If you're a buy-side analyst, your morning probably looks something like this: you open your inbox to find dozens of broker reports. You check Bloomberg. You log into two or three broker portals. You scan your internal Slack channels for anything flagged overnight. By the time you've triaged everything, a good chunk of the morning is gone, and you haven't done any actual analysis yet.

Roughly 40% of an analyst's work week (about 24 hours) goes to reading, triaging, and manually pulling data out of documents. The research itself is incredibly valuable. The process of consuming it? Not so much.

In this post, we'll walk through what broker research actually is, how research management systems (RMS) try to organize it, why the traditional approach is falling apart, and how AI is changing the game for analysts who'd rather spend their time building conviction than scrolling through PDFs. We'll also cover how AllMind AI brings broker research from trusted sell-side sources directly into a unified workspace where institutional teams can search, synthesize, and act on it in minutes instead of hours.

What Is Broker Research and Why Does It Matter?

Broker research (or sell-side research) is the reports, financial models, earnings estimates, price targets, and investment theses produced by analysts at investment banks and brokerages. Over 90% of equity fund managers rely on it as a critical input for their investment decisions.

The big names producing this work include Goldman Sachs, J.P. Morgan, Morgan Stanley, Barclays, and hundreds of independent shops like Bernstein and Redburn Partners. The output covers everything from equity research reports and sector analyses to buy/sell/hold ratings, macro commentary, thematic reports, and trade ideas. At any given time, thousands of sell-side analysts are putting out material across every asset class and geography.

The sheer volume is hard to overstate. S&P Global's Aftermarket Research collection has over 40 million cumulative analyst reports from 1,800+ global providers. LSEG gives access to roughly 1,300 active research providers covering 22,000 companies across 90 countries from more than 18,000 analysts. AlphaSense aggregates broker research from 1,000+ sources.

Here's how broker research actually gets distributed:

• Direct email from broker sales desks, still the most common and most fragmented channel
• Proprietary broker portals, where each bank has its own site with its own login
• Bloomberg Terminal via the internal RMS
• LSEG/Refinitiv Workspace, which caps downloads at 150 pages per day per user
• S&P Capital IQ Pro and FactSet, aggregators with varying coverage depth
• Third-party marketplaces like RSRCHXchange (now part of Liquidnet) and Smartkarma

This is the root of the problem. Analysts end up checking multiple systems, reconciling overlapping coverage, and manually keeping track of which reports they've actually read. Investors pay 10-15% higher broker fees for access to a research analyst and 20-40% more for a top-rated one. Yet the infrastructure for consuming that expensive research is still surprisingly clunky.

What Is a Research Management System (RMS)?

A research management system is software that helps institutional investors organize, manage, and act on investment research. It's the central workspace where analysts create and share internal research, PMs review recommendations, and compliance teams maintain audit trails. Think of it as the nerve center of the buy-side investment process.

What an RMS typically does:

• Research aggregation: pulling internal notes and external broker research into one place
• Organization and tagging: linking research to specific securities, sectors, themes, and watchlists
• Distribution: routing relevant research to the right people on the team
• Consumption tracking: recording who read what and when (critical for MiFID II)
• Search and retrieval: finding past research across internal and external sources
• Compliance and audit trails: keeping records of research interactions and investment decisions
• Broker vote and commission allocation: evaluating broker quality and directing soft-dollar payments
• Research budgeting: managing spend under MiFID II unbundling requirements

The major RMS platforms on the market:

The RMS space has gone through four rough phases. Desktop thick-client systems around 2000. Partial cloud migration around 2010. Fully cloud-native platforms with real-time collaboration around 2020. And starting in 2024, GenAI-powered platforms with natural language querying and cross-document synthesis. AllMind AI was built natively in this fourth phase, designed from the ground up for AI-powered research workflows rather than bolting AI onto legacy architecture after the fact.

How MiFID II Changed the Broker Research Landscape

MiFID II, implemented on January 3, 2018, forced European asset managers to unbundle payments for research from trading execution. Research that had been quietly bundled into trading commissions for decades suddenly needed to be priced explicitly. That single change reshaped the entire sell-side research industry.

The effects were fast and painful. Analyst headcount at the top 12 investment banks dropped from roughly 4,400 in 2012 to 3,500 by mid-2019. Research budgets fell 10-30% across the industry (some estimates put it as high as 50%). Coverage of EU-listed firms declined about 10% relative to US peers. The estimated total pre-MiFID II research cost was $5 billion, and Oliver Wyman estimated $1.5 billion in potential lost revenues for research providers.

The consolidation only intensified from there. Payments to the top 10 brokers in the average research budget climbed to 54.6% in 2023, up from 52% in 2019. Research budgets fell another 6.5% in 2023 alone. Fewer analysts covering more companies, producing less differentiated research, competing for smaller wallets.

