The rise of generative AI brings with it new approaches to the ways in which investing and management of market risk should be handled. The author of this article considers how data will be used, and explains why AI is a "co-pilot."

Anush Newman, CEO and co-founder of commercial data solutions provider JMAN Group – which has offices in London, New York and Chennai – explains how the use of generative AI is redefining the future of investment strategy. The firm works with private equity organizations and portfolio companies. (JMAN is a Baird Capital portfolio company.)

The editors are pleased to share these views and invite readers to respond. The usual editorial disclaimers apply to views of outside contributors. Email tom.burroughes@wealthbriefing.com

Ever since its arrival, ChatGPT has dominated the business narrative with its ability to generate deceptively human-like text capabilities. It can summarize lengthy documents, create reports and other vital business communications, research the most complex economic trends and industries, and even code. The result is a huge opportunity for businesses to automate key processes, streamline and enhance overall operations, especially in the current climate. The investment field is no exception.

A new age of investing
Traditionally investment strategies were formulated by combining human intuition and experience, usually supplemented by a basic level of market analysis. But with the exponential growth of data and an increasingly competitive and changeable marketplace, this approach is reaching its limits. At the same time, the data skills deficit means that few private equity firms or other types of asset management companies have the sufficient in-house human skills to analyze the vast amount of data now available. It is clear that we require new ways of closing this intelligence gap. Enter the game-changer: AI.

Together, generative AI and machine learning are accelerating a new age of data-driven decision-making in investment. It is now possible to automate the analysis of millions of data points – market trends, economic data, company financials, academic posts, news sentiment, and more – to report back on important trends and insights in minutes. 

Generative AI can also deliver data-driven insights that challenge conventional investment strategies, uncovering patterns, correlations, and opportunities that human analysts might otherwise overlook. Through the creation of synthetic data that replicates actual market prices, economic indicators and customer behaviors, investors are now able to test their investment thesis under various conditions and scenarios in order to optimize their portfolios more effectively. This allows for investment managers to execute trades with unparalleled accuracy and efficiency to mitigate risks and provide higher returns.

Also fundamental to this shift is the opportunity to overcome the fallacies of human emotion in the investment decision-making process. It is well documented that, whether aware of it or not, emotional biases can get in the way of investing, leading to bad decisions and poor returns.  By marrying human judgement with data-driven recommendations backed by deep factual analysis, investors minimize this risk and make more informed, rational decisions. The approach not only results in better decision-making but also fosters a more stable and rational market environment.

Building the data foundations
The reality though is that unlocking the full potential afforded by generative AI requires a solid data foundation. Yet PE firms, like most other organizations, often lack basic data skills across their teams. Without the ability to fundamentally understand statistics and ask good questions of data you cannot begin to effectively use even the most straightforward data analysis techniques or technology platforms. 

Investment and portfolio managers will be unable to apply data insights safely in their day-to-day working life because they are unable to assess the accuracy of results and fully determine their meaning. Consequently, many find themselves solely reliant on their data experts. This naturally creates bottlenecks and single points of failure, but it also severely inhibits a firm from becoming truly data driven. 

So what is the best course of action for PE firms seeking to use AI to enhance their investment strategy? The answer is far from straightforward. It will depend on the commercial strategy of individual firms and their portfolio companies. What part of my existing investment process can be automated, augmented or improved by using AI or better analytical techniques? This is a great starting point for identifying high ROI use cases; choose one and demonstrate value, grow momentum and buy in from the organization before building a broader infrastructure for further value capture. 

Further down the line it is likely that there will also be a strong business case for investment in upskilling and retraining staff across the board.

This should include everyone, including all senior teams. Even today, it still surprises me how few senior stakeholders 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 secondhand account of somebody else’s understanding, no matter how advanced it may be, could never substitute for your own personal analysis. By building up your own expertise now, you and your senior team will be able to ensure that your data-led decisions are the best possible choices.

AI as a co-pilot, not a replacement
But while AI is a powerful co-pilot, it’s not a replacement for human expertise. Even as it continues to improve efficiency and decision-making in the investment sector, AI still has limitations when dealing with vast unstructured datasets, natural language understanding, and complex contextual analysis. 

And, as with all exciting disruptions, the increased reward is mirrored by increased risk, from increased vulnerability to cybersecurity attacks to privacy and ethical concerns. In this way, the need for human intervention remains paramount for navigating these complexities and delivering sound recommendations which align with individual investor goals.

Alongside these risks, PE funds and management teams should be simultaneously assessing the danger of maintaining a legacy business model: are your competitors adapting faster than you, are new players entering the market, are consumer behaviors changing as a result of easier access to AI tools? These are just a subset of the questions investors should be asking to de-risk their investments, but they will also help them leverage AI to turbo-charge their returns.

Embracing the AI revolution
The integration of AI into investment strategy is far from another technological upgrade but rather a defining shift in the financial paradigm.

In the coming years, we can expect generative AI to play an even more dominant role in investment thesis, from enhancing predictive analytics, automating trading strategies, hyper-personalizing investment solutions, improving risk assessment, delivering real-time sentiment analysis, and beyond. 

At the same time, as AI technology develops it is likely that we will see even greater focus on the development of investment-specific tools and applications. This will lead to more accurate, agile and effective strategies while ultimately redefining the approach to investment strategy.  

With this, the reality is that PE firms cannot afford to lag behind the AI curve. Of course, there may be extra requirements needed to meet these new developments, not least reskilling or upskilling employees, hiring new personnel, and potentially embarking on structural change. However, in an increasingly AI-driven future, it will, most certainly, be an investment which pays dividends.