Deal Smarter: How AI Transforms M&A Workflow

Artificial intelligence has become one of the most discussed technologies in finance. In M&A specifically, many conversations still sit at a conceptual level. People talk about “AI-powered deal sourcing” or “automated diligence” without clearly explaining what that actually looks like inside an active deal team.

Over the last several months, our team has been embedding AI directly into our day-to-day M&A workflow.

This article outlines how we are currently using AI across three core areas of the M&A process: automated deal sourcing across defined geographies, AI assisted landscape mapping and deal screening, and iterative analyst feedback loops to continuously improve output quality.

Our objective is to show how AI can be engineered into the workflow of a deal team to expand coverage, increase consistency, and allow human analysts to focus on higher value judgment.

Table of Contents

• Why AI became necessary in our M&A workflow
• Part I: Turning deal sourcing into a continuous process
• Part II: Creating a better starting point for deal evaluation
• Part III: Training the system the same way we train analysts
• How the analyst role evolves with AI
• Governance, NDAs, and responsible use
• What we’ve learned from embedding AI into the workflow

Why AI became necessary in our M&A workflow

Traditional M&A sourcing is constrained by time and attention. Even experienced teams operate within a narrower slice of the market than they’d like. Deal flow is shaped by a limited number of inbound brokers, proprietary outreach, and manual market mapping. Screening follows the same pattern. Analysts review teasers, scan CIMs, build rough comps, and prepare early investment memos under tight timelines.

Over time, those constraints begin to shape decision-making. Teams react to what’s in front of them instead of continuously observing what’s out there, not because they lack discipline, but because there’s only so much a small group can realistically track at once.

This is where AI has begun to play a meaningful role for us. Not as a replacement for judgment or experience, but as a way to enhance how the team works. Instead of periodically checking the market, we can monitor it on an ongoing basis. Instead of screening one opportunity at a time, we can structure and compare early views in parallel. The work becomes less episodic and more systematic.

What makes this useful isn’t automation for its own sake. It’s systematization. AI allows M&A teams to turn what used to be ad hoc research into living, iterative systems.

Part I: Turning deal sourcing into a continuous process

The first layer of our system focuses on deal discovery. We operate within a defined geographic scope, primarily Western Canada, and look for businesses that fit specific size, industry, and transaction criteria. Traditionally, monitoring this universe would require manually checking dozens of broker sites, listing platforms, and newsletters.

That kind of monitoring is hard to sustain over time. Coverage depends on habit, memory, and available time, which makes it easy for gaps to form. We wanted a way to observe our market continuously without adding more manual work. To do that, we built an AI-assisted sourcing pipeline that runs in the background.

At a high level, the system performs four functions.

1. It scans the public web daily for new deal listings and broker postings within our defined geographic and industry parameters.
2. It extracts structured information from unstructured listings such as company description, location, stated financials, and transaction context.
3. It stores these opportunities in a centralized drive and database environment.
4. It flags new entries for downstream screening.

This approach expands coverage and consistency. We’re no longer limited to a short list of familiar platforms, and every opportunity is parsed into the same core fields, regardless of where it originates.

Over time, this creates a live market map. We can observe individual deals, trends in volume, sector mix, regional clustering, and broker activity.

Part II: Creating a better starting point for deal evaluation

Once deals are sourced, the next constraint appears. Early-stage screening has traditionally been one of the most time-intensive parts of the junior M&A workflow. Analysts review teasers, extract limited financials, compare to precedent deals, and prepare internal summaries. Much of this work is repetitive, yet requires care and context.

This is where our second AI layer comes in. We use AI, including Gemini-based workflows, to perform first pass landscape analysis and deal screening.

When a new opportunity enters our system, we pass the teaser or listing information into a screening workflow that performs several tasks.

