Three ways AI can help financial advisors

Artificial intelligence (AI) can enhance financial advisors' practices by automating administrative tasks, improving decision-making, and enabling more personalized service. With AI, advisors can offer better, data-driven advice while saving time on repetitive tasks.

The use of artificial intelligence (AI) has skyrocketed this year and will likely become mainstream—even in our relationship-based industry. Many advisors believe that AI will have a direct and measurable impact on the client-advisor relationship in the coming years. Let's explore how one branch of AI—machine learning—could help you transform your practice, and key considerations should you decide to use it.

What’s machine learning AI?

Machine learning AI uses algorithms trained on data sets to create models. These models are used to complete certain tasks such as analyzing data, classifying information, or suggesting products based on past behaviour—think of the recommendations you get when you’re shopping online or using a streaming service, for example. Generative AI, on the other hand, is used to create content, and it’s beyond the scope of this article. 

Three ways AI could help you grow your practice and deepen relationships

Simply put, machine learning AI is a practice management tool that may help you increase efficiency and have more consultative client conversations. Within a matter of minutes, it can process a significant volume of data to help you:

1 Qualify leads

Based on your target market and other parameters, AI can scour publicly available information to find prospective clients that fit your criteria—saving you time and effort. Over 9 out of 10 advisors believe AI can help grow their book of business organically by more than 20%.¹

2 Identify at-risk clients

You can use machine learning AI to create a list of clients you haven't heard from or have expressed specific concerns about their plan. This information can help you take preventive action to preserve the relationship. Over one in five advisors believe AI can help them segment clients to further understand acquisition, growth, and retention goals.¹   

3 Offer more personalized recommendations

AI can provide in-depth insight about existing and prospective clients to help you prep for meetings. For example, you might use AI to:

• Search for news about the client's business, such as a merger or acquisition
• Extract data from your reporting tools
• Examine behaviours, such as website visits and online activities

All three can help you tailor your advice to your clients' specific needs. Additionally, many advisors believe that AI can help identify proactive cross-selling opportunities.¹    

Key considerations for using machine learning AI

Before doing anything with AI, be sure to check with your home office and compliance team first. Many firms have created AI governance and risk management frameworks, and it’s essential that you comply with these policies and procedures, which may cover:  

• Approved uses for AI
• Protecting confidential data
• Cybersecurity protocols
• Record retention
• Required training

Why AI? The potential for more human interaction—not less

As a financial professional, you want to spend your time helping clients, not looking for them or dealing with administrative tasks. With the appropriate safeguards, machine learning AI has the potential to help you minimize the time you spend on administrative tasks so you can spend more time on the person-to-person tasks that demonstrate your value and help you build your practice.

What are the risks of using AI in financial advisory services?

While AI offers many benefits in financial advisory services, there are certain risks that advisors must consider, including::

• Data privacy concerns: AI systems rely on large datasets to provide insights, which could include sensitive personal financial information. If not properly secured, this data can be vulnerable to breaches, risking client confidentiality.
• Algorithmic bias: AI models may inherit biases from the data they're trained on, potentially leading to skewed or unfair recommendations for clients. This could impact the quality and impartiality of the financial advice provided.
• Over-reliance on automation: Relying too heavily on AI for decision-making may cause financial advisors to overlook nuanced factors or unique client needs. AI should complement, not replace, human judgment and personalized advice.
• Regulatory and compliance risks: As AI tools evolve, there are challenges around ensuring compliance with industry regulations, such as PIPEDA in Canada, which governs data privacy. Advisors need to be aware of legal constraints when using AI-powered tools.
• Implementation and integration challenges: Integrating AI systems into an existing advisory practice can be complex, requiring time, effort, and expertise. There may also be compatibility issues with legacy systems or resistance to new technologies from clients or staff.

AI security and data privacy: protocols every advisor should know

As AI tools are integrated into financial advisory services, security and data privacy must be top priorities. Advisors should be aware of the best practices and regulatory requirements to protect client information and maintain trust. Here are essential protocols to consider:

• Data encryption: Ensure all sensitive client data is encrypted both at rest and in transit to prevent unauthorized access.
• Secure AI models: Use AI models that are regularly updated and tested for security vulnerabilities to prevent data breaches or manipulation.
• Compliance with regulations: Adhere to data privacy laws like PIPEDA (Personal Information Protection and Electronic Documents Act) in Canada to safeguard personal and financial information.
• Transparency with clients: Disclose to clients when AI tools are used, how their data is protected, and how decisions are made, ensuring transparency and trust.
• Data minimization: Limit data collection to only the essential information required for providing advice and services, reducing exposure to unnecessary risks.

Cost–benefit analysis: is AI worth the investment for advisors?

Adopting AI tools in a retirement advisory practice requires an evaluation of the potential costs and benefits. While the initial investment in AI technology can be significant, the long-term advantages can outweigh the costs, especially when AI helps improve efficiency, enhance client engagement, and drive better financial outcomes. Here are the key factors to consider in the cost–benefit analysis:

• Initial setup and ongoing costs: The cost of integrating AI tools, including licensing, training, and system updates, can be high. However, many tools offer scalable solutions that can grow with the firm.
• Time savings: AI can automate routine administrative tasks (like data entry and report generation), freeing up advisors to focus on high-value client interactions. This can result in substantial time savings and productivity increases.
• Improved client outcomes: AI can analyze large datasets and provide personalized, data-driven insights that improve the quality of financial advice, leading to better client outcomes and potentially higher retention rates.
• Competitive advantage: Using AI can differentiate your practice in a competitive market, attracting tech-savvy clients who value efficiency and personalized service.

 Frequently asked questions

1 “AI in wealth management: A financial advisor study,” Accenture, 2022.

2 “131 AI Statistics and Trends for 2025,” National University, 2025.