Key takeaways
- A majority of asset managers—over 80%—are already using or piloting AI, but most are still feeling limited by issues with their data, figuring out where AI fits into their business, and compliance.
- AI is making the most headway for advisors in reporting, compliance, tax planning, and onboarding—with fully automated investment decision-making still a ways off.
- Clients already expect personalized experiences from other parts of their financial lives, and asset managers who can deliver that at scale—across a large book, without proportionally growing headcount—will be better positioned to meet that bar. AI can help.
- The firms seeing real results tend to follow a similar pattern: they make sure their data is accessible and start with repeatable workflows that take up the most time.
Introduction
Asset managers have no shortage of AI ambition. Recent data put more than 80% of asset managers already using AI or actively piloting it, with 55% having integrated it into at least one investment process.
And the AI asset management market is predicted to hit $48.6 billion by 2035, according to one market study. But look more closely at what’s under the hood, and you’ll still find some basic challenges: data quality issues, unclear business cases, regulatory hurdles, concerns about accuracy.
This guide breaks down what AI in asset management looks like today, including the real opportunities it opens up, and what separates the firms making an impact with AI from those stuck in pilot mode.
What is AI in asset management?
Artificial intelligence in asset management means using machine learning, large language models, generative AI (GenAI), and agentic AI systems to analyze data, automate workflows, and support investment decisions—at a speed and scale no human team can replicate.
While traditional systems follow rules like: ”If a client’s portfolio drifts beyond a set threshold, rebalance it” or “If a trade hits a stop-loss, exit the position,” AI adapts as new information comes in.
For example, instead of rebalancing a portfolio on a set schedule, an AI system can look at a client’s tax situation, your firm’s current investment views, and any client-specific restrictions all at once—then show the advisor a list of suggested trades to review.
How AI is transforming the asset management industry
1. Automating operational workflows
The middle and back office in asset management—reconciliation, investor reporting, compliance packs, research summaries—has always been heavily manual.
GenAI is starting to chip away at that load, with 69% of asset managers stating that the most tangible benefits they’ve seen from AI so far come down to enhanced operational efficiency, according to Mercer.
Firms are using it to draft fund and investor reports from raw data, assemble regulatory filings, and summarize research and meeting notes, with humans still reviewing and signing off.
2. Enhancing the client experience
AI does not deliver true one-to-one personalization overnight, but it makes far more granular service practical. Firms can use AI to draft more targeted outreach, support client onboarding journeys, and strengthen client relationships with more personal recommendations—without straining the team.
3. Improving risk analysis and compliance monitoring
Most risk workflows in asset management are built around periodic review, like quarterly stress tests or monthly compliance checks. And surveillance has traditionally worked from samples. Because manually reviewing every trade or account interaction was never realistic at scale, firms would pull a representative slice and work from that. The frameworks were designed around what was practical at the time, but weren’t ideal.
With AI, continuous monitoring and comprehensive surveillance become increasingly practical. Models can run in the background across an entire portfolio, flagging unusual concentrations or exposures as they develop. On the compliance side, trading activity and communications can be monitored in near real time across all accounts—so the picture firms are working from is as complete as possible.
4. Accelerating data-driven investment decisions
Most firms are working from a similar pool of information—earnings transcripts, news, filings, internal research. The advantage comes from how much of it you can actually act on, and how quickly—that speed is increasingly where firms find their competitive edge.
The problem is that the volume of data and signals flowing through any given day is more than any team can realistically work through manually. Being thorough takes time, and moving fast means accepting gaps. AI reconciles that tradeoff—models can read across large datasets quickly, flagging what’s relevant and surfacing what’s changed, so analysts are focused on what actually warrants their attention. According to McKinsey, this use of GenAI could deliver an 8% efficiency impact in investment management workflows.
An advisor’s guide to getting AI right
Move from experimenting with AI to building it into your firm.
