In this second blog of the four-part series, we’ll focus on the initiation phase of the deal cycle and the subsequent step of matchmaking.
While several tools are included as potentially useful examples, we are still in the process of testing such tools out. Clearly, the cut-over to agentic AI isn’t truly a big bang approach but rather a wobbly curve – certainly, within the context of financial institutions.
For example, the bottleneck in early-stage M&A isn’t strategic thinking—it’s information processing at scale. In the future, solving M&A problems will likely be more structural in nature than performance related. Thus, the adoption curve may see more acceleration where compute power makes sense for commoditized tasks. However, as we move along that curve, adoption could fluctuate depending on risk. Not every step in this process is meant to be automated at the same pace or degree.
And the most relevant case of that is in the human-critical thinking behind the development of an organization’s M&A strategy and objectives. In general industry — it is nascent, if not rare, for a CEO to allow full legitimacy to a machine. However, AI does serve as an assistant.
The Human Element – Prioritizing Your Objectives
Prioritizing your M&A objectives is critical prior to engaging any AI tools. This is where the human components of the deal team take center stage in creating the strategy and defining specific problems to solve or areas that can become more efficient. All the technology tools at your disposal are useless if your objectives are poorly defined.
In any M&A process, there are usually multiple objectives. In the bank and credit union sector, as an example, typical objectives include:
- Leadership Change – a retiring CEO wants to sustain a field of membership and culture
- Asset Portfolio Re-assessment – an institution is looking to divest branches that are no longer within strategic concentration
- Maximizing Liquidity –the institution requires a cash injection for operating or growth purposes
- Product Efficiency – cross-selling retail products and services to a customer base that has never been the target of such marketing
- Acqui-hire – obtaining a lending team, executives and a book of business for revenue right away from that commercial expansion
The objectives are human-driven. These are ‘You’ driven. And these objectives will affect many of the subsequent steps taken in the M&A process. This includes which potential partners you approach, the deal structure you propose, and the AI-powered assistance you seek.
Opportunities for Machine-Driven Efficiency
Starting the M&A process can be slow, noisy, and time-consuming. The initiation phase is labor-intensive but low leverage. Even seasoned financial institution CEOs spend an inordinate amount of time scrolling their rolodexes. Outcome variance is driven less by insight and more by coverage and timing.
Based on our view in the bank and credit union sector, we see potential opportunities in three areas at the onset of the deal cycle that could benefit from agentic AI:
Research
Banks and credit unions rely on established intelligence sources or advisors to understand potential merger collaborations. The real opportunity with agentic AI is not to replace these sources, but to orchestrate and activate them. By deploying agents that continuously monitor, pull, and interpret signals across regulatory filings, local market activity, leadership changes, and balance sheet trends, a bank or credit union can evolve from static, periodic analysis into a living system of intelligence.
As a small pilot, the team at ALM First used Claude Cowork to listen across multiple media sources: CUSO news, regional association press releases, personnel promotion announcements, and industry print. Then the agent ran aggregations on a weekly basis across regions to unravel trends or major market moves. Where it took an analyst days to pull the information, now took the bot far less time and it was a push notification on Sunday night to the M&A team.
In the M&A space, growth has sometimes depended on timing, relationships, and a fair amount of luck—being in the right conversation when a struggling institution becomes open to a merger, or when a large employer becomes receptive to a new financial partner. Agentic AI doesn’t replace those human dynamics, but it dramatically increases the odds of being in the right place at the right time.
Matching
Many bank and credit union CEOs may have specific M&A targets in mind based on their own networks. In practice, this means a dynamically re-scored pipeline of merger candidates or growth opportunities. Imagine a credit union tracking a long list of potential partners: smaller institutions showing deposit pressure, credit unions with aging membership bases, or those struggling with technology modernization. Instead of revisiting that list quarterly, agentic AI can surface real-time signals—an unexpected CEO retirement, a spike in loan delinquencies, a new branch closure, or shifting NCUA ratios—and automatically elevate those institutions at the exact moment they become actionable.
For example, PQ-Suite recently released DealChase.ai which is an overarching assembly of AI tools. Notably, their integration with CorpDev.AI allows you to take advantage of multi-model AI to simultaneously to search 70M+ institutions for prospect prioritization. Human oversight is still a mandatory requirement since someone must vet the quality of the list.
Connection
Connection doesn’t have to be limited by who you know. It can be expanded based on who can be systematically discovered. AI Relationship Mapper Agent @ MuleRun and Aria M&A Intelligence’s Relationship Mapping Agent are tools that could expand the knowledge of a network. Both tools use publicly available data.
Traditional reliance on personal networks can create a myopic view of the world. Bots today can dynamically graph relationships across Board members, prior deal teams, advisors, investors, operators, and executives. The expansiveness of both personal and professional social tools has dramatically changed the way the game is played.
Early-stage M&A is becoming less about building static target lists and relying primarily on individual network reach, and more about probability-based origination supported by stronger systems.
A target that looked unattractive last quarter may now be gaining momentum, entering a new market, hiring key talent, or becoming more open to a transaction. Likewise, a target that once appeared highly attractive may no longer be the right fit.
This is where agentic AI holds the most promise. It allows teams to continuously monitor the market, refresh targets as conditions change, and connect signals across performance, product activity, hiring patterns, leadership movement, capital activity, and other indicators of strategic relevance.
However, new tools do not reduce the importance of human decision-making. If anything, it increases it. People still need to decide what matters most, define the guardrails for the search, assess strategic and cultural fit, and determine when to move from research to outreach. Agentic AI can improve speed, coverage, and signal detection, but cannot replace discernment. It also does not eliminate execution risk. Poor prompts, low-quality data, or overreliance on automated outputs can still lead to false confidence, misguided outreach, or missed opportunities.
Achieving greater efficiency at the beginning of your M&A process is not defined simply as using more AI, rather it is about using AI wisely and productively. In the next blog, we’ll explore Agentic AI in due diligence and integration.
Learn more about DealChase at DealChase.ai
Want to discuss your credit union’s merger strategy & process in more detail? Contact ALM First’s team of experienced advisors to learn more.
