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.

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:

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.

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.

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.

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.

Intro

For years, hedge funds and large institutions have used models to automate trading decisions. But the artificial intelligence tools available today—ones built on machine learning and natural language processing—are a different animal, and they’re reshaping what’s possible for independent advisors across research and portfolio management.

Here, we look at how those tools work, where they fit into your investment process, and what they actually mean for your clients.

What is AI in investing?

AI in investing means using AI-powered tools to support how you research and manage portfolios, or how you communicate with clients. Tools built on large language models (LLMs), for example, can scan an earnings call or SEC filing in seconds, flag when a portfolio has drifted based on your investment strategy, surface tax-loss harvesting opportunities, or pull together a client report without starting from scratch.

For most independent advisory firms, the practical value is in how much of the routine work AI can absorb. That frees up time for the high-value work that needs human input—like building out a financial plan or helping a client stay the course through a rough market.

How AI is reshaping the investment landscape

AI is changing how independent advisors work in three meaningful ways:

The industry is already preparing for this shift. In Accenture’s 2025 North American Wealth Management Advisor Survey, 96% of advisors said generative AI has the potential to revolutionize client servicing and investment management. For independent advisory firms, the practical question is which parts of your process to let AI handle—and how to keep your own thinking at the center of it.

Benefits of using AI in investing

Faster and more comprehensive research

AI changes the shape of your research day. Instead of trying to keep up by skimming headlines and a handful of filings, you can have a broader universe—including investment opportunities across sectors—monitored and distilled for you. This way, you spend your time on the few items that genuinely warrant a closer look. Over time, that means you miss fewer important developments, you see patterns sooner, and you can bring more context into client conversations without adding late nights.

Improved portfolio consistency and performance

While most advisors don’t struggle with having an investment philosophy, it can be a struggle to execute it perfectly, every time, across every account. When AI helps monitor portfolios against your rules and surfaces where action is needed, it becomes easier to keep allocations, risk levels, and tax decisions aligned with your stated investment strategy.

Enhanced client communication and experience

Clients rarely see your research process or your internal dashboards. What they see is how clearly you explain what’s happening and what you recommend they do next. AI helps by giving you better starting points for that communication. It can draft review notes, explanations of complex changes, and talking points tailored to each client’s situation. That makes it easier to be timely and consistent, even when markets are noisy and your calendar is full.

More capacity without added headcount

A lot of advisory work is cognitively demanding; a lot of it is also repetitive. When AI picks up the repetitive parts of investment work—idea screening, summarizing information, routine portfolio checks—you create space for higher-value tasks without immediately needing to hire. In practice, that can be the difference between running at a constant sprint and having enough margin to think, plan, and reach out to clients before they reach out to you.

Healthier economics over time

Better use of time and more reliable execution add up. As AI helps you streamline research, monitoring, and communication, you tend to need fewer point solutions and less manual support for the same level of service. That gives you more flexibility in how you grow: you can decide to improve margins, reinvest in client experience, adjust pricing, or some mix of all three—without compromising the quality of your work.

Risks and limitations of AI in investing

Data quality and algorithmic bias

AI will sometimes give you a confident answer even when it’s working from incomplete or skewed data. For advisors, that means checking the basics: What period does this data actually cover? Does it capture different rate environments and market conditions, or just the last decade? Are there sectors, geographies, or asset classes it barely sees? Treat AI output as a draft—something to sample and spot-check against source materials—rather than a finished conclusion.

Over-reliance on automated recommendations

The goal with AI technology is to use it heavily for the right things—research, screening, monitoring—while staying actively engaged with what it produces. The risk isn’t using AI too much, it’s reviewing its output too little. AI can rank ideas, generate trade lists, and propose portfolio changes, but you’re still accountable for every recommendation that goes to a client. A simple test: if you can’t explain a move in plain language without saying “because the system recommended it,” you need to look more closely before acting.

Regulatory uncertainty

Regulators are paying close attention to how firms use AI in advice and portfolio management. They expect you to understand how these tools influence your recommendations, manage the associated risks, and document your review process clearly—including any disclosures required around AI-driven recommendations. Increasingly, regulators, including FINRA, expect firms to have written procedures that specifically address AI use, not just the ability to explain outputs after the fact.

