Enterprise RIAs and Generative AI: Pilot Programs for Internal Efficiency in 2026

Enterprise RIAs and Generative AI: Pilot Programs for Internal Efficiency in 2026

Our 2026 benchmark analysis reveals that AI has transitioned from a futuristic concept to a tangible tool for generating operational alpha within RIA firms. However, the deployment strategy is markedly pragmatic: rather than focusing on speculative, client-facing applications, firms are prioritizing internal process automation, data analytics, and compliance workflows. This approach yields immediate efficiency gains and, critically, establishes the data infrastructure necessary for future, more advanced AI deployments.

The report highlights that enterprise RIAs, in particular, are experimenting with generative AI in carefully controlled pilot programs. A leading use case is the automation of client meeting note summarization. This seemingly simple application offers significant potential to free up advisor time, improve data accuracy, and enhance compliance.

Why Pilot AI Internally?

Several factors contribute to the strategic decision to pilot AI for internal use cases:

• Data Security and Compliance: Internal applications offer a more controlled environment for managing sensitive client data. RIAs can implement robust security protocols and ensure compliance with regulations such as Regulation S-P and other privacy mandates before expanding AI's reach to client-facing interactions.
• Risk Mitigation: AI models are not infallible. Internal pilots allow firms to identify and address potential biases, inaccuracies, and other risks associated with AI before they impact client relationships.
• Building Institutional Knowledge: By starting with internal applications, RIAs can build a deeper understanding of AI's capabilities and limitations. This knowledge is crucial for making informed decisions about future AI investments and deployments.
• Demonstrating ROI: Internal pilots provide a clear and measurable return on investment (ROI) by automating tasks, improving data quality, and reducing operational costs. This helps justify further investment in AI technologies.

The task of summarizing client meeting notes is often time-consuming and prone to human error. Generative AI offers a compelling solution by automatically generating concise, accurate summaries of these meetings.

How it Works

Generative AI models, trained on vast datasets of text and code, can analyze meeting transcripts or recordings and identify key topics, action items, and client sentiments. These models can then generate summaries that capture the essence of the meeting in a fraction of the time it would take a human.

Benefits for Enterprise RIAs

• Increased Advisor Efficiency: By automating the summarization process, advisors can free up valuable time to focus on higher-value activities such as client relationship management and financial planning.
• Improved Data Accuracy: AI-powered summarization reduces the risk of human error and ensures that meeting notes are accurate and consistent.
• Enhanced Compliance: AI can be programmed to identify and flag potential compliance issues discussed during meetings, helping firms mitigate regulatory risks.
• Better Client Service: Accurate and readily available meeting summaries enable advisors to provide more informed and responsive service to their clients.
• Scalability: As firms grow and the volume of client meetings increases, AI-powered summarization can scale to meet the demand without requiring additional staff.

Implementing a Pilot Program

To successfully pilot generative AI for summarizing client meeting notes, enterprise RIAs should follow these steps:

1. Define Clear Objectives: What specific goals do you want to achieve with the pilot program? For example, reduce the time spent summarizing meeting notes by 50%, improve data accuracy by 20%, or identify potential compliance issues in 90% of meetings.
2. Select a Suitable AI Vendor: Research and evaluate different generative AI vendors that offer solutions for summarizing meeting notes. Consider factors such as accuracy, speed, scalability, security, and cost.
3. Establish Data Security Protocols: Implement robust data security protocols to protect sensitive client information. This includes encrypting data at rest and in transit, controlling access to data, and ensuring compliance with privacy regulations.
4. Choose a Pilot Group: Select a small group of advisors to participate in the pilot program. This allows you to gather feedback and refine the process before rolling it out to the entire firm.
5. Provide Training and Support: Provide comprehensive training and support to the advisors participating in the pilot program. This includes educating them on how to use the AI tool, how to interpret the summaries, and how to provide feedback.
6. Monitor Performance and Gather Feedback: Continuously monitor the performance of the AI tool and gather feedback from the advisors participating in the pilot program. Use this information to identify areas for improvement and refine the process.
7. Evaluate Results and Plan for Future Deployment: At the end of the pilot program, evaluate the results and determine whether to expand the use of generative AI for summarizing client meeting notes to the entire firm.

As Golden Door Asset's 2026 RIA Technology Benchmark Report emphasizes, the "Core-and-Spoke" architecture is now ubiquitous within the RIA industry. This architecture, centered around a Customer Relationship Management (CRM) platform, provides the foundational stability required for scalable growth and effective AI deployment.

The Role of the CRM

The CRM serves as the central operational hub, integrating essential platforms for portfolio management, financial planning, and data aggregation. This unified view of client data is critical for training and deploying AI models effectively.

According to our analysis, 92% of firms with five or more distinct technology tools have a clearly identifiable CRM platform (e.g., Salesforce, Wealthbox, HubSpot). These platforms act as the integration hub for other core components of the advisory business.

Key "Spokes" and Their Integration with AI

• Portfolio Management & Reporting: Platforms like Black Diamond and Addepar are essential for managing and reporting on client portfolios. AI can be integrated to analyze portfolio performance, identify investment opportunities, and generate customized client reports.
• Financial Planning: Tools such as RightCapital and MoneyGuidePro are foundational for creating financial plans. AI can be used to personalize financial plans, optimize asset allocation strategies, and provide proactive advice to clients.
• Data Aggregation: The anonymized tool NDEX was detected in 71% of the firms in our study, highlighting the importance of data aggregation for a unified view of client assets. AI can be integrated to cleanse, normalize, and analyze aggregated data, providing valuable insights to advisors.

Vendor Selection Considerations

When selecting vendors for your "Core-and-Spoke" architecture, prioritize those that offer robust integration capabilities and support AI-powered functionalities. Look for vendors that:

• Provide open APIs for seamless data exchange.
• Offer pre-built integrations with other popular RIA technology platforms.
• Have a clear roadmap for incorporating AI into their products.
• Prioritize data security and compliance.

The success of any AI initiative hinges on the quality and availability of data. RIA firms must ensure that they have a robust data infrastructure in place to support AI deployments.

Key Data Considerations

• Data Quality: Ensure that your data is accurate, complete, and consistent. Implement data cleansing and validation processes to improve data quality.
• Data Governance: Establish clear data governance policies to define roles and responsibilities for data management.
• Data Security: Implement robust data security measures to protect sensitive client information.
• Data Accessibility: Make sure that data is readily accessible to AI models. This may involve creating data warehouses or data lakes.
• Data Privacy: Comply with all applicable data privacy regulations, such as GDPR and CCPA.

Generative AI holds immense potential to transform the RIA industry, but its successful deployment requires a pragmatic and strategic approach. By focusing on internal use cases like summarizing client meeting notes, enterprise RIAs can realize immediate efficiency gains, build institutional knowledge, and establish the data infrastructure necessary for future, more advanced AI deployments. The "Core-and-Spoke" architecture provides a solid foundation for AI adoption, and data quality and governance are critical for ensuring the success of AI initiatives. As AI technology continues to evolve, enterprise RIAs that embrace a data-driven and strategic approach will be well-positioned to thrive in the future.

Ready to explore how generative AI can transform your RIA firm? Contact Golden Door Asset today for a personalized consultation and learn how our research-backed insights can help you optimize your technology stack and achieve your business goals.

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