This interview is a rewind from our Virtual Summit. Today I am here with Dr. Karl Michael-Popp, Senior Director of Corporate Development at SAP. Karl is a board member of a German Association for Post-Merger Integration, a member of the German Association for Mergers and Acquisitions, and chairs the program committee of the European Workshop on Merger Integration. In this episode, we discuss the future of M&A. We are going to be discussing what tasks are automatable in the M&A process.
To kick-off, could you briefly describe your role at SAP?
I am part of the corporate development team at SAP, and I am running product due diligence and post-merger integration projects. I am also working on the definition of the M&A processes that we use.
What is the future of M&A?
I think the future of M&A is a lot different than what we see today. Automation will play a major part in big changes that are ahead. The annoying tasks that we see today will be replaced by software, automats, and robots, so there will be much more time to focus on mitigating risk, solving problems, planning integration, and such.
How did you come up with the idea of automating M&A?
I work at a software company and I see a lot of great innovation around me with predictive analytics, natural language processing, machine learning, and robotic process automation. Life around me and life at my workplace is very different, which kept annoying me, so, to bridge that gap, I started working on the automation of M&A processes.
What areas of M&A do you specifically look at for automating?
We look at the end-to-end M&A process, from early stages and forming a strategy, to closing and integration. I have also created a new book talking about what tasks are in the M&A process, how can they be automated, which data are used and that could be the basis for the automation.
When you look at the ability to automate some of these tasks, are there areas that you think are generally easier than others, as far as starting points?
Absolutely. The best starting point is a data lake, with some automation on top, where you could look at the data lake as a data room that companies offer nowadays. Many companies offer certain automation as part of their data rooms, analysis of documents, analytics, etc. That is the typical starting point, as here you have a lot of data and can easily start applying the household machine learning analytics and other tools.
In what time frame do you foresee this? We are starting to see data rooms and the legal industry using contract analysis tools, but there seems to be a pretty big gap from that era to the regular practitioner that’s coming in and typically doing diligence. What kind of timeline are you seeing before we see a big shift in this area?
The competition will drive the technology forward. Examples in the software business are automated tools, tools for analyzing licenses of open source components, and even tools that can do things architects can do, which is based on source code. The key hurdle we have today is bringing all of that together because, in the end-to-end automation, you don’t want to have islands of automation everywhere. You want to have a joint basis to integrate all the available automation and that will take some time. I would say that 3 to 5 years from now we will be able to see a lot more automation.
When you talk about creating a data lake out of a data room and starting to do more automate analysis, can this actual data change the M&A approach itself?
Absolutely. I believe that will be a shift of the human capacity and M&A process away from searching and analyzing into mitigating, making decisions, etc. As an example, say you have four weeks of due diligence, where you wait 2 or 3 weeks for the results of legal analysis from legal advisers. Instead of that, you could have a draft version based on an automated analysis in 1 or 2 days, where major risks are automatically found.
I am curious if we can define automating a task. Can you share your approach to automating processes in M&A?s
The area of analysis and even construction of the needed documents is an area where we are very advanced in applying modern technology. There are tools out there that can do really fast automated analysis and identify certain risks, but also help you find things that you are looking for, such as search machine learning. Some tools can summarize contracts and show you a created non-existing contract that includes all developed clauses that are active today. That is a huge jump in productivity.
For example, you can imagine having a data room, where you can ask the data room and the intelligent assistant all the questions you have and have it answer based on the information in the data room. If you look at the automated ability, you could look at the task and see what are the different activities in this task. A lot of decisionmaking tasks can be automated today. Compared to what an employee had to deal with in the past, now with machine learning, even highly sophisticated tasks could be easily supported and improved with technology.
Can you tell me about some of the tools that can help?
It’s hard to get an overview of what tools are out there. One thing that is missing today is transparency on which tools are available for which tasks and how they compare. What we need is an exchange of information, experiences, and feedback from using things like contract analysis, analytics, natural language processing of documents in the data room, and we need more sharing in the community. In Germany, we have a very active community with several working groups in specific topics regarding automation, and we recently published some results from the German Association of M&A in our magazine, which is called M&A Review.
That is interesting. In M&A Science, the initiator was the fact that all these different companies have their approach and I focus a lot on people challenges because that seems to be one of the most difficult thi8ngs to solve. I don’t think you can automate that human-to-human part.
Let’s say during due diligence you have a question about a customer. Even when you talk to a person in a target company you could have support from a tool that shows all the relationships around this entity. You can have all the massive information in the data room to give you a background without having to look through hundreds of documents in the data room around this customer. Clearly, person-to-person is important, but so is augmenting and assisting in using all the data in the data room that a single person is not able to capture as a whole.
When you look at automation, what are the central goals around that?
The perfect automation would include an end-to-end platform or data integration layer. You would have the opportunity to plug-in specific tools for specific tasks, apply metrics, and even use auto-coupling data from different phases of the M&A process. You could go back and forth, and use this capability to not only compute but also analyze and interpret large amounts of data.
Have you seen much interest in this or are people satisfied with their current structure?