AI use cases
in asset management
Client onboarding and account management
Onboarding is often the first place clients experience a slowdown, but AI can speed the process up in the following ways:
•
Document intake and KYC checks
AI tools can read identity documents, custody statements, and suitability forms, extract key fields, and map them into your CRM or portfolio system—so staff are verifying exceptions rather than keying everything in by hand.
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Risk-profiling and suitability
AI can review a client’s risk profile and flag anything that doesn’t line up—like a mismatch between what they said they wanted, what they actually own, and what’s required for compliance—so advisors can focus the conversation on what needs attention.
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Account setup and funding
Once the intake is done, AI can handle the routing—matching new accounts to the right custodian and model portfolio, tracking progress, and flagging whoever needs to act when something gets stuck.
Reporting and client communication
Reporting and client updates are where clients see the story of your decisions; they are also where many teams burn hours assembling docs and charts.
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Performance and review reports
Generative models can pull performance data, benchmark comparisons, and risk metrics from your systems and draft client‑ready report narratives, so teams focus on editing and judgment rather than assembling paragraphs from scratch.
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Meeting and call summaries
Transcription and summarization tools can capture key points, decisions, and follow‑ups from client calls or investment committee meetings, then push those notes into the CRM or research library.
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Client outreach
AI can segment clients based on holdings, behavior, and recent interactions, then propose short, tailored messages—for example, explaining a strategy change or addressing a drawdown—that advisors can personalize and send.
Tax planning
Tax planning is high-impact for clients but hard to do at scale—the analysis is manual, and most advisors can only go deep on it for a fraction of their book.
•
Document analysis and opportunity surfacing
AI can read client tax documents—1040s, paystubs, account statements—extract what’s relevant, and surface high-impact planning opportunities, like Roth conversion windows or tax-loss harvesting candidates, in minutes rather than hours. Hazel, for example, does this directly within the platform.
•
Scenario modeling
Models can run what-if analyses on demand—income changes, withdrawal strategies, capital gains timing—so advisors can compare outcomes without juggling multiple tools or spreadsheets.
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Deliverables
AI can help generate personalized tax plans and letters that advisors review, edit, and send—shifting the work from building from scratch to refining and approving.
Billing and fee management
Billing might be a back office task, but the moment there’s an error, that becomes a tough conversation that the advisor has to have with a frustrated client.
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Fee calculation and validation
AI can automatically check that clients are being charged the right fees, catch errors like missed discounts or wrong rate tiers, and flag them before they ever hit a client’s account.
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Reconciliation and exception flagging
AI can identify mismatches between custodial data and internal records, helping teams catch and resolve discrepancies before they compound.
Compliance monitoring and regulatory reporting
Compliance teams are under pressure to cover more ground without proportionally more headcount. And when surveillance only covers a slice of activity, things that should be caught can fall through—potentially exposing firms to more risk.
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Communications and trading surveillance
AI can scan emails, chat logs, call transcripts, and trade records for patterns associated with misconduct or policy breaches—such as off‑channel communications or unusual account activity—escalating only the most relevant cases for review.
•
Regulatory filing preparation
AI can pull holdings, transaction, and disclosure data to draft asset-manager-specific filings—13Fs, Form ADV updates, AIFMD reports—leaving compliance teams to review and refine rather than build from scratch each cycle.
AI portfolio management and investment strategies
1. Algorithmic trading and execution optimization
About one-third (35%) of asset management firms point to investment selection and portfolio building as one of the main drivers behind their AI adoption, according to EY.
Instead of trading on a fixed schedule, AI can learn from a firm’s own trading history to suggest better timing, trade sizes, and order routing—especially for larger trades.
The goal isn’t to hand control to a model—the trading desk still makes the calls on execution and portfolio construction—it’s to make execution more informed by the firm’s own data. (Though, for independent advisors, this level of execution optimization is mostly institutional territory for now.)
2. Predictive analytics for market trends
AI can scan prices, company fundamentals, earnings calls, and news to spot opportunities that fit your investment approach—like a stock where sentiment has shifted but the portfolio hasn’t caught up yet, or a sector where a key trend is picking up steam. The practical application is a filter that surfaces ideas for analysts to review and act on.