Integration challenges with legacy systems

A good AI tool should make your day simpler. When it sits outside your custodian, planning software, and CRM, you end up moving data around by hand and creating new places for mistakes. The clunkiness of that process wears on teams quickly and makes it harder to trust what you’re seeing. It’s worth piloting any new tool on a real slice of your workflow first—exactly as you’d use it with clients—before rolling it out more broadly.

How to evaluate the best AI investing app for your practice

Start with your workflow

Most advisors start by comparing features. But features aren’t really the right starting point—your workflow is. If rebalancing taxable accounts is eating up your week, that’s a very different shopping list than if your problem is getting client reports out the door. Figure out where work is piling up or where things are falling through the cracks, and use that to back into the specific capabilities that would actually make a difference.

Define what good looks like

Before you start demos, get specific about what a strong output actually looks like in your workflow. Otherwise, it’s easy to get impressed by capabilities you’ll never use.

Check how it fits your existing systems

Ideally, you want tools that connect directly to the systems your team already lives in. The real test is whether it actually simplifies how your team works day-to-day. If you’re exporting data from your custodian, importing it into the AI tool, then manually updating your CRM with the output, you might be doing more work, not less.

Pressure-test for your regulatory environment

Verify SOC 2 certification, strong encryption, and clear data usage policies. For advisory specifically, look for audit trails, supervision features, and zero data retention agreements with third-party AI providers—meaning client data isn’t being used to train models outside your control. A vendor that’s built seriously for regulated environments will have answers to these questions without hesitation.

You should also be able to explain any AI-generated output clearly to a client or a regulator—and that’s something the tool should make it easy to do.

Test it on real work

Run the tool on actual clients and portfolios, not a curated demo dataset. Check how much editing the outputs need, whether junior team members can use it without hand-holding, and whether it holds up under time pressure. Adoption is where a lot of tools fail.

Don’t overlook client-facing output

If the platform can generate reports, planning summaries, or client communications that are ready to share with minimal editing, that’s time recovered from some of the most labor-intensive work in the business. Look for platforms that let you set parameters around tone, format, and firm-specific preferences so that the output actually sounds like your firm consistently, without needing a lot of heavy editing before going out to a client.

Weigh cost against operational impact

Look beyond the subscription fee—factor in implementation time, training, and any workflow changes. The more useful question is what the tool is worth in hours saved, errors avoided, and consistency gained. Some of the more capable platforms are more accessible than many advisors expect. Start with one workflow, define what success looks like before you begin, and treat AI outputs as drafts to review rather than decisions to accept.

The future of AI and investing for financial advisors

The tools available today are already capable. Where things are heading is deeper integration—AI that connects more seamlessly across the platforms and workflows advisors rely on every day.

Client meetings will lean more on tools that update projections and model scenarios in real time, so the conversation stays on trade-offs and priorities rather than pulling numbers together. Monitoring will shift from scheduled reviews to something more continuous—alerts that surface when something actually needs attention.

How much of that you’re positioned to take advantage of depends on the decisions you’re making now about how AI fits into your practice.

And integration and monitoring aren’t even the biggest shift coming. The bigger shift is in personalization. As AI draws on a fuller picture of each client—how they’ve behaved through past market cycles, major life changes, spending patterns—advice gets sharper and more tailored than anything a handful of questionnaire answers could produce.

Will AI replace financial advisors?

Research suggests that nearly 80% of affluent households still prefer a human relationship for core financial advice, according to McKinsey. 

That said, AI does change the role of the advisor in a few ways:

From stock picker to decision editor

AI can surface options, run scenarios, and flag risks at scale. The advisor’s job is to decide what’s actually appropriate for each client and why—bringing judgment, context, and accountability that no model can replicate.

From data gatherer to behavioral coach

Portfolio analysis and reporting can largely be automated. What can’t be automated is helping a client interpret a volatile market, avoid a panic decision, or stay aligned with goals they set when conditions looked very different.

From one-to-one service to scaled personalization

By offloading routine analytics to AI, advisors can deliver more timely, tailored touchpoints across a larger book—while freeing up time for the deeper planning conversations that actually move the needle for clients.

How forward-thinking advisors are using AI to grow

At its best, AI doesn’t change what good investing looks like—it just makes it a lot harder to let things fall through the cracks. Advisors who’ve built it into their investment process are running deeper research, catching tax opportunities across their book, and implementing their strategy more consistently across every account.

