By Karim Awad, Founder of Matsu Partners.
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Matsu Partners (currently fund-raising) is seeking to apply AI and digitalisation techniques used by large private equity firms, to support the origination, diligence, and operational improvement of a target company in South-East Asia.
Feeling pain: Traditional search
Picture this: You spend hours cultivating brokers, networks, and other sources to find opportunities. You then spend months engaged in diligence, pouring over historical data, haggling with sellers, and seeking to set-up a deal. And if you make it to acquisition, the likelihood of operational challenges surfacing, which diligence missed, is not idle. Even after 5+ years of operations, whom do you then exit to?
This may sound like the lifestyle of a Searcher. But it describes the typical challenges faced by any investment professional, including those in the largest private equity and sovereign wealth funds globally.
Most will simply “rinse and repeat”. However, the past several years has seen exciting innovations within the AI world, which are filtering through amongst large institutional investors. Those most innovative have deployed these to address the challenges above, de-risking their investments and improving the likelihood of successful exits.
AI: Why so many get this wrong?
With ChatGPT and related developments, AI has returned in vogue. But even amongst institutional investors, there is a struggle to translate this into tangible gains – why?
Often there is a mindset of quick-wins: that through a relative “flick-of-a-switch”, that deploying these tools will demonstrate instant impact. But besides tech companies, how many firms can you name whose business has been transformed by AI?
In short, they exist, but they do not roll-off the tongue. And that’s because AI is a journey.To extract the most, it requires thinking of data and technology from end-to-end. This is difficult – it requires a willingness to learn, experiment, and ultimately change – and where an organisation whole-heartedly must embrace that change.
However, for a searcher, it’s “organisation” is only one or two people. And upon acquisition, it can sponsor and nurture that change to overcome any resistance. The latter goes beyond AI to any revenue or operating improvement – so what can be done?
Where AI can tilt the odds in a Searcher’s favour: The Search
Origination is an obvious area. AI can be used to quickly filter through large databases to find relevant companies, should you have an industry in mind. This will be more refined than manually filtering by industry category or NAIC classification codes.
There are a lot of “AI companies” effectively offering these services. But you’d be right to ponder what difference this makes? In isolation, not much.
But when combined with alternative data, this can (seismically) move the needle. Alternative data comprises non-financial metrics on a company describing how different stakeholders interact with it: varying from customers, employees, and even how the firm engages publicly. All firms will have a digital footprint that can support this analysis.
Bringing this together alongside peers, can be enlightening. At a basic level, this highlights relative competitive positioning, traction, and pressures. More sophisticated AI analytics would score this data, and gauge how a company is likely to grow (or contract) within 12 months. It would also perform this at scale across 10,000s of companies.
Understanding this can make the difference between an investment destined to perform, or one due to enter distress – despite both appearing equally attractive. The Search Fund community focuses a lot on having excellent managers – but buying an inherently rotten business is like running a 100m race with ankle chains – even if you’re Usain Bolt, we know how that will end.
AI can also be used to identify people, varying from industry experts, senior advisors, and even potential employees or customers. This can facilitate warm introductions to a target, and identify personnel that can support an acquired company, but is a topic saved for another forum.
Due Diligence is also a beneficiary, regardless of the technological sophistication of the Company. This can be used to analyse transaction data, and can shed considerable insights into a target’s customers – and how they’re likely to behave post acquisition.
This becomes impactful when assessing 10,000s if not 100,000s of line entries (and where excel begins to struggle). From understanding persona types, purchasing and customer acquisition patterns, through to the risk of churn, and underlying price elasticities, basic descriptive analysis can shed light on these issues. This becomes invaluable especially when evaluating recurring revenues from uncontracted sources.
AI can build on this, predicting when customers will churn, the likelihood of new customers being acquired, products / services that can be cross-sold, and how customers will respond to varying price changes. Knowing this before you acquire a company can genuinely improve your odds of success.
That sounds great, but does this really work in practice?
Besides my years of applying these methods within private equity, the above has helped when I’ve looked at acquiring veterinary clinics in Singapore and Hong Kong.
These methods helped identify and rank every clinic in these city-states. Using public information, I could measure how busy these clinics were, whether they were renowned for veterinary services, and even determine if they had toxic cultures, or if the clinics needed refurbishment. And when it came to DD, I received transaction data where I could confirm if what a seller was telling me was borne-out by customer behaviour.
The latter may be surprising for small companies, typically with 5 employees or less, and with dated IT systems. But it highlights that sellers will accommodate these requests, even in a competitive sales process – and for those who don’t, it serves as a potential red-flag into its revenues, or underlying controls and systems.
Is that all AI can do to help Searchers? Of course not!
This only represents the start – remember, it’s a journey! Where AI comes into its own is upon acquiring a company, helping it to digitalise its operations. From an APAC standpoint, this is highly relevant as a lot of SMEs suffer from technological debt – where companies are constrained by legacy systems, resulting in value being lost.
A whole series of articles can be written on this topic. In brief, though seeking productivity improvements is an obvious area to target, be it through applications to back-office administration through to customer service chatbots, where I have found AI to be most profound is in understanding and reshaping the customer life journey.
There are a series of “touch-points” both the Company and customer share. AI can assimilate all these datapoints, and help better understand the number of interactions prior to making a purchase, and how that customer evolves during its “lifetime”.
This can then give rise to improvements in customer experiences, diminishing pain-points, and leading to new products / services that enhance their commitment to the firm – and help inspire referrals that unlock higher revenue growth.
An important aspect to remember though, is this requires a corporate culture receptive to these changes. From my experience, this relies on being able to lead change management across a firm: whether it’s generating calls-to-action, through to ensuring teams are adequately aligned and incentivised. Often the efficacy of AI really boils down to how successful these change management initiatives are executed.
Concluding thoughts
Innovation within AI continues to develop at a stunning pace. Being able to articulate how this can support a searcher from start to finish, besides providing a competitive advantage, can reinforce credibility with a seller in outlining how you can sustain and build-upon their heritage after they exit. Good luck with your AI journeys and feel free to reach out via Linkedin if I can be of any help!
About the Author
Karim has c.18 years of financial services experience gained within M&A, Private Equity (both as investor and in operational value creation), and within transaction services at the Big-4. He has lived across Asia since 2012, and previously worked at EQT Asia, where he led their value creation efforts across their APAC portfolio for 4 years, focusing specifically on data science and digitalisation. He is a trained data scientist, with degrees from Insead, Imperial College London, and the University of Cambridge.
About Matsu Partners
Matsu Partners is an AI-Assisted search fund focused on an acquisition in SE Asia. This revolves around a series of AI tools, including those that can identify and rank 125,000+ companies, along with identify over 70,000+ professional profiles, to support a Search. This was launched in late-May 2024 and is currently fund-raising.
