Over the last decade, “robo-advisors” have gone from a niche idea to a standard option on many brokerage and banking platforms.
Behind the marketing, these services rely on algorithms, data, and often AI tools to build and adjust portfolios in a structured way.
This article explains, in simple terms, how AI-powered or algorithmic robo-advisors typically build portfolios, monitor them, and react to market changes.
It is educational only and does not recommend any product or strategy.
What is a robo-advisor?
The SEC Investor.gov describes robo-advisers as registered investment advisers that use computer algorithms to provide automated investment advisory services, often with limited human interaction.
FINRA groups robo-advisors under “automated investment tools.” These tools collect information from the user and then use software to generate portfolio allocations or ongoing management.
In practice, many well-known robo-advisors:
- Operate through websites or apps
- Ask a series of questions about goals and risk tolerance
- Invest mainly in low-cost ETFs
- Monitor and rebalance portfolios automatically
Some platforms now add AI and machine learning to parts of this process, especially in data analysis and optimization. Others still rely mostly on rule-based models.
Step 1: Onboarding and the questionnaire
Everything starts with data collection.
According to the SEC’s Investor Bulletin on robo-advisers, most platforms ask for information such as:
- Age and basic personal details
- Investment goal (for example, retirement, general investing)
- Time horizon (how long until the money might be needed)
- Comfort with risk and market ups and downs
- Basic income or asset information in some cases
FINRA notes that digital tools may use this input to categorize clients into model portfolios or risk profiles, rather than designing a completely custom portfolio for each person.
AI and machine-learning components can appear here when:
- The system learns from large datasets how similar users behaved
- Models adjust question weightings or refine risk scoring over time
But even with AI, the process still boils down to:
questions → risk profile → target allocation.
Step 2: Translating answers into a risk profile
Once the questionnaire is complete, the robo-advisor turns the answers into a risk profile.
Academic and industry reports describe common categories such as:
- Conservative
- Moderate
- Aggressive
Each profile usually corresponds to a target mix of assets. For example:
- Conservative: more bonds and cash-like assets, fewer stocks
- Aggressive: more stocks and growth-oriented positions, fewer bonds
FINRA’s work on digital investment advice explains that algorithms may use optimization models (such as versions of modern portfolio theory) to map risk levels to asset mixes.
AI tools can refine this mapping by:
- Testing many possible allocations in simulation
- Analyzing historical relationships between asset classes
- Adjusting to new data over time
The end result is still a target allocation that the platform shows to the user as a pie chart or percentage breakdown.
For background on allocations in general, see on saveurs.xyz:
What Is Asset Allocation? and
What Is a Diversified Portfolio?.
Step 3: Building with ETFs and index funds
Most robo-advisors invest through ETFs and index funds, not individual stocks.
Schwab explains that its Intelligent Portfolios program builds portfolios using a diversified set of low-cost ETFs across different asset classes, including U.S. and international stocks, bonds, and sometimes alternative assets.
Investopedia notes that as of 2025, robo-advisors typically:
- Use ETFs as building blocks for broad exposure
- Choose funds that track stock and bond indexes
- Combine them into diversified portfolios based on the user’s risk profile
AI and analytics can help in:
- Screening ETFs based on cost, liquidity, and tracking error
- Checking correlations between asset classes
- Testing how different combinations behaved in past market conditions
However, the core structure is simple to describe:
Risk profile → target percentages → basket of ETFs or index funds that match those targets.
For an introduction to these building blocks themselves, you can read:
Both pieces are written for beginners and stay in descriptive mode.
Step 4: Automatic rebalancing
Over time, markets move.
That means the original percentages drift away from the target mix.
Schwab’s documentation on Intelligent Portfolios explains that its robo-advisor:
- Monitors portfolios daily with algorithms
- Checks whether any asset class has moved outside a pre-set “band” around its target
- Places trades to bring weights back toward the original allocation when a threshold is triggered
Investopedia’s overview of how robo-advisors handle volatility describes similar behavior across many platforms: rebalancing either on a schedule (for example, quarterly) or when allocation drift crosses certain limits.
AI systems can support this process by:
- Identifying patterns in drift across many client portfolios
- Optimizing trade size while looking at transaction costs and taxes
- Adjusting thresholds for when to rebalance, based on testing
Still, the simple idea remains:
Rebalancing = selling a bit of what went up, buying a bit of what went down,
to bring the portfolio back to the original mix.
Schwab and other firms clearly state that diversification, asset allocation, and rebalancing do not guarantee profits or protect against losses in declining markets.