There is a lot of interest in this. The key thing that keeps people in M&A and corp dev departments from rapidly adopting tools is that it takes time to get get a little more transparency on what’s cooking in the market and some time to understand that this solution is the right one to use for screening and other activities in the M&A process. There are a lot of screening tools out there with a lot of automation, but it’s hard to figure out which one of the available solutions is the best solution for you. This is why we need to share experiences more.
Are there some recent things that you worked on that you are very hopeful about?
I think that we will see widespread use of specialized end-to-end M&A project management platforms, with or without a data room, and they run from portfolio management strategy, screening all the way through to the integration. Specialized project management system providers provide layers for plugging data rooms, which is a good starting point for further specialization of automation tools, such as legal analysis of contracts in the data room. The idea is to understand what data is needed in different phases and then apply everything on top of that based on a standardized API model.
Why is it more challenging to integrate the businesses post-M&A than the activities involved in the course of pre-M&A?
What you need to do is focus on questions, issues, problems, and budget regarding the integration as early as possible. At SAP, in due diligence, we look at the target, do the diligence, but at the same time, we also do preliminary integration planning and try to harden our plans to make them bulletproof, to make sure we have enough budget and resources for post-merger integration. We have the same people in diligence and integration and that has maximized the use of due diligence results. So, starting early and working with the same people essentially helps improve the diligence process for future acquisitions.
Does GDPR represent an issue with regard to the automation of due diligence or integration for sensitive personal or employee data?
Absolutely. All the machine learning and analytics, even in diligence when looking at a list of employees of the target, are massively impacted by the GDPR. If you look at machine learning, GDPR is one of the key issues we have, because you do need a lot of data, but you can’t use it because it’s personal, identifiable information. GDPR is an issue, but it is important to understand that PII is only a small part of the data lake, and modern, sophisticated tools have ways to either avoid or anonymise this type of data.
Do you envision the tools you mentioned being available to small or middle-market practitioners, or only for large firms?
That is one of the goals. At SAP, we were looking at different tools for screening companies, and we saw that some of the tools are only offered as a service or as part of a software. I think there are still some obstacles present regarding screening tools, but there are also some innovative companies out there which offer a more affordable price tax for small and medium-sized companies as well.
Is there a best practice for ingesting a data room into a data lake, so that analytics can be best performed? What tools have you tried so far which have given you the biggest impact on automation?
For automation, we need a reference schema for the information or the data that we look at in the end-to-end M&A process. In my book, I explain a data model for the M&A process, which is quite complex. I believe that there will be a standardization of data, which will make it easier to extract the information . You don’t have to necessarily extract the data from the data room to create a data lake, as you can also work with a federated set of information collected from earlier or later phases, that are in the data room. It will take some time to come up with an appropriate schema, but some smart tools can help and assist you in merging different data sources.
Your book is coming out. Can you tell me a bit more about that? What can someone who’d like to learn more about this topic expect to get out of it?
We wanted to create a book for practitioners. Besides a more academic view on whether the task is automatable or not, there is also a lot of hands-on content included, such as questions that you might raise when running the task, intellectual property due diligence being one of the examples. There is a lot of information on how to run certain tasks as well.
How have you justified the cost of automating your M&A process and the proposed return on investment? What were the key areas in the business plan to justify the cost?
I think that the advantage you will get from having a lot of the information that drives decisions helps mitigate or make fast changes, will provide the value that pays for the increased speed in getting all the information and handling risks.
Do you have any advice you’d like to share with forward-thinking M&A practitioners?
There are two things, One is, if you are interested in making an integration successful, you better focus on thorough preparation for it. There is work that needs to be put into making an integration plan. The second thing is, stay tuned to what is happening in the increasing automation in the M&A process. Like Amazon’s Alexa, on top of the data room, soon we will be able to ask what information is in the data room, and get a fast response. More automation is coming, so be open to it, as it will improve the processes.
Do you foresee the valuation of firms diminishing due diligence to the ongoing COVID crisis?
If you exclude companies in deep financial trouble, the first question is, who wants to sell their company during the COVID crisis if they are financially viable? You will have a tough time cherry-picking very good companies for a very low price, unless they are in financial trouble and you come in as a savior. It depends on the specific market for target companies that you are looking at. Even if the valuations, in theory, will go down, essentially, as a seller, you don't want to sell if the market is down.
Have you seen more companies using automation during the COVID-19 shutdowns or as a slowdown?
Crisis, no matter if it’s financial or health-related, it’s always creating pressure for companies to increase productivity and lower costs, and that usually leads to an increase in automation. That is something we see today from an SAP point of view.
What are your thoughts on automating your M&A process and doing trending across multiple acquisitions?
Companies that provide you with portfolio management and overview of potential deals and different pre-deal cases and also analytics across different fields that will be done and are part of due diligence. There will be a lot of opportunities to compare different companies, different integration projects, different swimlanes in the pre-deal phases - I think that is already out there, as I have seen some glimpses of cross-deal, cross-process analytics that you can use to compare and improve things.
What is the craziest thing you’ve seen in M&A?
One of the craziest things is what I call cascading integrations, where you acquire a company that has acquired another 5 to 10 companies, and you want to integrate it. However, what happened in the past is that those other companies weren’t properly integrated, so you suddenly have to integrate 11 companies, instead of one.
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