From trusted information to better decisions: Reflections on how investment operating models are evolving in the age of AI

The recent Investment Data and Technology Summit in Sydney brought together several familiar themes reshaping investment organisations today.

Artificial intelligence (AI) featured prominently across discussions, alongside data management, operating model transformation, and the growing complexity of multi-asset portfolios.

What stayed with me after the event was a broader question: As firms invest in AI, what is the operating model they are ultimately trying to create?

Much of the industry’s effort over the past decade has gone into strengthening data platforms, modernising technology infrastructure, and improving operational efficiency. Those investments continue to matter. Increasingly, however, the focus is shifting towards how those foundations can support better decisions, faster responses to changing conditions, and more effective navigation of an increasingly complex investment environment.

Several discussions reinforced the importance of trusted information. The industry has long recognised it as a critical foundation of effective decision-making. Investment organisations have spent years working to improve data quality, governance, and control. AI is placing renewed attention on those foundations because it tends to expose weaknesses that were previously easier to overlook.

At the same time, the challenge extends beyond accuracy. Information also needs context. Data becomes more valuable when people can understand what it means, why it matters, and how it informs the decisions they make. This helps explain the growing interest in semantic models, contextual data frameworks, and new ways of delivering information through natural language interfaces and visual tools.

Investment organisations have spent years working to improve data quality, governance, and control. AI is placing renewed attention on those foundations because it tends to expose weaknesses that were previously easier to overlook.

The discussion is shifting from collecting information to making it easier to interpret, contextualise, and act upon.

One theme I found particularly interesting was the gap between individual productivity and broader organisational value. Many professionals are already using AI tools to work more efficiently. Tasks such as generating content, conducting research, and analysing information can often be completed faster than before. Yet translating those gains into enterprise-level outcomes appears considerably more challenging.

That distinction matters. A successful operating model is measured by how effectively the organisation performs as a whole. It depends on governance, workflows, operating disciplines, and shared trust in information. Individual productivity can contribute to those outcomes, but does not guarantee them.

Discussions around human oversight pointed to a broader reality: advances in AI do not reduce the importance of human judgement. Instead, they heighten the need for confidence in decision-making, particularly in areas where outcomes have significant investment implications.

Tasks such as generating content, conducting research, and analysing information can often be completed faster than before. Yet translating those gains into enterprise-level outcomes appears considerably more challenging.

As technology becomes more capable, organisations need greater clarity around accountability, transparency, and decision ownership. In investment management, confidence in a decision depends not only on the outcome, but also on understanding how it was reached and whether it can be trusted.

Private markets illustrate this particularly well. As investment teams work towards more connected portfolio views, they often encounter underlying information that remains fragmented across documents, systems, and reporting formats. As public and private market exposures become more integrated, the quality, accessibility, and context of information become even more important.

From my perspective, that points towards a broader shift in operating model design. The objective is not simply to add AI to existing processes. It is to create an environment where trusted information, appropriate governance, effective workflows, and human judgement work together more effectively.

As public and private market exposures become more integrated, the quality, accessibility, and context of information become even more important.

Leadership also emerged as a recurring factor. As operating models evolve, senior leaders play a critical role in setting direction and defining the outcomes the organisation is trying to achieve. That includes identifying where AI can create value as an enabler of business outcomes and where other approaches are more appropriate. The greater challenge is translating that ambition into action: identifying where value will be created, aligning investment with those priorities, and maintaining the organisational focus required to deliver enterprise outcomes.

The firms that make the greatest progress may not be those that adopt technology the fastest. Their advantage is likely to come from understanding where technology can genuinely improve outcomes, building the foundations that support that ambition, and maintaining the discipline to focus on problems worth solving.

As the industry focuses on what AI can do, an equally important question is whether investment organisations can use it to strengthen the quality of decisions made across the enterprise. Firms that can transform trusted information into consistently better decisions will be best positioned to create lasting value in the years ahead.