---
title: "How fund managers get considered when investors ask AI"
description: "By being the fund family assistants find in trusted fund research, with fees and facts consistent everywhere. Five firms already hold 58% of US fund assets."
canonical: "https://underneath.agency/resources/asset-managers-fund-demand-ai-search"
published: 2026-10-07
updated: 2026-10-08
publisher: "Underneath (https://underneath.agency/agent)"
entity: "https://underneath.agency/.well-known/entity.json"
---
Guide · AI search

# How does an asset manager get its funds considered when investors and advisers ask AI?

By making sure assistants find your fund family in the independent research they already trust, with fees, tickers and strategy facts that match everywhere. Investors and advisers now use AI to research funds, and the answers lean on third-party sources and familiar names. For a mid-size or newer fund sponsor, that turns AI visibility into a question of whether your strategies make the shortlist at all.

## The short version

1. The industry is concentrating: the [Investment Company Institute](https://icifactbook.org/pdf/2026-factbook-ch2.pdf) reports the five largest fund complexes held 58% of US mutual fund and ETF assets at the end of 2025, up from 35% in 2005, while firms ranked 11 to 25 fell from 21% to 14%.
2. New funds need to be found quickly: [Cerulli Associates](https://etfexpress.com/2026/06/16/new-etf-launches-far-exceed-closures-cerulli-associates/) counted 953 active ETF launches in 2025, and 92% of that year’s ETF closures were subscale products under $50 million.
3. Investors already research with AI: in an [Investing.com survey](https://hedgefundalpha.com/news/retail-investors-now-use-ai-to-inform-investment-decisions/) of 938 US investors, 54% had used chatbots such as ChatGPT for investing research.
4. Advisers, the gatekeepers for many funds, are adopting it too: 51% use generative AI, according to a [Broadridge and Financial Services Institute study](https://www.broadridge.com/jp/press-release/2026/advisors-signal-desire-for-more-technology-and-product-education).
5. Answers lean on independent sources: in a [study of banking questions](https://arxiv.org/abs/2509.08919), 64.6% of the sources AI engines cited were editorial or third-party sites, against 34.1% brand-owned.

A note before you read: this article is about how fund companies show up in AI answers. Nothing in it is investment, legal or compliance advice.

## Who chooses funds today, and what is a fund client worth?

Two groups choose: self-directed investors on platforms, and financial advisers who pick funds for client portfolios.

The market is large and still growing. The ICI’s [2026 Fact Book](https://icifactbook.org/pdf/2026-factbook-quick-facts-guide.pdf) puts US-registered fund assets at $45.1 trillion, with 76.0 million households, 56.4% of all US households, owning funds. Investors can choose from 16,829 investment companies, including 8,030 mutual funds and 4,813 ETFs, offered by 772 fund sponsors.

A fund client is worth recurring fees on the assets that stay. The revenue line is simple: assets multiplied by the expense ratio, every year. That ratio keeps shrinking. In 2025 the simple average expense ratio for index equity ETFs was 0.45%, but the asset-weighted average was 0.14%, because investors put most of their money into the cheapest, largest funds. When fees are thin, scale decides who earns a living, and scale comes from being considered.

The adviser channel adds a second buyer with different questions. Advisers want product education, not just product names. In the Broadridge and FSI survey of 428 advisers, 82% said better training and awareness of tools would help them grow, and 48% said they need to build their knowledge of crypto, a category many fund sponsors now package in ETFs.

## Where does AI already sit in fund research?

At the research stage: investors and advisers use AI to explore and compare before they buy, not usually to decide.

The evidence comes mostly from surveys, and the best available ones are run by companies with research products to sell, so treat them as directional:

- **US investors.** The Investing.com survey (March 2026, drawn from its own users) found 62% had used AI tools to help with investment decisions and 54% had used chatbots such as ChatGPT for investing research. 39% worried about incorrect or misleading recommendations.
- **Global retail investors.** [Finimize’s Modern Investor Pulse](https://finimize.com/business/press/modern-investor-pulse-2026-q3-40-per-cent-of-retail-investors-use-ai-for-research-weekly), a survey of 2,808 investors, found 40.9% use AI for research at least weekly. US investors were the most likely to never use it, at 24.4%.
- **New investors.** In the same survey, AI-generated research was the first catalyst for 4.5% of investors who started in the last two years, roughly double the 2.5% among longer-standing investors.
- **Advisers.** Broadridge and FSI found 67% of advisers under 45 use generative AI, against 43% of those 65 and over.

