Redesigning investment manager operating models for an AI-driven future

For many years, operational transformation has been driven by efficiency programs, platform upgrades, regulatory remediation, and targeted automation.

While still important, these no longer reflect the scale of the challenges underway. The industry is being rapidly reshaped by the confluence of artificial intelligence (AI), data proliferation, and structural shifts in capital markets, putting pressure on operating models that were not designed for today’s pace, complexity, and need for adaptability.

Demands on the operating model are expanding with the need to support faster decision-making, greater personalization, stronger resilience, and more connected execution across the enterprise.

That pressure is building from several directions. Investment strategies are becoming more complex as firms broaden their exposure across private markets, digital assets, and multi-asset strategies. Distribution models are evolving as institutional capabilities are adapted for wealth and retail channels. Clients are looking for outcomes, with greater expectations around transparency, responsiveness, and tailored insights. At the same time, regulatory scrutiny continues to intensify, especially around valuation, transparency, controls, resilience, and accountability.

Each trend introduces new operational demands. Together, they are redefining what firms need from their operating model. The model must support innovation and control, customization and scale, global consistency, and local delivery. At the same time, firms are shifting from periodic reporting and retrospective analysis to real-time, insight-driven decision-making.

Clients are looking for outcomes, with greater expectations around transparency, responsiveness, and tailored insights.

Growth and scale are not interchangeable​s

This is where many organizations are feeling the challenge most acutely. They may be growing in assets, clients, markets, and product breadth, but growth does not automatically create scale. Size adds operational demands while scale creates the ability to reduce them without adding equivalent cost, risk, or friction.

In conversations with clients, that distinction is becoming increasingly important. Many have made meaningful progress modernizing parts of their environment, yet transformation remains more difficult than it should be. The time to market for new products is often delayed. Data may be available, but it is not always consistent, timely, or trusted. Processes work, but often rely on significant coordination across teams and systems. Transformation initiatives are delivering value, while the broader opportunity lies in scaling those benefits across the enterprise.

The common reality is operating models are being asked to support a broader and more dynamic set of business outcomes than they were originally designed for.

In conversations with clients, that distinction is becoming increasingly important. Many have made meaningful progress modernizing parts of their environment, yet transformation remains more difficult than it should be.

The capabilities gap is now more visible

The next phase of transformation will require a stronger set of enterprise capabilities with data at the center. A reliable, connected foundation that serves as a trusted source of truth across investment, operations, risk, finance, client reporting, and regulatory functions is essential. Without it, insight remains fragmented and AI’s potential is constrained.

Infrastructure that is modular and scalable is also key. Future operating models must support faster solutioning, easier integration of new capabilities, and the ability to scale as priorities change. This means creating models with capabilities used across the enterprise, rather than supporting individual systems, products, or asset classes.

AI then becomes a way to amplify the model. Its greatest value will come when embedded into business outcomes and operational workflows, improving automation, insight, efficiency, resilience and speed to market. Agentic AI signals an even more significant shift from tools that assist individual users to digital agents that can orchestrate multi-step workflows across systems, controls, and teams.

AI then becomes a way to amplify the model. Its greatest value will come when embedded into business outcomes and operational workflows, improving automation, insight, efficiency, resilience and speed to market.

Building the foundations for AI-enabled execution

AI’s potential extends far beyond productivity gains and task automation. As firms begin to explore agentic AI, the opportunity shifts towards orchestrating complex workflows across functions, systems, and decision points. Realizing that potential, however, depends on more than technology. AI-enabled execution requires operating models built on trusted data, clear governance, strong controls, and well-defined accountability. Without these foundations, firms risk amplifying inefficiencies rather than improving performance.

This is where the conversation about operating model modernization needs to evolve. Existing models have supported firms through years of growth, market change, and regulatory complexity. The issue is not that existing models have failed; it is that expectations have changed. Models built around periodic processing, manual exception management, and product-specific workflows are being stretched by demands for real-time insight, faster solution design, and increasingly autonomous execution. As AI becomes embedded within business processes, weaknesses in data quality, governance, and operational design become more visible, amplifying both regulatory and reputational risk.

For leaders, this creates an opportunity to redesign with greater intent. The firms that make progress will rethink the relationship between platforms, processes, people, and location. They will ask where activity should be standardized, where expertise should remain close to the client or market, and where technology can remove friction from both the employee and client experience.

While every firm’s transformation journey will be different, three priorities are likely to shape the next generation of operating models.

The first priority is data management. As data volumes grow across public, private, and digital markets, the ability to govern, connect, and distribute data will become one of the most important determinants of operational performance. The opportunity is to turn complexity into actionable insight.

The second is AI adoption. In the near term, AI can help teams identify exceptions, surface insights, and reduce manual workload. Over time, agentic AI will support more end-to-end execution, with humans setting intent, applying judgement, and overseeing outcomes. This marks the transition from automation to intelligent orchestration.

The third is the platform ecosystem. Future operating models will be shaped by how effectively firms connect internal platforms, external partners, data providers, and market infrastructure. The objective is to create an ecosystem that is interoperable, resilient, and capable of supporting business change without rebuilding from the ground up each time a new partner joins the operating model.

From today’s foundations to tomorrow’s execution

The evolution will happen in phases. Today, many firms are focused on strengthening data-centric platforms, simplifying operating environments, and introducing more conversational ways for teams to interact with information. Tomorrow, digital agents will increasingly coordinate activity across functions, creating a hybrid workforce where human expertise and AI-enabled execution work together. Looking further ahead, emerging technologies may enable more predictive operating environments , helping firms anticipate issues, identify opportunities, and respond to client needs before they fully materialize.

Emerging technologies may enable more predictive operating environments , helping firms anticipate issues, identify opportunities, and respond to client needs before they fully materialize

A leadership challenge

For chief operating officers, operational heads, and transformation leaders, this is as much as a cultural challenge as it is a technology initiative. Are operating models truly designed for scale, or have they simply grown? Which capabilities can be evolved, and which need to be redesigned? Is your organization building the foundations for AI-enabled execution, or merely automating existing complexity?

The firms that answer these questions with clarity will be best positioned to turn complexity into advantage. In an AI-driven future, operating model design will sit much closer to the center of business strategy. It will be one of the critical mechanisms for turning strategy into execution.