AI in wealth management: Built to scale

Moving from experimental to exponential

As many financial services and capital markets firms pivot their respective businesses toward the higher, stable returns of advice, competitive pressure to retain and win clients in the wealth management industry has never been greater. Wealth customers are requiring highly digitized experiences and, at the same time, demanding more for less. It's becoming increasingly difficult for wealth managers to stand out in a crowded space, and the required investment to compete is negatively impacting profitability.

Accenture recently interviewed 100 strategy, digital and technology executives across a range of North American wealth management firms to separate "signal from noise" when it comes to artificial intelligence (AI) and wealth management. Our results demonstrate most wealth managers see a real opportunity to move the financial needle by adopting AI over the next two to three years. However, more than 80% of firms are stuck in the proof of concept stage today, with efforts narrowly siloed within a department or team.¹ This inability to scale is a significant barrier to unleashing the greatest benefits from AI and strengthening competitive position.

The AI strategic imperative

There is approximately $78 trillion of assets in motion for wealth managers to capture, underpinned by the global expansion of the affluent middle class, women with wealth, and the wealth created by entrepreneurs and business ownership.² Seizing this opportunity requires a significant shift in strategy and approach, as wealth managers recognize how the digital agenda of the past has quickly become the digital imperative of the future.

Simply put, there is tremendous opportunity to capture more value from AI. With proven use cases as a starting point, the AI journey can be accelerated―allowing wealth managers to seamlessly move from theory to execution and capture benefits realized from scale. We see a rapid evolution from today's world of targeted application to a future of embedded innovation, making it clear why AI should be part of your near-term execution strategy.

Today: Proving the value and building a foundation

Wealth management and AI come together at five primary points along the value chain: client engagement, product and pricing, client experience, productivity and operational efficiency. According to Gartner, by the end of 2024, three-quarters of enterprises will have shifted from piloting to operationalizing AI.³ In the wealth management industry, early AI adopters focused largely on risk and compliance functions. More recently, attention has shifted to marketing and service. Our survey results indicated up to 80% of respondents reported they're either deploying or scaling both client- and advisor-facing AI-powered technology. By keeping the client at the heart of every interaction, powered by cutting-edge AI-driven analytics and delivered with increased personalization, advisors can better understand and anticipate clients' needs.

Wealth management is poised to capture potential from AI to the benefit of both the top and bottom line. We've seen this firsthand―helping players of all types, sizes and strategies launch their AI journey with measurable results. Our experience shows it's not atypical for a single use case to generate a 20%+ uplift, growing both existing clients as well as revenue from new clients. When multiple use cases are pursued a combinatorial effect occurs, and the longer-term uplift could easily be doubled or more.

Tomorrow: Accelerating the AI journey

As companies learn from their initial use cases and the potential becomes clearer, AI quickly elevates to a C-suite priority. However, truly realizing that potential is much harder. Based on our survey results, 76% of executives struggle with how to scale AI across the enterprise. We observe three common roadblocks to scale:

• Foundational data capabilities: Data privacy is paramount in wealth management, and there are substantial regulatory requirements to consider, especially in light of CCPA and GDPR. Firms should not only understand where their sensitive data sits, but also put in the right controls and tools to protect against both internal and external threats.
• Governance and risk management: AI decisions in wealth management have a real bearing on people's lives. Placing decision-making capability in the hands of a machine raises big questions around ethics, trust, legality and responsibility. Both explainability and oversight are essential, with a human + machine approach.
• Employee adoption: New ways of working are required to achieve the full potential of AI. Our survey indicates the most long-term value for AI is perceived in the front office. In an industry where a traditional playbook is still the norm, wealth managers cannot underestimate the criticality of engaging advisors early in the journey.

Despite these challenges, we know achieving scale is not ambiguous or luck. Our research shows companies who are successful have nearly 2x the success rate and 3x the return from AI investments versus those stuck in proof of concept.⁴ There are three distinct success factors that could help wealth managers overcome roadblocks and realize AI's full benefit:

1. Drive "intentional" AI: Leaders drive AI from the business strategy, not the technology. To build momentum, undertake projects in a few areas, creating a cadre of evangelists. Define an operating model with processes and owners for measuring value, supported by appropriate levels of funding. With clear accountability, leaders could complete successful AI programs 3-5X faster.
2. Tune out data noise: Leaders recognize the importance of business-critical data, with greater ability to integrate both internal and external sources. Using the right AI tools—such as cloud-based data lakes and data science workbenches with model management—could enable not just data maintenance and consumption but also enhance "trusted AI" governance and model explainability.
3. Treat AI as a team sport: AI is not a one-time event, but rather an ongoing and iterative process as the data landscape and underlying technologies evolve. Leaders have recognized executive sponsorship is not enough, and effective scaling calls for embedding multi-disciplinary teams throughout the organization. The better the blend of skills, the more sustainable the result, re-enforcing a constant commitment to business value.

The future: A digital platform mindset

Looking ahead, we can envision a path where AI moves beyond current use cases, adding sophistication that fundamentally allows for new ways to interact with clients and transform advisor and firm productivity. Focus may shift to intelligent product creation, hyper-personalized and omni-channel client experiences, and paraplanner automation to increase advisor time on portfolio and relationship management. Stretching beyond five years, there will be new ways of wealth management innovation not yet imagined, in response to disruptive forces as well as how AI itself will continue to evolve.

Admittedly there is still a long way to go on this journey, but with 84% of our survey participants agreeing that AI will transform the industry, firms should be on this path today. Whether your firm is just shaping its vision or already on the way, three core actions can help:

To run a marathon you must be able to run a mile. Implementing AI requires preparation, training and time to make sure the business, operations and technology are ready to undertake the journey—both from the advisor and client perspectives.

Success is not just about speed, money, more data or a single leader—it's about moving deliberately, aligning investments to the right places, driving the right insights and bringing the right capabilities. For wealth managers, if one thing is clear, it's that the time to act is now.

¹ AI: Built to scale

² Accenture-Orbium Wealth Management C-Level Survey 2020

³ Gartner identifies top 10 data and analytics technology trends for 2020

⁴ AI: Built to scale