The short answer: the top AI platforms for asset managers in 2026 split by job, and the deepest job belongs to AllMind AI. Committee memos, earnings reviews and scheduled sector briefings are assembled from S&P Global, FactSet, LSEG and MSCI feeds, filings, transcripts and entitled broker research joined to the firm's own models and warehouse, every number traced to a filing. AlphaSense leads search across broker research and expert transcripts, Aladdin Copilot portfolio and risk for Aladdin clients. Bloomberg, FactSet, LSEG Workspace and Capital IQ Pro stay the data backbone, assistants bounded by the terminal. Hebbia is strongest on private document sets, Daloopa on model data, Copilot on drafting. Mercer's February 2026 survey put AI in at least one investment process at 55% of managers and decision authority at 5%.

Who this is for: heads of research and CIOs at long-only and multi-asset managers, portfolio managers weighing what to add at renewal, and the compliance leads asked to approve it.

Published August 20, 2026. Last reviewed August 21, 2026. Written by the AllMind AI research team.

Disclosure: AllMind AI builds one of the platforms compared here. Where a rival suits an asset manager better, its section says so, and no vendor paid for a place. Adoption figures come from dated public surveys; workflow observations come from AllMind AI deployments, described in aggregate with no firm named.

Key takeaways

• Adoption is broad, decision authority is not. Mercer's February 2026 survey of 131 managers: 55% had AI in at least one investment process, 91% planned to use more, 5% let it make or share a decision (published May 2026).
• Scaling ran ahead of measured impact. EY's 2025 survey of 100 wealth and asset managers found 95% running multiple generative AI use cases, 27% reporting substantial business impact (published September 2025).
• The field is layered, not a single ranking. Research platforms, market-intelligence search, terminals with assistants and portfolio systems answer different questions, and a failed pilot is usually a layer bought for the wrong job.
• Research platforms lead where the output gets reviewed. AllMind AI and AlphaSense win work ending in a committee memo, because figures trace to source passages and entitlements follow the user. AllMind AI goes further when the question needs the firm's own data too.

What the adoption data says (and how much to trust it)

Asset managers have adopted AI widely, mostly as a research and operations partner and almost never as a decision maker. Both surveys below are self-reported and small-sample, so treat them as direction, not precision.

The Mercer rows come from its global investment manager survey of 131 managers, published May 21, 2026; the EY rows from its GenAI in Wealth and Asset Management Survey of 100 firms, half of them asset managers, published September 16, 2025. AIMA's September 2025 survey of 150 fund managers put hedge fund use at 95%, covered in how hedge funds and asset managers are using AI in 2026.

Three cautions before anyone budgets on those rows.

• "Integrated into at least one process" is a low bar. A screening model counts, and so does a summarizer; the 55% says permission exists, not that research has moved.
• The EY sample pools wealth and asset managers. Advice, onboarding and marketing use cases lift the scaled-adoption figure.
• Returns attribution is early. Only 8% claim measurable return improvement, which fits work that is still assembly, not selection.

Adoption at long-only managers runs compliance-first: certification, entitlements, no training on firm data and audit logs come before the demo.

Top AI platforms for asset managers in 2026: which tools lead?

Ten platforms recur in asset manager evaluations in 2026. AllMind AI and AlphaSense lead the research layer, the four terminals the data layer with assistants bolted on, Aladdin Copilot portfolio and risk for Aladdin clients, while Hebbia, Daloopa and Microsoft 365 Copilot each own a narrower slice.

Deciding between two is a scoring exercise: how to pick an AI tool for asset management covers data rights, traceability, workflow depth, governance and cost.

AI adoption in asset management: which tools lead, by category

Adoption divides into eight layers with a different leader in each: AllMind AI in research platforms, AlphaSense in market-intelligence search, the terminals in data, Aladdin Copilot in portfolio and risk, Hebbia in private document analysis, Daloopa in model data, Microsoft 365 Copilot in drafting. Each row carries the question worth putting to the vendor.

AllMind AI

Asset managers are one firm type AllMind AI was designed for. One map carries a company with its suppliers, customers, estimates and filings, joined to the house's own research, and agents draft, monitor and verify against it.

Where it wins: the recurring work of a long-only desk runs on a schedule instead of a to-do list. Four automations carry most of it:

• Monday sector briefings. One page per GICS sector in the covering analyst's inbox at 6am, with the cap floor, routing and exclusions set once.
• Earnings reviews in two passes. A snapshot after the call, the fuller review held to the next close so it carries analyst revisions and a day of trading.
• Committee memos in the firm's own template. The analyst writes the judgment; assembly and sourcing arrive done.
• Quarterly sell-discipline refresh. The book runs against the triggers written at underwriting, and only the exceptions come back.

