From advisors to algorithms: The shift in wealth guidance

Finextra

7 hours ago

From advisors to algorithms: The shift in wealth guidance
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Editorial
This content has been selected, created and edited by the Finextra editorial team based upon its relevance and interest to our community.
For decades, wealth management was a relationship business. Clients used to meet their financial advisors in offices, discuss life goals, and trust human judgment to guide their financial futures. However, over the last decade, a quiet revolution has taken place: one that's replacing handshakes with algorithms and intuition with machine learning.
Today, millions of people receive financial advice not from a person, but from a platform. Robo-advisors, AI-driven planning tools, and hyper-personalised investment apps are reshaping how we think about wealth, and who gets to build it.
However, according to McKinsey, by 2034, 'at current advisor productivity levels, the advisor workforce will decline to the point where the industry faces a shortage of roughly 100,000 advisors.'
McKinsey data shows that advice revenues have been the main economic driver for the US wealth management industry. Revenues 'generated from fee-based advisory relationships [...] have grown from approximately $150 billion in 2015 to $260 billion in 2024, and growth in the number of human-advised relationships has outpaced population growth by three times in the same period.'
Amid the growing demand for advice, declining advisor head count and addressing the shortage with an advisor talent and productivity system, the quiet revolution has advocated for the recruitment of new-to-industry advisors. By improving the advisor career path for entry-level talent, there is also a clear path for new sources of talent by targeting career switchers.
Recruitment will not be enough. This is where generative AI comes in. McKinsey's estimate reveals that even a '30-40% average advisor adoption of more wealth-management-specific gen-AI-enabled tools and processes across the value chain and across the full advisor population by 2034 can deliver 6-12% of time savings – and, in turn, increase advisor capacity.'
From suits to software
Addressing a 100,000-advisor capacity shortage will be no easy feat, especially with the first wave of robo-advisors having emerged in the early 2010s, offering low-cost, automated portfolio management. They were simple, rules-based systems, efficient, but impersonal. Fast forward to 2025, and the landscape has evolved dramatically.
Modern AI-driven platforms don't just rebalance portfolios. They analyse behavioural patterns, anticipate life events, and tailor advice to individual goals, risk appetites, and even emotional states. What began as a cost-saving tool has become a sophisticated engine for personalised wealth guidance.
As Sébastien Payette, director consulting expert, financial services, CGI, writes, robo-advisors are becoming an 'integral part of investment strategies for both retail and high-net-worth (HNW) and ultra-high-net-worth (UHNW) investors.' He points to three key trends:
'Hybrid advisory models – Traditional financial institutions are incorporating robo-advisors alongside human expertise, offering a blend of automated technology and personalised, face-to-face financial guidance.
Hyper-personalisation – Advanced robo-advisors utilise AI-driven insights that integrate financial market data with investors' digital footprints, tailoring investment strategies to unique financial goals, risk appetite, and life stages.
Diversified asset offerings – Robo-advisors are expanding beyond equities and fixed-income products to include alternative investments such as derivatives, real estate, private equity, and cryptocurrencies, broadening the scope of automated wealth management.'
What AI does better
Payette also explains that smarter AI and predictive analytics through robo-advisors 'will leverage even more advanced AI capabilities to predict market trends and mitigate risks, leading to increasingly optimised portfolios and investment strategies.'
AI's appeal lies in its speed, scale, and objectivity. It can:
Monitor markets and portfolios in real time.
Optimise tax strategies across multiple jurisdictions.
Detect behavioural biases and nudge users toward better decisions.
Offer 24/7 access to insights, no appointment necessary.
For younger investors and the mass affluent, these tools offer something traditional advisors often couldn't: accessibility. With lower fees and intuitive interfaces, AI has opened the door to wealth planning for millions previously priced out of the conversation.
As the World Economic Forum states, 'large language models (LLMs) are still evolving. They are expanding beyond robo-advisors, progressing from chatbots to assistants to agents, reducing the advice gap for retail investors.
'The essential question is can AI systems deliver the level of emotional intelligence and empathy required by investors to share information and needs in ways they do with their human advisors, i.e., could machines ever replace human advisors?'
But what's lost?
Despite its advantages, AI lacks something essential: human empathy. Financial decisions are rarely just about numbers. They're about fear, ambition, family, and identity. A human advisor can read between the lines, sense hesitation, and offer reassurance. An algorithm, no matter how advanced, can't replicate that, at least not yet.
There's also the question of accountability. When an AI-driven platform makes a poor recommendation, who's responsible? The developer? The firm? The user? As AI takes on more advisory functions, these questions become more urgent — and more complex.
Can AI wealth models be credible? Yes, by processing vast datasets with speed and precision. Agentic AI can also integrate collaboration with human experts, which strengthens the credibility of the systems.
Can AI wealth models be reliable? Yes, AI is consistent and free from human error, but there are transparency concerns. LLMs can offer guidance and with modules such as retrieval augmented generation (RAG), context is enriched.
Can AI wealth models be intimate? Yes, AI can recognise sentiment, but cannot offer the lived experience and cultural nuances that humans can.
Can AI wealth models be self-oriented? Yes, AI can operate without personal biases, but the data its trained on or how it's deployed by financial firms may introduce conflicts of interest. Regulatory oversight may be needed to ensure AI acts in the best interests of the clients, and recommendations are not biased.
The rise of hybrid models
Rather than replacing human advisors, many firms are now blending the best of both worlds. This transformation or phase of the evolution requires a balanced approach that capitalises on AI systems but preserves human touch. This is how trust-based relationships can be built in wealth.
These advisors can use AI to handle data-heavy tasks, while humans focus on relationship-building and strategic guidance. It's a model that promises both efficiency and empathy, and it's gaining traction fast.
In conversation with Finextra in 2023, Renato Miraglia, head of wealth management and private banking, Italy, at UniCredit, says that human beings will continue to be essential in the relationship with private and wealth clients into the future, but the use of data and technology will change the analytical tools with which client materials are produced. He cites the value of generative AI to create highly personalised and detailed real-time reports and analyses on the peculiarities of each client's position.
'The wealth manager of the future will use new and sophisticated tools to analyse risks and investment opportunities – and this will improve the accuracy of financial planning. The next generation of tools consider key elements in an integrated way, taking a holistic view of factors such as the interaction with real estate or business assets, changes in lifestyle, and elements of risk that can be secured through various forms of insurance,' Miraglia elaborates.
The next evolution
The future of wealth guidance may not be about choosing between humans and machines, but about redefining the role of each. As generative AI becomes more conversational and emotionally intelligent, it could take on more nuanced advisory roles. Meanwhile, human advisors may evolve into financial coaches, helping clients navigate not just markets, but meaning.