Trade blotter reporting

You can now download trade blotter reports on your own schedule, without a lengthy process with our support team. Experience a streamlined system where you can: 

• Generate trade blotter reports across all of your accounts.
• Choose a custom date range, with up to two years of history.
• Receive reports within a minute’s time.

New in Model Marketplace

We’ve launched additions to the Altruist Model Marketplace:

1. Altruist Equal Weight Large-Cap Direct Index
2. Eventide Faith-Based Models

We also improved Explore & Compare, making it easier than ever to find the right fit. Now you’re able to compare up to 4 models side by side with deeper analytics on sectors, regions, style, yield, and more.

Custom roles

Altruist now has custom roles built around your firm’s structure. Each of your team members gets exactly the access they need. Here’s how it works. You can either: 

• Choose from predefined roles for specialized functions, such as Trader
• Build your own custom roles from scratch or start from an existing role and tailor it to your team

Why AI matters for financial advisors

Client expectations have changed faster than most advisory tech has. People want answers quickly and to feel like their advisor really knows them. But that kind of service is hard to deliver at scale. The pressure shows up in a few specific places:

• Competitive pressure: Clients increasingly expect the kind of proactive, personalized service that used to be reserved for ultra-high-net-worth relationships. And the window to get ahead of this is narrowing as AI is quickly moving from differentiator to baseline across much of the financial services industry. (Some estimates suggest that 90% of finance teams globally are expected to use at least one AI-enabled tool by the end of this year.)
• Operational demands: The administrative layer of running a practice is substantial—compliance monitoring, performance reporting, meeting documentation—and it grows alongside your book.
• Scalability: There’s a ceiling to how many clients one advisor can serve well without sacrificing quality, which means you traditionally couldn’t serve more clients without adding headcount.

AI helps by automating the operational layer, helping you streamline and deliver more personalized service at scale, and raising the ceiling on how many clients you can serve well without a proportional increase in overhead. 

How AI is used in finance today

AI shows up differently depending on what problem you’re solving. Three core technologies are worth understanding, as knowing what each is built to do makes it easier to spot where they’re most useful.

Machine learning and predictive analytics

Machine learning (ML) is what allows AI to get better at predictions over time without being manually updated. It works by finding patterns in historical data—and in finance, those patterns do a lot of useful work: risk scoring, identifying clients who might be disengaging, triggering rebalancing reviews, forecasting market trends. The more data these models see, the sharper their predictions get. (Whether that’s your firm’s data or broader financial datasets depends on the platform and your data permissions.)

Natural language processing and sentiment analysis

Natural language processing (NLP) is how AI reads and makes sense of text, including earnings calls, regulatory filings, news articles, and client emails. Instead of a human analyst spending days combing through documents, NLP can extract the relevant figures, flag what changed, and surface patterns across thousands of sources at once.

Sentiment analysis is a specific application of this—e.g., reading the emotional tone of news articles to gauge how markets are reacting, or scanning client emails and meeting transcripts to spot referral opportunities or satisfaction signals before your next touchpoint.

Generative AI and large language models

Unlike ML, which analyzes existing data, generative AI (GenAI) produces new content from it. In financial services, that means drafting client communications, summarizing complex documents like estate plans or tax returns, generating scenario models, and producing first drafts of compliance documentation. It’s the newest of the three AI technologies and is still maturing, but it can cut meaningful time out of day-to-day knowledge work.

AI applications for your practice

The technology is only as useful as what it actually does inside a firm. Here’s where advisors are seeing the most meaningful impact.

Portfolio management and investment optimization

Portfolio tools now use AI to monitor drift, flag rebalancing triggers, and stress-test allocations under different scenarios. Many can also generate proposed portfolios based on a client’s goals and risk profile. These are the same mechanics that powered early robo-advisors, but they’re now showing up in software designed for human advisors.

Document processing and workflow automation

A large share of operational work still revolves around paperwork. AI can pull information from incoming documents (e.g., account transfer forms, loan applications, regulatory filings), validate the data, and move it to the right systems. Tasks like reconciliation or onboarding paperwork that used to require manual entry can instead move through a lot of the process automatically.

Client service and conversational AI

Some firms are starting to use AI assistants to handle routine client questions—checking balances, pulling transaction history, or retrieving documents. When something requires an advisor’s input, the system logs the interaction and routes it to the appropriate person, maintaining the right level of oversight.

