'll tell you exactly where automation will have the most impact.
5. Map the information flow. How does a customer order go from initial inquiry to fulfilled service? Every manual handoff is an automation opportunity.

The Bottom Line

Technology due diligence isn't about finding businesses that already use AI. It's about finding businesses where the gap between their current operations and what's possible with modern automation is large — and closable.

That gap is where your returns come from. The businesses that score 2-3 on AI readiness today, with a clear path to 4-5, are the ones that will deliver the strongest multiples at exit.

If you're evaluating a target and want a technical assessment of its AI readiness and automation potential, let's talk.