Post Merger Integration on the beach
pmiGPT: Good morning, Peter, how can I support you today?
Taking into account the milestones of our latest target and considering your email communication on the closing conditions, the closing should take place in the next few days.
Shall we have a look at the plan for Day One?
Peter: Please provide me with the complete schedule for Day One. Consider our usual procedure and also look at the latest discussions with the target's works council.
pmiGPT: I'll get straight to work, boss.
…
Peter: Oh, I almost forgot – I’ll also need the scripts for the speeches, the presentations and my moderation cards with the key points.
pmiGPT: Of course, as soon as I have the schedule ready, I'll get right on it.
…
As Peter sips his cappuccino, he thinks about the fact that his avatar could actually handle the job on Day One – allowing him to extend his workation by another three days...
Science fiction or soon to be reality?
Back to reality. For now, these are still just dreams.
Although artificial intelligence is already used in one in five M&A transactions (BAIN, M&A Insights, March 2025), its large-scale application primarily focuses on the transaction process — specifically, the phase before signing. After signing or closing, AI usage remains limited today.
Setting aside the fact that an avatar on Day One might help Peter optimize his personal work-life balance, it certainly doesn’t contribute to building goodwill with new employees.
Artificial intelligence is already significantly reducing manual effort in many areas. Generative AI unlocks entirely new possibilities and will become even more influential in the months and years ahead.
The scenario from the introduction is still futuristic. How long that remains the case largely depends on creativity and the willingness to experiment. Technology must be embraced to drive progress — and the first step is to start experimenting.
To make that process a little easier, I’ll share some use cases and ideas here. I won’t bother with the obvious no-brainers, like having written communication proofread or generating custom images to support messaging.
Supplier Screening Support
Wherever there is data — preferably large amounts of it — artificial intelligence can be leveraged effectively, delivering significant efficiency gains. This is why it has been widely adopted, particularly during the transaction phase, with a strong focus on due diligence.
We can feed the AI with all supplier contracts and have it identify “critical” passages. This not only saves us the time we would otherwise spend reading but also allows us to focus immediately on the AI-prioritized “red flags.” For example, we can mitigate risks through the change-of-control clause. After all, no one wants to hear from their suppliers: “It was nice having you as a customer — until yesterday.”
Similar approaches work in any area with numerous contracts or large volumes of data. In sales, this applies to customer contracts; in HR, to remuneration agreements, and much more. Of course, data protection and GDPR compliance are ensured, provided that key principles are followed when selecting the AI model, the place of hosting, and configuring various AI settings.
Reorganization – Ready on Day One
Now, let’s take a step into the future. Following the acquisition, the accounting departments — I like this traditional term; it fits well into our modern discussion — of both companies are set to merge. This isn’t just about consolidating a location and a management level; it’s also about modernization — introducing agile processes and increasing efficiency.
There are countless articles on this topic online, along with best practices from large, mid-sized, and small consulting firms. So why not feed all these organizational charts and concepts into our pmiGPT? We can also include the current organizational structures of both accounting departments, along with growth plans for the coming years. And, of course, we won’t forget to incorporate other relevant framework conditions.
Then, pmiGPT will generate suggestions for the structure of the new accounting department — including a detailed description and an analysis of the respective advantages and disadvantages. In a single step, it will optimize management spans and, who knows, maybe even consider the team members’ star signs — for particularly energetic collaboration.
When employees arrive at the office on Day One, they’ll find themselves standing in front of a large table displaying the new seating arrangements — almost like a wedding reception.
Phew! Maybe that’s a bit too much automation and top-down decision-making. But some of these approaches significantly boost efficiency and are no longer just a futuristic vision.
Avatars for accounting standards
After our journey into the future, let’s return to what’s already possible today. On Day One, Target employees face a lot of new information — from parking and canteen use to booking meeting rooms and understanding the buyer’s accounting standards.
Of course, all of this could be documented in a traditional how-to guide. But that approach feels outdated. For years, learning content has been delivered through videos featuring someone reciting the information. AI can already do this much more effectively.
Let’s have an avatar deliver the training. With AI-driven learning, it can even use pedagogical techniques to make the content more engaging and easier to absorb. Digitalization offers countless possibilities — documents and videos can be searched or tailored to specific target groups.
But this is just the beginning. If something small — or even significant — changes, the avatar can update the content with a single click. In the past, this would have required reshooting and editing an entire video.
Here, AI not only enhances efficiency and effectiveness but also improves quality at the same time. The best part? This isn’t a vision of the future — it’s already a reality. And who knows, involving people from both organizations in the avatar creation process might even become a key step toward cultural integration.
Making cultural differences visible
Cultural integration — the key term for our next use case. At the core of cultural integration — and here, “integration” does not mean assimilation — is the mutual recognition of differences. The famous elephant in the room needs to be brought into the spotlight.
In the past, this required extensive processes. First, surveys; then, data analysis; and finally, workshops with mixed teams from both organizations. The outcome was a visualization of cultural differences.
A complex process that not only takes time but can also only begin after Day One. And with all the urgent tasks that need to be tackled after Day One, cultural integration often takes a back seat.
Culture is particularly evident in communication — on websites, in job postings, in external and internal content (aka posts), and even in emails. So why not feed this data into an AI and let it analyze the cultural differences? This technology has been available for some time and is already being used in other areas.
This provides a starting point for discussions within teams and among managers — meaning cultural integration is already underway. All without requiring extensive time from employees and, most importantly, without delay.
Culture Clash Meter
Once we have analyzed cultural differences with AI and then refined them through input from those involved, the first step is successfully completed.
Going forward, it’s especially important to recognize when discussions in meetings or conversations shift into the cultural realm — often at the expense of constructive dialogue.
In larger meetings, moderators are often brought in to oversee discussions. With their experience and a targeted briefing, they ensure conversations stay on track and intervene when cultural differences lead to unproductive exchanges.
Today, bots are already recording numerous meetings. If AI can analyze cultural differences, it will eventually be able to detect them in real time — or at least with minimal delay.
Now, we let AI calculate an indicator. The more frequently cultural misunderstandings or culturally driven discussions arise, the higher the value climbs — and it decreases as these instances become less frequent. This creates the Culture Clash Meter, a tool that can be used in every meeting, video call, phone conversation, or face-to-face discussion.
Integration Path Optimization
Last but not least — even if the scenario from the intro isn’t reality yet, we can still hand over the plans of all individual workstreams, functions, or teams to AI for analysis. By supplementing these with relevant topic details and overarching milestones, we create a more comprehensive picture.
This enables AI to identify dependencies that we previously mapped out manually. The insights provide teams with valuable input for discussions and collaboration — an essential step in growing together and ensuring the success of post merger integration.
The road ahead
How far are we from AI not only creating the post merger integration plan but also detailing every step — what needs to be done, when, and by whom? And how long until it takes over these tasks entirely, just like the avatar delivering the Day One speech?
Generating such a plan might take hours, maybe even days — but what is that compared to 100 days of integration or even three years to finalize every detail?
Today, we are still far from this scenario. However, AI already offers numerous opportunities to enhance post merger integration. The maturity levels of these use cases vary, and the full potential of AI in this field is far from being realized.