Fraud detection and cybersecurity

AI monitors transactions, login patterns, and device and location signals continuously. When something looks unusual, AI systems can flag it immediately and trigger verification steps or temporary blocks before fraudulent activity has a chance to progress or spread.

This is also the most established AI use case in financial services: Roughly 60% of financial institutions use AI for fraud detection, and in the U.S., that number climbs to about 91% of banks that are using it.

Risk assessment and credit scoring

AI evaluates creditworthiness using traditional data—credit history, income, collateral—alongside non-traditional signals like utility payments and cash flow patterns. The result is a more complete picture of an applicant’s financial health than standard scoring models would typically capture.

Algorithmic trading

Many market participants now use AI-driven systems to analyze real-time data and execute trades based on predefined strategies.

Around 70% of all global trading volume is actually now executed by these algorithms. Independent advisors usually aren’t running these models themselves, but their clients’ portfolios are operating in markets where algorithmic activity plays a significant role.

Regulatory compliance and anti-money laundering

AI-driven RegTech tools handle much of the ongoing monitoring—scanning transactions and client records against regulatory rules and watchlists, then surfacing anything that needs review. They also track regulatory changes as they happen, rather than catching up after the fact.

Benefits of AI
in finance

Where AI proves itself is in the day-to-day economics of running a firm:

• Operational efficiency and time savings: Preparation, documentation, and follow-through work—drafting agendas and recaps, updating CRM records, extracting tasks from conversations—gets handled automatically, freeing up more of an advisor’s week for actual client work.
• Enhanced client experience and personalization: Advisors arrive at every interaction with a clear view of recent activity, open issues, and past commitments—enough context to tailor recommendations and follow-ups to a client’s real circumstances rather than working from memory or partial notes. The economics back it up: BCG research finds that firms using AI to personalize client interactions have seen 10–15% revenue increases and up to 30% reductions in churn.
• Improved risk management: Continuous monitoring surfaces early signals—portfolio drift, concentration risk, unusual account activity, disengaging clients—and supports more consistent documentation, reducing the quiet gaps that tend to become compliance or suitability problems later.
• Cost reduction and scalability: As the practice grows, more of the added administrative work—notes, forms, updates, record-keeping—gets handled automatically. Firms can absorb higher client volumes without proportionally increasing headcount.
• Data-driven decision making: Consolidating portfolio data, tax-lot details, cash flow needs, and client communication into a single view means advisors spend less time assembling information and more time evaluating the tradeoffs that actually require their judgment.
• Tax planning: AI can ingest tax returns, account statements, and client financial data to surface optimization opportunities (e.g., Roth conversions, timing moves, deductions) and model scenarios in real time. For example, Hazel’s AI tax planning tool can turn a client’s tax return into a personalized strategy—with scenario modeling and client-ready deliverables—in minutes. This helps save advisors approximately $5,900 per client in taxes and 5 hours per week, on average.

Challenges of AI in finance 


The benefits of AI in finance are meaningful—but they’re also not without tradeoffs. Understanding where AI adoption gets complicated is part of making a good decision about where and how to start.

• Data privacy and security: AI tools need data to work—and in an advisory context, that means account numbers, tax IDs, client statements, and communications. The practical concerns are specific: Data leaving the firm’s environment to third-party vendors, staff pasting sensitive information into unmanaged tools, and limited visibility into how long data is retained or who can access it. Any platform you evaluate should have clear answers on things like role-based access controls, encryption, audit trails, and clear limits on what data the tool can access.
• Algorithmic bias: AI models learn from historical data—and historical data often reflects historical inequities. That can look like lending decisions that disadvantage certain zip codes, risk scores that penalize gaps in work history, or service models that route some segments to slower responses. A related challenge is transparency: When a model is complex or vendor-hosted, it can be genuinely difficult for an advisor or compliance officer to answer “why did the model recommend this?”—which is a real problem in a fiduciary context.
• Legacy system integration: Most firms are already operating across a mix of custodial platforms, portfolio accounting systems, CRM tools, and document management—plus whatever spreadsheets and macros have accumulated over the years. The common pain points are duplicated data, delays between when data updates and when you can act on it, and APIs that are limited or outdated. Integration work also tends to take longer and cost more than the AI pilot itself, which is worth factoring in before you’re already committed.
• Implementation costs and expertise gaps: The cost of adopting AI goes beyond the vendor contract. It includes data preparation, integration, and ongoing monitoring. Some firms are showing early ROI, but it’s not universal yet: 1 in 5 finance teams currently report returns above 20% on their AI initiatives. Most firms can experiment with low-code tools, like Hazel, which is built to handle the integration and workflow complexity, so firms don’t have to build that capability themselves.

