Artificial intelligence has become a central topic in asset management, featuring prominently in industry discourse and client conversations alike. Yet when we applied a purpose-built semantic search system to Morningstar’s internal Manager Research notes (2019-26), the qualitative assessments written by our global team of 130 analysts as part of their due diligence on approximately 3,200 rated strategies per year, we found that meaningful AI adoption is concentrated among fewer than 20 firms, most of them with long-established quantitative or systematic capabilities. Beyond this small group, the gap between rhetoric and reality remains wide.
What’s Actually Happening
The industry at an inflection point. The step-change is not that AI has arrived in investment management. Machine learning, for example, has been a feature of quantitative investing for over two decades. The step-change is that generative AI has made a different kind of capability widely accessible: the ability to synthesize large volumes of unstructured information, parse documents, and scale research coverage without requiring the specialized infrastructure that traditional machine learning demanded.
For quantitative teams, generative AI and its recent coding capability are accelerating existing workflows, compressing model development cycles, and automating tasks. For fundamental managers, it is opening a door that was previously closed: scalable, systematic research without needing to build or train their own models. Instead, they can leverage powerful pretrained LLMs directly.
To learn more about Morningstar’s primary research on how AI is being deployed across the asset management industry, and why its role remains largely focused on enhancing productivity rather than driving investment decisions, please access our full report.
