AI Makes M&A Target Search More Efficient

Mergero Capital AG specialises in mergers and acquisitions. Its work follows a pattern familiar across the M&A field: before the actual negotiations begin, a great deal of expert time goes into identifying suitable target companies and buyers.

Empirica is developing MGX technology together with Mergero. The goal is to make M&A target search more efficient, put existing data to better use and reduce the manual work of M&A professionals. This is a substantial AI and software development project that combines Empirica's technical delivery with Mergero's M&A expertise.

Why does finding the right buyer take so much time?

The M&A market has plenty of potential buyers and plenty of companies, but connecting the two efficiently is not straightforward.

M&A professionals work with a large volume of information. Details about companies, buyers, investment criteria and previous conversations accumulate across several different sources, and identifying the right parties often rests largely on an individual professional's experience and network.

This is a typical expert-work bottleneck. The work is not routine, so it cannot be automated with rules, yet it repeats in much the same form from one assignment to the next and moves exactly as fast as one person can review the material. Mergero's aim was to put that information to systematic use and support the professional in identifying potential M&A opportunities.

What does MGX do?

Empirica and Mergero Capital AG are developing MGX together. It brings together information generated during the M&A process and uses AI to analyse it.

AI helps process large volumes of data, identify relevant connections and surface potentially interesting companies and buyers for the professional to assess.

The goal is not to replace the M&A professional's judgement, but to automate and speed up the time-consuming stages of the process. Final assessments and decisions stay with the professional.

How is the work divided between AI and the professional?

M&A is a good example of expert work where the greatest benefit of AI comes from processing large volumes of information and automating preparatory work.

Instead of the professional manually reviewing a large number of companies and other material, the technology helps identify the essential information and brings the most interesting options forward for closer assessment. This directs the professional's time to where human expertise creates the most value.

Stage of work	Who does it
Reviewing large volumes of material	AI
Surfacing the essential information	AI
Assessing companies and buyers	The professional
Interaction between the parties	The professional
Negotiations and closing the deal	The professional

What does building technology for a professional's working environment require?

MGX has been built for the real working environment of M&A professionals from the outset, not as a separate experiment alongside it.

Three things carry particular weight in the development work:

• Usability: the technology is part of the professional's normal process, not an extra step beside it.
• Security: M&A involves confidential information, so controlling how that information moves is a basic requirement of the delivery.
• The professional's role: AI supports decision-making, and people remain responsible for the assessments.

The aim is to make it possible to handle a larger pool of companies and buyers efficiently without the quality of expert work suffering.

What can other professional services firms learn from this?

MGX illustrates a broader shift in expert work. Many organisations hold a great deal of valuable information, but making use of it still takes a considerable amount of manual work. AI can process material, identify connections and do preparatory work far faster than traditional methods.

The markers of this kind of work are the same regardless of industry:

• The work repeats in much the same form, but every case differs slightly, so it cannot be automated with rules.
• The quality of the outcome depends on how much material there is time to review.
• The information the work needs already exists, but it is scattered across several sources.
• The same professional does both the preparatory work and the actual assessment.

If you recognise your own organisation in these, it is worth working out which stages are involved and what they cost on an annual basis. Technology like MGX is built as a custom AI delivery and software development, but what decides the benefit is which stage of the work the AI is aimed at. Empirica is a Microsoft partner and a Claude Partner Network member.

At its best, AI does not replace the professional but gives them better tools. In the case of MGX, the goal is simple: more time for doing deals, less time spent reviewing information by hand.