AI regulations and compliance in financial services

The regulatory framework around AI in financial services is still catching up to the technology. Current SEC fiduciary guidance makes one thing clear: fiduciary responsibility doesn’t transfer to an algorithm. If an AI tool influences a client recommendation that causes harm, the advisor is still accountable for that outcome—meaning that human oversight remains a compliance requirement.

Beyond fiduciary duty, federal and state privacy regulations govern how client information flows through AI systems, and those rules continue to evolve. Understanding what your platform does with client data—and documenting it thoroughly—is increasingly a baseline expectation for running a compliant practice.

And because there isn’t a fully settled regulatory framework at the moment, advisors adopting AI today should be especially careful when choosing the platforms and tools to incorporate into their practice. Understanding third-party policies will be a meaningful risk management decision.

The future of AI in finance

The current state of AI in financial services is genuinely useful—but the more significant changes, shaping the future of finance, are still ahead.

Agentic AI for autonomous financial workflows

The next frontier is AI solutions that can orchestrate entire processes from start to finish—things like tax planning, compliance documentation, report generation—with humans stepping in to review and approve rather than manage every step. These systems are early but moving fast, and they’re likely to reshape how firms operate over the next few years.

Finance leaders are already anticipating the shift, with 75% expecting agentic AI to be a routine part of their operations by 2028, handling complex workflows with humans stepping in to review rather than manage every step.

Hazel’s tax planning tool, for example, is already doing this for advisors, using AI to ingest client data, surface opportunities, and produce client-ready strategies with the advisor in the loop at the right moments.

Hyper-personalization and embedded finance

As AI gets better at modeling individual client behavior and preferences, the quality and timeliness of personalization improve with it. The direction this is heading is financial guidance woven into the platforms clients already use daily—banking apps, payroll portals, commerce platforms—proactive, context-aware, and available well before the quarterly review. For advisors, that raises the bar on what clients will expect from every interaction, even if they’re not the ones building those experiences directly.

AI for financial inclusion

One underappreciated implication of AI in advisory is what it does to the economics of who you can serve. When routine work is automated, the cost of serving smaller accounts drops, making it more viable to extend quality advice to emerging or mass-affluent clients who didn’t previously fit the traditional model.

And the impact extends to lending, as well. For example, credit unions using AI-driven credit scoring have seen a 40% increase in loan approvals for women and people of color, without an increase in default risk.

How financial advisors can start using AI

If you’re ready to put AI to work in your firm, here are some practical steps to get started:

1. Evaluate your current technology and tools: Look for where data has to be entered manually or where information gets lost between systems. Those friction points usually signal where AI can add the most immediate value.
2. Identify your highest-impact use cases: Pick one obvious or persistent bottleneck in your practice—meeting documentation, client onboarding, compliance reporting—and start there. Early, visible wins make it easier to build support for broader adoption.
3. Choose a platform with AI built in: Standalone AI tools often require extra integration work that can offset their benefits. Platforms like Altruist build AI-powered workflows directly into the custodial experience, so the capability is already there when you need it.
4. Train your team on the tools—and their limits: One of the biggest risks with AI is trusting it blindly. Make sure your team knows how to use the features effectively and where outputs need human review before anything reaches a client.
5. Track what actually changes: Before you roll out anything new, set a baseline for key metrics—time spent on specific tasks, onboarding timelines, client response times. Compare before and after so you know which uses of AI are worth expanding.

Building a more efficient advisory practice with AI

The advisors who will get the most out of AI are the ones who are clear on what they’re trying to build and strategic about where technology can help them get there faster. AI works best as an extension of a good advisory practice, where the client relationship, the trust, and the judgment remain yours.

What AI can do is take the operational weight off your plate, so more of your time goes toward the work that actually matters. 