The pattern we infer from these numbers: AI is becoming the place where a fund category is first explained and a shortlist first forms. The purchase still runs through a platform, an adviser or a model portfolio, but the names on the shortlist were often set earlier.

## Which questions do investors and advisers ask about funds?

Category, cost, structure and fund-family questions, plus education questions from advisers. We drafted the sample prompts below to show the shape of these questions; none was logged from a real investor or adviser.

| Who asks | Illustrative prompt |
|---|---|
| Self-directed investor | “What’s the lowest-cost way to own the S&P 500 in an ETF?” |
| Self-directed investor | “Active bond ETF or bond mutual fund for a taxable account?” |
| Income investor | “Which dividend ETFs have the longest track records?” |
| Adviser | “Who issues buffer ETFs, and how do their caps and costs compare?” |
| Adviser | “Explain interval funds for private credit: liquidity, fees, risks” |
| Home-office research | “Fund families with the strongest active fixed income ETF lineups” |

These questions matter because the categories that are growing are the ones that need explaining. In the [MMI-Broadridge survey](https://s203.q4cdn.com/209299927/files/doc_news/AI-Product-Innovation-and-Next-Generation-Investors-Set-the-Course-for-the-Future-of-Asset-and-Wealth-Management-MMI-Broadridge-Surve-KR08K.pdf) of asset and wealth managers, 72% put active ETFs among their top three growth categories, and 78% of asset managers named them a key growth area. A new active ETF or interval fund is exactly what an adviser asks an assistant to explain.

## Why are the stakes higher for mid-size and newer fund sponsors?

Because the industry is concentrating, launches are at record levels, and AI answers tend to default to familiar names.

The ICI data shows the squeeze. The five largest fund complexes now hold 58% of mutual fund and ETF assets, and the ten largest hold 72%. From 2015 to 2025, 408 sponsors entered the US market and 515 left. Index funds, mostly run by the largest complexes, now hold 52% of long-term fund assets.

At the same time, the shelf is getting more crowded. [ETFGI](https://etfgi.com/node/28333) counted a record 2,759 new ETFs listed worldwide through November 2025, including 1,033 in the United States. Cerulli reports that 83% of ETF issuers intend to launch at least one active ETF in 2026. Most closures hit funds that never reached scale: Cerulli says they “did not attract adviser and end-investor interest.”

AI answers may add to the advantage of size. In a [controlled test](https://arxiv.org/abs/2606.17443) by Xi Chu and YuPeng Hou, assistants recommended a well-known brand 100% of the time when products were otherwise identical, though a rival’s small, visible quality edge broke that default. The test used consumer products, not funds. Still, a default toward the familiar name would fit what the ICI data already shows about fund assets: a few complexes dominate.

Our inference: for a sponsor outside the top tier, a fund that assistants cannot explain or place in its category is a fund that starts each conversation from zero.

## How does AI visibility turn into assets under management?

Through the shortlist: an answer names fund families or tickers, the investor or adviser checks the documents, and assets follow.

The path looks like this, and each step is our inference about how a documented buying process meets AI research:

1. **Category answer.** An investor asks how to get exposure to a theme, or an adviser asks how a structure works. The answer names a handful of fund families or tickers.
2. **Due diligence.** The investor opens fact sheets, ratings and the prospectus; the adviser checks the firm’s approved list and research notes.
3. **Purchase.** The trade happens on a platform or inside a model portfolio, often with no visible link to the AI answer. The platform itself may be picked through AI too, as our guide to [how investing apps get chosen](https://underneath.agency/resources/investment-platforms-customers-ai-search) explains.
4. **Recurring revenue.** The assets pay the expense ratio for as long as they stay, which is why one adviser adding a fund to a model can matter more than many single purchases.

This is why measuring AI’s effect through website visits undercounts it for fund companies. The adviser who first learned about a strategy from an assistant may never visit the sponsor’s site before placing it in a model. The same blind spot shows up well beyond funds, as our piece on [why analytics miss AI visibility](https://underneath.agency/resources/why-analytics-miss-ai-visibility) explains.

