How Asset Owners are Rethinking Talent and Leadership: AI and the Long Game

Much has been written about how artificial intelligence is transforming investment operations, service delivery, and portfolio analytics at asset owners. Far less attention has been paid to a more difficult and arguably more consequential question: What does AI mean for talent, leadership, and institutional capability in long horizon investors?

We held conversations with sovereign wealth funds, public and corporate pension plans, endowments, and superannuation funds across North America, Europe, the Middle East, and Asia Pacific, representing trillions in assets. Drawing from these insights, we outline five emerging themes that are reshaping how these institutions approach talent and leadership in an AI‑enabled world.

The most immediate talent impact of AI is being felt inside core HR processes, not headcount decisions. Across asset owners, AI is already embedded in the front end of hiring:

• Mapping and identifying potential candidates using platforms such as LinkedIn
• Screening CVs and accelerating longlist development
• Using vendors (e.g., Qneiform) and AI agents to augment talent research
• Structuring assessment processes, including interview guides, interview note synthesis, and reference summaries

This shift is also beginning to influence how roles themselves are defined. As one asset owner Head of HR observed: “Every approval for an external hire now comes with a question: can any part of this role be performed by AI?”

Importantly, these applications are aimed at speed, consistency, and signal clarity, not outsourcing judgment. Final hiring decisions still sit firmly with humans.

This reflects a broader philosophy: AI is used to reduce friction in the system so people can focus on higher value evaluation and decision making.

Performance management is one of the clearest examples of AI changing how work is managed, even where formal frameworks remain unchanged. Organizations are deploying AI to:

• Translate role descriptions, strategic plans, and annual priorities into clearer individual goals
• Synthesize 360 degree feedback and draft initial performance narratives
• Rapidly generate tailored learning content to support emerging capability needs

Crucially, AI is not replacing qualitative and quantitative assessment. In several cases, it is being used to nudge managers toward deeper, more specific insights, for example, prompting clearer evidence or reflection while preserving open ended human input.

The result is higher quality dialogue, with less administrative burden and more time available for coaching and development.

Despite widespread speculation, AI is not being used as a primary justification for hiring fewer junior people. Many asset owners are already lean, and leaders are acutely aware of the long-term risks of weakening their future talent pipeline.

What is changing is the definition of readiness. As one leader put it: “The bar has changed, not the need.” Organizations are re-examining:

• What junior roles are for when AI handles more foundational analysis
• Which skills signal long term potential in an AI enabled environment

Against this scenario, there is growing emphasis on learning agility and adaptability, comfort moving between technical and conceptual domains, and hybrid intellectual profiles (e.g., technical training paired with broader critical thinking).

The guiding principle heard repeatedly was, “Train for the athlete, not for the sport.” Given how fast the technology is changing, organizations shouldn’t just hire someone who knows today’s technology because it will change. Instead, the approach should be hiring someone who demonstrates learning agility so they can perform any “sport.” 

A strong people centric philosophy is a common refrain. At the same time, expectations are becoming clearer and more explicit. As institutions move forward, there is a growing recognition that everyone must be brought along, even as the bar continues to rise.

While some individuals later in their careers may choose not to deeply reskill, for those with a meaningful runway ahead, upskilling is increasingly expected. Leaders and employees alike are expected to understand how to work with AI tools, question their outputs, and apply them responsibly.

AI is already influencing:

• Workforce planning
• Business planning
• Learning delivery through faster creation of tailored content

In the near term, many organizations are relying on change agents embedded within teams to support adoption, recognizing that resistance often stems from unfamiliarity rather than opposition.

The most profound talent shift is at the leadership level. Across asset owners, AI is:

• Freeing leaders from time intensive synthesis and preparation
• Enabling more focus on big picture thinking, judgment, and orchestration

As a result, organizations are rethinking what they expect from leaders:

• Encouraging experimentation and intelligent failure
• Providing direction without dictating tools or solutions
• Reframing jobs around capabilities rather than static role definitions
• Creating conditions for learning, autonomy, and creativity

While AI fluency is increasingly valued, most institutions acknowledge it remains underdeveloped across leadership populations. As a result, assessment and development efforts are focusing on signposts of readiness, curiosity, resilience, and adaptability rather than narrow technical knowledge.

Leadership, in this context, is less about knowing the answers, and more about creating environments where the best thinking can emerge.

Across these conversations, it has become clear that AI is a work in progress for organizations, acting as a “stress test” that reveals how institutions think about talent, risk, judgment, and the kind of leaders they want to build.
For long horizon investors balancing financial mandates with national and intergenerational responsibilities, the question is not whether to adopt AI, but how to do so without eroding the very capabilities that underpin long term success.

The organizations making the most progress are quietly re architecting how talent is hired, developed, evaluated, and led, recognizing that in an AI enabled world, human judgment becomes increasingly more valuable.