1. Business classification
   AI categorizes the company by industry, end market, business model, and value chain position.‍
2. Market landscape framing
   An initial view of the competitive landscape, including likely peer groups, market dynamics, and business drivers is generated.‍
3. Metric extraction and estimation
   Where data exists, the system structures revenue, EBITDA, margins, and growth indicators. Where data is missing, it flags uncertainty rather than inventing precision.
4. ‍Screening against defined parameters
   We provide the AI with explicit investment parameters and evaluation metrics. These can include size ranges, margin profiles, industry preferences, geographic fit, and strategic rationale. The system scores the opportunity against these criteria and explains why.
   

The output is a structured screening memo. This memo includes a company summary, a market overview and an initial strategic fit assessment.

Part III: Training the system the same way we train analysts

The most important design choice we made was keeping analysts in the loop. Every AI-generated screening is reviewed by an M&A analyst before it goes anywhere else. They assess accuracy, logic quality, financial framing, and relevance to our internal standards. Corrections are made directly in the document and are fed back into the workflow so the system can improve over time.

We regularly perform side-by-side comparisons of AI-generated screens to traditional manual ones. An analyst will review a teaser or CIM the way they always have. In parallel, the AI produces its own structured view of the opportunity. We then compare them by asking:

• Where did the AI miss nuance?
• Where did it overstate confidence?
• Where did it surface insights faster than a human would?

These comparisons are used to refine prompts, parameter definitions, evaluation rubrics, and output structure. Over time, the AI’s outputs become less generic and more aligned with how an M&A analyst actually thinks. This process mirrors how junior analysts are trained. They draft, receive feedback, and gradually internalize standards. We are effectively doing the same thing with an AI system.

Without feedback loops, AI outputs quickly stagnate. They remain polished, but shallow.

With structured review, the system improves at prioritizing what matters, flagging risk, and framing ambiguity. This is the difference between using AI as a writing assistant and using AI as part of an analytical system.

How the analyst role evolves with AI

As more of the mechanical work is supported by AI, the analyst role starts to shift. Time that used to be consumed by hunting for deals, cleaning listings, or rewriting similar screening templates is freed up and redirected. That time moves upstream into interpreting what the system surfaces and pressure testing early signals, and downstream into building sharper theses, engaging earlier with management teams, and thinking more deeply about risk, fit, and integration.

What changes most is not the volume of work, but the nature of it. Analysts are no longer primarily processors of information. They become evaluators of it. The work is less about moving deals through a checklist and more about asking better questions sooner.

This also has implications for training. Junior team members are exposed to a wider range of deal archetypes earlier in their careers. Instead of working through one opportunity at a time in isolation, they begin to see patterns across different sectors, geographies, and business models. Learning happens through review and comparison, not repetition alone.

Governance, NDAs, and responsible use

We are intentionally phasing this system. Today, the focus is limited to pre-NDA data such as teasers, public listings, and market information. This allows us to stabilize workflows, accuracy, and internal controls before expanding further. 

As the system matures, we are exploring how AI can responsibly support later stages of the process, such as NDA processing, document intake, and early diligence organization. Any expansion beyond pre-NDA use would be tightly scoped, reviewed by the deal team, and designed to support human analysis rather than replace it.

This will require close collaboration with legal and compliance stakeholders, along with careful design around confidentiality, permissions, and data handling. Decision-making, judgment, and accountability remain with the investment team at all times.

What we’ve learned from embedding AI into the workflow

What this work reinforced for us is that AI is most effective when embedded into a system, not used as a standalone tool. Output quality depends on clearly defined parameters, investment criteria, and evaluation metrics, and human feedback remains the core driver of improvement. AI expands what a small team can observe and process, but it does not replace experience, skepticism, or decision ownership.

Looking ahead, we believe AI will increasingly shape how M&A teams source markets, frame opportunities, and manage information by building analytical infrastructure around deal teams. Our current system is an early version of that philosophy in action.

If you are a founder, broker, or advisor working with an interesting business, we are always open to reviewing opportunities and exchanging perspectives. Visit our website and send us a message to see the latest updates on our mandate and current areas of focus.