Why does any of this matter for research management? Because MiFID II created real demand for better tools to track research consumption, allocate budgets, evaluate broker quality, and prove compliance. Every interaction with broker research (who accessed it, when, and how it influenced decisions) became auditable. That regulatory pressure pushed firms toward formal RMS platforms and, eventually, toward AI-powered alternatives that help teams do more with less.

Why Broker Research Workflows Are Still Broken

Despite billions spent on research and technology, the day-to-day reality of consuming broker research is still painfully manual. Three problems keep showing up.

Information overload

Equity research analysts work an average of 60 hours per week. About 40% of that, roughly 24 hours, goes to manual data gathering, document reading, and routine analysis. Coverage expectations have ballooned from 30-40 companies in the early 2000s to 50-60+ today for buy-side analysts. During earnings season, 20-30 companies might report in a two-week window. That's hundreds of broker reports landing on your desk in the span of a few days.

One firm, Minotaur Capital, processes over 35,000 articles per week. Federal Reserve research has shown that information overload "exhausts investors' processing capacities, deteriorates decision accuracy, and increases estimation risk." The problem isn't a lack of information. It's the inability to pull signal from noise at the speed the market demands.

Fragmentation

Analysts burn hours reconciling data across Bloomberg, Refinitiv, Capital IQ, various broker portals, email, and internal systems. Each platform has its own interface, its own search logic, its own data format. A single company might have 15 different brokers covering it, each distributing reports through different channels. Just keeping track of whether you've seen the latest note from each one is a job in itself, let alone comparing their views.

The synthesis bottleneck

This is the problem nobody has really solved yet. Multiple brokers cover the same stock, often with very different takes on valuation, growth, and risk. The most valuable thing an analyst does is weaving those perspectives together alongside their own proprietary work (management meetings, channel checks, internal models) into a differentiated investment thesis.

But that synthesis work, the highest-value intellectual activity, gets crowded out by the time spent on lower-value reading and data extraction. A traditional coverage initiation takes 4-8 weeks per company. Quarterly model updates eat about 100 minutes of manual data extraction from filings per company. Searching a single broker report for a specific data point? At least 15 minutes per report.

Traditional RMS platforms collect documents but can't synthesize across them. Search is still mostly keyword-based. There's no cross-document analysis for comparing views across multiple analysts. They aggregate passively without surfacing what actually matters.

How AI Is Changing the Way Analysts Consume Research

The shift happening right now is simple to describe but profound in practice: analysts are going from reading research to querying it. Instead of opening 30 reports and scanning them one by one, you ask a question across your entire research library and get a synthesized, cited answer in seconds.

The adoption numbers tell the story. A 2024 Deloitte survey found over 65% of asset managers had integrated AI into their investment processes, up from 45% in 2021. A Mercer survey of 150 asset managers showed 91% are either using (54%) or planning to use (37%) AI in research and portfolio construction. The global AI-in-asset-management market is expected to grow from $3.68 billion in 2023 to $17.01 billion by 2030.

Semantic search

Traditional search forces you to guess the exact words a broker analyst used. Semantic search understands what you mean. Search for "TAM" and you'll also get results mentioning "addressable market" and "market size." You stop missing relevant research just because the terminology didn't line up.

Generative summarization

Instead of reading a 40-page equity initiation end to end, analysts can ask pointed questions: "What's this analyst's bear case?" or "What revenue assumptions drive the price target?" The AI pulls the relevant passages, synthesizes them, and gives you a cited answer you can verify. Bloomberg launched AI-Powered Document Insights in April 2025 for natural language querying of company documents. FactSet's Transcript Assistant claims a 50% speed-up in finding key points from earnings calls.

Cross-document synthesis

This is the capability that directly attacks the synthesis bottleneck. Instead of reading six broker reports on the same stock to manually compare their views, AI can analyze all six at once, showing you where they agree, where they disagree, and what assumptions explain the differences. Hours of comparison work collapses into seconds.

The efficiency gains

AI can bring a 60-hour analyst work week down to roughly 36 hours. New coverage initiation shows 61% time savings (from 60 hours to about 23.5). Firms using AI-powered research tools report around 40% reductions in research costs. As one AlphaSense user put it: "The generative search summarizes key points so I spend less time reading and more time validating hypotheses."

A few honest caveats

Not every firm has moved at the same pace. A 2025 ESMA report found that only 0.01% of 44,000 UCITS funds formally incorporate AI/ML in investment strategies. The CFA Institute has flagged concerns about AI eroding critical thinking if it's treated as a replacement rather than a tool. And hallucination risk is real, which is why any serious platform uses RAG (Retrieval Augmented Generation) with source citations as guardrails. Every answer needs to trace back to a specific document and page. AI should sharpen analyst judgment, not substitute for it.

How AllMind AI Brings Broker Research into a Unified AI Terminal

AllMind AI tackles the broker research problem by pulling research from trusted sell-side sources directly into a single AI-powered workspace, alongside filings, IR materials, internal documents, news, and alternative data. Instead of bouncing between Bloomberg, email, broker portals, and shared drives, analysts work from one terminal that ingests everything, organizes it, and makes it all queryable through natural language.

