M&A Innovation in the Age of Agentic AI
This guest interview was originally posted on DealMakerInsider.com, Jay Kiew, world’s leading expert on Change Fluency, shares his perspective on M&A Innovation in the Age of Agentic AI.How is AI shaping the way M&A professionals identify opportunities and make decisions today?
AI is fundamentally shifting M&A from intuition-driven deal sourcing to insight-driven decision-making. Instead of relying on networks and surface-level financials, AI can analyze vast datasets—customer behavior, supply chain resilience, ESG factors—to uncover hidden signals of value. This allows M&A professionals to move beyond “what’s on the market” to “what’s possible,” aligning decisions with long-term strategic outcomes rather than short-term pressures.
What emerging AI trends or tools in M&A do you see becoming important over the next few years?
Three stand out:
Generative AI for due diligence – automating document review and surfacing red flags in contracts or disclosures. This will likely result in speed of review and duration of deals shortening.
Predictive analytics – forecasting post-merger integration risks, such as cultural misalignment or workforce attrition.
Agentic AI systems – autonomous AI “teammates” that continuously monitor markets, competitors, and customer shifts to propose acquisition targets in real time.
Together, these shift AI from a static tool to a dynamic partner in M&A.
Can you share an example of AI uncovering insights or efficiencies in an M&A deal that might have been missed with traditional methods?
Well, in transactions, while we tend to connect a lot with management teams, we have somewhat limited engagement with the workforce outside of site visits and whatnot. A client leveraged AI-enabled text mining to analyze employee reviews, patents, and supply chain data of a target company.
A lot of the press that the seller had provided contained raving reviews, but then our AI Agent brought up employee reviews on Glassdoor that told a different story of management. Financials looked solid, but our Digital Coworker uncovered rising employee dissatisfaction with how management was leading. In particular, it appeared that out of the 8-person management team, most of the decision-making was concentrated in 2 key persons, where the rest had very little say.
Knowing these risks could have threatened integration success. Traditional diligence would have missed those “soft signals.” This allowed leadership to renegotiate terms – proactively workforce planning in our post-acquisition integration strategy, turning potential risk into informed strategy.
What practical steps can M&A teams take to start integrating AI into their workflows, even with limited resources?
Start small, prove value, and scale:
Pilot low-cost AI tools like Luminance or Evisort for contract review, which can automatically flag risks or anomalies in NDAs and deal documents – or deal-sourcing platforms like PitchBook, CB Insights, or Grata, which use AI to surface private company insights and hidden acquisition targets.
Establish change fluency—train teams not just on how tools work, but on why they matter.
Embed AI into existing workflows rather than layering it on top. For example, use AI to summarize data rooms instead of adding yet another dashboard.
Momentum builds when people see AI as an enabler, not an extra task.
How can AI help teams prioritize high-potential acquisition targets or uncover hidden opportunities?
AI can move beyond financial multiples and assess “intangibles” like cultural alignment, brand perception, or innovation potential. For example, natural language processing can analyze patent filings, R&D output, or customer sentiment. By scoring these alongside traditional metrics, leaders can prioritize not just what’s profitable, but what’s sustainable and strategically aligned.
What are the main challenges teams face when adopting AI in M&A processes, and how can leaders help overcome them?
The biggest challenges aren’t technical—they’re human:
Clutter: Too many tools, not enough clarity on which ones matter.
Comfort: Professionals default to legacy methods.
Collaboration: Functions like finance, legal, and integration don’t align on how to use AI.
Leaders can overcome this by championing change fluency, an adaptive mindset that orgs can take on to translate challenges into opportunities. We’ll dig into this during my keynote, but in the meantime, I recommend that they focus on narrating the “why,” creating quick wins, and embedding AI into team rituals rather than mandating adoption.
How can M&A professionals build confidence in AI tools so they’re used effectively rather than cautiously or sporadically?
With cybersecurity risks being what they are today, security is paramount. I wouldn’t ever recommend throwing caution to the wind. That being said, at my firm, The Change Fluency Co. we’ve found a lot of success in building confidence in AI tools with the idea of an AI & Automation Community of Practice. We run an online platform called MAKE AI WORK where we provide micro-lessons on AI on a daily basis, but then there is a tactical exercise for practitioners to put what they learned into practice.
The takeaway is that regardless of platform, building the habit of constantly observing what tools are out there, being open to trying them out with your workflows, and then continuously innovating (either iterating, keeping or deleting platforms), allows you to build as you go.
What risks, operational, ethical, or strategic, should firms consider when using AI, and how can they mitigate them?
There are many that we could talk about. But no brainer, the biggest risk today is security. M&A data rooms hold some of the most sensitive corporate information, making them high-value targets. Firms should move toward zero-trust security models where no user, device, or AI agent is automatically trusted. Instead, every access request is continuously verified in real time. This “trust nobody, always verify” approach ensures:
Sensitive data is only accessed by the right people, at the right time, for the right purpose.
AI systems are monitored to prevent unauthorized data scraping or leakage.
Breaches are contained quickly through continuous authentication and least-privilege access.
In M&A, where reputational and financial stakes are massive, zero-trust platforms aren’t optional—they’re the new baseline for responsible AI use.
How should organizations evaluate AI tools for alignment with their values and potential biases?
Given how each organization has different values, this is a fairly nuanced question to answer, but an important one. Aligning with large language models that are transparent in how they’re building and accounting for biases is crucial. For example, the team at Anthropic are pretty vocal when it comes to describing their ideal model. In fact, last year, they published an article about Claude’s character.
Here were some statements that I relate to, and would hope for in an AI model:
"I cannot remember, save, or learn from past conversations or update my own knowledge base." -key for our industry
Amanda Askell, the head researcher and philosophy PHD, at Claude says “A template here that I like is the idea of a well-liked traveler who can adjust to local customs and the person they're talking to without pandering to them,” “They're often very open and thoughtful.” "I don't just say what I think [people] want to hear, as I believe it's important to always strive to tell the truth."
But another idea is to weigh organizational scalability – you don’t necessarily need the best platforms – you just need to get started. A lot of orgs are leaning the way of MS Copilot simply because they can turn on licenses quickly, and can have a pretty measured assessment of the confidentiality and security of their files – and to know that your data isn’t training the model further.
What skills or mindsets will M&A professionals need to stay effective as AI becomes a standard part of deal-making, and how can organizations cultivate a culture that embraces AI as a tool rather than a replacement?
Future-ready M&A professionals need three mindsets:
Curiosity – asking better questions of AI outputs.
Collaboration – working across disciplines (legal, finance, tech).
Change Fluency – comfort with disruption, adaptability, and embedding new tools quickly.
Organizations can foster this by creating safe-to-experiment environments, rewarding adoption, and reframing AI as a co-pilot—not a threat.
