Large institutional asset owners face the challenge of first defining what AI truly entails
When assessing the impact of artificial intelligence (AI), large institutional asset owners face the challenge of first defining what AI truly entails. The potential of AI to identify investment opportunities, revolutionize industries, and enhance productivity has been widely acknowledged. However, understanding its application in specific tasks necessitates a clear definition, as reported in an article by Top1000funds.com.
Defining AI
According to Jacky Chen, director of total fund completion portfolio strategies at OPTrust, AI involves “systems, tools, and machines that are programmed to think and act or learn like humans.” AI distinguishes itself by its capacity to learn and adapt its rules or algorithms over time, allowing it to “improve continuously by observing patterns.”
Ari Shaanan, managing director of digital innovation and private markets solutions at PSP Investments, notes the shift from deterministic outcomes to probabilistic outcomes in AI. This shift, he says, aligns more closely with the non-deterministic nature of human decision-making, where inputs can yield a range of outcomes.
“It is actually much closer to the way the human brain works, in the sense that if you give someone the same inputs, if you give them the same core set of environments and contexts, people will react differently, depending on whatever is the day, the time, the hour, the additional thousands of potential inputs in there,” he says.
The interaction between humans and AI has also evolved, enabling more “natural interactions.”
“It just facilitates interactions between people and computers. It’s the next evolution,” Shaanan adds.
Peter Strikwerda, global head of digitization and innovation at APG Asset Management, defines AI as “the ability of computer systems to execute cognitive tasks that require human intelligence, without human intervention.”
Fei Fei Li, inaugural Sequoia Professor at Stanford University's Computer Science Department and co-director of Stanford’s Human-Centered AI Institute, asserts that AI represents “a genuine inflection point of technology” and one that is “ready to really transform businesses, to deliver products and services that would really have mass value.”
Shaanan emphasizes the importance of developing in-house AI expertise to support analysts and portfolio managers. “We spent a lot of time actually sharing the knowledge that my team has gained on these AI projects with our investor teams, to think through the impact it could have on the portfolio. We’ll share knowledge from our projects, but we’ll also interface a lot with our partners in what they’re doing on AI in their portfolio. And then we’re trying to bring that back again to our investors and actually more just stimulate sort of a PSP-wide level discussion around AI and upskill everybody in terms of knowledge on the topic, how to use it, where it’s valuable, where it can make a difference, where it’s going to impact society,” he says.
“We’re really trying to raise PSP’s game in this from a knowledge perspective, more than anything,” says Shaanan. PSP applies a rigorous use-case test for AI applications, ensuring potential benefits outweigh costs.
Productivity gains and ethical considerations
Rest superannuation fund, managing $49.3 billion assets, is actively participating in the Microsoft 365 Copilot Early Access Program, incorporating AI into familiar tools like Word and Excel. Chief technology and data officer Jeremy Hubbard notes that AI within Microsoft 365 has already led to personal productivity improvements, paving the way for further customization based on Rest's unique data.
Rest has developed its own version of ChatGPT, named RestGPT, utilizing Microsoft's Open AI GPT 3.5 model. The implementation aims to enhance internal communication and interaction through Microsoft Teams.
While Rest currently has a small internal innovation team, Hubbard envisions building a community around AI, focusing on delivering multiple “proof-of-value experiments.” The fund sees potential value in automating software engineering processes, such as code rewrites, with measurable time and cost savings.
“For me, what’s exciting there is we can estimate with our estimating methodology, this is how long it would have taken a team of developers to update, and then we can do the same thing with AI. And we’ll be able to have, I think, a really black-and-white view that this saved us x hours or x weeks, and x hundreds of thousands of dollars. That’s emerging, but that’s another space where I think we can prove very tangible value,” says Chen.
Chen stresses the importance of ethical guidelines and regulatory frameworks as AI advancements continue.
“Strong regulatory frameworks and ethical guidelines will be crucial. That requires us, as a society, to see collaboration between industry, governments, and also the public. We are all stakeholders in this, in these discussions, and we need to make sure that there is a collaborative effort that helps us to shape the landscape going forward, and we [that] are not just wanting to focus on the innovative side. We need to make it inclusive,” he says.
