In its bid to be an early adopter of AI, CalSTRS, the $358.4 billion pension fund for California’s teachers, has written a set of generative AI (GenAI) policies and guidelines, conducted an AI cost-benefit analysis and identified a pipeline of scalable use cases for the technology that all demonstrate business value.
CalSTRS is also well down the road on board and staff training efforts (staff will attend an AI bootcamp this September) to upskill the team in a sweeping change management effort.
Documents from a recent board meeting presented by April Wilcox, senior investment director, David Liao, director of enterprise IT solutions and innovation and Vaishali Dwarka, director of enterprise strategy management, laid out the next stage in a strategy where CalSTRS has committed to becoming a leader in the field.
As of August 2024, a total of 25 candidate AI use cases have been documented and analysed across more than ten business programs, six of which sit in investments.
Here, the technology will support predictive dashboards, cash flow forecasting and data augmentation and will help staff draft memos and knowledge share. It will be applied to portfolio and research intelligence, manager due diligence and facilitate the legal documentation behind the investment process, including reconciliation in private assets.
GenAI will be used to query data, generate summaries, gain insights into markets, ask questions and provide correlation analysis, they detailed. It will enhance risk management, optimise portfolio management and create operational efficiency where CalSTRS estimates the technology could increase efficiency by 85 per cent whereby processes that used to take 2.5 hours, take just 15 minutes.
In a joint presentation, Accenture’s Ted Kwartler and Eyal Darmon detailed how integrating AI comes with important questions for institutional investors. CalSTRS must ascertain if its people, data and technology are ready for AI and investors also have to know where they will apply AI, and how to balance the value and risk of the technology.
Risks include the potential for unreliable outputs or the unauthorised disclosure of confidential information. Alongside security vulnerabilities like fraudulent attacks and disinformation, the technology also carries the risk of bias and will usher in changes in the workforce, which can exacerbate user mistrust of AI.
Integrating AI impacts investors’ risk exposure and shifts cost models. Decision-making models will be data-driven and CalSTRS will be dependent on ecosystem partnerships, they said. AI implementation also comes with multiple costs like tech infrastructure, data management and governance as well as the cost of running pilot projects, innovation labs and talent development.
The role of the board
In their presentation, Kwartler and Darmon also detailed the important role of the CalSTRS board in monitoring the rollout of AI.
For example, the board needs to stay informed about market trends which means monitoring the evolution of AI and how peer organisations are leveraging the tech. The board needs to encourage agility in AI decision-making to respond to industry shifts and have a firm grasp of the strategy. They also need to maintain an understanding of the AI program’s alignment with CalSTRS’ strategic goals and stay abreast of how AI investment is budgeted and its performance measured.
The consultants counselled on the importance of tying costs to mission-driven outcomes – suggesting starting small and scaling strategically. Quick wins are a good way to showcase upfront value which in turn builds momentum for long-term investments and will drive the transformation.
Using board notes that charted the evolution of AI, Kwartler and Darmon illustrated that Agentic AI, which acts on our behalf, planning and executing tasks end to end, is now capable of placing orders, scheduling repairs, or optimising workflows without needing a human to step in.
Alongside text (the most advanced domain, which has already passed medical, law, and business exams), AI is now able to write code for programmers and translate foreign languages.
AI also has multiple use cases outside the investment process including supporting payments to reduce repetitive interactions and freeing up staff time for other tasks.
