Leaders Rethink Operating Models for the AI Era at AssetOps London

At AssetOps London senior operations leaders came together to explore how asset management firms are redesigning their operating models to strengthen data foundations and prepare teams for AI-enabled transformation.

Asset management operations are entering a new phase of transformation. 

One fascinating insight AsseOps London last week was that modernisation can no longer be treated as a series of isolated technology projects. Building resilient, scalable, operations requires firms to connect all aspects of business operations, from strategy to client outcomes, into a single operating vision.

As one senior asset management operations leader put it: “You can no longer talk about an operations function in isolation, or a tech function in isolation, or a data function in isolation.”

Operating Models Are Becoming Enterprise-Wide

Strong foundations still matter, of course, including controls, operational discipline, technology, and resilience. But today’s operating environment demands adaptability.

Speakers highlighted that firms are now managing a broader ecosystem of public and private markets, platforms and tools. This complexity means operations leaders need a clear view across the whole organisation, not just individual functions.

Several leaders argued that the most effective firms will be those that define an “operating vision” linked directly to business strategy. That means understanding where to partner, where to build internally, where to simplify, and where to invest for long-term advantage.

Data Foundations Are Now a Baseline Requirement

The discussions also showed how central data has become to the future of asset operations. In private markets, in particular, leaders described the challenge of working with unstructured information, inconsistent terminology, and institutional knowledge that often sits in people’s heads rather than formal systems.

One private markets operations executive noted: “Private markets still rely heavily on human interpretation and unstructured information.”

That creates a difficult balance. Firms need to standardise repeatable processes and build trusted data foundations, while preserving the judgment, narrative, and context that drive investment insight.

Speakers suggested that clean, governed data may no longer be a source of competitive advantage on its own. Instead, it is becoming the baseline. The real differentiation will come from how firms interpret that data, combine it with human expertise, and use it to make better decisions.

AI Transformation Requires New Change Muscles

AI was, unsurprisingly, one of the most discussed topics of the day. But speakers were careful to distinguish between AI hype and the practical realities of implementation.

One transformation leader argued that AI-driven change is different from traditional transformation because it touches every role, evolves quickly, and creates uncertainty around workflows, skills, ethics, and client expectations.

“The technology itself is built to be iterative, but our change process is built to be linear.”

That mismatch is creating new challenges for firms. Traditional change programs are designed to move toward a clearly defined endpoint. AI transformation is more fluid. It requires leaders to test, learn, adapt, and keep employees engaged.

The human element was central to this conversation. Speakers emphasised that AI adoption cannot simply be “done to” employees. People need to understand the purpose of the change, see how it affects their work, and be empowered to help shape new processes.

Modernisation Depends on Handoffs, Partners, and Controls

Legacy modernisation was another major focus. While technology is advancing rapidly, leaders cautioned that the hardest problems often emerge between systems, teams, and service providers.

As one senior operations and technology leader said:

“Technology on its own, whether it is AI or anything else, is wonderful. But when you have the handoffs in process, sometimes it breaks.”

This is especially relevant as firms work with multiple providers, each developing their own tools, platforms, and AI capabilities. Speakers noted that provider innovation can create new opportunities, but it can also introduce risk when feeds, controls, and reconciliations change across interconnected systems.

The conversation pointed toward a more partnership-led model. Rather than viewing outsourcing purely as a cost lever, firms are increasingly looking for co-creation, better assurance, and stronger alignment between internal teams and external providers.

Leading the Future of Asset Operations

The future of asset operations will be shaped by more than technology. AI, automation, data architecture, and modern platforms all have a critical role to play, but transformation depends just as much on strategy, culture, governance, and execution.

AssetOps created a forum for senior leaders to benchmark those challenges, share practical lessons, and explore what resilient, scalable, and digitally enabled operations should look like.

As the conversations at AssetOps London showed, leaders who want to take their operations to the next level will create models that can absorb complexity and support responsible modernisations and innovation.

____

To hear more conversations on leadership, operational transformation, and the future of asset operations, join us at AssetOps Chicago on August 11th, 2026.