Artificial Intelligence continues to gain traction across the investment management industry, but the path from early experimentation to large-scale transformation remains challenging. In our previous article, we examined the foundations of AI adoption and the early barriers highlighted in the first chapter of the MDOTM and EY research paper. That opening chapter set the scene: AI is becoming a structural capability, but most firms have yet to move beyond initial pilots.
This new article focuses on the third chapter of the research and explores what it truly takes to scale AI across an investment organization. If the first wave of adoption has been defined by curiosity, experimentation, and isolated initiatives, the next one will be defined by integration, consistency, and enterprise-wide impact.
Why Scaling AI Matters
Many firms launch AI pilots that successfully demonstrate potential but fail to translate into real operational or strategic change. This is not because the technology is lacking—on the contrary, early results often prove its value—but because pilots remain disconnected from the broader organization. The research makes a clear point: AI creates meaningful value only when it becomes part of the firm’s structural architecture, influencing how decisions are made and how processes operate at scale.
Scaling AI means moving from prototypes to systems, from one-off experiments to repeatable models, from isolated insights to integrated capabilities.
Building the Foundations for Scale
The third chapter outlines the conditions required for AI to grow from local success stories into a widespread, trusted part of the business. These conditions span strategy, operating model, technology, governance, and culture. Each must evolve in parallel.
1. Strategic Alignment
AI must be linked to concrete business outcomes. Firms that move beyond pilots treat AI as a long-term growth driver rather than a collection of tactical tools. This means clearly defining where AI will create value, such as:
- improving investment outcomes
- enhancing client personalization
- increasing operational efficiency
- supporting scalability across functions
When strategy guides the roadmap, AI becomes part of the firm’s identity rather than a side project.
2. An Operating Model Built for AI
As AI adoption expands, organizations require a coordinated model to support it. Early experimentation may be centralized, but scaling demands collaboration across investment, risk, distribution, operations and technology teams. Successful firms introduce structures that promote:
- shared development standards
- cross-functional ownership
- transparent coordination of use cases
- a shift from isolated pilots to reusable approaches
This ensures that AI becomes embedded in day-to-day decision-making rather than remaining externally bolted onto existing processes.
3. Scalable Technology and Data
Pilots often rely on temporary setups—custom code, manual data preparation or isolated environments. At scale, this is not sustainable. Firms need robust, enterprise-grade foundations:
- consistent, governed, high-quality data
- modular architectures that allow new use cases to be added quickly
- environments that support model deployment and monitoring
- sufficient computational capacity, often cloud-based
A scalable technology stack enables repeatability, reliability and faster time-to-market for new AI applications.
4. Governance That Enables Responsible Growth
As AI becomes more integrated into core workflows, governance becomes a critical enabler. Firms need clear frameworks for model oversight, explainability, accountability and risk management. Strong governance builds internal confidence and allows innovation to proceed with clarity and control.
5. Culture, Skills and Confidence
Ultimately, scaling AI is as much about people as it is about technology. Teams must understand how to use AI tools, trust the insights produced, and feel supported throughout the transition. Firms that succeed invest in:
- role-specific training
- clear communication around AI’s purpose
- internal success stories that build momentum
- a culture where humans and AI work together
This cultural shift transforms AI from a technical asset into an organization-wide capability.
Rethinking Processes, Not Just Accelerating Them
One of the strongest messages from the research is that scalability requires reimagining processes rather than simply making existing ones faster. The real impact of AI comes not from incremental improvements, but from redesigned workflows where automation, predictive analytics and content generation reshape how work is done.
For example, instead of using AI just to summarize information, firms can redesign entire research workflows. Instead of producing manual client materials, firms can generate tailored proposals and reports automatically. Instead of treating AI as a tool for speed, they can use it to fundamentally elevate the scope and depth of what teams can deliver.
A New Phase of Transformation
The third chapter makes it clear: the real value of AI lies in scale. Firms that can align strategy, technology, processes and people are beginning to unlock advantages that go far beyond productivity. They are reshaping their operating models, enhancing investment decision-making, strengthening client engagement, and building more adaptive organizations.
The industry is entering a new phase—one where AI is no longer a series of pilots, but a core foundation for competitiveness. The firms that embrace this transition will not only accelerate their transformation, but define the future of investment management
