The Intelligence Era in Asset Management 

What will it take to build an enterprise that can compete in the age of AI? 

Over the past year, we brought together senior leaders from across the asset management industry to explore this simple but important question. While firms were at different stages of AI maturity, executives consistently described many of the same challenges. The discussion shifted from individual AI use cases to a broader question: How do organizations need to evolve if intelligence is to become an enterprise capability rather than another point solution?

Across those conversations, the same barriers kept coming up. Despite differences in size, operating model and technology landscape, leaders pointed to the same structural barriers preventing AI from delivering enterprise-wide value. More importantly, they increasingly saw AI not simply as another technology, but as a catalyst for rethinking how knowledge, decisions and expertise move across the organization.

This playbook explores that shift, and what it will take for asset managers to move from fragmented AI initiatives to an enterprise designed for intelligence. 

Access to AI may be getting easier, but scaling it is not. Firms still need to connect fragmented data, knowledge, workflows and governance throughout the enterprise.

Across asset management firms with different operating models, technology landscapes and levels of AI maturity, executives identified eight themes shaping intelligence:

1. AI is advancing faster than organizations are changing: Technology capabilities are evolving rapidly, while operating models, governance and ways of working struggle to keep up.  
2. Intelligence remains fragmented across the enterprise: Critical knowledge sits across people, functions, platforms, documents and partners, making it difficult to access, reuse and apply consistently. 
3. Data alone is no longer enough: Years of data modernization have created stronger foundations, but data by itself doesn’t create better decisions.
4. AI initiatives are repeatedly rebuilding the same foundations: Teams recreate prompts, governance models, integrations and business logic for individual use cases, slowing delivery and making AI harder to scale.
5. Operational complexity has become the biggest barrier to transformation: Adding AI to fragmented processes can create more complexity rather than less, making workflow redesign as important as the technology itself.
6. Competitive advantage is shifting from models to enterprise intelligence: As foundation models become widely accessible, differentiation increasingly comes from combining proprietary data, business knowledge and operational context.
7. Governance is more important than ever: Risk, compliance and oversight need to sit inside enterprise workflows so intelligence can scale safely and consistently.
8. Building intelligence is fundamentally an operating model transformation: AI changes how work, decisions and expertise move through the organization.  

Taken together, these themes suggest the industry isn’t struggling because it lacks AI investment or capable models. Intelligence remains fragmented as it tries to move through organizations that were never designed to connect and apply it at enterprise scale. 

Three structural barriers explain why:

1. Intelligence remains trapped inside organizational silos
   
   Asset managers possess enormous amounts of knowledge. Investment research, portfolio insights, regulatory interpretation, operational expertise, client interactions and historical decisions all represent valuable intellectual capital. Yet much of this intelligence remains isolated within business functions, individual teams or technology platforms.
   
   As Lucas Cortini, investment product analyst at Schroders, put it, “The data is not centralized and just to gather the data you need to go through many different tools and places. It’s just very hard.”
   
   As a result, organizations repeatedly solve the same problems, recreate the same analysis and make similar decisions without benefiting from collective enterprise learning. 
   
   AI can generate new knowledge, but it cannot easily access intelligence that remains fragmented across disconnected systems, inconsistent processes and institutional memory.
   
   Until organizations make enterprise intelligence discoverable, connected and reusable, AI will continue to amplify fragmentation rather than eliminate it. 


2. Organizations optimize individual tasks instead of redesigning decision-making 
   
   Many AI initiatives begin by automating existing activities. “Teams are spending hundreds of hours every month collecting documents, storing them, but then having to type data points from a PDF into a downstream system,” explained Oliver Wedlake, director of sales, EMEA, at Canoe. “It’s a workflow that shouldn’t exist today.” 
   
   While these improvements create measurable productivity gains, they rarely change how the organization actually operates.
   
   The executive discussions highlighted a common challenge: many firms have become better at accelerating individual tasks without fundamentally redesigning how decisions are made across the enterprise. 
   
