AI for Acquisition Target Screening

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-business-AI-and-acquisition-target-screening-adults

1. What should you define before asking AI to screen M&A targets?

   1. The price you are willing to pay for each target
   2. Your acquisition thesis and specific screening criteria
   3. The names of all employees at each target company
   4. The integration timeline for the eventual acquisition

2. What is the primary advantage of AI-assisted screening over a human panel reviewing 25 targets under time pressure?

   1. AI has access to private financials that humans cannot see
   2. AI applies criteria consistently across all targets without fatigue or anchoring bias
   3. AI makes the final acquisition recommendation
   4. AI can negotiate with target founders directly

3. A target company scores 9.1 out of 10 on your screening matrix. Your deal lead says 'this one feels right.' What is the most appropriate next step?

   1. Initiate a term sheet immediately based on the strong score and deal lead instinct
   2. Move the target to the primary diligence queue, including founder conversations and a data room request
   3. Accept the score as the final acquisition decision
   4. Lower other targets' scores to justify focusing on this one

4. What is the purpose of a 'hard no' criterion in M&A screening?

   1. To automatically eliminate targets that violate a non-negotiable requirement regardless of their total score
   2. To discourage investment committees from questioning the screen methodology
   3. To narrow the target list to one company as quickly as possible
   4. To give AI a criterion with binary output for easier scoring

5. Which of the following is the most useful public data source for AI-assisted M&A target screening?

   1. The target's internal financial projections shared with investors
   2. SEC filings for public companies, Crunchbase funding data, LinkedIn company profiles, and press releases
   3. Confidential employee surveys from Glassdoor
   4. The target's private data room documents

6. What is the most valuable output of AI-assisted M&A target screening according to professional practice?

   1. A ranked list of targets by total score
   2. A rationale column showing specific criterion-level reasoning for each target's score
   3. A recommended acquisition price for each target
   4. A projected integration timeline for each target

7. AI is screening 25 acquisition targets and one target has no public financial information available. How should this be handled?

   1. Exclude the target from the screen entirely since AI cannot score it
   2. Score all other criteria and flag the financial criterion as unverifiable pending outreach
   3. Ask AI to estimate financials from the company's employee count
   4. Assign a neutral score of 5 to financial criteria automatically

8. What risk does treating a high AI screening score as a final acquisition recommendation create?

   1. It may upset the target's founders before negotiation begins
   2. It bypasses the primary diligence required to assess culture, founder fit, private financials, and integration risk
   3. It prevents the investment committee from being involved
   4. It overvalues the target's revenue multiple

9. After initial conversations with three short-listed targets, you learn that one of your original criteria is not relevant to current deal reality. What should you do?

   1. Keep the original criteria to maintain screening consistency
   2. Update the criteria to reflect new information and re-run AI scoring on the remaining targets
   3. Drop the criterion for the target you prefer without updating others
   4. Ask AI to tell you whether the criterion should be dropped

10. AI screens 25 targets against 6 criteria and identifies 4 in a short list. What is the correct interpretation of targets 5-25?

    1. They are permanently eliminated and should never be revisited
    2. They are lower priority given current criteria — criteria changes or new information could re-rank them
    3. They should be acquired at a lower price to diversify the portfolio
    4. They represent poor acquisition targets across the entire market

11. What does AI generate when asked to produce 'due diligence questions' for each short-listed M&A target?

    1. Generic legal questions that apply to all acquisitions
    2. Questions specific to each target's screening gaps — areas where the data was thin or criteria were partially met
    3. Questions the target's founders will find easy to answer
    4. A legal opinion on acquisition risk

12. Your acquisition thesis is 'accelerate enterprise expansion.' A target scores 9/10 but has only SMB customers. What is the right AI-assisted next step?

    1. Override the thesis and prioritize the high-scoring target anyway
    2. Ask AI to assess whether the target's product and sales motion could realistically serve enterprise customers post-acquisition
    3. Reject the target because it does not fit the thesis
    4. Lower the enterprise criterion weight retroactively to improve the target's score

13. Why can't AI assess founder fit in M&A target screening?

    1. Because founders never appear in public information
    2. Because founder fit requires direct conversation and relationship judgment that goes beyond document analysis
    3. Because AI is not allowed to evaluate individuals
    4. Because founder fit is irrelevant to acquisition success

14. A target company's AI screening score drops from 8.2 to 5.9 when you add a new customer concentration criterion. What does this signal?

    1. AI made an error that should be reported
    2. The target has significant customer concentration risk that the original criteria did not capture
    3. Customer concentration is not an important criterion and should be removed
    4. The target should be immediately eliminated without further review

15. What is the correct framing of an AI-generated M&A target screen for an investment committee presentation?

    1. 'AI has identified these as the best acquisition targets in the market.'
    2. 'Based on our defined criteria applied consistently to available public data, these targets warrant primary diligence. Here is the specific rationale and gaps for each.'
    3. 'AI recommends we acquire Target A based on its score.'
    4. 'These targets were selected based on management's intuition, validated by AI.'