Your Research Analysts Spend 60-70% of Their Time Searching, Not Analyzing
Here's what that looks like in practice: jumping between Bloomberg terminals, SharePoint folders, S&P Global reports, GuidePoint transcripts, and ThirdBridge interviews. Each source requires separate searches. Each insight needs manual extraction. Each investment memo starts from scratch.
AlphaSense's 2025 State of AI for Business and Finance study confirms the scale: organizations juggling multiple market intelligence platforms lose 6-10 hours weekly per professional to fragmented search. For a 25-person research team, that's 250+ hours weekly: six full-time positions' worth of capacity buried in manual overhead.
One global investment firm managing over $40 billion in assets faced exactly this challenge. Here's the framework they used to reclaim that time, and why this approach works for any financial institution.
What AI Research Infrastructure Actually Delivers
AI research infrastructure, the connected systems that unify data sources, automate analysis, and preserve institutional knowledge, creates three distinct advantages.
1. Speed: From Days to Hours.
Firms that compress research cycles from days to hours respond to market opportunities while competitors are still gathering information. Proper infrastructure can reduce a 40-hour deal analysis to 10-15 hours of focused strategic work.
2. Compounding Intelligence: Research That Gets Smarter.
When insights flow into unified systems, each analysis strengthens the foundation for future work. Research quality improves as the knowledge base grows, so the tenth deal analysis happens faster and better than the first.
3. Compliance Confidence: Traceability Built In.
Complete source traceability built into the infrastructure means every conclusion connects directly to supporting evidence. Regulatory audits become straightforward rather than scrambling to reconstruct the analysis trail.
The $40B firm achieved all three advantages through a systematic three-phase approach. Here's how it works.
A Three-Phase Framework for AI Research Infrastructure
- Phase 1: Discover Where Infrastructure Creates Value
Start by mapping how analysts spend time over 30 days. Track which systems they access, where information exists in multiple locations, and where analyses get repeated because prior work isn't discoverable. This diagnostic typically reveals that 60-70% of research hours go to information gathering rather than strategic interpretation.
How the $40B firm applied this:
SoftSnow applied the AI Opportunity Matrix™, our proprietary framework that identifies high-leverage pain points, documenting how analysts source, review, and synthesize information across typical deal workflows. The team cataloged data access patterns through platforms like S&P Global, GuidePoint, and ThirdBridge, evaluated existing tools, and identified where manual processes consumed time better spent on strategic judgment.
The Matrix helps organizations distinguish between speed bottlenecks, accuracy gaps, and visibility limitations. This clarity enables leadership to prioritize investments that deliver measurable productivity gains.
For the $40B firm, the AI Opportunity Matrix™ revealed three priority areas:
- Standardizing document intake across disparate sources,
- Improving research traceability for compliance confidence
- Reducing manual summarization time that kept analysts from higher-value interpretation work.
Key deliverable: A prioritized AI Opportunity Matrix™ showing exactly where infrastructure changes create the most business value, organized by impact and implementation complexity.
- Phase 2: Design Infrastructure Around How Analysts Work
The breakthrough happens in Phase 2 when you translate friction points into technical design.
Three questions shape this translation: What should research agents accomplish? What standards must outputs match? Where should intelligence aggregate?
How the $40B firm applied this:
The team made a strategic choice that proved critical for adoption: anchor infrastructure in SharePoint, where analysts already stored 80% of internal research. Meet analysts where they work instead of asking them to change behavior.
Building on the Matrix findings, SoftSnow translated each opportunity into design criteria using our Three-Pillar Framework:
- Right Prompts define what research agents should accomplish, from summarizing regulatory filings to extracting risk factors from earnings calls to highlighting key data points across expert interviews.
- Right Context embeds your firm's specific analysis standards, compliance requirements, and research methodologies directly into system design so outputs match institutional expectations without constant correction.
- Right Data connects governed intelligence sources, including market data terminals like Bloomberg, S&P Global, and Moody's Credit Ratings, company filings, expert networks, and internal research archives into unified access patterns.
With the data hub established in SharePoint, SoftSnow helped the firm evaluate what AI capabilities would layer on top. The evaluation covered three solution categories: financial point solutions that embed AI into data vendors analysts already use, document intelligence tools capable of summarizing and searching across unstructured content, and AI orchestration platforms that connect multiple data sources and automate retrieval workflows.
This layered approach delivered flexibility. The infrastructure could accommodate new capabilities without replacing working systems.
Key deliverable: A technical blueprint showing how prompts, context, and data connect to create intelligent research capabilities that strengthen with use.
- Phase 3: Deliver Through Phased Rollout
Implementation strategy matters as much as technical design. Here's where most firms either build momentum or stall.
Start with one high-impact workflow that's frequent, time-consuming, measurable, and straightforward. Strong candidates include earnings transcript summarization, searchable expert interview repositories, or regulatory filing analysis.
How the $40B firm applied this:
SoftSnow is guiding the firm through a phased implementation that translates the AI Opportunity Matrix™ into living operations. The approach started with high-volume, routine research tasks before expanding to complex analysis workflows, ensuring early wins build momentum for broader adoption.
Current deliverables include three critical enablers:
- A Governance Guide establishing security standards and data-syncing protocols that maintain compliance while enabling intelligent automation.
- Prompting Libraries providing optimized workflow templates for analysts, ensuring consistent, high-quality outputs across research teams.
- An AI Platform Evaluation Matrix offering rubrics for cost, scalability, and compliance that help leadership assess new capabilities as the AI landscape evolves.
Early indicators show significant progress. The firm is tracking toward significant reductions in document search overhead and measurable increases in research coverage breadth, and complete source traceability across all investment memos. Which means analysts now spend their time on interpretation and strategy, not hunting for documents.
Key deliverable: Fully adopted infrastructure with quantified performance gains, ongoing quality assurance, and an expansion roadmap that evolves alongside market needs.
What This Looks Like Across Other Financial Institutions
The same framework works beyond investment management.
The pattern applies everywhere financial firms synthesize information to make decisions: lending, underwriting, risk assessment, and regulatory reporting.
What matters is the orchestration. Each tool handles one job well. Infrastructure connects them into a system that delivers value consistently.
Building AI Infrastructure That Grows Smarter Over Time
Traditional research operations reset with each project. Analysts gather information, synthesize insights, write memos, and then repeat the process. Intelligence doesn't accumulate.
AI research infrastructure changes this dynamic. Each document processed teaches the system your firm's analysis standards. Each search refines relevance algorithms. Each workflow completion creates templates for similar future work. Each question answered strengthens the knowledge base for faster subsequent responses.
This compounding effect distinguishes infrastructure from point solutions. It's why successful approaches center on unified systems rather than isolated tools, start with workflow analysis instead of vendor evaluation, and measure institutional knowledge retention alongside efficiency gains.
The path forward starts with understanding where infrastructure creates value in your operations, translating those opportunities into technical design aligned with how your teams work, and building organizational confidence through measured rollout that proves value incrementally.
Ready to map where AI infrastructure would create the most value in your research operations? Let's explore your highest-impact opportunities.
