Implementing Artificial Intelligence: Getting to Results

Nicsa’s 2024 Asset & Wealth Management Summit highlighted Artificial Intelligence’s (AI) promise to transformative the industry. Yet, the journey from concept to practical deployment is fraught with challenges. In the session "Implementing Artificial Intelligence: Getting to Results," a panel of experts unpacked their experiences, sharing both triumphs and trials in AI implementation.

Vanessa Touma, Head of CX Strategy, Distribution Intelligence and Digital Sales, Invesco moderated the discussion revolving around finding the right AI objectives, building organizational trust for adoption, and defining success metrics.

Speakers included:

Rob Pettman, Chief Revenue Officer and President, TIFIN

Zar Toolan, Principal, Wealth Platforms, Data & AI, Edward Jones

Here are the key takeaways.

Aligning AI with Business Goals
The panelists emphasized that successful AI implementation starts with aligning use cases with overarching business goals. Edward Jones, for instance, aims to become a "knowledge-powered advice firm" by 2030. AI use cases like their Model Assistant and generative search are strategically designed to enhance efficiency and customer service, contributing to the firm’s ambitious growth targets.

Case Studies

• Model Assistant: This tool integrates generative AI to provide tax-optimized portfolio recommendations, allowing financial advisors to focus more on client relationships. The result? A reported 70-80% time savings for branch teams.
• Generative Search: By streamlining access to over 60,000 knowledge assets, this tool reduces service call volumes, enhancing operational efficiency.

Start with Simpler Use Cases
Experts recommended starting AI adoption with use cases that have a higher tolerance for error. These "low stakes" applications, like automating routine client queries or internal document searches, offer immediate efficiency gains while allowing teams to learn and refine their AI strategies.

Building Organizational Trust and Achieving Buy-In

Creating a Coalition
Building internal trust for AI adoption is a challenge, often requiring a coalition of stakeholders from various departments, including technology, finance, compliance, and HR. Achieving buy-in isn't just about presenting a compelling tech solution; it requires demonstrating clear business value.

Overcoming Misconceptions
Addressing organizational myths and misconceptions about AI is crucial. Education and transparency about AI's capabilities and limitations can help demystify the technology, fostering greater acceptance.

Pilot Projects and Measured Risks
Launching small, pilot projects can build confidence and showcase AI’s potential without committing extensive resources. This iterative approach allows organizations to mitigate risks while gradually scaling up their AI initiatives.

Governance and Responsible AI

Establishing Governance Structures
The panelists underscored the importance of robust governance structures to guide AI implementation. This includes cross-functional teams that oversee AI projects, ensuring they align with ethical standards and regulatory requirements.

Guiding Principles
Edward Jones established five guiding principles for AI implementation: human-centered design, trustworthiness, transparency, data privacy, and accessibility. These principles help ensure AI solutions are both effective and ethically sound.

Regulatory Preparedness
With regulatory bodies like the SEC beginning to scrutinize AI use, maintaining comprehensive inventories of AI applications and ensuring transparent data usage are becoming essential. Firms must prepare for increased regulatory oversight by documenting their AI practices and ensuring compliance.

Defining and Measuring Success

Clear Metrics for Success
Defining what success looks like for AI projects is pivotal. Metrics should go beyond cost savings or efficiency gains to include improvements in customer experience, risk reduction, and business growth.

Iterative Learning and Adaptation
AI implementation is a continuous learning process. Firms must be willing to iterate and adapt their strategies based on feedback and evolving business needs. The panelists encouraged firms to remain flexible, adapting their AI initiatives as they learn from initial deployments.

Conclusion: The session "Implementing Artificial Intelligence: Getting to Results" illuminated the multifaceted journey of AI deployment. From setting clear objectives and gaining organizational trust to establishing governance and measuring success, the path to effective AI implementation requires strategic planning, cross-functional collaboration, and a commitment to responsible practices. As firms navigate this complex terrain, the insights shared by the panel provide a valuable roadmap for leveraging AI to achieve meaningful business outcomes.

Nicsa remains at the forefront of fostering innovation and collaboration among professionals in the industry. We invite you to join us at our next event, where you'll have the opportunity to engage with industry leaders, explore cutting-edge technologies, and gain valuable knowledge to drive your business forward. Don’t miss the chance to be part of our dynamic community shaping the future of asset and wealth management: SLF2025

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