An EY study of the wealth management industry highlighted the leading priorities leadership teams should consider to adapt to the rapidly evolving drivers of value creation.
The wealth management industry is undergoing structural changes driven by a number of factors, including a historic wealth transfer and the emergence of cutting-edge technologies like artificial intelligence (AI). In its latest report, EY uses a so-called Strategy Radar to map convictions, including the trade-offs they must make between growth and efficiency, offense and defense, or immediacy and long-termism.
According to the global consultancy, 10 convictions were highlighted as the key priorities to consider.
1. The strategic meaning of AI for wealth management
The report said that the practical implications of AI can viewed through a “5E” schema: staff enablement, workflow efficiency, client experience, front office effectiveness, and guided business engineering.
Despite the transformational impact, the main challenge named was institutional resistance. EY advises wealth management leaders to view AI implementation as “a redesign of operating and governance models with humans in the loop, not an IT project — driving change across silos, spanning workflows, and scaling controlled adoption”.
2. Cohort-specific value propositions unlock profitable growth
While client segmentation is well established, firms are still catching up in translating evolving expectations into “proactive, cohort-specific experiences”. According to EY research, this includes areas like financial planning (45% of mass affluent clients) and values-based investing (29% of mass affluent and 23% of high net worth clients).
The key takeaway for senior leaders is to embed cohort differentiation into code, translating segmentation into distinctive client journeys, offers, and services.
3. Simulation turns psychometric insight into higher conversion rates
Although successful relationship managers and wealth advisors have historically been able to translate insights about clients into higher conversion rates, many firms struggled to act on behavioral signals in real time and at scale. AI-driven simulation applied to psychometric profiling has the potential to fill this gap and drive profit growth.
“The next competitive edge may come from a deeper understanding of how clients make decisions,” EY said. “Deliver distinctive client experiences via personalized offers triggered by intent signals, with mandate proposals aligned to client risk preferences, priorities, and goals.”
4. Sustainable private markets growth requires liquidity discipline
While private markets are all the rage, wealth managers will need to better communicate liquidity expectations. Semi-liquid structures may drive wealth flows into asset class but exiting the underlying assets is not easier especially during market stress.
EY suggests wealth managers to calibrate allocations against explicit client liquidity budgets by “embedding those constraints into mandate terms, portfolio construction, and product selection”.
5. Pricing complexity intensifies as scrutiny shifts to provable value
Pricing is becoming a challenge, not only in terms of transparency but also perceived value from clients. Legacy practices, such as charging fees based on assets under management, will become harder to defend. In addition, regulators are also increasing scrutiny with supervision in some markets looking beyond fee disclosure to consider service delivery and fair value.
“Future pricing power will depend on realigning price with service intensity and provable client benefit,” EY stressed. “Differentiate more clearly between lower, simpler charges for increasingly standardized features, and higher fees for more tailored services where complexity and intervention remain hard to substitute.”
6. The accelerated rise of self-directed investors
According to EY, AI is reducing barriers to investing and mature markets will see about 35% fully and 50% partially self-directed clients in coming years. As a result, wealth managers are at risk of “silent attrition”, especially among self-reliant, cost-sensitive investors with perceived above-average financial literacy.
“The strategic challenge for wealth managers is not how to prevent self-direction, but how to keep it within the ecosystem,” EY said. “Separate downside defense from upside offense — intervening early to retain clients at risk of silent asset leakage and focusing growth efforts on clients open to keeping hybrid mandates within existing relationships.”
7. Live tax visibility as the catalyst for net-outcome transparency
As the wealth management market becomes more cross-border and multi-platform in nature, additional factors like tax are becoming an increasingly important area to monitor. At the same time, manual cross-border tax support will become increasingly uneconomic as complexity rises, making automation a must-have.
“Extend net-of-fees reporting discipline to foreign exchange, tax and inflation — building a broader sense of outcomes, improving accountability and moving conversations from what must be explained to what firms can influence,” EY suggested.
8. Global competitiveness blends efficiency with market adaptation
Key markets cluster around different models – adviser-led in the US and Australia, relationship manager-led in Switzerland, product-led in Asia, and banking-integrated across much of continental Europe. At the same time, scalable, global efficiency will also be critical.
EY advises wealth managers to combine agile coverage with function-led efficiency at the core, noting that “growth-market exposure and multi-hub franchise design will become central to growth, margins, valuation, and long-term relevance”.
“Localize target segments more sharply, but without increasing local product variation — using stronger standardization to make greater use of a more centralized operating backbone,” it added.
9. Risk and compliance shift from static defense to real-time trust
Risk and compliance burdens are increasing from rising client expectations and fresh regulations across resilience, AI governance, financial crime, and customer outcomes. EY underlines that functions need to shift from “documenting policy execution to demonstrating continuous control to enable change and business”, with the speed of transformation exceeding the speed of governance which has led to bottlenecks that slow product launches and project rollouts.
“Integrate controls seamlessly into code, turning the second line of defense from spreadsheet owner into platform controller — enabling real-time querying by the first line, third line and supervisors,” EY explained.
10. Personal advisory engines emerge as reliability earns data access
Banks face a major threat of disintermediation in early stages of client journeys with increasing testing of AI for wealth guidance. With each off-platform AI interaction weakening firms’ visibility of client intent, needs, and behavior, earning trust and data access is critical. Institution-controlled AI advisory engines, using verified financial data within a controlled environment and connected seamlessly to human advisors will be a differentiator that external AI tools cannot easily match.
“Winning firms will close the proactive advice gap quickly, keep more of the advisory journey inside the bank, and make human escalation frictionless,” EY highlighted.
“Establish a reinforcing loop within the firm, in which more usage improves the advice, and better advice drives more usage – shaping client journeys better than is possible from episodic human interactions.”