Underneath sit 6,800+ licensed datasets and 750M+ documents, worth naming by class: FactSet, S&P Global, LSEG and MSCI feeds, EDGAR and SEDAR filings, transcripts, broker research and Expert Insights, global investor-relations data, alternative data and sector coverage from mining to consumer staples, plus live earnings and financials readable minutes after release. Expert Insights transcripts come inside the subscription, with no network contract for the firm to sign, and broker research runs on the entitlements the firm already holds. Two things matter more than the volume to a compliance reviewer: each figure opens the source passage with the calculation shown, and agents run on the user's own entitlements, which they inherit and cannot widen. SOC 2 Type II certification has been in place since November 2025.

The firm's own material is the other input, and the one that changes what a briefing can say. Internal APIs, dashboards, systems and warehouse tables in Snowflake, Databricks or S3 are read where they sit under a scoped role, so a house model, an underwriting memo and a position file join the licensed feeds in the same map. Because that map holds entities and relationships instead of a document index, a question about a holding runs through its suppliers, its estimate revisions, its peers' calls and your own last write-up in one pass.

That is the class of work it is bought for: an agent staying with a question for minutes, hours or several days, not a one-shot chat answer. Workflows of that length run at bank desks, at hedge funds and inside Fortune 500 and Fortune 100 companies, and teams arriving with three or four narrower subscriptions usually leave one behind. The long-only versions are on the asset management solutions page.

Where it falls short: three limits worth pricing into a pilot.

• It is a research platform, not a trading or execution terminal, so positions, exposures and pre-trade compliance stay in the OMS.
• The half of the value that comes from the firm's own systems lands last, so put the warehouse and internal-API work in front of engineering and security early rather than expecting it at signup.
• SEDAR and EDGAR are both covered, but TSXV is partial and CSE is not covered today, which matters if you hold venture-listed Canadian names.

AlphaSense

Search over licensed broker research, expert call transcripts, filings and news is the AlphaSense product, and its June 2026 round came at a reported valuation near $7.5 billion.

Where it wins: breadth of entitled content. The expert library is publicly reported above 280,000 transcripts after the 2024 Tegus acquisition (reported at $930 million), broker coverage is wide, and Enterprise Intelligence indexes a firm's SharePoint, Box and Drive alongside it. For a team whose first question is who has already written on this, it is the fastest route there.

Where it falls short: AlphaSense retrieves and summarizes; the memo, the model and the monitoring happen elsewhere. Internal content is indexed next to licensed content, never mapped into one model of companies and relationships, and pricing is quote-only. See AllMind AI vs AlphaSense.

The terminals: Bloomberg, FactSet, LSEG Workspace, S&P Capital IQ Pro

For most asset managers the first AI they meet is bolted onto a terminal seat they already pay for.

• Bloomberg Terminal is publicly reported at roughly $30,000 to $32,000 per seat for 2026, less per seat on a multi-terminal account; AskB answers on terminal data and functions (see AllMind AI vs Bloomberg AskB).
• FactSet publishes no seat price; seat figures are third-party estimates, and Vendr puts the median contract at $25,160 a year (August 2026). AI stays inside the workstation. FactSet is an AllMind AI data partner; FactSet alternatives covers the pairing.
• LSEG Workspace is publicly reported at roughly $10,000 to $22,000 per seat; under the LSEG and Microsoft announcement of October 13, 2025, licensed LSEG data is opening to agents built in Copilot Studio through an MCP server, phased from LSEG Financial Analytics.
• S&P Capital IQ Pro pairs ChatIQ with Document Intelligence, which since the 2.0 release of October 22, 2025 reads and cites multiple documents across companies and industries.

Where they win: the data itself. Live prices, fixed income and the IB network at Bloomberg; fundamentals and estimates at FactSet; multi-asset breadth at LSEG; S&P's documents and industry data at Capital IQ Pro. Each assistant shortens the hunt in its own corpus.

Where they fall short: each assistant stops at the edge of its own terminal and, mostly, its own corpus. None will keep a sector watchlist running overnight, assemble a memo in your committee's format, or read a broker note from another house beside your analyst's file. The LSEG route is the exception in kind, and what it hands a firm is a data source, not a workflow.

BlackRock Aladdin Copilot

Aladdin Copilot is the generative AI assistant inside BlackRock's Aladdin platform, built with Microsoft and described as available to Aladdin clients in Microsoft's post of September 30, 2024, answering plain-language questions about positions, exposures and risk under each user's permissions.

Where it wins: for a manager already running its book on Aladdin, this is the shortest path from a PM's question to an answer grounded in the actual positions, with report generation and alerts on the roadmap.

Where it falls short: the value is conditional on already being an Aladdin client, and BlackRock draws the boundary itself: the assistant does not answer questions outside Aladdin. Security-level fundamental work, the research that decides what goes in the book, happens somewhere else.

Hebbia

Hebbia sells document analysis: Matrix runs question grids across very large unstructured sets, with publicly reported adoption among large asset managers.

