Bringing AI to Practice Management
Financial firms are actively using artificial intelligence to address bottlenecks and improve operational efficiency. While AI’s promise to deliver personalized advice at scale continues to grow, advisers are already applying AI tools to streamline participant education and client communications.
Recently, Vanguard introduced a generative AI tool for its 150,000 advisers, enabling them to create customized content for client communications. This tool tailors market summaries based on clients’ financial knowledge, life stage, and preferred tone.
Commonwealth Financial Network is also integrating AI technology from Zocks to boost adviser efficiency by reducing manual data entry and enhancing client interaction continuity across its network.
According to a report from HFS Research and Infosys, banking and financial services are moving toward an AI-first approach, prioritizing automation to improve efficiency and create value. AI budgets in this sector are projected to grow by 25%, making up 16% of technology spending. Key investments focus on data modernization and generative AI software, with primary use cases in data analysis, process automation, marketing, and software development.
Bidirectional AI Integration
AI supports financial coaches by automating follow-ups after client calls, encouraging clients to act on discussed steps. At the same time, AI nudges potential clients who engage with digital platforms to reach out for human assistance, creating a two-way support system.
Edwin Jongsma, Vice President of AI and Integration at Financial Finesse, highlights that digital platforms assist coaches, making the interaction mutually beneficial rather than one-sided.
Recent research from the Financial Planning Standards Board shows that over 75% of financial planners believe AI improves client service, and 60% say it enhances advice quality. Two-thirds of firms already use or plan to adopt AI within the next year, with half of planners expressing a positive view of AI.
Practical AI Use Cases Today
An April report from Capital Group outlines several current AI applications in advisory practices:
- Client meetings: AI-powered notetakers prepare agendas, capture meeting notes, and integrate them into CRM systems. Zoom leads this space with 27% of advisers using its AI notetaking software, followed closely by Fathom and Jump.
- Client emails: AI drafts or improves emails using tools like Grammarly, ChatGPT, and built-in features in Microsoft Outlook and Google Gmail, saving time and ensuring clarity.
- Marketing: AI assists in creating presentation outlines, generating social media ideas, and even developing marketing strategies to support advisers’ content efforts.
- Large document analysis: AI tools can quickly extract relevant information from tax returns, powers of attorney, wills, and other complex documents.
- Tax planning: Some advisers leverage AI for scenario analysis, helping them evaluate tax implications of decisions involving equity compensation, Roth conversions, and capital gains.
Advisers are also exploring AI to enhance sales efforts. Amy Chou, COO of Addition Wealth, explains that AI can improve prospect list building by focusing on specific niches and locations, and automate outreach via emails and calls. This approach helps advisers sell, retain, and acquire the right clients more effectively.
From Generative to Agentic AI
AI is evolving from generative models that create content based on prompts to agentic AI, capable of performing tasks and making decisions within defined parameters. Agentic AI aims to handle transactions across various platforms to resolve client or plan sponsor needs swiftly.
Currently, most agentic AI implementations are internal or in pilot phases, with limited client-facing applications. A recent Wolters Kluwer survey found that 6% of finance leaders currently use agentic AI, while 38% plan to adopt it within a year. If adoption reaches 44% by 2026, this would mark a significant increase.
Challenges Ahead
As AI integration deepens, advisers face key challenges including data quality and access, security and privacy concerns, and talent shortages. Only 23% of banking and financial services companies have mature AI governance and risk management in place. Additionally, 45% of AI talent is sourced externally, highlighting a gap in internal expertise.
Further Reading
- Explore AI courses for financial professionals
- AI tools tailored for finance
- Regulators urged to take risk-based approach toward AI
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