9.2/10

Session Rating

6

Total Sessions

16 hrs

Saved Per Deal

AI Training for M&A Due Diligence

How a global advisory firm accelerated AI adoption across their deal team

A global PE and advisory firm headquartered in New York City was feeling the squeeze. M&A timelines were compressing, deal volume was climbing, and the team's manual research and analysis workflows weren't keeping up. Leadership knew AI could help, but they faced the classic adoption gap: some team members were experimenting with ChatGPT on their own, while others hadn't touched an AI tool at all.

There was no shared vocabulary for talking about AI, no way to capture what was working in early experiments, and automation opportunities were hiding in plain sight inside daily workflows. They needed more than a demo day. They needed a structured program that would build real AI fluency across the entire team, ground training in their actual work, create internal champions, and surface the automation opportunities nobody had time to find.

What We Did

AI Experts designed and delivered a four-phase training program built around the team's actual due diligence workflows - not generic demos or canned exercises.

We started with discovery: interviewing internal champions and workflow owners to map the due diligence process and identify high-frequency tasks where AI could deliver immediate value. From there, we moved into champion development, working directly with early adopters to plant real case studies into the curriculum. The idea was simple - peer credibility beats outside expertise, so we armed champions with wins before the first training session.

Training delivery consisted of 6 sessions total: 4 structured training sessions plus 2 office hours. Every session used live experimentation with actual deal materials. No simulations, no hypotheticals - the team worked on real documents from real deals.

The final phase was activation. Champions shared their wins with peers, new automation opportunities surfaced organically, and the team started building custom AI agents for complex, multi-step due diligence workflows.

The Breakthrough

One workflow stood out above the rest: a multi-step process for analyzing client documents, conducting deal review, and generating risk assessments. What previously took days of manual work was compressed into a structured AI workflow. The team built custom GPTs - purpose-built AI agents - that handled the heavy lifting, freeing analysts to focus on judgment calls and client communication instead of document processing.

This wasn't a theoretical exercise. The team deployed these agents into their active deal pipeline and started seeing results immediately: 16 hours saved per deal on document analysis and risk assessment alone.

Measuring What Matters

We evaluated the program using the Kirkpatrick Model, the gold standard for learning and development effectiveness. The results told a clear story:

• Reaction: 9.2/10 average session rating from anonymous participant feedback
• Learning: Measurable improvement in pre- vs. post-training AI literacy assessments
• Behavior: At the 30-day follow-up, the team was actively applying AI tools to daily work
• Results: Custom AI agents deployed in production; internal champion promoted to an enterprise AI role

That last point bears repeating. The internal champion who led adoption through this program was promoted to an enterprise AI role - a direct result of the initiative's visibility and success. The program itself became a model for AI rollout across other divisions in the firm.

Why It Worked

Four design principles made this program different from the generic AI workshops that most teams sit through and forget:

• Their files, their workflows. We didn't teach generic AI. We taught AI applied to M&A due diligence using their actual documents and templates.
• Champions first. We built peer credibility before training the broader team. When a colleague shows you a win they got from AI, it lands differently than when an outside consultant does.
• Hands-on from day one. No slides-only sessions. Every training included live work with real deal materials.
• Built for sustainability. The goal wasn't dependence on AI Experts. It was building internal capability that compounds long after the engagement ends.

The team is now iterating on their own - building new prompts, refining their AI agents, and sharing techniques across the group without any external support. That's the outcome we design for.

This case study has been anonymized. Metrics and outcomes are accurate to the engagement.