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Nicsa’s Asset & Wealth Management Summit featured an exploration of how to put Artificial Intelligence to work within industry organizations. The “AI Unleashed” session highlighted adoption trends, use cases, challenges, and future projections. Leaders in asset management and banking discussed AI’s evolving role, emphasizing the importance of thoughtful implementation, regulatory preparedness, and cultural adaptability. The panel focused on which AI strategies are delivering the most value, from investment management and client engagement to compliance, risk management, and reporting.

Accenture’s Senior Manager Michael Sands moderated the discussion among a panel of leading experts including:

Kate Chatzopoulos, Independent Consultant

Carl Lingenfelter, Artificial Intelligence Product Manager, Northern Trust

Tod McKenna, Global Head of Data Science and Artificial Intelligence, Citi Securities Services

Following are key takeaways, focused on the transformative impact of artificial intelligence across the financial services sector:

Key Takeaways

1. Adoption and Use Cases
• The adoption of AI, particularly generative AI, has accelerated across financial services, driven by its potential to improve efficiency, streamline operations, and enhance client experience. Banks and asset managers are leveraging AI for a range of applications, from virtual assistants that aid client service teams to tools that automate post-trade processing and help prioritize client inquiries. These internal applications remain the primary focus due to concerns around data security and regulatory compliance in public-facing tools.
  
  
• Four major use cases were identified: improving client engagement, operational efficiency, investment insights, and risk management. For example, some tools help identify compliance gaps during onboarding, while others aggregate vast datasets to generate insights rapidly. The potential to embed AI into everyday processes offers employees “superpowers,” enabling them to work faster and more efficiently, particularly when equipped with AI tools for document generation and data retrieval​.
  
  
2. Hurdles and Regulatory Challenges
• The adoption of AI faces numerous hurdles, primarily around regulatory compliance, data privacy, and the lack of standardized industry guidelines. Currently, AI in financial services operates in a largely unregulated space, though frameworks such as the EU’s AI Act and the NIST standards for AI risk management are starting to provide some guidance.
  
  
• The complexities of scaling AI in regulated industries, combined with concerns about data security and transparency, mean that institutions must carefully select use cases and establish safeguards. This cautious approach often requires organizations to rank potential projects by risk and expected value, using frameworks to identify the most viable, low-risk AI applications. These processes help financial firms avoid over-commitment to AI initiatives that might not yield measurable benefits.
  
  
3. Measuring Success
• Measuring the success of AI initiatives was highlighted as a critical, ongoing challenge. Financial firms are encouraged to assess potential projects from the outset using business-case metrics such as time saved, efficiency improvements, or error reduction.
  
  
• Failure is expected, with leaders emphasizing that AI implementations will not always deliver intended outcomes. However, this iterative process of trial and error is necessary for refining AI applications and achieving successful results. Leaders recommend tracking performance against baseline metrics, such as manual effort versus AI-enabled performance, to gauge improvements over time​.
  
  
4. The Role of Cultural and Structural Adaptations
• The shift towards AI necessitates cultural adjustments within organizations, including fostering a culture of innovation and adaptability. This involves creating environments where employees can experiment with AI, learn from their experiences, and integrate AI into their workflows without fear of job loss.
  
  
• Data centralization and compatibility are also essential for AI to operate effectively. Many financial firms face challenges due to siloed and incompatible datasets, underscoring the need for data normalization. Leaders noted that new hires often come with a high level of AI literacy, and future employees will expect AI tools to be readily available. Meeting these expectations may require structural changes in organizations, as well as enhanced data infrastructure​.
  
  
5. Future Outlook and Potential Impact of AI
• Looking forward, the panelists envisioned a future where AI plays a central role in generating investment insights and handling complex financial data for alpha generation and superior asset management. As AI continues to evolve, financial firms may be able to process vast amounts of complex data quickly, allowing them to offer highly personalized and automated services to clients.
  
  
• In the long term, the panelists speculated that AI could lead to a nearly frictionless financial ecosystem where client needs are met through hyper-personalized, AI-driven solutions. In this scenario, human employees could transition primarily to roles as risk managers and relationship managers, overseeing AI-driven processes to ensure accuracy and regulatory compliance. However, the future of AI in asset and wealth management will be influenced by regulatory and technological barriers, as well as societal concerns such as job displacement.
  
  
6. Engaging the C-Suite and Justifying AI Investments
• Communicating the value of AI to executives is crucial for securing buy-in and budget allocation. Panelists emphasized the importance of a clear “storytelling” approach to articulate how AI applications align with the company’s strategic goals and contribute to efficiency and revenue growth. Many organizations are implementing small-scale AI projects to demonstrate initial successes, building momentum and trust in AI technology within the C-suite and across teams.
  
  
• By framing AI as a tool that augments human capabilities and enables new efficiencies, rather than a replacement for employees, firms can foster a positive reception and gain the backing needed to drive AI adoption across the organization​.
  
  

Conclusion: The “AI Unleashed” session highlighted both the transformative potential of AI in financial services and the challenges associated with its implementation. From regulatory uncertainties to cultural shifts, financial institutions must navigate a complex landscape to successfully integrate AI into their operations. The leaders stressed that a strategic, metrics-driven approach to AI adoption, coupled with a strong focus on data infrastructure and employee adaptability, will be essential as AI continues to reshape the industry.Top of Form

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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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