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M&A Operations Diligence.
Handheld magnifying glass looking at a representation of a manufacturing operations

Mergers and acquisitions (M&A) operations diligence is a key component of many business acquisitions. And, it is a time-consuming and often, resource-intensive; as a usable output typically requires a significant amount of research, analysis, and interpretation. AI is fast making inroads into operations diligence and providing a significant degree of support while reducing analysis time and improving the interpretive results. AI is helping early adopters manage and streamline the M&A operations diligence process, while reducing costs and increasing time-to-complete efficiency. Following are ten ways AI is supporting M&A operations diligence:

Due diligence analysis: AI’s ability to analyze large amounts of data, interpret and, provide insights into key metrics and trends is allowing organizations to more quickly make better informed decisions.

Financial modeling: The creation of accurate financial models and projections garnered from historical data is a specialty of AI. The result is often improved accuracy, reductions in human error and occasionally unexpected insights that human might miss due to unconscious biases, and/or blindness because the insight simply falls outside of their past, direct, experience e.g. it’s never come up for them before so it’s not recognized.

Risk assessment: AI is a powerful and capable tool with respect to recognizing potential risks and identify areas of concern. This allows organization to address prospective risk by developing mitigation strategies in advance of their being needed.

Document analysis: Unlike you and I, AI never gets fatigued while analyzing legal documents and contracts. The result is faster completion times and better quality in terms of identifying potential issues and areas deserving concern. Our time can then be spent focusing on and then addressing the specific issues needing attention.

Competitive analysis: Another instance where AI never gets fatigued and is much faster and effective executing these tasks. We can focus on addressing important information, conclusions, insights and potential market opportunities.

Regulatory compliance: AI can balance reported information and simultaneously analyze it against regulatory requirements. The outcome can be a 1-to-1 acknowledgement of hits or misses against regulatory requirements and perhaps more importantly provide an output calling out areas requiring additional clarity and/or validation to ensure compliance.

Customer analysis: In analyzing customer data AI is providing insights into customer behaviors, current preferences and future prospective interests. This is allowing organizations to conduct more definitive studies and as a result to better and more quickly tailor their marketing strategies and product offerings to the needs and ever-changing wants of their customers.

Human resources analysis: AI is providing more complete analysis of HR data and removing potential unintentional biases while doing so. That spans employee performance, retention rates, benefits consumption and origination sources. This is providing insights into actual / potential workforce issues, opportunities and is correlating what are the employee recruitment strategies and sources which result in the most stable and highest performing employees.

IT systems analysis: AI is proving to be particularly capable in analyzing IT infrastructure and applications to identify and target potential issues and areas of concern, including hardware, processing and cybersecurity risks. Its coding ability can also be leveraged to evaluate code efficiency and code creating security weakness / attack points.

Post-merger integration: AI is proving to be tremendously useful for post-merger integration work. This is an entire set of subject matter on its own. Suffice it to say, AI support of integration activities in part lies in its capability to in detail analyze, plan, forecast (expense, returns and synergies), mitigate risk and tracking progress associated with post-sale integrations.

AI is already well on its way to transforming the M&A operations diligence process. It’s providing valuable insights and support throughout the deal lifecycle including post-sales business integration. In leveraging AI-powered tools and strategies, organizations are reducing costs, decreasing time-to-completion and increasing output efficacy which is leading to organization make more and better-informed decisions. Through leveraging its capabilities in analyzing data, creating financial models, assessing risks, or analyzing IT systems, AI is supporting all aspects of M&A operations diligence. How do you plan to bring up your level of AI knowledge, capability and utilization?

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