Here are 7 key takeaways from their insightful discussion.
1. Artificial Intelligence will have a significant impact on private markets, from investment decision making through to operations, investor relations, compliance and much more.
AI, and particularly GenAI is democratizing information access within firms and across industry. PE firms in particular see AI as a real value driver. Many high-volume PE workflows are reasonably standardized and commoditized; handing these over to AI/ML will free up staff to focus on higher-value work. GenAI also enables analysts to compile and analyze large datasets without needing to be tech experts. This saves time and provides more opportunity for strategic thinking – often starting with a broader, cleaner set of data.
AI will change how firms operate across the value chain, from deal sourcing through to investor relations. It will enable greater industry and sector specialization, and the ability to better analyze underlying investment risk. The winners will be firms that embrace AI to build IP in their areas of investment specialization.
2. Addressing AI implementation challenges and considerations in advance is critical to successfully embedding it across your organization.
Given AI’s transformative disruptive potential, leaders need to think through how to manage an array of potential challenges. These include governance, data security, the speed at which technology is moving, evaluating risk, and the human element around AI implementation. Putting in place a robust governance framework is critical, as regulators undertake more hands-on involvement in how AI is used.
AI is heavily dependent on data, and many asset management firms are still very unstructured when it comes to managing their data and knowledge assets. Data security is critical, particularly as GenAI puts more information in the hands of a broader cross-section of the organization – and potentially outsiders. AI technology is moving very quickly and much of it still isn’t mature; it might be better to bet on solving a specific use case than on a particular piece of technology. Once they take the AI plunge, many firms struggle when scaling from experimentation to industrialization, particularly if they try to implement AI on their own.
Fund managers are also grappling with understanding the potential value of implementing AI, versus assessing potential risks. Many lack the internal know-how needed to conduct such risk assessment, much less implement the technology. Here, working with partners and talking with peers can be a critical part of the process. The human element incorporates everything from having the understanding and skillset to implement AI successfully, to managing the resistance to change and fear of the unknown that will inevitably encounter.
3. (Gen)AI’s proliferation will have far-reaching implications for your talent, including recruitment and retention strategies, and workforce training.
As with any transformation process, your (Generative) AI journey may encounter internal resistance and fear of change. It is important for leadership to openly acknowledge that the nature of work change – and that this may be uncomfortable. Nevertheless, the benefits to individual workers are clear. Many firms estimate staff spend 20% or more of their time finding information. GenAI will speed that process and deliver better insights and results.
As the nature of work changes, training and onboarding will also be impacted. For decades, young employees have learned the business by performing mundane, repetitive tasks – exactly the sort of foundational work GenAI will eliminate. For years we’ve heard that digital-native workers are not interested in the sort of boring, repetitive operational tasks. Part of AI’s promise is greater job fulfillment. Expect new roles to develop, with data scientists playing a central part. Traditional roles will change, and some will be eliminated, but people of all ages who are open to change will find exciting opportunities – just as they’ve done during previous technological shifts.
4. ‘GIGO’ still counts: the fundamental importance of high data quality and robust data infrastructure.
GenAI streamlines and automates the ability to collect, clean, and standardize data and provides unprecedented opportunities to derive business insights and operational alpha. But the rule of ‘garbage in, garbage out’ is more critical than ever. Bad data quality exponentially increases the probability of AI leading you down the wrong pathway. Strong data governance and security are critical, and a robust data infrastructure has never been more vital.
If your data is a mess or your governance structure is unclear, start getting your house in order now, even before your AI project begins. Such upfront work is time-consuming, but critical to achieving the desired results and quickly demonstrating proof of value for your AI projects. Cultivate the right in-house expertise – or partnerships – to ensure a long-term, sustainable data strategy, infrastructure, and governance that sets up future AI applications and use cases for success. And of course, information security and cybersecurity are more critical than ever, so don’t cut corners here.
5. Model governance is critical to support accuracy and believability.
The beauty of Generative AI is how it democratizes information access: anyone can use it and benefit from it without needing to understand where all the data is stored or how the technology works. However, GenAI has also become infamous for ‘hallucinations’ – the generation of made-up ‘facts’. That’s one reason why robust governance is so critical, from ensuring data quality, to having clear processes for ‘training’ models correctly, and built-in access to source documentation and data so analysts can quality-check AI-generated conclusions.
Whether you build and train your own models or source them from a partner, model explainability is critical to developing and maintaining trust within your organization and with external stakeholders. We can expect regulators to mandate governance standards and require firms to demonstrate accuracy, fairness, and a lack of bias. A natural segway to the topic of regulation.
6. Understanding coming AI regulation – and how to prepare for it.
Regulators are becoming increasingly focused on the role of GenAI in finance. Key areas of focus will include ensuring fairness, preventing bias, demonstrating data quality and auditability. It will be helpful to think in terms of three P’s – Patchwork, Prescriptive, and Proof.
Patchwork, because a host of state, federal, and international governments and agencies will enter the fray, making it very difficult to have commonality across the regulatory framework. Prescriptive, in that they will be less concerned with understanding the underlying technology than in ensuring the appearance at least of fairness and avoidance of bias. And Proof, in that regulators will expect you to demonstrate that you have taken the right steps to ensure that AI-driven results are non-biased. There will be fine line between proving an understanding of why the AI model has made the decision it made and revealing your firm’s ‘secret sauce’. Of the three Ps, ‘Prove it’ will quite possibly be the most onerous.
7. Tips for starting your AI journey successfully.
Leveraging AI and GenAI will be vital for business survival and growth. Given AI’s promise, it makes sense to start now rather than waiting for the technology to mature. In any case, the technology is moving so quickly that it will probably look different at the end of a 6-month implementation project. Here are 7 tips for getting starting.
- Start at the right size for your company and avoid overhyping and overreaching.
- Keep abreast of developments in AI technology and how it can integrate with your existing data, tech stack, processes, and operational requirements.
- Spend time understanding how AI will impact your workforce, including your ability to attract and retain new talent.
- Evaluate relevant, actionable use cases for implementing AI. Think in terms of reaping business benefits and not just the ability to apply technology to a problem.
- Define internal use cases and build a small team of people who are excited about AI and interested in working on it.
- Be willing to make mistakes, fail quickly, learn, and move forward – while staying focused on your end objectives.
- And finally, find a good partner to work with or use as a sounding board on your AI journey.
AI will fundamentally change how Asset Management and Alternatives businesses operate in the coming years. It is critical to begin your journey to embrace AI starting with internal data and internal operations. Private models deployed securely in private instances can help you address some of the concerns around data security and enable methodical and measured implementation.
Contact Linedata to learn how we are helping firms like yours address these challenges.