How leading wealth advisors are using AI to stay competitive

Research shows that more and more wealth management forms are embedding AI-driven analytics into their client advisory processes, with benefits ranging from improved portfolio performance to, faster response times and a reduction in compliance management.

According to Morgan Stanley’s 2024 Wealth Management Technology report, robo-advisory assets under management grew by 58% year-on-year, with AI driving much of this expansion through improved performance and client experience. Furthermore, 68% of financial services firms (including wealth managers) now prioritise AI in risk management and compliance initiatives (KPMG: The generative AI advantage in financial services).

With the value proposition only growing, industry forecasts project AI managed assets to reach over $6 trillion by 2027 (Netguru: AI in wealth management, transforming financial planning and investment approaches). Deloitte projections cited by the World Economic Forum strikingly predict that by 2028, AI driven investment tools will become the primary source of advice for around 80% of retail investors (World Economic Forum: AI, wealth management and trust: could machines replace human advisors?).

Whilst, some advisors have raised concerns about AI replacing them, firms that are integrating AI throughout all of their operational practices are reporting 25-40% improvements in operational efficiency. The threat therefore, isn’t AI, it’s being left behind whilst competitors embrace it.

How advisors are embracing AI

The practical applications extend far beyond robo-advisors. According to an Accenture survey of 500 financial advisors, 96% believe generative AI can revolutionise client servicing and investment management, with 97% foreseeing significant impacts on their work (Accenture: Using generative AI to power growth for wealth managers).

Current implementations include:

Portfolio management and rebalancing - AI-driven tools enable dynamic asset allocation adjustments that respond to market shifts faster than manual processes. Informa report that 76% of firms have experienced efficiency gains through AI integration, particularly in portfolio rebalancing operations (Informa: Navigating AI in wealth management, balancing tech and human touch).
Compliance and risk management - EY estimate that AI-powered compliance tools can reduce compliance management time by 75%. In a heavily regulated industry, it’s essential firms continuously assess how they can reduce risk exposure.
Client communication and service - Firms are increasingly adopting chatbots, and automated reporting systems to handle routine client interactions, allowing advisors to dedicate more time to complex planning conversations and relationship building.
Predictive analytics - Advanced AI systems can identify patterns in client behaviour, market conditions, and risk factors that would be impossible for humans to detect manually, enabling improved insight driven conversations.

Challenges

The real competitive advantage often comes from understanding and overcoming specific challenges that can derail implementation.

Data privacy and regulatory compliance

The 2025 GDPR amendments mandate that businesses need to provide clear explanations around how AI processes personal data, with consumers reserving the right to contest automated decisions.

The SEC has also prioritised AI oversight in 2025 examinations, with particular focus on AI note-taking tools and automated client communications. The margin for error is shrinking fast.

How advisors can stay compliant

Implement AI systems with built-in audit trails that document decisions and data access points
Establish clear client disclosure protocols before deploying any AI tools in client-facing situations
Create a compliance review process specifically for AI outputs, not just human advisor recommendations
Partner with suppliers and specialists who can demonstrate regulatory compliance documentation, not just product features.

AI bias and decision quality

Experts advise that AI systems can inadvertently perpetuate bias, especially in areas like portfolio recommendations and client segmentation. Regulatory authorities including the FTC, DOJ, and Consumer Financial Protection Bureau have signalled this is an enforcement priority.

Beyond regulatory risk, comes quality risk. AI systems trained on historical data can make recommendations that don't account for unprecedented market conditions or individual client circumstances that fall outside normal patterns.

How firms can minimise risk

Never deploy AI decision-making systems without human oversight and approval processes
Regularly audit AI recommendations against diverse client portfolios to identify potential bias patterns
Train employees to recognise when AI suggestions don't align with client-specific circumstances
Maintain detailed documentation of when and why you override AI recommendations
Test AI systems on edge cases and outlier scenarios, not just typical client profiles.

The trust gap

When markets become volatile or personal circumstances become complex, clients need human judgement and empathy.

The firms who are winning aren't choosing between AI and human advisors; they're optimising practice by strategically combining both.

How firms can maximise adoption

Position AI as your team's enhancement tool, not a replacement for advisor relationships
Be transparent with clients about where and how AI contributes to their portfolio management
Reserve complex, emotionally charged conversations (estate planning, divorce, business succession) for human advisors
Use AI-generated insights as conversation starters, not final recommendations
Train advisors to explain AI recommendations in plain language that builds rather than erodes confidence

Ineffective or lack of AI strategies

The biggest mistake firms make is adopting AI tools without a clear implementation strategy, that lacks investment in employee understanding and training.

How to overcome it

Start with one specific process where AI can deliver measurable improvement (like automated portfolio rebalancing or compliance documentation)
Measure baseline metrics before implementation so you can demonstrate ROI
Dedicate resources to comprehensive employee training, not on just how to use the tools, but when and why to use them
Create feedback loops where advisors can report what's working and what isn't
Be prepared to iterate and adjust your approach based on real-life results

What success can look like

Those firms experiencing the strongest results from AI adoption share common characteristics:

They maintain the human element in client relationships - AI handles data analysis, routine communications, and operational tasks, whereas advisors focus on strategy, complex planning, and relationship management.
They're transparent about their AI use - rather than hiding AI implementation, they proactively explain to clients how technology enhances their service delivery.
They invest in training and change management - technology adoption isn't just about buying tools, it's about changing workflows, mindsets, and practices.
They start small and scale intentionally - rather than attempting wholesale transformation overnight, they identify high-impact use cases, prove the value, and expand where necessary.

AI adoption in wealth management is a competitive necessity, but successful adoption requires careful planning, comprehensive training and strategic investment.

If you’d like more information on the topic, support with your hiring strategy or guidance on securing a new role please contact one of our financial services consultants.

Sources