   True transformation requires organizations to rethink entire workflows, not simply replace isolated activities with AI. 
   
   The greatest opportunity lies in redesigning how intelligence flows between people, systems and decisions, enabling humans and AI to work together as part of a connected operating model. 


3. Every AI initiative starts from scratch
   
   Perhaps the most consistent frustration expressed by executives was the repeated effort required to deliver every new AI capability.
   
    “The repetition of things that go on throughout the organization is such a massive waste,” said Yugo Ashida, head of ISS enterprise architecture, at Fidelity International. “But at the same time we can’t seem to get organized to show these are the central things that we’re going to do to enable people to experiment in a safe way. These are the standard kits, that means we only have to do that once.
   
   ”The result is an organization that scales AI projects rather than enterprise intelligence—and these are often doomed to fail. As one cloud architecture leader observed, “I’ve personally seen tens of AI pilots fail. The models and tools are amazing. They go through a two-, three- or four-week proof of concept and get there, and then someone asks, ‘How are you going to get that data into one place?’ There’s no answer. So we see a lot of projects just fall down there.”

The debate over whether organizations should adopt AI has largely been settled. The question now is how enterprises need to change so intelligence can move continuously across people, systems and decisions.

For decades, technology transformation has focused on digitizing processes, integrating applications and modernizing data. The intelligent enterprise operates differently. Rather than treating intelligence as something created by individuals or isolated applications, it treats intelligence as a strategic enterprise asset, one that is continuously captured, connected, governed and reused across every business function. 

This changes where competitive advantage comes from. As foundation models become more powerful, accessible and interchangeable, the model itself becomes less differentiating. Sustainable advantage comes from what surrounds it: proprietary knowledge, business context, operational expertise, governance and the ability to orchestrate across the enterprise.

While every organization will evolve differently, the architecture of intelligence rests on four interconnected layers: knowledge, intelligence, orchestration and learning.

1. Enterprise knowledge
   
   The foundation is the organization's collective knowledge: structured and unstructured data, business policies, investment research, regulatory guidance, operational documentation, client interactions and institutional expertise. Rather than remaining isolated within individual functions, this knowledge becomes shared enterprise context that the business can access and reuse consistently. 
   
   
2. Enterprise intelligence 
   
   Knowledge alone doesn’t create value. It becomes intelligence when information is interpreted, connected and enriched with business meaning. AI agents, reasoning models, workflows and domain expertise turn raw information into trusted insights that can support decision-making across functions. 
   
   
3. Enterprise orchestration
   
   Intelligence only creates value when it influences decisions and actions. Orchestration connects intelligence with business processes, human expertise and enterprise systems so the right insight reaches the right person or agent at the right point in the workflow. Governance, security and compliance sit inside this layer, allowing intelligence to move safely across the organization.
   
   
4. Enterprise learning 
   
   Intelligence doesn’t stop once a decision is made. Every decision, exception, interaction and outcome creates feedback that strengthens the enterprise over time. Learning becomes a continuous organizational capability rather than a periodic exercise.
   

Once the architecture is mapped, the next challenge is deciding where to begin. This was one of the most debated topics in our executive discussions. While every organization faced different commercial priorities, regulatory obligations and technology landscapes, there was striking agreement on the approach.

The firms making the greatest progress aren’t attempting to deploy AI across every function at once or pursuing isolated proofs of concept with no path to enterprise scale. Instead, they’re deliberately selecting a small number of high-value use cases that could solve immediate business challenges while simultaneously creating reusable enterprise intelligence. 

The goal is to establish the foundational capabilities that can be reused across future AI initiatives, helping teams adopt AI faster while reducing implementation effort, governance overhead and operational complexity. 

Viewed in this way, every successful use case becomes another building block in the enterprise intelligence architecture.

Across our discussions, executives consistently prioritized use cases with four characteristics:

1. They operate across multiple business functions 
   
   The greatest opportunities often sit where intelligence must move between investment teams, operations, compliance, risk and technology. These cross-functional workflows expose the fragmentation that prevents organizations from operating as a connected enterprise. 