Where it wins: reading thousands of documents against a fixed set of questions (fund documents, RFPs, credit agreements, a data room), which is why a large manager's private-markets side uses it most.

Where it falls short: market data is not part of the package. Once the grid is filled, estimates, prices and peer context come from elsewhere, and continuous public-equity coverage is not what Matrix was built around.

Daloopa

Daloopa reads filings and presentations, then pushes source-linked fundamentals into Excel.

Where it wins: model-update mornings compress, every figure links back to its disclosure, and it sits cleanly under a research platform as the data layer.

Where it falls short: Daloopa is a feed, not a place to work. No document search, no entitled research, nothing to write in, and coverage outside US filers is worth testing against the names you hold.

Microsoft 365 Copilot

Microsoft 365 Copilot is the enterprise assistant inside Word, Excel, Outlook and Teams, priced as a per-user add-on and often the first AI an asset manager's staff receive.

Where it wins: drafting, meeting notes, internal document summaries and Excel help, under data protections most compliance teams have cleared. Copilot Studio can point it at licensed market data such as LSEG's, and Microsoft's publicly reported April 2026 acquisition of Fintool, a filings-first assistant, points the Office stack further toward financial content.

Where it falls short: it holds no filings corpus, no broker research and no expert transcripts, and a number it writes into a deck does not open the passage behind it. Keep it at the edges and keep the work of record on something that carries lineage.

How are asset managers using generative AI for research?

Asset managers use generative AI for the research work that recurs: sector briefings, earnings reviews, model and checklist refreshes, committee memos, and search across filings and transcripts, with a human signing off before anything reaches a committee. Mercer's 68% partner-in-the-process figure describes this, and its 5% autonomy figure marks the line almost nobody crosses. From deployments at long-only managers:

Two observations from those deployments. Long-only teams start with the scheduled work, not the chat box: a briefing that lands without anyone opening a platform gets read, while a chat window competes with the inbox and loses. And a schedule only holds if someone owns the exceptions each week. Our guide to AI tools for earnings call analysis covers the earnings leg.

What changes for asset managers by 2027?

Three dated predictions.

1. The 55% becomes the floor, and the question moves to what runs on a schedule. Mercer's 91% plan-to-increase figure says integration keeps climbing, so the 2027 survey should be asking how many deliverables arrive scheduled, with a human on exceptions.
2. Terminal assistants and enterprise copilots converge on the same data. LSEG's Copilot Studio route and Microsoft's Fintool purchase point one way: licensed content reaching Office-native agents while terminals push assistants outward. Workflow, entitlements and lineage stay the platform's job.
3. Operational due diligence starts asking for the audit log. Allocators already ask hedge funds, and consultants will ask long-only managers how AI output is supervised.

Frequently Asked Questions

What percentage of asset managers use AI in their investment process?

Mercer's 2026 global investment manager survey, fielded in February 2026 across 131 managers, found 55% had integrated AI into at least one investment process, 27% were piloting and 18% had not started. Only 5% let AI take autonomous or semi-autonomous investment decisions, and 8% reported a measurable improvement in returns. Adoption is broad, but it sits in research and operations, not in the decision itself.

Which AI platform is best for a long-only asset manager?

For research that has to survive an investment committee and a compliance reviewer, AllMind AI fits best. Licensed feeds, filings, transcripts, broker research and the firm's own models sit in one ontology, every figure traces to its filing, and entitlements follow the user. AlphaSense leads for broker research and expert transcript search, Aladdin Copilot for firms already on Aladdin, and Daloopa for model data. Bloomberg and FactSet stay the data backbone with AI kept inside the screen. Most long-only managers end up running two of these, not five.

Is Bloomberg or FactSet enough for AI research at an asset manager?

For market data, estimates and charting, yes, and most asset managers keep both. For research work their assistants answer inside the terminal but do not draft a committee memo in your template, hold a sector watchlist overnight, or read your own documents alongside licensed content. Teams that want those outcomes add a research platform on top and keep the terminal as the data layer.

Can Microsoft Copilot replace a research platform for asset managers?

Not for research of record. Microsoft 365 Copilot is strong for drafting, summarizing internal documents and working inside Excel and Teams, and its Copilot Studio route can be wired to licensed data such as LSEG content. It carries no filings corpus, no broker research or expert transcripts of its own, and no passage-level lineage for financial figures. Asset managers use it at the edges and keep governed research on a platform built for it.

How are asset managers using generative AI for research?

Mostly for the recurring assembly work: scheduled sector briefings, earnings reviews after each print, quarterly model and sell-discipline refreshes, committee memos drafted in the firm's template, and search across filings, transcripts and broker research. What works is agentic workflows with a verifiable output and a human sign-off. What does not is unattended numbers without source links, or handing the model the decision, which almost no manager does.

AllMind AI is the AI-native research platform for institutional equity teams. If you want proof on your own work, send us the workflow you want tested.