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Legal AI will transform post-merger integration. Here’s how

A merger or acquisition only really begins once the ink has dried on the deal. As any lawyer who has worked on a post-merger integration knows, post-closing comes with a substantial checklist of legal must-dos that require diligent attention. Many of these tasks are labour-intensive, involving manually sifting through lots of data, legal documents and contracts. In this week's blog on AI in M&A, we look at how Artificial Intelligence can transform post-merger legal work and business integration in the first 100 days of a merger – and beyond.

AI can increase the odds of post-merger success

As we discussed in previous blogs, Artificial Intelligence is revolutionising M&A due diligence. So, given we can already use AI to gather, order, analyse and structure data – the building blocks for successful post-merger integration – can’t we also use it to drive and improve post-merger integration as well? We believe that the answer is yes.

Within legal, AI technology is becoming increasingly good at identifying and improving the tasks that need to be done after a deal is closed. Often, it can already perform these tasks faster and better than a team of people, and with more accurate, more detailed and more precise outcomes. In addition, AI technology helps create a solid base for making future decisions. It does this by providing reliable information, making sure processes and workflows are in sync and spotting chances in the market.

Here are three ways AI can already support the work of legal teams in post-merger integration.

1. Post-merger data migration, optimisation and analysis

The initial and crucial step in merging companies is to combine data from their different systems. The goal: to create a single, reliable and secure source of information. This process can be very complicated and time-consuming because it involves dealing with a lot of data from various sources. Generative AI tools can now automate this process. These tools learn from large public data sets and use this knowledge to process and interpret the data. The process will become even more efficient as these AI models are adapted to handle sensitive company data securely.

AI-based data conversion
There are two ways in which generative AI can streamline and improve data migration. First is changing the structure and interrelationship of data. Here, AI helps to convert and organise data into the specific format or structure that the new company needs.

The second way it helps is by improving the data quality, with AI scanning through different data sets to find and highlight any similarities or differences. This makes combining data from different sources more efficient. AI can also spot and fix errors or inconsistencies, making the data more accurate and so more trustworthy.

For example, during mergers, there's a window to refine the acquired entity's corporate housekeeping. By deploying generative AI on the raw data dump, the data is not just transferred but is also optimized and structured. This ensures a seamless fit into the acquiring company's systems, enhancing corporate governance post-merger.

AI-based data optimisation for decision-making
At the same time AI is organising and improving data, it can also analyse, sort and refine it. This is a big advantage, as the data that has been cleaned and organised can now be used to help make better decisions and reports. AI is great at quickly chewing through huge amounts of data, enabling it to provide accurate answers to specific questions almost instantly. For example, finding the best procurement contract becomes easy with AI. It can quickly analyse contract terms, prices and other important details, helping to make the right choice in a matter of seconds.

 

2. Post-merger contract lifecycle management

The data migration, optimisation and analysis outlined above ensures that Generative AI is a valuable tool in managing contracts after a merger. This ability goes beyond merely reading the combined company’s contracts, but rather “understanding” them. It involves not just summarising the contract contents, but also identifying potential issues or warning signs. AI can link contract terms to a broad range of legal knowledge and past case studies. It can compare the terms to previous legal outcomes, industry-specific regulations and current legal trends.

These deeper insights are extremely useful for renegotiating terms, aligning contracts with the new business goals and ensuring that they comply with legal regulations. Additionally, AI can identify how contracts from the merging companies align or differ. This helps smooth the integration process and aids in making more strategic decisions moving forward. In fact there are three ways in which AI can simplify and improve contract lifecycle management post-merger:

a). Enhanced analysis and active query resolution
AI is already good at closely examining and understanding every part of a contract, including its clauses, terms and conditions. But this is just the beginning. As AI becomes more advanced, it will be able to answer questions about contracts more accurately and confidently. For example, if you ask the AI when a contract ends, it will not only give you the end date but also explain what that means for you.

b). Faster legal compliance checks
One of the biggest benefits of AI in the legal field lies in accelerating legal compliance checks. As AI continues to improve, it is becoming a trusted source of legal knowledge. By combining generative AI with legal databases like Practical Law and Kluwer Navigator, it’s now much easier to look into specific legal issues. In a merger and acquisition, for example, a company can use AI to see how well its own data conforms with various legal requirements. For instance, AI can check if a contract meets certain criteria or whether the contracts of an acquired company comply with current laws.

c). Quicker decisions during evaluation moments
Completing the advantages for contract lifecycle management, AI can speed up decision-making during evaluations. Not only can it sort and rank contracts based on what they contain, but AI can also suggest changes to agreements with clients. For example: whether to continue an agreement, end it or renegotiate the terms. This simplifies the contract evaluation process, making it faster and more efficient.

 

3. Risk assessment and automated compliance checking

It’s no secret that the regulations around mergers and acquisitions are very complex. But the good news is that AI can help deal with them. For instance, AI can automate the compliance-checking process to ensure that a newly merged company meets legal standards in different areas or regions. This helps manage the risks and keeps everything under control after a merger or acquisition.

In fact, AI is great for systematically tackling important but not deal-breaking compliance issues raised during the due diligence phase. By identifying and incorporating existing rules, processes and best practices, AI can help ensure that the merged organisation meets the risk and compliance standards set by the purchasing company or the new company itself. This leads to a smoother transition.

AI also improves risk assessment by identifying weak spots, like contract terms that might need changes to avoid future problems. It can clear up any legal grey areas by matching real-life situations with legal rules, highlighting where there might be differences. AI’s skill in bringing together different types of data also makes it extremely easy to ask specific questions and get clear answers.

And AI is a big help in spotting financial risks that might not be obvious. For instance, it can speed through financial documents, transactions and compliance reports, comparing this information with regulatory databases and industry standards to find potential financial risks. This includes spotting patterns that might reveal under-insured assets or trends of not following rules. AI’s ability to predict based on current operations can also help it spotlight potential financial issues, allowing businesses to act before problems arise.

 

Coming next: automated compliance checking
Above is what is possible now. However, AI’s potential for automation means there is much more to come. For example, businesses will soon be able to employ AI to ensure that an acquired or newly formed company not only complies with today’s standards, but that it continues to do so as regulations change. Automatically. This will save significant amounts of time and cost, while reducing risk, too.

There are two very promising areas for automated AI compliance checking. One is to use it to safeguard regulatory adherence (GDPR, corporate obligations, antitrust obligations and even product regulations).

The second avenue for automated AI checking is KYC, AML and sanctions. In this context, enhanced network analysis could help to identify suspicious transactions, providing a more comprehensive view of potential criminal networks. Here, AI could simplify compliance through predictive analysis – modelling expected behaviour patterns to proactively identify and isolate potential criminal activities.

 

Conclusion: transforming business integration and operations

As legal AI continues to evolve, the landscape of post-M&A integration is transforming rapidly. Initially, the focus was on the immediate post-merger phase, the famous 100 days. Today, and increasingly going forward, the focus is on using AI to drive sustained business improvement and business optimisation far beyond the first 100 days.

Legadex is at the forefront of this evolution, helping businesses harness the full potential of these emerging AI applications. With our guidance and expertise, companies can navigate this changing terrain, making the most of new opportunities and advances in AI for even more efficient and insightful post-merger integrations.