2. They rely on complex knowledge rather than simple automation
   
   Processes involving regulations, investment mandates, client documentation or operational policies benefit disproportionately from AI because they depend on reasoning, context and domain expertise rather than repetitive task execution. 


3. They combine measurable business value with reusable capabilities 
   
   The strongest initiatives deliver immediate operational improvements while creating assets that other teams can use, from governed knowledge repositories to decision logic and shared enterprise context.


4. They strengthen enterprise governance from the outset 
   
   Successful programs embed governance, human oversight and auditability into their operating model from day one. That foundation makes it possible to reuse intelligence safely across the organization.

While every organization will define its own roadmap, our discussions highlighted several domains where firms are already generating measurable value while establishing reusable enterprise capabilities. 

• Regulatory interpretation and policy intelligence 
• Investment guideline management and monitoring 
• Investment research and knowledge management 
• Client servicing and relationship intelligence 
• Operational exception management 
• Enterprise search and knowledge discovery 
• Workflow orchestration across investment operations 
• AI-enabled decision support for portfolio, risk and compliance teams 

These aren’t simply AI use cases. Each implementation strengthens the shared knowledge, governance, workflows and decision capabilities upon which future initiatives build. Over time, those individual initiatives form a connected intelligence ecosystem: knowledge becomes easier to discover, decisions become more consistent and the organization becomes progressively better equipped to deploy AI at scale.

Problem: For most firms, creating and interpreting investment guidelines is still a manual process, slow and prone to errors. This process varies across teams and individuals, has no audit mechanism and cannot scale across the enterprise. This creates a bottleneck at compliance, one of the most crucial functions in the business.

Opportunity: Agentic AI provides a unified system capable of multi-step reasoning, validation and contextual decision-making with speed, consistency and auditability.  

A strong use case can prove the value of enterprise intelligence. But scaling that value requires something more: a common operating foundation that prevents each new initiative from becoming another point solution. 

Here are six moves leaders should make to scale: 

• Set one enterprise ambition. Define the value target in productivity, control quality, service responsiveness and time-to-market rather than counting pilots. 

• Fund the platform first. Approve budget for data products, context services, model gateway, evaluation harness and governance tooling before approving a long tail of isolated use cases. 

• Name one accountable executive. Split accountability slows scale. One executive should own platform outcomes end to end, with domain business owners accountable for adoption and value. 

• Mandate governed context. Require that priority AI workflows use approved data products, source retrieval, permissions and evidence capture. 

• Adopt supervised autonomy as policy. Draw clear thresholds for what can draft, what can recommend, what can act under approval and what must remain fully human-led. 

• Insist on a 90-day result. The first production workflow should be live in one quarter with a clear value baseline, quality target and control dashboard. 

Together, these actions provide a practical foundation for building enterprise AI capability in a sector where regulators already expect strong governance, traceability, testing and management-body oversight. 

Sapient Bodhi is Publicis Sapient’s agentic platform built for AI orchestration at enterprise scale. It brings agents, models and workflows into a single system designed to execute business processes according to embedded logic and rules without cloud or model lock-in. Bodhi’s library of pre-built, customizable agentic solutions runs with the right enterprise context, policies and data from the start. Teams design, run and monitor their agents from a centralized dashboard, making it easier to track value and manage risk across the business. 

Asset managers need an enterprise AI platform that turns data, enterprise context and organizational knowledge into better decisions at scale. 

That foundation starts with trusted data, shared enterprise context and organizational memory. It combines agentic architecture that coordinates work across the enterprise with governance built into every interaction and workflow. Together, these capabilities make AI more accurate, more transparent and easier to scale in a highly regulated industry. 

As foundation models become widely available, competitive advantage will come less from the models themselves and more from the enterprise operating system built around them. Firms that establish that foundation today can be better positioned to deploy new AI capabilities faster, strengthen governance and improve decision-making without rebuilding the platform for every new use case.