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All the Alphas: How Active Asset Managers Use AI-Powered Search to Save $21M+ and Outperform
The Existential Pressure on Active Management
Active investment managers were built to beat the market. But a decade of sustained passive inflows, fee compression, and underperformance across the industry has fundamentally changed the competitive calculus. According to Morningstar, passive funds now account for more than half of all U.S. fund assets — a share that continues to grow. For active managers, the consequences are direct: shrinking margins, eroding client loyalty, and relentless pressure to demonstrate value that justifies the fee differential.
In this environment, the firms that survive and differentiate will not be those that simply try harder. They will be those that find a structural edge — one that is scalable, defensible, and embedded in how the entire organization operates. Increasingly, that edge is hiding in plain sight: in the firm’s own data.
The Untapped Asset: Your Own Information
Across every function in an asset management firm — investment research, portfolio management, distribution, client service, compliance, M&A, and product development — data accumulates at scale. Investment memos, analyst reports, earnings call transcripts, CRM notes, performance models, client communications, market data feeds, regulatory filings, and shared drive documents all contain intelligence that could inform better decisions, faster.
Yet most of this information is invisible in practice. It lives in disconnected systems, inaccessible formats, and organizational silos that prevent the right insight from reaching the right person at the right moment. Portfolio managers build investment theses without knowing that a colleague wrote a detailed analysis of the same company six months ago. Distribution teams pitch clients without access to the full context of the relationship history sitting in CRM notes and email threads. Compliance teams conduct manual reviews that intelligent search could complete in a fraction of the time.
The cost of this fragmentation — measured in hours, decisions, and missed opportunities — is enormous. And it is entirely addressable.
AI-Powered Search: The Structural Edge
AI-Powered Search connects every information source in the firm — internal and external, structured and unstructured — into a single, intelligent knowledge layer. Every analyst report, every CRM record, every market data feed, every email thread, and every shared document becomes instantly searchable, with results ranked by relevance to the specific user, their role, and their current context.
The result is not incremental productivity improvement. It is a fundamental change in how the firm operates — one that compounds across every function, every team, and every decision made in the organization.
One firm quantified that impact at over $21M in annual savings — achieved by deploying a unified AI-powered search platform across its investment, distribution, and client service operations, eliminating duplicated research effort, accelerating client response times, and enabling analysts to spend more time generating insight and less time retrieving information.
What This Whitepaper Covers
“All the Alphas” is Sinequa’s definitive guide to how active asset managers can use AI-powered search to unlock competitive advantage across every dimension of the business. It is built around a framework that identifies five distinct categories of alpha — each representing a different function where information advantage translates directly into performance. The whitepaper covers:
- Competitive survival in the passive era — a frank analysis of the structural forces reshaping active management, and why data access has become the new battleground for differentiation
- The $21M case study — a detailed breakdown of how one asset management firm calculated and captured over $21M in annual value by deploying a unified AI-powered search platform across its operations, including the specific use cases, functions, and workflows that drove the return
- The five alphas — a function-by-function framework for activating intelligence across investment research, distribution and client service, transformation and operations, M&A, and product innovation — with concrete examples of what AI-powered search makes possible in each area
- Data maximization without disruption — how firms connect and activate all their data — internal and external, structured and unstructured — without replacing existing systems, eliminating vendor lock-in, or requiring lengthy, disruptive technology migrations
- Implementation considerations — what it takes to deploy enterprise AI search in a financial services environment, including security, compliance, and data governance requirements
Who Should Read This
This whitepaper is written for senior leaders in active asset management who are responsible for competitive strategy, operational efficiency, or technology investment — including Chief Investment Officers, Chief Data Officers, Heads of Investment Research, Heads of Distribution, Chief Operating Officers, and digital transformation leaders evaluating AI platforms for financial services.
Download the Whitepaper
Why AI-Powered Search Is the Right Foundation for Financial Services AI
As asset managers evaluate generative AI, large language models, and AI assistants for investment research and client operations, the question of the retrieval layer becomes critical. A generative AI assistant is only as good as the information it can access — and in a financial services context, that access must be precise, permission-aware, auditable, and grounded in verified internal data.
Sinequa’s enterprise AI search platform is purpose-built for this requirement. It provides the retrieval infrastructure that makes AI assistants trustworthy in regulated financial services environments — connecting to all internal data sources with full security inheritance, returning only content each user is authorized to access, and providing the provenance and traceability that compliance teams require.
For active managers building an AI strategy, the search layer is not a supporting technology. It is the foundation.
Frequently Asked Questions
In investment management, “alpha” refers to the excess return generated above a benchmark — the value that active managers are paid to deliver over and above what a passive index fund would return. In the context of this whitepaper, “All the Alphas” extends the concept beyond portfolio returns to describe the performance advantage that AI-powered search creates across every business function: investment research, distribution, client service, operations, M&A, and product innovation. Each function has its own form of alpha — insight gained faster, decisions made better, time recovered and redeployed.
Passive investing has grown dramatically over the past decade, driven by lower fees, consistent benchmark performance, and increasing institutional skepticism about the ability of active managers to deliver sustained outperformance after fees. As passive strategies capture a larger share of AUM, active managers face a compounding challenge: shrinking fee revenues, rising pressure to demonstrate value, and the need to do more with smaller teams. AI-powered search addresses this by eliminating operational friction and enabling every function in the firm to operate with a structural information advantage.
The $21M annual savings figure comes from a real asset management firm that deployed a unified AI-powered search platform across its investment research, distribution, and client service operations. Value was generated across multiple dimensions: analysts recovered hours per week previously spent searching for information across disconnected systems; distribution teams accessed full client relationship context before pitches, improving conversion rates; client service teams resolved inquiries faster by surfacing relevant account history and product documentation in seconds; and compliance teams reduced manual review time through intelligent search across regulatory and internal documentation.
In an asset management context, AI-powered search connects a wide range of internal and external sources: investment research reports and memos, earnings call transcripts, financial models and spreadsheets, CRM records and client communications, email and collaboration platform content (Teams, Slack), market data and news feeds, compliance and regulatory documentation, product documentation, portfolio management system data, and shared drive content. All sources are indexed into a unified, permission-aware knowledge layer that respects existing security controls.
Yes. Enterprise AI search platforms like Sinequa are designed specifically for regulated industries. They inherit existing security permissions from connected systems — meaning users only ever see content they are already authorized to access. All data remains within the firm’s controlled environment (on-premise or private cloud), with full audit logging, role-based access controls, and data governance capabilities required by financial regulators. Sinequa holds SOC 2 compliance and is used by leading financial services organizations globally.
AI-powered search and Retrieval-Augmented Generation (RAG) are complementary. AI-powered search is the retrieval layer — finding and ranking the most relevant documents and data across the firm’s information ecosystem. RAG uses that retrieved content to ground the responses of a large language model, enabling AI assistants to answer questions based on verified internal data rather than general training knowledge. For asset managers, this means an AI assistant that can answer questions like “what is our current thesis on semiconductors” or “what did we tell this client last quarter” by retrieving and synthesizing actual internal documents — not generating plausible-sounding but unverified responses.
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