How the investment industry is rethinking the operating model in the AI era
Key takeaways
Investment firms increasingly view AI adoption as a redesign of the operating model rather than simply a technology upgrade or cost-efficiency exercise.
- Institutions are rethinking entire roles and workflows while considering the long-term effects of AI on career paths, team dynamics, and organizational structures.
- Demand is growing for cross-functional technology and finance expertise, analytical and interpretation skills, critical thinking, judgment, decision-making, and communication capabilities.
- Firms remain reluctant to move to full automation without human oversight, with analytical rigor and human insight continuing to underpin investment decision-making.
The integration of new technologies has profound implications for workflows and organizational structures across the investment industry.
The deployment of artificial intelligence (AI) is transforming workflows and reshaping organizational structures across every industry – and the investment management sector is no exception.
CFA Institute recently conducted a series of roundtables spanning asset management, private banking, private markets, technology and human resources to understand the implications for how investment firms are structured, staffed and managed.
Our conversations revealed a consistent message: institutions increasingly view AI adoption not as a technology upgrade, but as a redesign of the operating model itself.
The CFA Institute Research and Policy Center recently launched a new research series exploring the implications of the integration of artificial intelligence on capital markets and the investment profession. Explore Artificial Intelligence and the Future of Finance: A Framework for Structural Change.
A fundamental shift
AI is now an operational reality in investment management, with material productivity gains already visible across client servicing, investment analysis and operations.
There is excitement around the potential to improve client reporting, portfolio rebalancing, research, due diligence, risk detection, sustainability analysis – and many other use cases.
The breadth of potential applications for AI means institutions are looking beyond automating existing processes and rethinking whole roles and workflows.
However, we also heard from multiple institutions that implementation is not simply a cost-efficiency exercise. The long-term impact on roles, career paths and team dynamics is an integral part of AI integration strategies.
Organizing for AI
AI adoption raises important questions about how firms structure their operations:
Firms we spoke to are weighing centralized versus decentralized AI capabilities, and modern platforms against legacy systems that are often run separately from the business. Centralized implementation and data teams can bridge the gap between technology teams and front office functions and help scale adoption by replicating successful use cases across business units.
Many institutions are developing internal solutions rather than relying solely on external providers. Technology departments are seen as a key enabler, and their role is increasingly evolving into a validation and infrastructure function that supports business-led applications.
The successful deployment of AI capabilities depends on close engagement between tech teams and front-office functions to ensure tools are practical and relevant. As a result, institutions are looking to develop multi-skilled teams and embed quant capabilities within the front office rather than in isolation.
Ownership of the AI roll-out, meanwhile, remains unresolved: infrastructure may sit with the technology department, but accountability for its use in the investment process itself is frequently less clearly defined.
New roles, new skills
As AI integration accelerates, institutions are increasingly looking for a broader and more interdisciplinary skill set.
Human oversight is shifting from individual tasks to designing processes, setting targets, validating outputs and carrying accountability when something goes wrong. However, performance outcomes remain the ultimate measure of success, and institutions remain reluctant to move to full automation without human oversight.
Firms are increasingly seeking:
- Cross-functional expertise spanning technology and finance.
- Analytical and interpretation skills to validate AI outputs. Demand is rising for critical thinking, judgment, decision-making and oversight alongside technical skills.
- Communication capabilities to translate complex insights into actionable advice. Communication, storytelling and stakeholder management are seen as key gaps.
This shift reflects a broader evolution in the investment industry, as more roles move from execution to judgment and insight.
Balancing efficiency and understanding
While AI can significantly improve productivity, wealth and asset managers are looking to ensure that efficiency gains do not come at the expense of deep understanding. Analytical rigor and human insight continue to underpin investment decision-making.
Junior talent is in a difficult transition: firms are reconsidering junior hiring, but they acknowledge the need to develop tomorrow’s leaders – especially around critical thinking, judgement and ethics.
Upskilling is now a strategic issue. Many institutions are investing in continuous, role-specific learning to support new workflows rather than one-off qualifications.
CFA Institute is responding to demand for AI content with curriculum touchpoints, wealth-management programs and prompt-engineering training. The 2027 curriculum for the CFA® Program, for example, includes a new learning module introducing financial data science, AI, and large language models.
Evolving expectations
In the AI era, institutions will need to engage with a more technology-driven generation of future investors who will have different expectations for advice and decision-making.
Integration, skills and strategic clarity will remain differentiators. Firms must define clear problems, embed AI into workflows and training, and align insights with client risk profiles.
When it comes to adopting AI, the emphasis across our roundtables was clear: redesigning the operating model and governing it well matters more than speed alone.
This article is part of our series featuring insights from investment management firms on the challenges facing the investment industry in a changing landscape. Discover how industry leaders are responding to shifting market, regulatory, and talent pressures while building the capabilities needed for the future.
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