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Data Analytics and AI in M&A: Uses and Limitations, Interpreting Conflicting AI Results, Eliminating Bias

Recording of a 90-minute premium CLE video webinar with Q&A

This program is included with the Strafford CLE Pass. Click for more information.
This program is included with the Strafford All-Access Pass. Click for more information.

Conducted on Wednesday, June 14, 2023

Recorded event now available

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This CLE webinar will examine applications of data analytics and artificial intelligence in the M&A process. The panelist will discuss the use of data analytics and AI in evaluating potential targets, how they can inform due diligence of a target company, and the challenges of reconciling conflicting sets of data and AI conclusions.


AI and data analytics are transforming the M&A process. Data analytics can better gauge the value of a deal by broadening the number of factors considered. By analyzing both internally and externally generated data an acquirer can gain a more comprehensive overview of a prospective target.

Predictive tools can analyze data to quickly highlight worthy M&A deals and create an investment hypothesis based on the analysis. The findings can then direct the due diligence process once a target is identified and serve as a roadmap for steps to take post-acquisition.

AI has distinct limitations. Data streams can be deceptive, and AI tends to trust data as binary. AI systems function better in closed environments--they are not trained to interact with other AI systems--but business and legal decisions can be very nuanced. Deal parties and their counsel must be able to interpret conflicting AI results and reconcile disparate data sets to form conclusions about how to proceed. They must also be on the lookout for yellow flags presented by the data and bias inherent in AI processes.

Listen as Mark Stignani, Partner at Barnes & Thornburg, discusses the ways in which data analytics and AI can enhance the acquisition process and the limitations of AI which must be taken into account.



  1. Why we need AI--more data gives more useful results
  2. Sources and types of data
  3. Latest developments in AI/machine learning – large language models (GPT), contract analysis and predictive coding, natural language processing
  4. How data can be used to examine a company's culture, people, finances, brands, legal disposition
  5. Limitations of AI
  6. AI whispering
    1. Ability to interpret conflicting AI results, connect disparate data sets
    2. Determining which AI results are more right at which time
    3. Connecting the dots, eliminating machine-borne bias
  7. Tasks where AI is highly useful
    1. M&A speed matching
    2. Licensing--in and outbound
    3. Reverse business/technology strategy
  8. The data trust pyramid--what data should be used
  9. Data whispering yellow flags


The panelist will review these and other important issues:

  • What kinds of data go into AI analysis, and what are the limitations?
  • How can data analytics and AI influence the selection of an acquisition target and the due diligence which must be conducted?
  • What is counsel's role in interpreting data and reconciling conflicting AI results?
  • How can AI be used strategically throughout a transaction, including post-acquisition?


Stignani, Mark
Mark Stignani

Barnes & Thornburg

Leveraging his deep background in and knowledge of artificial intelligence (AI) and machine-learning tools, alongside...  |  Read More

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