Automation in M&A

In today's digital era, the pervasive influence of technology is felt in every facet of business, and Mergers and Acquisitions (M&A) are no different. The arrival of innovation and automation will soon make their way to the industry, streamlining processes that could bolster productivity and facilitate smoother post-merger integration. In this article, Dr. Karl Michael Popp, Senior Director, Corporate Development at SAP, discusses automation in M&A. 

“In M&A, you have to make a thousand decisions which are not typical day-to-day decisions. But as soon as you have proper data, you can use them to augment the information for better decision making.” - Karl Michael Popp 

As an ambassador for M&A automation, Dr. Karl believes that the traditional approach to strategy often lacks structure and consistency and is clouded with bias. Software and automation, on the other hand, mitigate all these risks when provided with proper data. This is why he broke down strategy into smaller parts and came up with a structured model.

Components of strategy

1. Strategic entity - these are the high-level concepts used to define strategic goals. (Market, customers, suppliers, partners, solutions, etc.) 

2. Strategic assumptions  - these are assumptions about the entities.  

3. Strategic goal - these are the targeted goals brought about by the assumptions of the entity.

However, high-level strategic goals must be broken down further into smaller, more manageable tasks. This helps everyone in the company understand their role in achieving the goal.

Machine Learning in M&A Tools

Today, data room vendors are expanding their horizons beyond due diligence to include Post Merger Integration (PMI) and even earlier phases of the process. They are complementing their offerings by incorporating machine learning to provide a more advanced, accurate, and comprehensive analysis of data. 

For example, machine learning can automatically collect and combine growth rates and revenues to make predictions about potential revenue with the acquirer's sales force. Tools like these move us from making assumptions, instead enabling more accurate predictions based on formalized assumptions and data.

Augmenting Technology with Decision Making

M&A processes are filled with thousands of decisions that deviate from typical day-to-day operations. With proper data, these decisions can be made more effectively. Machine learning algorithms can collect information, make proposals, and ultimately enable better decisions. These advanced technologies can supplement decision-making processes with critical data, leading to outcomes that are more calculated, precise, and likely to succeed.

Incorporating Data Modeling into Organizational Culture

One of the most intriguing aspects of modern M&A is the ability to incorporate quantifiable aspects of an organization's culture into data models. It's now possible to leverage surveys and other data-collection methods to gauge the morale, motivation, and potential issues within the acquired team during the integration process. 

Understanding these factors, we can identify and address critical elements such as attitudes, norms, and beliefs. While not all aspects of culture are quantifiable, structured data can significantly contribute to shaping the acquired employees' experience and ensuring a smoother transition.