What if you could experiment with your acquisition target’s core product to conduct your integration planning before the deal closes?
I recently saw an article in the Financial Times looking at how Bain was using generative AI to generate a mockup of a prospective acquisition target to assist their PE clients during due diligence.
The article itself is light on details, and the work focused mainly on how they looked at SaaS providers to evaluate how sustainable and flexible their business would be to a potential acquirer in the new AI world. The article also prompted an idea.
Instead of evaluating AI readiness, technical teams could use this for the initial integration preparations and planning long before the deal is closed.
Think about it: if this is a strategic acquisition, you want something where you can understand what is being incorporated, determine its capabilities, identify where there is overlap, and get an idea on what the migration path would look like. This feeds directly into identifying the risks, complexity, and level of effort. All questions that can now be addressed much sooner.
Having an AI generated approximation would highlight the various user workflows that will go beyond migrating the users. Identifying the different user types such as customers, employees, third-party integrations. User interactions such as user creation and authentication. API access between services, partners, and now AI agents.
This will give the integration team a start at addressing answers to the following questions: How will the users be migrated? What will be their experience on Day 1? How will the existing onboarding processes support the migration, or will a new process need to be created?
In other words, identifying the big boulders early enough to identify the critical dependencies and integration risks. Even if the early assumptions are incorrect, this will provide a stronger starting point than not having one at all.
Most important of all is that it will allow you to be more laser-focused while performing the initial discovery once the deal is closed.
Going back to the FT article, let’s look at a SaaS company as an example. An AI generated mockup could approximate a prospective acquisition target. We know enough of their application and customer base that we can approximate the look and feel as well as the underlying data models. We can identify what the workflows look like to determine how much of it overlaps. We can identify how the app itself is built to know the underlying architecture of the system, identify incompatible APIs and shared libraries. All feeding into the integration decisions.
This isn’t meant to replace the discovery phase. Instead, it will lead the team leading the discovery effort, and who may ultimately design the migration path for the integration, to ask better questions. It will also reduce the risk of surprises coming up midway through the implementation phase whether it is a missing dependency or a new gap that must be addressed.
At the end of the day, using generative AI to create a mockup is a faster way to identify and reduce risk and frontload as much of the discovery and design so you aren’t wasting weeks between close and day 1 fleshing out the integration plan and can execute instead. This also enables better resource planning ensuring proper resources are lined up, costs can be reduced, and ultimately fewer surprises during the integration phase.
Every acquisition begins with uncertainty. The question isn’t whether you’ll discover integration challenges. It’s whether you’ll discover them before or after the deal closes. If you’re exploring how AI can accelerate the technical due diligence and integration planning, I’d welcome the opportunity to compare approaches.