3. Risk assessment and scenario modeling
Risk teams are using AI—including predictive models—to stress-test portfolios more frequently and across a broader range of conditions.
That means catching things like overexposure in a sector or a single position earlier than a quarterly review would. For independent advisors, the firms and model providers you work with are increasingly applying these same approaches.
The AI playbook for your firm
Our guide covers everything from data readiness to vendor security—so you can move forward with confidence.
Best practices
for AI adoption
in asset management
1. Start where the workflow is stable, and the payoff is clear
Begin with use cases like reporting, compliance documentation, research summaries, or onboarding support—areas where the process is repetitive, the risks are lower, and success is easy to measure. Leave higher-stakes investment decisions for later, once the data, controls, and teams are ready.
2. Fix the data before you scale the models
Most AI projects stall for the same reason: the data feeding them is incomplete, inconsistent, or scattered across systems. One way to get ahead of it is to consolidate onto a platform that keeps information connected from the start.
3. Train people to use it in their day-to-day
The practical goal is not broad enthusiasm; it is making sure analysts, advisors, risk teams, and operations staff know how the tool fits into their actual decisions and controls.
4. Put governance in place early
Before you roll out any AI tool, decide two things: What it’s allowed to do, and who reviews its outputs before they reach a client. A drafted email that goes out unreviewed, or a recommendation acted on without a human check, is where things go wrong. For compliance purposes, make sure you can explain what the tool surfaces and why—that starts with knowing how it works, what data it’s pulling from, and having a clear review process before anything reaches a client.
5. Measure one level deeper than efficiency
Time saved is useful, but it is not enough on its own. Track whether the tool improves turnaround times, reduces errors, increases coverage, sharpens decision-making, or changes client outcomes—then refine from there.
How asset managers can get more from AI
Most of what makes AI work in asset management comes down to data access—clean, connected, front-to-back. That’s harder to achieve than it sounds when custody, portfolio management, and reporting all live in different systems.
Altruist brings all of that together in one place, which is why advisors on the platform are better positioned to get real value from AI tools as they mature.
FAQs
Will AI replace human financial advisors?
No. AI handles repetitive tasks well—drafting reports, flagging compliance issues, summarizing notes. What it can’t replicate is the trust, judgment, and emotional intelligence that define good client relationships. The goal is using AI to handle the routine so advisors can focus on the work that actually requires them.
How much does implementing AI cost for smaller advisory firms?
It depends on your approach. Building custom AI solutions is expensive and usually impractical for independent advisors. A more realistic starting point is an integrated custodial platform that already has AI built in—like Altruist, which combines custody, portfolio management, and reporting in one place with Hazel AI available alongside it.
What are the biggest risks of using AI in investment management?
Three main ones: Data quality (AI is only as good as the data feeding it), transparency (you need to be able to explain why a model surfaced a recommendation), and regulatory scrutiny (oversight expectations are rising).
How should advisors disclose AI usage to clients?
Be clear about where AI plays a role in your process and that a human is reviewing and approving decisions. Regulatory guidance is still evolving, but the direction is consistent: disclosures should accurately reflect any material role AI plays.
What AI tools are currently available for independent advisors?
There are several AI tools across research, compliance, and CRM—but for most independent advisors, the more practical question is whether the tools you’re considering integrate cleanly with your custodial platform. That’s why Hazel is a solid option. It works across custodians, and for advisors who also custody with Altruist, it connects directly alongside portfolio management, reporting, and billing for an even more seamless experience.
Hazel is an artificial intelligence tool offered through Altruist Corp (“Altruist”). Outputs generated by Hazel are for informational purposes only and should not be relied upon as legal, compliance, financial, tax, or investment advice. Hazel’s capabilities are evolving and may be subject to limitations based on data input, user configuration, and system permissions. Altruist does not guarantee the accuracy or completeness of Hazel’s outputs.