## What decides whether a fund family is named?

Mostly independent editorial coverage, plus the accuracy of the firm’s own data. Platforms document only part of this.

**Documented by platforms.** Google says its systems give even more weight to strong expertise and trust signals for “Your Money or Your Life” topics, which include financial stability, and that such content “must be highly accurate and consistent with established expert consensus.” Google also says [AI Overviews and AI Mode](https://developers.google.com/search/docs/appearance/ai-features) may use “query fan-out,” running several related searches across subtopics before answering.

**Observed in studies.**

- **Editorial sources lead in finance.** In the banking study by Mahe Chen and colleagues, editorial and third-party sites made up 64.6% of cited sources, brand-owned sites 34.1% and social sites 1.2%. Bankrate and NerdWallet led the domains. That study covered banks, not funds.
- **Third-party mentions travel with recommendations.** Across the brands in [our brand study](https://underneath.agency/research/brand-entity-ai-recommendations-study), every tenfold rise in the number of independent sites that named a brand went with 4.7 times the odds of being recommended. For a fund family, those sites are fund research databases, adviser trade press and financial media.
- **Stale pages travel.** In [our pricing study](https://underneath.agency/research/ai-pricing-accuracy-study), 61.9% of software prices quoted by assistants were fully faithful to the official page, and for 39 of 64 prices that differed, the same figure appeared on another page of the vendor’s own site.

**Our inference for fund companies.** The pricing study covered software, but the lesson carries: an old fact sheet with last year’s expense ratio, or a data vendor with an outdated strategy description, can become the version an assistant repeats. Fund facts live in many places (your site, prospectuses, data vendors, ETF databases, platform listings), and assistants can pick up any of them.

## What does GEO look like for a fund company inside the marketing rules?

For a fund sponsor, generative engine optimization (GEO) makes accurate, compliant facts and credible coverage easy for assistants to find.

1. **A clear entity.** Make the relationship between the parent firm, the fund family brand and each fund unambiguous, with consistent names and tickers across your site and data vendors.
2. **One version of the facts.** Expense ratios, inception dates, strategy descriptions and share classes should match across fact sheets, data vendors and platform listings. Retire stale PDFs rather than leaving them online.
3. **Category education.** Explain the structures you sell (active ETFs, buffer ETFs, interval funds) in plain language, including risks and who they do not suit. These are the pages advisers and assistants need. [How to fix wrong brand information in AI answers](https://underneath.agency/resources/fix-wrong-brand-information-in-ai-answers) covers tracing errors back to their source.
4. **Independent coverage.** Portfolio manager commentary in trade and financial press, analyst coverage, and inclusion in research databases give assistants third-party evidence. Our guide on [how brands build authority for AI search](https://underneath.agency/resources/how-brands-build-authority-for-ai-search) covers the method.
5. **Compliance from the start.** The SEC’s [investment adviser marketing rule](https://www.law.cornell.edu/cfr/text/17/275.206%284%29-1) prohibits advertisements that include “any untrue statement of a material fact,” and fund advertising has its own SEC and FINRA rules. GEO content goes through the same review as any other marketing.
6. **Measure across engines.** Assistants differ in what they cite, so check several of them; see [why tracking ChatGPT alone is not enough](https://underneath.agency/resources/is-tracking-chatgpt-enough).

No one can promise that an assistant will name a particular fund. GEO improves the evidence assistants find; whether a strategy suits an investor stays a decision for the investor and their adviser.

## What can’t the research tell fund marketers yet?

Whether AI visibility moves fund flows, and how assistants treat individual funds as opposed to firms.

- **No fund-level study.** We found no independent study of how assistants choose among specific funds or ETFs. The closest evidence covers banks, consumer products and firm-level brand visibility.
- **Vendor data.** The investor surveys come from companies selling research tools, and their methods are their own.
- **No public link to flows.** No study we found connects a fund family’s AI visibility with net new assets.

## Where should a fund company start?

Start by asking assistants the category and structure questions your strategies answer, and record which firms and sources appear.

That first check shows whether assistants place your funds in the right categories, whether your fees and facts are quoted correctly, and which research sites shape the answers. It also shows where a competitor with a weaker product is named simply because it is better documented.

