how the resulting insights were presented to and understood by the board. Regulators increasingly view AI as part of the story of how a decision was made, and that story must be coherent, transparent and defensible under examination. Integration: Where AI Either Pays off or Exposes Weaknesses The real test of any transaction is integration. Announcements are made at signing; value is realized — or lost — in the months and years after closing. Here again, AI has significant potential and significant limitations. On the potential side, AI can rapidly map overlapping systems, products and processes across two organizations. It can identify redundant branches and platforms, highlight operational bottlenecks, and model how different integration scenarios are likely to affect customer behavior, deposit stability and service levels. Once integration begins, AI-driven monitoring can track key performance indicators in near real time, flagging early signs of attrition, fraud, operational incidents or service degradation that may have previously gone unnoticed until quarter-end reports. What AI cannot do is make the strategic and cultural choices that determine whether an integration succeeds. It does not decide which system becomes the system of record, which products are sunset, how risk appetite is harmonized, or how policy and culture conflicts are resolved. It will, however, expose unresolved decisions and weak governance faster and more visibly. Unclear ownership, conflicting procedures and lax access controls all tend to surface quickly when AI systems begin analyzing integrated data. Culture is another area where AI’s limits are evident. No amount of analytics can eliminate the friction that comes from combining institutions with fundamentally different approaches to risk, compliance or customer treatment. At best, AI can help identify where cultural conflicts are manifesting in metrics; it cannot resolve them. Characteristics of Institutions That Use AI Effectively in M&A Experience across banking and financial services suggests that institutions gaining real value from AI in M&A share several characteristics. They have strong fundamentals: relatively clean and well-governed data, clear documentation, and mature integration playbooks. AI enhances these strengths rather than compensating for deficiencies. They treat AI as a professional tool — powerful but fallible. Deal teams receive training not only on how to use the tools, but also on their limitations and failure modes. Senior leaders, including general counsel, CISOs and heads of corporate development, remain actively engaged in decisions about where and how AI is deployed. They embed AI into existing governance frameworks instead of creating parallel, unregulated workflows. Model risk, third-party risk and AI governance structures are incorporated early in planning. AI vendors and data brokers are evaluated with the same rigor as core technology providers. The mindset is one of defensibility: Decisions are made with an eye toward how they would be explained to regulators, shareholders or courts if challenged later. Most importantly, these institutions keep human accountability front and center. Regardless of how sophisticated the tools become, a named individual — or body — still owns the recommendation to proceed with a transaction at a particular price and on particular terms. AI may inform that recommendation, but it does not replace the judgment. AI is now a permanent feature of the M&A landscape in banking and financial services. The key question is not whether it will be used, but how. Institutions that treat AI as a powerful assistant, integrate it into strong governance, and remain candid about their own data and cultural realities are best positioned to use it to kill bad deals earlier, surface risks sooner, and preserve more of the value they promise. Those that treat it as a black-box oracle will eventually be reminded — by regulators, investors or events — that accountability never left human hands. This article was drafted by Spencer Fane attorneys Shelli Clarkston, Mike Patterson, and Shawn Tuma, Chief Information Officer Allen Darrah, and Paul Schaus, the managing partner and founder of CCG Catalyst. Regulators increasingly view AI as part of the story of how a decision was made, and that story must be coherent, transparent and defensible under examination. 10 | The Show-Me Banker Magazine
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