The automation changes the process, not the underlying market risk.
Step 5: Tax-loss harvesting and other automated features
Many robo-advisors add extra features, especially for taxable accounts.
Investopedia notes that large platforms often automate tax-loss harvesting. That means:
- The algorithm looks for positions trading below their purchase price.
- It may sell those positions to “realize” the loss for tax purposes.
- It then buys a similar (but not identical) fund to maintain the overall allocation, while respecting rules such as the U.S. wash-sale rule.
This process aims to optimize after-tax results within legal limits.
It is algorithmic, and in some cases AI supports:
- Identifying replacement funds
- Managing many small trades across thousands of accounts
- Balancing tax effects with tracking error and transaction costs
Other automated features can include:
- Automatic cash sweeps into or out of portfolios
- Goal-tracking dashboards that compare progress to the user’s target date or amount
- Alerts when contributions pause or goals drift off track
These are still tools, not guarantees.
Regulated firms usually state clearly that tax-loss harvesting and other features do not ensure profits or eliminate tax obligations.
Step 6: Where “AI” fits vs simple automation
Not every robo-advisor uses advanced AI.
A 2024 paper on customer trust in robo-adviser technology notes that robo-advisors are algorithmic tools, and some—but not all—use AI techniques.
You can think of three layers:
- Basic automation
- Fixed rules: “If risk profile = moderate, then use allocation X.”
- Scheduled or threshold-based rebalancing.
- Algorithmic optimization
- Portfolio models like modern portfolio theory or variations on it.
- Rule-based tax-loss harvesting and drift controls.
- AI and machine learning
- Pattern recognition across large client datasets.
- Improving questionnaires and risk scoring.
- More complex optimization under multiple constraints.
From the outside, a user mainly sees the interface: sliders for risk, goal-based charts, and automated adjustments.
Behind the scenes, the level of AI can vary a lot between providers.
Regulators remind investors to read the firm’s Form ADV brochure and website to understand its methods and limitations.
Step 7: Human oversight and the shift to hybrid models
Early robo-advisors focused on digital-only advice.
Recent news shows a shift toward hybrid models that combine automation with human advisors.
Barron’s reports that several large banks have closed stand-alone robo platforms and moved clients into services that pair automated portfolios with human support, often due to profitability and adoption challenges for pure robo services.
Schwab describes its own model as automated investing with human help when you need it, combining robo algorithms with access to service professionals and, in some programs, dedicated planners.
The SEC and FINRA emphasize that, even with automation, firms must:
- Provide clear disclosures about services and fees
- Maintain compliance programs and supervision
- Make sure portfolios line up with the information they collect
So AI and automation handle many day-to-day tasks, but human oversight remains important for design, supervision, and support.
Step 8: Risks and limitations of robo-advisors
Regulators and industry research highlight several educational points:
- Market risk still applies. Robo-advisors use diversified portfolios, but Schwab and others state that diversification, asset allocation, and rebalancing do not ensure profits or protect against losses in falling markets.
- Limited personalization for complex situations. The SEC notes that robo-advisers may not be designed to handle complex financial needs, such as unique tax issues, concentrated positions, or unusual goals, without additional human input.
- Questionnaire quality matters. FINRA’s work on digital advice warns that weak or incomplete questionnaires can lead to portfolios that do not really match a client’s situation.
- Technology and data risks. Industry papers on digital investment advice highlight cybersecurity, data protection, and system reliability as key risks that firms must manage.
These limits do not mean robo-advisors are “bad” or “good.”
They show that automation changes how portfolios are built, but does not remove the usual risks of investing and technology.
Conclusion
AI robo-advisors build portfolios by collecting information through questionnaires, turning it into risk profiles, and mapping those profiles to diversified mixes of ETFs and index funds.
SEC and FINRA materials, along with education from firms like Schwab and independent overviews from sites such as Investopedia, show a common structure across platforms: algorithm-driven asset allocation, automatic rebalancing, and sometimes tax-loss harvesting, all supervised by compliance programs and, increasingly, human advisors in hybrid models.
Automation and AI can make portfolio management more systematic and accessible, but they do not remove market risk, technology risk, or the need to understand basic concepts like diversification, time horizon, and fees.
For beginners, the most practical takeaway is that robo-advisors are structured systems for building and adjusting portfolios—not magic boxes—with strengths, limits, and assumptions that are easier to see once you understand the building blocks described throughout saveurs.xyz.