By Alex Bajdechi, VP of Global Sales, Deal Engine
There is a useful signal hiding in plain sight for anyone paying attention to how private equity firms are deploying AI.
One of the most consistent themes across PE deal teams right now is the push to automate how they evaluate inbound deal flow from investment banks. Screening CIM decks faster, applying investment criteria earlier in the process, reducing the time associates spend on preliminary assessment. This is not a fringe initiative; it is quickly becoming standard.
What that tells us is something important about the other side of the table. If PE firms are investing in processing inbound more efficiently, it is partly because the volume and pace of what investment banks are sending them has increased. Bankers are busy. The pipeline is moving faster. And the pressure to get in front of the right buyers, earlier, has never been higher.
So what is actually happening inside investment banks right now?
The proprietary outreach challenge
Investment banking has always been a relationship business. The best bankers are not the ones who send the most emails or make the most calls. They are the ones who seem to reach the right people at exactly the right moment, with something relevant to say.
That instinct is real. But increasingly, it is being shaped by information.
In a tighter, more competitive market, the timeline for building founder and business owner relationships has extended significantly. As John Farrugia, CEO of Cavendish, noted in a recent webinar, it used to take 12 to 15 months to develop those relationships before a mandate. Now it can take twice that long. Which means bankers need to be engaging earlier, staying relevant for longer, and doing so across a much larger universe of potential clients than before.
The challenge is not effort. Bankers are working hard. The challenge is coverage, and the ability to act on the right signal at the right time.
Relationship people, not salespeople
Here is something worth understanding about how investment bankers actually think about BD. John put it plainly: his team are not salespeople. You cannot get them to operate like an outbound sales floor. They are relationship people. They build trust over time, they understand a business, and they earn the mandate through genuine engagement, not volume activity.
That distinction matters enormously when you think about how AI and data tools fit into their workflow. You are not trying to turn bankers into something they are not. You are trying to give them better reasons to reach out, better context when they do, and better visibility into what is happening in the companies they care about, so the relationship stays warm without being manufactured.
The goal is to slot into how they already work, not change it.
Buyer list building and the cost of leaving stones unturned
One of the most concrete examples of where this plays out is in buyer list construction. Building a comprehensive buyers list for a deal has traditionally been a significant undertaking. At Cavendish, an associate would typically spend three weeks putting together a buyers list for a mandate. By using agents trained on investment criteria, that same list can now be produced in four hours and comes back with roughly twice as many buyers on it.
That is not just an efficiency gain. It changes what "leave no stone unturned" actually means in practice. A wider, more intelligent buyers list means better outcomes for clients, stronger competitive positioning for the bank, and a more compelling story to tell PE sponsors about how the advisory process works.
What good infrastructure actually looks like for an investment bank
Buyer list construction is one use case. But the underlying capability that makes it possible is worth examining more closely, because it applies across the full IB workflow.
The firms getting ahead are not just using AI for point tasks. They are building a coverage infrastructure that connects their proprietary relationship context, third-party market data, and live company signals into a single operating layer. The result is a system that works continuously in the background, surfacing the right information to the right person at the right time.
In practice, this shows up in a few specific ways.
Market monitoring gives BD teams a live feed of signals across their target universe: leadership changes, facility expansions, new funding rounds, hiring patterns, and other indicators that a business owner may be ready for a conversation. Instead of waiting for the annual check-in or relying on a banker's memory, the system flags when something relevant has changed. That is the trigger for a timely, contextual outreach rather than a generic one.
For sponsor coverage and buy-side advisory mandates, the same intelligence applies in a different direction. When an IB is pitching a PE firm on a buy-side mandate, being able to demonstrate that agents have been trained to identify companies the way that firm's investment team actually thinks is a meaningful differentiator. It shifts the conversation from "we have a good network" to "we have a systematic process for finding what you are looking for."
And across all of it, the context that accumulates over time compounds. Relationship history, meeting notes, previous deal activity, signals tracked across months: all of it becomes part of the institutional memory that helps bankers act with more precision and less duplication. The coverage does not reset every time a new analyst joins the team or an MD moves to a different sector focus.
This is the part of the AI conversation in investment banking that tends to get skipped over. There is a lot of discussion about automation and speed, both of which matter. But the more durable advantage comes from a firm having better, more structured knowledge about its market than its competitors do, and using that knowledge consistently across every client interaction.
Where the PE and IB worlds are converging
What is striking about the challenges investment banks are navigating right now is how structurally similar they are to what PE firms have been working through. The same underlying problem, fragmented signals, relationship context scattered across inboxes and CRMs, difficulty acting on the right information at the right time, is showing up on both sides of the deal.
PE firms have been building proprietary data engines for a while now. The most sophisticated are codifying their investment theses, monitoring their universe continuously, and using that structure to power AI applications that surface better opportunities faster.
Investment banks are beginning to think in exactly the same way. The ones moving fastest are not adding more tools. They are building better coverage infrastructure: a system that turns market activity, relationship history, and company signals into actionable intelligence that helps bankers do what they do best, with fewer gaps and better timing.
That is the convergence worth watching. And it is why the conversation about AI in M&A advisory is no longer just about deal screening or document automation. It is about the full origination workflow, from proprietary outreach to relationship management to buyer identification, and building the data foundation that makes all of it work.
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