If you’re thinking about what that looks like in practice, talk to the Altruist team about how an AI-integrated platform can fit into how you run your practice today.

How to be the advisor for decades to come?

According to research from Cerulli Associates, by 2048, nearly $105 trillion in wealth will go from Baby Boomers and older generations to their heirs in the Great Wealth Transfer. And as that wealth changes hands, advisors have an opportunity to connect with these next-gen clients and set their firms up for success for decades to come.

In an ideal world, an advisor would automatically win the next generation of investors. But that’s not always—or even often—the case. Recent data from The Harris Poll found that nearly half of younger Americans (43%) plan to switch asset managers from their parents’ current provider after they receive their inheritance.

Clearly, a pre-existing relationship isn’t enough to get you in the door with the next generation of investors. So, the question is, how do you position yourself as the right asset manager for millennials, Gen Z, and younger generations to come?

Let’s take a look at seven strategies that will help you win this next generation of clients—and position yourself as an effective asset manager for younger clients.

Strategy #1

Treat next-gen clients like new clients

Next-gen clients won’t necessarily inherit their parents’ advisor relationship—it’s one that has to be earned on its own terms. That’s why it’s helpful to see these heirs as a brand new partnership.

Think about what you do when you’re trying to win a new client. How do you make the initial connection? How do you show them that you’re the right advisor for their long-term financial goals? Whatever strategy you typically use to win new business, apply to next-gen clients.

For example, regular contact is a must for securing new business. So, as you’re trying to secure next-gen partnerships, it’s important to have a strategy for when, how, and how often you’re reaching out.

And while there is no “one-size-fits-all” cadence for potential client communication, it’s important to note that if you want to win this next generation of investors, you’re probably going to need to be reaching out more than you’re used to—and more than you connect with their parents.

According to research from The Harris Poll, 42% of younger Americans said they want to consult with their financial advisor at least once a week—compared to just 4% of older investors.

Strategy #2

Use your existing relationships to get in the door

Now, while you’ll want to build trust with next-gen clients in the same way you approach connecting with new clients, that doesn’t mean you have to start from ground zero. You can (and, in most situations, should) use your existing client relationship to your advantage—and as a way to “get in the door” with the next generation.

For example, you might invite your client’s children to annual reviews or schedule financial meetings with not just your primary client, but their entire family.

Then, as you start to build the relationship, you can look for opportunities to connect with next-gen clients directly—for example, by scheduling one-on-one meetings to talk about their future financial goals, educating them on relevant wealth management topics (like tax-efficient investing or estate planning), or reviewing your advisory services, financial services, and what you can offer in a partnership moving forward.

Strategy #3

Combine digital and IRL support

Many financial professionals assume that younger investors prefer to connect with their advisors and manage their investments via the digital space. And there is definitely some truth to that. As Grealish said in his recent interview:

“Younger generations are…digitally native. They’re not going to expect to consume information about their portfolio investments through monthly statements mailed to them, or emailed to them. Folks are going to expect to be able to have 24/7 access through a thoughtfully designed digital platform, to have digital-first interactions.”

However, for many next-gen clients, digital channels are just one piece of the puzzle. In addition to digital support, many younger investors also want to forge a more analog, real-life relationship with their financial advisor. For example, while The Harris Poll found that 17% of younger investors want a digital relationship with their advisor, it also found that those investors actually prefer in-person (29%) or phone (24%) interactions with their financial planner over digital methods like email (19%) or text (17%).

So, if you want to appeal to next-gen clients, it’s important to take a hybrid approach to the relationship—and offer a variety of ways to communicate, connect, and work together, both in the digital space and the “real world.”

For example, when working with younger investors, you might give them access to Altruist’s client portal, where they can access their portfolios and investment information 24-7, in real-time, from any desktop or mobile device—and then supplement that digital experience with quarterly in-person meetings, educational opportunities, and/or frequent phone calls.

Strategy #4

Show next-gen clients the value only you can provide

For next-gen clients, investment tools like AI-powered robo-advisors or self-directed index funds are not only the norm, but—in many cases—the go-to. For example, research from SurveyMonkey found that Robinhood is overwhelmingly the investment platform of choice for millennial (40%) and Gen Z (32%) investors.

And as these tools have become increasingly popular with younger investors, there’s one question you can pretty much guarantee will be on their minds when they’re deciding whether they want to work with you: “Why do I need to hire you—when these tools can deliver the investment support I need for free?”

