AI has transitioned from a CIO-exclusive concern to a broader C-suite priority at Merger and Acquisition (M&A) firms (according to Moore Kingston Smith). Firms can use AI to automate many stages of the M&A process, saving costs and improving productivity.
AI’s ability to extract, analyse, summarise, structure and generate data means its users must channel it into clear-cut strategies. Of course, that is easier said than done. Let’s explore how attitudes towards AI can translate into actionable strategies that leverage AI’s undeniable benefits.
Consider all of the nebulous language surrounding the integration of AI into M&A activities. Clichés like ‘AI will revolutionise M&A and ‘AI can help M&A stakeholders turn to more value-adding activities’ may be true (depending on how they deploy the AI). While it’s true that AI excels at analysing large swathes of data and identifying underlying patterns, it might be challenging to visualise what that looks like in practice.
But regurgitating cliches isn’t necessarily a practical way to approach AI implementation. Integrating AI isn’t just about ticking a box for the benefit of a C-suite executive. As a starting point, we suggest conducting an operational review and identifying where your business is least operationally efficient. For example:
Once you've identified which processes, you can explore which tasks might benefit from AI-powered automation. As M&A Science suggests, AI can automate low to medium-skilled tasks. Examples of these tasks include:
Let’s explore four of them in greater detail.
Extracting data from M&A is typically a low-medium skilled task. We’ve written extensively about how challenging it is to maintain high accuracy when manually entering information from documents. Even the most skilled data entry operators are bound to include ~1% errors.
Plus, for time-pressured M&A employees, manually copying deal information is frustrating. To quote Matt Lewis, an AI consultant who has worked with M&A advisories: “Not just across the corporate finance industry, but across the board, you've got data analysts and data entry operators who are sitting on a machine entering data.”
Extracting data from M&A processes is an unrewarding task. We’ve spoken to hundreds of clients over the years, and none have ever enjoyed data extraction.
Data extraction also represents a way of structuring unstructured data. Rather than just capturing the raw data from the page, AI can ‘read’ it and populate a predetermined output. For M&A specialists, providing shared access to structured data is a central part of the deal cycle – from exploration and due diligence to post-merger/acquisition integration.
When finding financial information about buyers or prospects, AI can (be trained) to enrich the data, such as with details of previous M&A activity, past transactions and financial performance. AI must be able to source its findings so deal executives can validate them with a single click.
Like data extraction and structuring data, the benefit of data enrichment is that it frees deal executives from repetitive labour. After all, AI doesn’t get bored, tired, distracted or ill.
M&A Science suggests using AI for ‘general idea generation’, which most likely refers to identifying potential acquisition targets. Using AI to complete market research allows M&A firms to generate strategies for widening the acquisition pool.
We’ve written for VentureBeat about how AI’s limited decision-making capabilities mean it struggles to map consequences. Its business advice tends to veer on the side of genericism, too – case in point:
If generic ideas are what you’re looking for – such as corporate gifts or away-day ideas – then idea generation via AI might be useful.
AI offers numerous benefits that fit into three general categories: improving productivity, saving time and cost savings.
Investopedia explains that to work in M&A requires ‘a strong proficiency in accounting, finance, law, strategy, and business.’ It does not require a strong proficiency in copying data into spreadsheets or Googling financial information. Freeing employees from these tedious tasks makes for more productive work days focused on building long-term (and profitable) relationships with clients.
Saving time means you might be able to save labour costs. For example, our client YouLend notes that our AI has allowed them to grow without increasing their team.
Plus, if you’re currently using a low-functioning or unscalable solution, replacing it with a high-performing AI alternative will save time by removing manual touchpoints.
You can save costs by focusing on revenue-boosting activities and eliminating operational flimflam. Plus, AI might also help you recoup costs by:
If these benefits sound spurious, alternative investor Unigestion saved 75%+ of their costs by implementing a robust, AI-automated data extraction solution. Ultimately, a high-quality AI solution for M&A will pay for itself – several times over.
Humans are not going to become obsolete. That seems unanimous across all reputable M&A sources.
What is going to become obsolete are firms that don’t leverage AI to improve operational efficiency. The M&A sector has faced many post-pandemic challenges, including increased interest rates, regulatory changes and a thick layer of geopolitical tension. AI technologies can mitigate the effects of changing regulations, reduced deal-making activity and (stretched-thin) internal resources.
Yet, AI’s integration is bound to ruffle feathers with internal and external stakeholders. For example, if clients learn that their M&A agency is using AI, they may wonder whether they can complete the transaction themselves. After all, if they can finesse the M&A details using ChatGPT, why would they bother with costly human interaction?
Ultimately, AI’s continued improvements are contingent on M&A professionals using AI technologies. There’s not a huge range of acclaimed AI-powered technologies specialised for M&A currently on the market. Examples of what does exist include dealroom AI like DealRoom and deal sourcing and marketing tools like Sealk (though there is a glut of AI-based technologies geared towards financial services professionals).
Meaningful interaction with current AI-powered data management tools will help AI developers specialise solutions for M&A purposes. Of course, you could build an internal AI solution, but I’ve written before about how difficult that can be.
If using an external AI tool, it’s essential to check that it uses (real and powerful) AI. AI washing is a serious hurdle when scoping AI solutions, as some AI vendors don’t actually use AI at all. Their intelligent capabilities are merely the product of automation workflows or (reputedly) outsourced employees.
So, while AI-powered automation can deliver several compelling benefits, consider the following questions before implementation:
Langcliffe International is a corporate deal advisory, specialising in deals from £1 to £20 million. For their operations, high-quality, confidential data is paramount.
When they realised that they weren’t managing their data's potential effectively, they turned to an AI-driven approach. Now, AI enriches and sources the data in their proprietary database. Langcliffe plans on eventually leveraging its AI agents to suggest targeted client recommendations.
Mark Eardley, CEO of Langcliffe, describes their AI strategy: ‘If we can gradually perfect the data on buyers worldwide, we can offer vendor advisors a far better service in terms of suggesting buyers for the companies they bring to the market, i.e., their sale mandates.’
Langcliffe’s journey highlights a growing use case in M&A for AI to improve data quality for buyers, sellers and vendor advisors. Throughout our interview with Langcliffe, they emphasised data quality as the most important factor in their AI’s performance – a value that we completely share.
AI’s spread into M&A generally centres around preserving the talents of M&A professionals by automating away the tedious parts of their work days. When carefully integrated into legacy IT architecture and workplace culture, AI can tangibly boost productivity, save costs and form better operational processes.
Yet, some of the culture surrounding AI for M&A may require improvement: pivoting from vague notions of operational efficiency to clear-cut data management strategies. A practical, results-focused approach will ensure you skate around AI integration’s pitfalls. The result should be the ability for employees to turn to more value-adding activities – no clichés necessary.
Interested in extracting data from financial documents? Evolution AI’s award-winning data extraction tool offers several unique benefits, including:
Book a demo with our financial data project team or email hello@evolution.ai to discover more.