Aggregation from trusted sources

AllMind AI integrates broker research from established sell-side providers so clients get research delivered directly within the platform. No more checking six different portals for the latest reports on a single name. The research shows up in one place, already indexed, searchable, and ready for analysis.

Search and synthesis that actually works

Once broker research is inside AllMind AI, you can query it the way you'd ask a colleague a question:

• "What are the bear-case assumptions for this company across all broker coverage?"
• "Which analysts raised their price targets after last quarter's earnings, and why?"
• "Compare the revenue growth assumptions across all sell-side models on this name"

AllMind AI pulls the relevant passages from multiple broker reports, synthesizes the findings, and gives you cited answers that link back to the original sources. This is the cross-document synthesis piece that traditional RMS platforms just don't do.

From weeks to minutes

Initiating coverage on a new company used to mean weeks of reading broker research, building a model from scratch, and drafting an investment memo. With AllMind AI, teams can go from a new name to a differentiated view in a fraction of that time. AI handles the aggregation, data extraction, and first-pass synthesis. Analysts focus on what they're actually good at: judgment, conviction, and differentiated thinking.

Built for institutional teams

AllMind AI processes over 30PB of raw market data each month and integrates live exchange data from 60+ venues. The platform handles internal document ingestion (memos, models, slides, spreadsheets) so firms can query across both external broker research and their own proprietary work in a single interface. Every AI-generated insight includes source citations for auditability, and analysts always stay in the loop as the final decision-makers.

We also apply domain-specific AI models trained for financial analysis. If you're looking at a healthcare company, there are models trained specifically on clinical trial data working alongside others trained on healthcare earnings transcripts, all running in parallel. That's how we're able to generate contextually relevant insights rather than generic summaries that miss the nuance of a given sector.

Getting Started with AI-Powered Broker Research

You don't need to rip and replace your entire tech stack to make this work. Most institutional teams start with a focused pilot:

1. Pick your highest-friction workflow. For most firms, it's earnings season triage or new name initiation, where the volume of broker research overwhelms whatever manual process you have in place.

2. Centralize your research inputs. Pull broker research, internal notes, and key data sources into a single platform instead of keeping them scattered across five different systems.

3. Start querying instead of just reading. This is the real mindset shift. Once your analysts experience asking a question across 20 broker reports at once and getting a synthesized answer in seconds, the old way of doing things feels impossible to go back to.

4. Measure the time savings. Track how many hours your team spends on research aggregation and synthesis before and after. Teams using AllMind AI typically see about a 60% reduction in research busywork, freeing analysts to spend more time on things that actually move the needle: management meetings, channel checks, and building differentiated theses.

If your team spends more time collecting and reading research than acting on it, Request Demo to see how AllMind AI brings broker research from trusted sources directly into a unified AI terminal.

Frequently Asked Questions

What is broker research aggregation?

Broker research aggregation means collecting sell-side research reports from multiple investment banks and independent providers into one searchable system. Traditional aggregation platforms like Bloomberg, FactSet, and AlphaSense centralize access. AI-powered aggregation takes it further with natural language search, automated summarization, and cross-document synthesis across your full research library.

What is a research management system in finance?

A research management system (RMS) is software institutional investors use to organize, manage, and act on investment research. It typically handles aggregating internal and external research, tagging content to securities and sectors, tracking consumption for MiFID II compliance, managing broker votes and commission allocation, and maintaining audit trails. The major platforms include Bloomberg RMS, FactSet IRN, VerityRMS, and Bipsync.

How does AI improve investment research workflows?

Three main ways. Semantic search helps analysts find relevant research regardless of exact terminology. Generative summarization condenses long reports into targeted, cited answers. And cross-document synthesis compares perspectives across multiple broker reports simultaneously. Together, these capabilities can cut the 24 hours per week analysts spend on manual reading and extraction by about 60%.

What are the MiFID II requirements for research management?

MiFID II requires European asset managers to unbundle research payments from trading execution, putting an explicit price on research. Firms have to track which research was consumed, by whom, and when. Budgets need to be set and managed transparently. All of this creates demand for RMS platforms that can log consumption, allocate costs, evaluate brokers, and produce audit trails.

How do buy-side analysts use AI for equity research?

Analysts use AI across the full research workflow: triaging incoming broker reports with automated summaries, pulling key data points from filings and transcripts, comparing estimates across multiple sell-side models, drafting investment memos with citations, and monitoring positions against new research and news. The point isn't to replace analyst judgment. It's to get rid of the manual busywork that keeps analysts from spending time on higher-value work.

Anwaar Malik, CEO of AllMind AI

This article is powered by AllMind AI, the AI terminal for institutional investors. AllMind AI unifies filings, broker research, internal documents, news, and alternative data into a single AI-powered workspace, helping investment teams cut research busywork by roughly 60% and go from new names to differentiated views in minutes instead of weeks. Request Demo to see it in action.