While there is undeniable excitement about AI’s transformational opportunities, we are past the hype cycle—AI is already starting to reshape planning and client service. And as prudent professionals are finding out, integrating AI into financial planning comes with challenges.

Effective integration requires careful consideration, ongoing education, and alignment with core human values such as empathy and trust. But even with careful adoption, the sentiment surrounding AI’s arrival is not about fear—it’s an anticipation of a positive transformation many in the industry see coming.¹

The best and most-likely future scenario is one in which the planner remains the strategist, decision-maker, and relationship-builder, while AI acts as the ever-ready analyst, assistant, and operational engine. To set that up properly from the start, planners should pay attention to three key concepts:

1. Level-Setting the Expectations of AI

AI is here now and offers tangible benefits today, such as helping with routine tasks. As technology evolves, its ability to influence financial planning relationships and its underlying technology will expand.

2. Preparing for AI Integration

Financial planners will want to take steps to fully understand and prepare for the changes AI brings to their business, including knowing how to select the right tools, communicate effectively about AI, and capitalize on its benefits while mitigating potential risks.

3. Acknowledging Challenges and Opportunities

Financial professionals play a pivotal role in balancing AI’s efficiencies with human-centered values. This involves ensuring AI isn’t over-emphasized or over-promised while still offering thought leadership on its broader industry potential.

Learn the Differences in AI Types

In its infancy, artificial intelligence relied heavily on rules-based engines—systems that could only function within the boundaries of explicitly coded logic. These early tools are powerful but typically limited to predictable inputs and outcomes.

Today, AI has advanced into adaptive, data-driven models powered by machine learning and neural networks. These systems learn from vast data sets, making nuanced decisions that once required human intuition.

However, financial planners should take a deliberate approach to ensure the AI being considered meets all of the requirements without exposing data to risk.

A working knowledge of all AI types is helpful for those considering using an AI solution:

Rules-Based Engines

The simplest (yet still powerful) form of AI, rules-based engines operate based on predefined conditions or “rules” developed by humans. When provided with a specific input, these engines follow programmed logic to produce a corresponding output. These are effective for well-defined scenarios where the rules are clear and consistent. Key examples are:

• Alerts: These are triggered based on set parameters. For instance, if a platform user’s savings rate falls below a threshold (say 10 percent of monthly income), the system could generate an alert notifying them of the issue. The underlying engine doesn’t analyze trends or behaviors—it simply identifies when conditions (the rule) have been violated.
• Solvers: These tools apply predefined logic to help users make decisions. For example, a solver might run a calculation to determine how much more a user needs to save monthly to meet a target retirement goal, based purely on input values like current savings, annual returns, and expected retirement age.
• Chatbots: A chatbot could provide answers to simple “if-then” scenarios such as “What are the hours of operation?” or “How do I reset my password?” By automating these tasks, human resources are freed for more complex challenges.

Machine Learning (ML)

Machine learning expands beyond fixed rules and uses algorithms to learn patterns and adapt over time. Unlike rules-based engines, ML models can process large datasets, identify correlations, and improve their accuracy over time based on real-world feedback. Key examples are:

• Transaction Categorizers: When financial transactions are pulled into the platform from external connections (banks, credit cards, etc.), a system might use machine learning to classify them. For example, grocery store transaction is categorized as “Food” or “Groceries.” Instead of relying solely on static rules (e.g., “Transaction from ACME is always Groceries”), the model learns from both prior categorizations and user corrections, improving accuracy over time.
• Account Type Mapping: A platform can expand this concept to efficiently match account types (e.g., 401(K), HSA, IRA). For example, if the system identifies transactions flowing into an account labeled “Traditional IRA,” the ML-powered engine ensures it’s correctly mapped to “Retirement – Traditional IRA” on the platform. This prevents manual errors and improves how data is organized for financial planning.

OCR (Optical Character Recognition)

A growing use case sees machine learning integrated into OCR technology to process complex documents, such as scanning tax returns. OCR can identify and extract mortgage interest or charitable donation amounts from scanned PDFs. It can then map these values to relevant financial categories, enabling the creation of summaries or automated reports.

Generative AI (GenAI)

Generative AI, currently one of AI’s most advanced forms yet, goes beyond identifying trends and instead predicts future scenarios or creates entirely new outputs based on the data it is trained on. How and where it might fit into financial planning has yet to be determined as it needs strict oversight to be compliant. What makes it distinct is its ability to generate insights or content that feels truly “creative” or human-like. Key examples are:

• Predictive Financial Planning & Analysis (FP&A): Generative models can simulate future outcomes by analyzing historical data and applying predictive insights.
• Content Creation: Generative AI assists in drafting templates for financial plans or articles. An example: If a user wants content related to retirement planning, the system might create a first draft of an article or report—highlighting strategies for maximizing savings or optimizing post-retirement income streams.

