by Anush Newman, CEO and Founder of commercial data solutions provider JMAN Group
Ever since the arrival of ChatGPT and its near human-like ability to generate text, speech, code and everything in between, artificial intelligence (AI) has dominated the narrative. Dive into real-world examples and you’ll likely find a trove of different ways it is transforming modern business practice. In marketing, for example, AI tools are now widely being adopted to help create personalised content at scale, generate novel concepts and explore new creative possibilities. Equally, AI’s ability to automate routine administration is redefining the shape of customer service, with more self-service options and quicker response times. Amongst all this though, one of the most profound, yet largely unreported, impact AI integration is having can be found in the investment world.
According to PWC, assets managed by algorithm-driven and increasingly AI-enabled digital platforms will surge to almost US$6 trillion by 2027, nearly double the figure for 2022. A separate analysis by McKinsey & Company reveals that investment firms using AI-driven tools increased portfolio returns by 15% and reduced management costs by 20%.
This trend signals a broader one across other aspects of the asset and wealth management industry, particularly in Private Equity (PE) as firms have started raping up AI use in their investment and portfolio management strategies.
But to fully understand why AI is having such a radical impact on investment strategy and what it means for startups, let’s first consider the once traditional way of doing things.
The changing face of investing
Every successful investor will know the value of a strong hunch. While valuations and investment strategy focus on conscious reasoning as the key to a good investment, most investors have placed just as much importance on their relationships, experience and a certain ‘gut’ feeling. In most cases this has been supplemented by only a basic level of financial and commercial diligence – historical profit margins, revenue and customer analysis based on aggregated data in conjunction with relatively high level market studies and management forecasts.
But rising asset prices and a competitive market can make it risky to rely solely on these methods. Added to this is the wider context of inflationary pressures and volatile economic conditions which have continued to hamper performance and reduce asset values. At the same time higher interest rates have increased debt servicing costs – all of which is making it harder to buy and sell assets; crucial to the PE lifecycle of generating returns and raising new funds
These conditions, together with awareness of the power of AI and data analysis rising across the board, has created an environment where PE firms and venture capital portfolios are coming under increasing pressure to improve insight into valuation processes from regulators and investors alike. Historically, investors have wanted visibility into just the core financial trends. Now they have much higher expectations for transactions on a data front row, and are much more interested in understanding the ‘how and why’ certain financial and operational trends are occurring. While there is still a need for investment managers to utilise their experience, knowledge and network, this shift towards data-driven strategy is promoting a culture where factual analysis is the basis of investment decisions. An example of this was the Financial Conduct Authority’s Chief announcement that PE firms must increase transparency and data sharing, or risk regulatory measures further down the line.
This all sits alongside increased interest in leveraging the AI investment opportunity too. From our experience, most Limited Partners (LPs) and General Partners (GPs) are eager to invest in assets where they can unleash the power of AI to drive growth and efficiencies, while avoiding assets where AI could adversely disrupt their business model.
AI applications and adoption
In terms of what this means for the startup community, the upshot is that a robust data strategy is no longer a ‘nice to have’ but a commercial imperative. Founders can no longer afford to consider their data narrative towards the point of investment but must prioritise a holistic approach to their data and secure a deep understanding of that data from day one. Fail to do so and the reality is that it will be incredibly difficult to take advantage of the opportunities and/or avoid the risks of AI.
Take for example, exit strategy. Usually data is not a priority for founder-led companies who are much more focused on securing growth. Not anymore. This shifting buyer behaviour is creating a growing expectation for management teams to provide broader and deeper datasets at exit. Potential investors are no longer happy to suffice with learning about basic profit and turnover figures – it needs to be evidenced by granular data and solid analytics. With this, it’s becoming increasingly critical for startup companies to place greater emphasis on data and analytics from the offset in order to support their ‘equity story’ and give investors comfort on past performance and future returns. With higher expectations because of common availability of tools and processes, the risk is if founder-led companies don’t do this, they will not achieve the best valuation they could and will have a more painful transition in the first year or two of the hold.
Startups can also harness this new data capability to increase valuation through shifting business and operating models. For example, once their PE firm has developed their value creation plan, they can use AI to monitor data aspects such as pricing, level sales and margins across key dimensions (ie. business unit, product category), and use it to help accelerate performance, decrease costs or spot problems arise. This can help ensure the maximum valuation further down the line; but can only be executed off the back of a solid data foundation.
Getting the data foundations in place
So, what is the best course of action for startups seeking to harness the power of data to boost investor appeal?
Generally speaking, taking full advantage of AI’s rapid progression will necessitate significant cultural and organisational changes – such as upskilling and retraining staff so they have the skills and knowledge to use data effectively.This should include everyone, including all senior members. Even today, it still surprises me how few business leaders are able to understand and interpret their core business data, instead relying on a handful of experts. After all, it’s impossible to know what you don’t know – and a second-hand account of somebody else’s understanding, no matter how advanced it may be, could never substitute for your own personal analysis. Only by building a solid data foundation early doors can founders ensure that they, along with their senior team, are well-positioned to pitch their equity narrative with confidence and boost investment appeal.
An innovation advantage
It’s not hyperbole to say that the rapid pace of AI integration is redefining the shape of modern investment. Within the decade we are likely to see increased automation and a range of innovative new approaches that challenge everything we thought we already knew about investment strategy. For startups who take up the mantle and harness the full power of their available data now, there will be a real opportunity to gain a huge innovation advantage.