If you want to secure those clients, you need to not only answer that question, but actually show them the value you, as an advisor, can offer—the value they will never be able to access if they stick to these digital, DIY investment tools.

For example:

• Private market access. Alternative investments like private equity and private credit are typically only available through financial advisors—and that can be a major selling point for younger investors looking to diversify their portfolios beyond the traditional stocks and bonds available via consumer investing apps. (With Altruist, the entire alternatives experience, from fund discovery to paperwork to billing, lives within the platform—making alternative investments available for clients in just a few clicks.)
• Portfolio-backed borrowing. With tools like Altruist’s margin loans, advisors can help investors borrow against their own investment portfolio—without selling their position. This liquidity strategy is accessible via advisory services—and can be a great way for younger investors to access needed funding (for example, to purchase a house or fund a business venture) without sacrificing long-term growth.
• Personalized financial planning. DIY tools can track a portfolio—but they can’t replace a human advisor who understands a client’s full financial picture. Advisors can offer personalized financial advice that’s tailored to the client’s specific goals or circumstances—for example, navigating a job transition, building an estate plan, or setting up college funds for their children.

Strategy #5

Embrace AI

AI is everywhere, including wealth management—and for many next-gen investors, AI competency is a must-have quality for financial advisors. For example, according to a recent study, 54% of millennial and Gen Z investors say they want to work with a financial advisor who understands and actively uses AI in their role—almost 20% more than Boomers (36%).

And it makes sense. With the right AI tools, you can streamline your operations, automate time-consuming tasks, and significantly reduce the time and energy required to deliver high-quality work—all of which allow you to provide a higher level of service and a better experience for your clients. As such, if you want to appeal to younger investors, embracing AI is an absolute must.

But not all AI is created equal. So what, exactly, are the “right” AI tools for financial advisors?

If you want AI to make a real impact in your firm and for your clients, avoid generic platforms and instead, look for tools that speak directly to the work you do with clients—like Hazel, Altruist’s transformative AI engine.

For example, Hazel AI Tax Planning reads 1040s, paystubs, meeting notes, email, custodial data, and other key tax-related documents to help advisors create personalized tax strategies and client plans in minutes.

When you embrace AI—and use it strategically in your business—you can essentially offer clients the best of both worlds. By combining the efficiency and intelligence of AI with the experience, support, and guidance of a real-time advisor, you can add more value and a better client experience than both DIY AI-powered finance tools and less tech-savvy advisors—which can help you stand out to next-generation clients.

Strategy #6

Lead with values, not just returns

Next-gen clients view wealth differently than older generations; for example, according to data from The Harris Poll, while older Americans are most likely to view wealth as a path to security (42%) or a tool to live their desired lifestyle (25%), younger Americans see wealth more as a way to build their legacy (22%) or to achieve personal fulfillment (18%). And as a result of this perspective shift, for many next-gen clients, values alignment isn’t a nice-to-have—it’s a non-negotiable. 

This identity- or values-centric approach to wealth building and management is reflected in how next-gen clients find, hire, and work with advisors. Some of the top reasons heirs cited for leaving their benefactors’ financial advisor included not having a similar investment philosophy (38%), values not aligning (33%), and the advisor lacking trustworthiness and integrity (25%). In addition, a recent report on sustainable investing found that 90% of millennial investors said they would choose their financial advisor based on their sustainable investment offerings.

So, if you want to get in the door with the next generation of investors, you need to lead with values—not just returns. 

What does that “leading with values” look like in practice? As you’re getting to know your client, ask what’s important to them; what industries, businesses, and/or causes (like sustainability or climate change) they support; and if there are any investment strategies or opportunities that feel misaligned with their values. Then, use that information to suggest financial strategies that help them not only grow their wealth, but also use it in a way that supports their personal values and the good they want to do in the world. 

You should also highlight how, as an advisor, you can better help next-gen clients navigate values-based investing vs. trying to invest on their own. For example, with DIY apps and tools, investors have limited options, like stocks and pre-built funds. But with Altruist’s personalized indexing, advisors can offer next-gen clients tax-aware, customized portfolios at scale—including portfolios that align with their specific values.