Internal Business Applications

AI that is used primarily for task automation and workflows that use rules-based logic to handle repetitive tasks. These use cases may be the financial planner’s best place to start when integrating the technology. Key examples are:

• Meeting Note Summarization: After a sales or client meeting, machine learning captures and summarizes important points (much like transcription software). This includes generating action items such as “Follow up with pricing details” or “Send updated investment proposal.”
• Task/Action Item Generation: The system automatically creates tasks based on meeting notes and assigns them to relevant stakeholders. For example, “Marketing team to update collateral” or “Client relations team to prepare presentation slides.”
• Personalized Communication: ML can tailor communication based on client preferences and engagement history. For example: If a client prefers quarterly reviews and is active on email, ML can identify those patterns and ensure personalized emails include a recommendation for scheduling their next quarterly meeting.

Use a Strategic Approach to Partnering with AI

Avoid the notion of using AI as a tool for everything. Introducing AI as a business solution first (e.g., internal workflows) makes adoption smoother, focusing on tangible benefits rather than futuristic promises. By providing realistic examples of AI-driven efficiencies across the platform and the business, users build trust and see its immediate value.

Stay current and be intentional when using AI. Once the technology matures, planners may see a shift in its positioning to more predictive planning and deeply personalized recommendations, aligning with the rapid evolution of generative AI.

Lay the Foundation for AI Integration

To get started on the road to using AI, we recommend a deliberate, structured approach:

1. Form an Internal AI Committee

If integrating AI into your organization, start by assembling a dedicated internal AI committee. This group should be diverse, including team members from various departments who can collectively identify areas where AI can deliver the most impact. The committee’s focus should go beyond just brainstorming; create a roadmap that prioritizes projects based on feasibility, potential ROI, and alignment with business goals. If integrating AI into a small practice, use these steps as your own framework:

• Step 1: Hold an initial discovery session to map out all repetitive and time-consuming processes that could benefit from automation.
• Step 2: Evaluate untapped opportunities for improving efficiency or offering enhanced services to customers. For example, could AI streamline supply chain logistics, improve customer support, or identify underserved market segments?
• Step 3: Assign responsibility for researching relevant AI tools and solutions, ensuring the team stays informed about emerging technologies. Provide ongoing training to keep committee members up to date on AI advancements.

By establishing this committee, you’ll create an ownership structure for AI initiatives and foster collaboration across business units.

2. Prepare Your Data

Data serves as the backbone of any successful AI endeavor, and preparing it effectively is essential. AI systems thrive on high-quality, reliable, and well-organized data, so dedicating resources to this step upfront will save time and optimize results in the long run. To prepare your data:

• Audit Existing Data: Start by identifying where data is stored, who owns it, and how current it is. Consolidate disparate sources into a unified repository for easier analysis and integration.
• Clean and Normalize Data: Clean up inconsistencies, duplicates, and inaccuracies that could affect the reliability of AI outputs. Standardize data formats to ensure uniformity, especially if you’re combining data from legacy systems or multiple departments.
• Improve Accessibility: Make sure your data systems are secure yet accessible to the AI applications. This may involve investing in cloud-based storage solutions, data pipelines, or data governance tools. Consult an IT professional as needed.

Careful and deliberate data preparation isn’t just a tactical checkbox; it’s a strategic investment that will increase the accuracy and effectiveness of the AI models you deploy later.

3. Focus on Immediate Wins

While the world of AI offers thrilling possibilities, it’s critical to start with simple, easily attainable benefits in a low-risk environment. Focusing on immediate wins helps build momentum while proving the value of AI to your clients and other stakeholders. Here’s how:

• Automate Administrative Tasks: Begin by identifying repetitive, low-value tasks such as data entry, appointment scheduling, or report generation. Employing tools like meeting note-takers or chatbots can significantly reduce manual workload and improve turnaround times.
• Enhance Communication Efficiency: Implement AI tools like automated email responders, language translation software, or even intelligent calendars that prioritize tasks. These tools deliver measurable productivity gains by reducing friction in everyday processes.
• Strengthen Data Security: Use AI for continuously monitoring networks, identifying anomalies, and predicting potential threats. Cybersecurity frameworks enhanced by AI are not only practical but also vital in today’s data-sensitive environments.

By concentrating on these kinds of quick wins, you’ll create operational improvements and build internal confidence in AI technologies, setting the stage for tackling more complex projects in the future.

As we continue to explore how planners can partner with AI, financial professionals can prepare by understanding these foundational steps and talking about potential solutions with informed colleagues.  By applying an intentional approach, you and your organization can effectively set the foundation for a successful AI transformation. Each step should build upon the last, ensuring your efforts are guided, purposeful, and outcome-driven.

To learn more about AI’s potential in financial planning, read our new eBook, AI in Financial Advice: What’s Next? Trends and Guidance for Forward-thinking Planners.

¹ Financial Planning and AI: Strategic Adoption, eMoney, 2025

DISCLAIMER: The eMoney Advisor Blog is meant as an educational and informative resource for financial professionals and individuals alike. It is not meant to be, and should not be taken as financial, legal, tax or other professional advice. Those seeking professional advice may do so by consulting with a professional advisor. eMoney Advisor will not be liable for any actions you may take based on the content of this blog.

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