One of the most frequent questions we hear from deal teams is:
If and how we can help them use AI, technology, and data to more effectively assess opportunities before they incur material due diligence (DD) costs?
For some of the most active investors, this is one of the highest-value problems to solve in M&A: extracting valuable (and hard-to-uncover) insights when deal budgets are at their lowest or entirely non-existent.
Why Early-Stage Diligence Matters
Broadly, these teams want to be able to use data at this stage to:
- Improve how they manage expectations of both the seller(s) in their initial approaches and their internal stakeholders.
- Mitigate the risk of deals failing later on due to factors they may have reasonably been able to anticipate upfront.
Failure to do this properly at the outset can trigger allergic reactions from having to swallow dead deal costs at a later stage - or having to go the long way around to reset expectations (on both sides of the table).
Naturally, the more deals a team looks at, the greater the risk. So the struggle is most definitely real.
Breaking Down the Problem
My team and I have now spent months speaking to investors and tech experts from across the industry about the problem and potential solutions. But we had to start by getting more granular.
Firstly, we distinguished between:
- The sort of “screening” that an investor might do to build its pipeline (applying one or more filters on a pool of potential targets to whittle the list down).
- The sort of “screening” done when wanting to get a better feel for a specific opportunity (now honing in on a specific target).
I now think of the former as screening and the latter as preliminary or early-stage diligence.
There are already many ready-made solutions (of varying degrees of efficacy) for the former. And it is the latter that we hear deal teams struggling with the most, which is where we focused our efforts.
A Two-Phase Approach to Preliminary Diligence
Going one stage deeper, when looking at preliminary diligence, it can be broken down into two phases:
- Accessing and reviewing public data.
- Undertaking an initial review of (usually a limited subset of) private data.
Given the data available for review, the challenges and solutions differ for each stage.
There is no single answer to “the best” approach. It all depends on the investor’s requirements and the available data. But the good news is that there is an ever-growing menu of options and if you can assemble and use a number of them in conjunction, you have a good chance of achieving what you’re looking for. That’s the secret sauce.
Over the next few weeks, I will share some of what we have uncovered.
What are your priorities when undertaking preliminary diligence?
