Why is application of AI being embraced to support smarter decision-making in strategic M&A deal analysis and evaluation? In prior posts we have mentioned the capture of an acquiring entity’s deal experience. We have all attended meetings where different organizations are considered as potential acquisition targets – and why the AI’s predicted outcome was not as expected at any point during the deal process.
Assuming your AI captures the actual outcome “experience” for every deal, agree on the criteria for defining deal success. Then, write machine learning models that consider actual deal outcomes in comparison to your organization’s experience in the deal evaluation process. Over time, have the AI check the information provided during the deal process and its predictions against the actual deal outcome. After the AI analyzes the expected vs. actual outcome data and its prior predictions, and this analysis is reviewed by your M&A team, determine how to update the AI so that it continuously “learns”. By incorporating more information into the AI’s knowledge base its deal evaluation intelligence increases. Over time, the AI “learns” more and more, and the M&A team’s AI Copilot becomes even more valuable during deal process management with a higher reliability factor for the information, recommendations, and predictions it provides.
You can see how your M&A AI Copilot will learn continually as it delivers more value in the form of not only more reliable predictions of deal outcome success but also improved insights and recommendations for the AI to apply during future deal process management.