The CIO role in an AI world
“I don’t know who the winners will be and quite possibly the winners might not even exist yet.” - Richard Tomlinson
Chat about AI is everywhere. Not joking, but I recently went to a meditation seminar called “Staying Grounded in an AI world”. This topic is rightly getting plenty of attention in investing too. In a field as dynamic as AI and investing, conclusions quickly become stale, so please tell me where you disagree.
On the CIO Chair podcast, where Sean Thompson and I interview CIOs, AI has come up frequently and almost exclusively positively. Most variants of the thought process is, if AI can process more information, find patterns and build models faster than humans, how can we rethink the decision process to humans plus AI and where will humans continue to make the highest impact?
What is AI already good at?
AI is exceptional at processing digital information. It can search documents, compare disclosures, identify changes in language, populate models and highlight inconsistencies between sources.
Rob Groves described its use in credit research. Analysts review financial results, public statements, sector developments and ratings information. Specialist AI tools can make this work easier and more scalable, keep it more up to date and expand the reach of an individual human analyst.
Andy Chorlton, CIO of Fixed Income at M&G, pointed towards where he thought the value was:
“We want our analysts to spend more time thinking and less time doing.”
In his framing, the analyst’s main value is not building models or processing information. It is challenging the conclusions and deciding: what does this information mean for us?
AI is currently strongest where the task can be specified, the information exists and the output can be checked. What did the company report? Which covenants changed? What growth assumptions sit inside the rating model? These questions help us improve our understanding of "what is the case tody". There is enormous value in answering this more fully and quickly.
Jeremy Rogers, CIO of Better Society Capital, described using several AI agents with different roles rather than relying on one system. He is using AI to identify missed information and blind spots. Importantly, the person leading the work at Better Society Capital previously worked as an investment manager and had excellent context of using AI. In this case, the technology is being designed around the investment process rather than bolted on from outside.
Investment decisions
The problem with replacing entire decision-making teams with AI, is analysis is not an investment recommendation. Many valuable assumptions on what the market, company or valuation might look like in future trumps understanding today's situation.
AI models can help here. What has happened to similar investments? similar themes? However, aside from the usual limitations of back-testing, this kind of analysis cannot tell us whether the future will resemble the past. It's important that we do not gloss over this point. One of the components of both analysis paralysis and overconfidence, is an understanding of how well you can make a prediction. For some there is never enough analysis, for others, their back-test is enough. Because investing is a long-term, fairly idiosyncratic business, no individual investment, or strategy can be relied upon to outperform indefinitely.
This was echoed with the CIOs we interviewed. None claimed an ability to make consistently accurate predictions and so this inability to predict the future is a structural problem not a technological one. Where technology has been directed towards markets, it has tended to be shorter-term anomalies. From High Frequency Traders to Renaissance Capital, the short- term reversions has proven fertile territory, but the barrier to entry has just been reduced for systematic investing.
It is likely humans will still be required for long-term outperformance. AI can interrogate an earnings-call transcript and explain management’s stated strategy. Alpha lies in deciding whether that strategy is achievable and, if not, how the miss feeds through to earnings and its borrowing profile. The analysis can be prepared of course by AI, but weighing it up is as much of an art as a science.
This moves the role of humans in the loop from producing reports to scoping and scrutinising them. Huge leverage can be delivered by outsourcing the production of analysis to AI, but the value of that analysis can only be unlocked if it is critically understood.
Preparing for an unknowable future
The message that our CIOs come back to is that good portfolios should not depend on correctly predicting the future. David Thompson, CIO of Zurich UK, described forecasting the next black swan as “a bit of a fool’s game”. Simon Pilcher said historical returns and correlations only take an investor so far. USS considers different futures and seeks to build a portfolio able to thrive in most and survive practically all of them.
Guillermo Donadini split risk assessment into two parts. One can be illustrated through stress tests. The other is whether decision-makers can “stomach that risk” at the bottom of a drawdown without feeling compelled to de-risk.
Guillermo, highlights the role of an accountable human, who has built relationships with and communicated consistently with an Investment Committee. AI cannot yet determine whether an investment committee will tolerate the risk profile of a portfolio.
I am not convinced that AI solves the knottiest problem: the long-term forecasting used in asset allocation, particularly where leverage is involved. It had not been solved pre-AI either. Better analysis of history and today may still produce only minimal improvements in forecasting.
So where can we point AI to help?
AI has made analysis easier, but the reality remains disconnected from some of the promise.
Area | Where AI can help | Potential impact |
Analysing history | Analyse larger datasets, include older records and test broader relationships. | High |
Understanding today | Combine data sources, compare disclosures and identify red flags, changes or opportunities. | High |
Forecasting | Generate scenarios and test assumptions. Less useful where the future differs from the past or risks emerge through new interactions. | Low |
Portfolio interactions | Look through asset-class labels to identify common factors, concentrations and second-order risks. | Medium |
Strategy and horizon | Test mandate limits, liquidity needs and consistency with stated objectives. Less able to assess stakeholder behaviour or the institution’s real time horizon. | Low |
The analysis of history and aggregation of current information are ripe for improvement. For example, systematic strategies should benefit and Private-market investors can use AI for opportunity scanning, due diligence and underwriting.
Overreliance
CIOs we have spoken to have concluded it would be a strategic own goal not to use AI to improve analysis. The danger arises when users fail to understand what can be inferred from that analysis and with what confidence.
Regular readers know my concerns about boiling complicated things down too far. Simplification can discourage consideration of scenarios that have not yet played out. Siddharth Chakravarty took us through the AI infrastructure build theme that is impacting every different market and Richard Tomlinson, CIO of Local Pensions Partnership Investments, calls some diversification “optical diversification”. Using AI to help draw out the common factor risks that are not easily captured through the industry sector might be useful in trying to understand portfolio-level behaviour, but it can also make us overconfident about what happens next.
So what?
AI can read more, compare more and calculate more than an investment team could manage alone. It should improve research and remove repetitive work.
The harder part is in forecasting. That requires an appreciation of horizon, interactions, human behaviour and what has been lost when the world is reduced to factors.
For now, humans retain primacy in framing the problem, understanding the institution and owning the decision. That boundary may move with time.
The risk is not only that investment teams trust an incorrect answer. It is that they lose the ability to challenge or probability weight a plausible one.
AI should help us analyse not think and the CIO’s role is to make sure AI helps as much as it can, but no more.
Next week's blog will be "Intuition revisited". As always get the blog delivered directly to your inbox on Home | Deciders | for mental fitness | change your mind. Other blogs in the CIO series What's happening in PE? Building winning investment teams
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