Key takeaways
- AI adoption is becoming commonplace; value creation is not.
- Governance is emerging as an early indicator of adaptability and future competitiveness.
- The next investment opportunity lies in AI diffusion, not just AI infrastructure.
- Active research is critical to identifying which companies can turn AI investment into shareholder returns.
Over the past two years, AI investing has largely been a story of infrastructure.
Markets have focused on hyperscalers, semiconductor companies and the extraordinary capital investment powering the AI ecosystem. Closer to home, much of the debate has centred on which Australian companies might be disrupted by the technology.
That's a worthwhile question. But increasingly, I think it's the wrong one.
The more interesting investment question today is not who is talking about AI, or even who is deploying it. It's who is creating sustainable competitive advantage from it.
That distinction has become a major focus of our research.
Over the past year, Fidelity has engaged directly with more than 30 ASX boards to understand how companies are governing AI, managing its risks and preparing their organisations for what comes next. Those conversations revealed meaningful differences in how companies are approaching the opportunity. Some are treating AI primarily as a productivity tool. Others are beginning to rethink business models, customer experiences and sources of competitive advantage.
For investors, those differences matter.
Moving beyond the AI winners and losers narrative
When AI first entered the market consciousness, the narrative was relatively simple: identify the winners and avoid the losers. But technological shifts are rarely that straightforward.
History shows that adaptability often matters more than starting position. The companies that ultimately create the most value are not always the earliest adopters. They are frequently the organisations with the culture, leadership and governance structures needed to evolve as the technology matures.
That insight became one of the key conclusions from our board engagement work.
Governance is proving to be more than a risk management exercise. It is increasingly a signal of organisational capability. It helps reveal whether companies can make disciplined decisions, allocate capital effectively and adapt when the competitive landscape changes.
For active investors, those attributes can be every bit as important as technological capability itself.
The debate is changing
What's particularly interesting today is how quickly the market conversation is evolving. Twelve months ago, investors were asking whether companies had an AI strategy. Today, they are asking whether those investments are generating returns.
Boards are beginning to focus on productivity outcomes, implementation costs, accountability frameworks and the economics of AI deployment. New questions are emerging around agentic AI, governance structures and how organisations measure value creation in practice.
In many respects, we are moving from the experimentation phase of AI to the execution phase. That's where active research becomes increasingly important.
The research question we're exploring now
The governance work has given us a valuable foundation. But it has also raised a much bigger question. If AI adoption becomes widespread, where will excess returns come from?
In our view, the next stage of research is likely to focus less on the technology itself and more on the second-order effects.
- How will AI reshape competitive moats?
- Which industries are likely to see margin expansion?
- Where will productivity gains accrue, and who will capture them?
- Which businesses have unique data assets, customer relationships or market positions that become more valuable in an AI-enabled economy?
- Most importantly, which management teams are demonstrating the ability to convert technological change into shareholder value?
These are not questions that can be answered through screens, datasets or company presentations alone. They require ongoing engagement with businesses, boards and industry participants.
Portfolio implications
For portfolio construction, this means we are becoming increasingly focused on a new set of signals.
We're interested in companies that can demonstrate disciplined AI investment rather than simply high AI activity. We're looking for management teams that are realistic about the challenges as well as the opportunities. We're examining whether competitive advantages are becoming stronger or weaker as AI adoption accelerates. And we're paying close attention to governance because, in many cases, it provides an early indication of a company's ability to adapt.
The reality is that we're still in the early innings of this transition.
The biggest investment opportunities may not come from identifying who is building AI. They may come from identifying who is successfully integrating it into their business model and creating lasting value as a result.
That's the next phase of research we're focused on. And we think it's where some of the most compelling opportunities for active investors may emerge.
