The AI Due Diligence Playbook for Technology Services M&A – Part 2

In continuation of Part 1, let’s look at the remaining questions, the third and fourth factors to consider, and draw the final conclusion.

4. How complex is the customer’s technology environment?

This is the brownfield insulation check, and it’s one of my favorite questions in any customer DD exercise. Ask the target to walk you through the architecture of their two or three largest clients. How legacy is the stack? How many integration points exist? How much of the target’s day-to-day work involves institutional knowledge of that specific client’s environment – the history of decisions made, the workarounds that exist, the things you’d only know if you’d been there for years?

The ‘deeper the complexity’, the ‘stickier the relationship’; that’s your moat. Document it.

5. Are the current billing rates sustainable?

Run a simple scenario: if the target bills at $30–$35 per hour for development work and AI tools reduce the required effort by 35–40%, what does the next contract renewal look like for a well-informed client? Likely a meaningful rate reduction or a smaller team request. How does that change the EBITDA picture over three years?

6. Is the target already moving or still?

Small services firms often don’t have the bandwidth to retrain their workforce, redesign their delivery model, and invest in tooling all at the same time. So I look for any evidence of intentional movement: an AI transformation roadmap, a pilot engagement where AI-augmented delivery was tested, even one outcome-based contract as a proof of concept. The absence of any of this in a company operating in 2026 gets priced in as an “AI readiness discount” and informs how I think about earnout structure and integration milestones.

Factor #3 — Workforce DD: Are You Buying Skills That Will Still Matter?

The people DD stream tends to focus on the same things every time: key-person dependencies, attrition rates, compensation benchmarks, retention liabilities. All important. But I now add a second lens: AI capability mapping.

Step 1 — Sort the workforce into three tiers

When I get the org chart and headcount breakdown, I classify every significant role category:

Tier	What It Means	Typical Roles	AI Risk
Tier 1: High Exposure	Tasks AI can now replicate or accelerate dramatically	Junior developers, manual QA testers, data entry, basic report writers	Very High
Tier 2: Augmentable	AI as force-multiplier; human judgment still leads	Senior developers, data engineers, cloud architects, project managers	Moderate
Tier 3: Protected	Requires domain expertise, relationships, or complex judgment	Solution architects, delivery leaders, AI/ML engineers, account managers, security specialists	Low

Step 2: Test whether current capability matches what’s needed

For Tier 2 and Tier 3 roles specifically, I want to know: are these people already working with AI, or working around it? During management interviews and reference calls, I ask:

• Are they using AI coding assistants or LLM-based tools in their daily work? How fluently?
• Can they prompt AI systems well, or do they treat AI as an occasional shortcut?
• Are they designing solutions that could be AI-augmented — or solutions that AI will eventually disrupt?
• Can they look at a delivery process end-to-end and tell me where AI belongs in it?

The answers are usually honest and revealing.

Step 3: Price the gap

This part matters for the deal model. After mapping current versus required capability, build a gap analysis by role band. Here’s a practical cost framework, with sources I’d stand behind:

Gap Type	Investment Range	Timeline	Source
Tool adoption — AI coding tools, IDE integration	$2,000–$5,000 per person	1–2 months	SmartDev 2025 TCO study on AI for SMEs; Coursera/LinkedIn Learning pricing
AI-augmented delivery workflow redesign	$5,000–$10,000 per team + change management	3–6 months	Delivery transformation program benchmarks
Specialized AI/ML skills development	$8,000–$20,000 per person	6–12 months	EY and Deloitte M&A integration benchmarks
Workforce right-sizing (Tier 1 displacement)	Severance + Tier 3 recruitment premium	6–18 months	2–3 weeks severance per service year; 15–25% recruitment premium for Tier 3 profiles

To make it concrete: a 200-person target with 40% Tier 1 exposure and 40% augmentable roles means roughly 80 high-exposure people, 40–50 of whom will likely need transition, and 80 who need structured upskilling at $5k–$8k each.

Step 4: Talk to the founder about AI

Honestly, this is the most important conversation in the whole workforce DD stream.

Not because founders run the engineering teams but because small companies move based on conviction at the top. I’ve seen firms with modest technical capability punch far above their weight simply because the founder was obsessive about a specific direction. I’ve also seen technically capable teams go nowhere because leadership didn’t believe in the pivot.

I ask founders of companies what they think when it comes to AI and how they drive productivity. Questions cover AI investments, team communications, experimentations/pilots. Their straightforward answers and honest views give an indication of how tough the integrations are going to be.

Factor # 4 What Changes in Your Deal Model

The AI readiness assessment feeds into three places in the valuation:

Revenue assumptions. If a meaningful share of revenue is T&M or fixed-price on AI-exposed workstreams, apply a rate-compression sensitivity. A 15–25% haircut on AI-amenable revenue over three to five years can meaningfully shift the EBITDA bridge. Model it explicitly don’t let it hide in a vague “macro risk” discount to the multiple.

Earnout design. If the deal includes earnouts or performance-linked payouts, Can anchoring a portion to AI adoption milestones rather than just revenue be a possibility? Percentage of engagements using AI-assisted delivery by Year 1. At least one new outcome-based contract per year post-close. AI-trained headcount percentage targets. Customer satisfaction improvement through faster, AI-augmented delivery. This aligns the founder’s incentives with the transition and it protects you if the transition doesn’t happen.

Integration budget. Build AI readiness costs training, tooling, possible restructuring, Tier 3 recruitment explicitly into your Day 1 integration plan. These are costs most acquirers find 12–18 months post-close. Finding them now gives you negotiating room and a more credible integration timeline.

Bringing it Home

The M&A community works hard at precision. We build three-statement models, we chase down revenue recognition policies, we spend weeks on customer reference calls. We’re good at the things we’ve always measured.

But right now, there’s a gap in how most of us underwrite IT services businesses. It’s not the financial model. It’s not the legal docs. It’s whether the delivery model we’re underwriting will still be the right one in three years.

Every IT/technologyservices business you look at in 2026 sits somewhere on a spectrum from AI-threatened to AI-enabled. Neither extreme is as simple as it sounds. The threatened ones can often be repositioned with the right investment and the right founder. The enabled ones are worth a premium. Most sit somewhere complicated in between.

The job isn’t to avoid one end of that spectrum. It’s to understand exactly where the target sits, what it costs to move it, and whether the price reflects that honestly.

AI due diligence isn’t a future best practice. It’s what the job requires today.

Put it in your scoping letter. Put it in your information request list. Build it into your financial model. And when you find a target that’s already doing this AI-native delivery, outcome-based pricing, a team that’s eager to evolve pay the right multiple for it without flinching. That’s where the value is going.

For founders reading this: none of this is meant to suggest that what you’ve built doesn’t matter. It matters enormously. The question this playbook is really asking is: are you positioned to take it forward into a world that’s changing? The services companies that will define the next decade won’t be remembered for how many people they employed at peak. They’ll be remembered for how well they used those people alongside the best tools of their time.

That’s the company worth building. And that’s the company worth buying.

Click here to read part 1 of the blog.

• SmartDev. (n.d.). Gen AI implementation cost for SMEs. https://smartdev.com/gen-ai-implementation-cost-sme/
• PwC. (n.d.). M&A outlook. https://www.pwc.com/gx/en/issues/c-suite-insights/the-leadership-agenda/m-and-a-outlook.html
• Equirus. (n.d.). How AI is revolutionizing M&A due diligence: The $236 billion tech transformation. https://www.equirus.com/blog/how-ai-is-revolutionizing-m-and-a-due-diligence-the-236-billion-tech-transformation