If your growth plan depends on new strategies reaching scale, or on getting onto advisers’ shortlists, [ask us for an AI visibility review of your fund lineup](https://underneath.agency/contact). We will test the investor and adviser questions behind your key strategies, trace the sources behind each answer, and plan the data fixes, coverage and education content, reviewed with your compliance team, that give your funds a fair chance of being considered. See our [generative engine optimization service](https://underneath.agency/services/generative-engine-optimization) for how we keep fund facts consistent across data vendors and build adviser education content alongside your compliance review.

## Frequently asked questions

### Do AI assistants recommend specific funds?

They often name fund families or tickers when asked category questions, but no independent study yet shows how they choose among specific funds. Treat every answer as a starting point.

### Why would an assistant quote the wrong expense ratio?

Usually because an old page says so. In our pricing study, most differing software prices also appeared elsewhere on the vendor’s own site, which points to stale pages rather than invention.

### Is GEO allowed under fund marketing rules?

GEO content is marketing content. It goes through the same compliance review as any fact sheet or article, under the advertising rules that already apply to your firm and your funds.

### Can a small fund sponsor compete with the largest complexes in AI answers?

It can be considered, but it starts behind. In a controlled test, assistants defaulted to the known brand when products looked identical, and a small, visible advantage changed that. Clear, documented differences matter most.

## Sources

- Investment Company Institute (2026), [2026 Investment Company Fact Book, Chapter 2](https://icifactbook.org/pdf/2026-factbook-ch2.pdf)
- Investment Company Institute (2026), [2026 Investment Company Fact Book: Quick Facts Guide](https://icifactbook.org/pdf/2026-factbook-quick-facts-guide.pdf)
- ETF Express (2026-06-16), [New ETF launches far exceed closures: Cerulli Associates](https://etfexpress.com/2026/06/16/new-etf-launches-far-exceed-closures-cerulli-associates/)
- ETFGI (2025-12-31), [ETFGI reports the global ETFs industry had a record 2759 new products listed at the end of November 2025](https://etfgi.com/node/28333)
- Investing.com, via Hedge Fund Alpha (2026-04-09), [Nearly Two-Thirds Of Retail Investors Now Use AI To Inform Investment Decisions](https://hedgefundalpha.com/news/retail-investors-now-use-ai-to-inform-investment-decisions/)
- Finimize (2026-07-07), [40% of retail investors use AI for research weekly](https://finimize.com/business/press/modern-investor-pulse-2026-q3-40-per-cent-of-retail-investors-use-ai-for-research-weekly)
- Broadridge and Financial Services Institute (2026-02-02), [Advisors Signal Desire for More Technology and Product Education](https://www.broadridge.com/jp/press-release/2026/advisors-signal-desire-for-more-technology-and-product-education)
- Money Management Institute and Broadridge (2025-11-21), [AI, Product Innovation, and Next-Generation Investors Set the Course for the Future of Asset and Wealth Management](https://s203.q4cdn.com/209299927/files/doc_news/AI-Product-Innovation-and-Next-Generation-Investors-Set-the-Course-for-the-Future-of-Asset-and-Wealth-Management-MMI-Broadridge-Surve-KR08K.pdf)
- Chen, Wang, Chen and Koudas (2025), [Generative Engine Optimization: How to Dominate AI Search](https://arxiv.org/abs/2509.08919)
- Xi Chu and YuPeng Hou (2026), [Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems](https://arxiv.org/abs/2606.17443)
- Google Search Central (2025), [Creating helpful, reliable, people-first content](https://developers.google.com/search/docs/fundamentals/creating-helpful-content)
- Google Search Central (2025), [AI features and your website](https://developers.google.com/search/docs/appearance/ai-features)
- Legal Information Institute, Cornell Law School, [17 CFR 275.206(4)-1, Investment adviser marketing](https://www.law.cornell.edu/cfr/text/17/275.206%284%29-1)
- Underneath (2026), [Do Wikipedia and schema make AI assistants recommend a brand?](https://underneath.agency/research/brand-entity-ai-recommendations-study)
- Underneath (2026), [How faithfully do AI assistants quote software prices?](https://underneath.agency/research/ai-pricing-accuracy-study)

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