How AI strengthens mid-market M&A deal pipelines: A conversation with Deloitte

AI-driven deal sourcing tools are gaining traction in M&A as firms look for more effective ways to identify and evaluate opportunities. This shift is especially important in the mid-market, where companies are less visible and information is often fragmented.

To understand how deal origination works in this space, I spoke with Bethany Winsby, Manager at Deloitte. Her role focuses on connecting private equity firms with high-potential acquisition targets, and she is increasingly using AI in her day-to-day work.

Bethany shared her perspective on how deal sourcing is evolving, where AI delivers the most value, and how technology will reshape the M&A profession.

Q. Can you tell me about your career and your role at Deloitte?

I started out through a degree apprenticeship, studying while working at a wealth management and pensions firm. It wasn’t an M&A role, but it gave me a strong grounding in financial markets and client relationship management.

Over time, I became increasingly drawn to the strategic side of transactions. I liked understanding the “why” behind deals and how they could be structured to maximize value for buyers and sellers. That led me to join Deloitte’s M&A tax team.

From there, I moved into deal origination and today, I manage mid-market transactions for Deloitte’s business services sector across the UK and Europe. This involves tracking a pipeline of opportunities, connecting with PE firms to understand their investment objectives, and matching them to acquisition targets.

Q. How does mid-market deal origination differ from larger deals?

In larger deals, firms are well-known and well-documented, so opportunities are easier to spot. In the mid-market, it’s very different. Early-stage companies and emerging businesses don’t always appear in traditional datasets, so you’re constantly tracking smaller, less obvious signals.

These signals come from all sorts of places. You might see a company mentioned in an industry article, notice hiring activity on LinkedIn, or come across founders through events and market conversations.

Unlike larger companies, I can’t rely on earnings reports or analyst coverage, as many mid-market businesses sit outside those formal reporting channels. 

Q. How are you using AI in deal origination?

When data is fragmented, that’s where AI adds value. A lot of the work in deal origination involves consolidating large volumes of information, reviewing it, and connecting the dots. This previously took significant time to complete manually, and AI streamlines that process. I can then focus on speaking with PE clients, understanding their priorities, and building those relationships.

When company data isn’t available, we use AI to build an informed view using proxy indicators. It can analyze similar businesses and sector patterns to form a reliable picture of how a company is performing and positioned. That’s particularly useful when assessing a company’s growth without hard financials.

Q. How do you turn that information into something your clients can use?

We capture this information in market updates, using AI to help shape clear narratives around deal opportunities.

I recently mapped themes across a market using around 200 reports. Normally, it would take days to read and synthesize that volume of material. With AI, I was able to quickly extract the insights and produce a first draft PowerPoint deck in an hour, which I then used as a starting point for investment discussions with clients.

AI allows me to spend more time interpreting what the information means for a specific opportunity. That leads to more productive client conversations and a stronger pipeline of actionable deals.

Q. How do you balance AI with your own judgment?

I use AI to help surface and organize information across the deal pipeline, but I always review the outputs. In M&A, context is everything. What looks like a signal in isolation isn’t always meaningful without the broader picture.

That’s where judgment comes in. AI can bring structure and speed to the analysis, but it doesn’t replace the need to interpret what the information is really saying about a business – its trajectory, its quality, and its suitability for an investor. That assessment has to come from a dealmaker.

Q. How do you think AI will change the skills junior M&A professionals need?

Junior professionals will need to become comfortable using AI as part of their core toolkit. That means knowing how to leverage it effectively, including how to structure prompts and get consistent, useful outputs.

A basic level of AI literacy is increasingly important – understanding what the tools are doing and the information they’re drawing on. That awareness is key as AI becomes more widely used across deal origination. 

AI is also changing how juniors are trained. A lot of the more repetitive or administrative parts of work can now be accelerated, which means they can spend more time building exposure to clients and developing commercial experience earlier in their careers.