Strategy #7

Connect via content

If you know anything about marketing, you know that if you want to appeal to a certain demographic, you need to connect with them where they already are. And if your target demographic is younger investors, “where they already are” is on social media platforms. 

According to research from Blackrock, roughly half of high-net-worth investors report being more likely to engage with an advisor who has an active social media presence—and nearly one-quarter of Gen Z adults (23%) say they wouldn’t even consider a financial professional that wasn’t publishing content on social media. 

Whether you’re looking to lock in next-gen clients or just to appeal to younger investors in general, the data is clear: publishing content on social media can be an incredibly effective way to do so.

• Choose one platform. To keep things manageable, think about what kind of content you like to make. If you’re skilled at creating video and visual content, go with Instagram. According to data from Sprout Social, 76% of millennial social media users are on the platform. If you want to focus more on text-based content, go with Threads. Not only are Threads users significantly more engaged than X users (On average, Threads posts have nearly 74% higher engagement rates than X), but many of those users would fall under the next-gen category (millennials and Gen Z are the most active users on Threads, with each generation having a 17% adoption rate). 
• Create the right kind of content. If you want your social media content to land with your ideal clients, creating content isn’t enough; you need to create the right kind of content. Think about what your next-gen and younger investors need to understand the most—for example, financial planning, taxes, inheritance basics, or how to purchase their first home—and then create content that speaks to those topics.
• Focus on value—not volume. When it comes to content, value always outweighs volume. Rather than trying to post every day, focus on creating content that packs a major value punch. That being said, consistency is important on social media—and sticking to a posting schedule is crucial for ensuring your content gets in front of your target audience. So while you don’t need to publish every day, you do need to choose a cadence that works for your schedule—and stick with it (for example, posting every Wednesday).

How Altruist can help you handle SPCX after it goes public

When a company this large enters indexes, the impact lands across your whole book simultaneously. That’s when the value of a predictable, repeatable process becomes particularly important.

Altruist’s direct indexing methodology is systematic, transparent, and rules-based. We’re not making a one-off exception for SPCX. If it meets the applicable eligibility criteria for a given index, it gets incorporated through our normal methodology at the next scheduled reconstitution—the same process, the same rules, regardless of which company is in the news.

That consistency is intentional, for SPCX and every stock. Advisors can trust that our process isn’t driven by market sentiment or headline pressure.

That said, not every client will feel the same way about SPCX. Some will want the exposure. Others may have concerns about valuation, concentration, governance, or simply prefer not to own the company. Both are reasonable positions, and advisors should have the tools to act on either one.

When large private companies go public, your clients notice

The SpaceX IPO is part of a broader wave that’s been building for years. The world’s most influential companies have stayed private longer, with substantial value creation happening before public market investors ever had access. Now, as that wave crests, advisors are fielding real questions about concentration, valuation, volatility, and index exposure.

What makes SPCX particularly interesting is how index providers are responding differently to it. Nasdaq and FTSE Russell have adopted fast-entry frameworks that could allow SPCX into major indexes shortly after listing. The S&P Dow Jones Indices, by contrast, isn’t expected to immediately include SPCX in the S&P 500 given its existing eligibility requirements.

There’s another nuance worth noting: a company’s headline valuation isn’t necessarily the same as its eventual index weight. Because most major indexes rely on float-adjusted market capitalization rather than total market capitalization, SPCX’s actual weight in many broad-market indexes may ultimately be smaller than the headlines suggest.

That divergence matters. Depending on how a client’s portfolio is benchmarked, their exposure to SPCX and the timing of it could look very different. Which means the same question lands differently depending on who’s asking it.

Altruist provides personalized indexing at scale

With Altruist Personalized Indexing, advisors have two straightforward options.

If a preference applies broadly—say, an advisor whose clients generally want to avoid high-concentration single-name risk—they can create a personalized index model that excludes SPCX and apply it across multiple accounts at once. If it’s specific to one client’s circumstances, that exclusion can be made at the individual account level instead.

Advisors don’t have to choose between a scalable investment process and client-specific customization. With the right infrastructure, they can have both.

How advisors can be ready for whatever goes public next

SPCX may be the headline today, but it will not be the last company to raise these questions. As more large, private companies enter public markets, the ability to respond quickly, consistently, and in a way that reflects each client’s individual needs will become a core part of what advisors offer. The advisors best positioned for that future are preparing for it now.