As technology and investment market trends converge, now is the time for PE firms to learn what AI really is.
One clear trend in the investment world right now is the growth of Private Equity deals. The first half of 2021 has seen PE deals worth more than $500bn; helping to propel global mergers and acquisitions activity to an all-time high.
The other trend is AI technology growth — despite being around for decades, and growing significantly since the arrival of cloud computing earlier this millennium— AI is still growing. This trend isn’t particularly new, but it means that AI capabilities are becoming more common place in more businesses; and leading businesses are taking advantage of the benefits of AI to outperform their lagging competition.
Combining these trends, it’s no surprise that there’s an increasing amount of investment activity involving companies that have AI as a core component of their proposition and enterprise value. AI companies raised a record $33 billion in equity funding in 2020, and what’s more, an increasing number of companies claim to be “AI-first”; using cloud technology, data science and engineering to deploy AI into different aspects of their business to operate better.
Time for PE to learn about AI
If AI is deemed to be a crucial differentiator for a company’s proposition, or a major value lever given the rich, unique dataset the company has, then it’s important this is well understood when assessing the company’s valuation and investment potential. When considering a deal with an “AI-first” company, any investor needs to be clear on the existing and potential value of AI within that company.
- Do buyers understand exactly what AI capabilities they’re buying?
- How do buyers evaluate and leverage the claimed AI capabilities?
- For data-rich companies that aren’t even claiming to use AI, how do buyers assess the potential value that AI could add to the acquisition?
Many investment brochures will advertise the organisation’s good data governance, rich data assets, scalable data platform, and leading AI capabilities that makes it a unique investment opportunity — but in many cases the truth may be it’s not so unique. After all, despite AI maturing in the last few years, it’s still like teenage sex — more companies are talking about it than actually doing it.
PE companies will use their preferred IT due-diligence and Commercial due-diligence partners to confirm a potential investment’s true capabilities, potential and risk; but these organisations rarely have the AI practitioner experience to robustly assess AI; and the remit of a typical IT DD and CDD exercise is too broad to provide any deep analysis within the AI domain.
The answer: AI due-diligence
Given the increasing importance of rigorously evaluating a company’s AI capabilities before investing, and the lack of detail IT DD and CDD studies provide for this, DataSparQ is finding itself developing valuable AI Due-diligence reports on potential assets for PE investors. Whilst a PPT slide deck isn’t the traditional output of a DataSparQ project (we pride ourselves on developing and running operational AI solutions in the real world; our outputs are usually more Python than PPT), our deep AI practitioner expertise means we know what good looks like, and because we understand what it takes to deliver AI successfully in the real world, we’re in a privileged position to help others.
Our AI DD service consists of both a “backward-looking” confirmatory assessment of a potential investment’s AI capability, as well as a “forward-looking” analysis of how AI could be used to unlock further hidden value in that business. Different investments often have a different focus; for businesses with established AI capabilities that give them a unique competitive advantage, the confirmatory assessment is more important. For data-rich companies early in their analytics journey, the forward-looking analysis is often more valuable.
The areas of investigation during an AI DD include:
- Data diagnostic — a review of the data collected, stored and used by the business. Is it done systematically or ad-hoc? How accurate, relevant and complete is it? How is it catalogued and governed?
- Algorithm diagnostic — an assessment of the algorithms and analysis used. Are they appropriate (meeting requirements for accuracy, explainability, auditability, bias, customisation)? Are they robust? Will they scale and remain competitive as the business grows and the market matures? Are they compliant with industry rules and regulations?
- Technology diagnostic — a review of the technology systems and architecture used to build and run these AI capabilities. Are they resilient? Will they scale to support future growth requirements? Are they cost efficient? Are they secure?
- Capability diagnostic — an analysis of the business’s way of designing, building and running AI solutions. Do the right skill-sets exist? Is the capability unique and sustainable? Does the way of working encourage innovation?
- AI Opportunity review — a process to identify, validate and quantify high-value opportunities for AI yet to be exploited. What hidden value levers can AI unlock? What incremental value does AI offer over a more low-tech solution? How feasible is each opportunity? What’s a realistic delivery timetable for value capture?
Given a modest amount of time with the management team, and other key individuals in the business who might not normally be involved in traditional DD exercises, these questions can be answered within the timelines for most DD processes. It can be efficient for the AI DD provider to work alongside the IT DD and CDD providers so the same questions don’t have to be asked twice.
As AI matures and becomes a differentiating value lever in organisations, it’s increasingly important for investors to put this capability under the microscope when considering deals. This means PE companies need to start asking new questions, and learning more about AI than the buzzwords and headlines in order to know what questions to ask, let alone answer them.
The innovation of AI technologies and methods is fast and shows no sign of slowing. It will be an ongoing learning journey for investors to ensure their assessments remain relevant. As a result, AI practitioners like us may find ourselves increasingly doing PPT as much as Python as we become the go-to service providers for AI DD.