An IT Leader’s Checklist for Deploying AI Agents in Financial Services

Four Areas Where AI Agents Drive the Most ROI in Financial Services

To deliver business value, financial institutions need to align AI strategies with organizational goals. Jim Wray, director of AI and productivity solutions for the M365 Copilot program at Microsoft, advises IT leaders to focus on four key areas:

1. Elevating the Employee Experience

AI bots can improve productivity by up to 40%, with the potential to automate work that currently consumes 60%-70% of employees’ time, according to the MIT Sloan School of Management.

For financial services teams, this means freeing advisers, underwriters and compliance officers to focus on higher-value activities, such as deepening client relationships or mitigating risks.

“You’re giving people time back with AI,” says Wray. “But what are they going to do with that time? Will they strategize new solutions, mentor colleagues, upskill their technical skills or strengthen client relationships?”

2. Reinventing Customer Engagement

Personalized, seamless customer experiences are essential for building loyalty and trust. AI can tailor financial recommendations, predict customer needs, and power conversational support for services such as loan applications or insurance claims.

The real value comes when leaders connect AI use to measurable business outcomes, such as higher conversion rates, reduced churn or faster service resolution times.

3. Reshaping Business Processes and Compliance

From fraud detection to regulatory reporting, financial institutions depend on highly accurate, compliant workflows. AI agents can accelerate repetitive processes and reduce manual errors, driving a 25% boost in productivity.

But the opportunity goes beyond automation. By redesigning workflows around real-time data, financial firms can improve transparency and stay ahead of compliance mandates. This shift requires change management and reskilling talent to work in a generative AI-powered environment.

4. Staying Ahead on Innovation

In financial services, being reactive isn’t enough. Firms must adopt proactive AI strategies to remain competitive. Wray suggests setting a value hypothesis for how AI agents will deliver impact; piloting programs; and tracking results against key performance indicators such as risk reduction, customer satisfaction and operational efficiency.

Your AI Agent Checklist for Financial Services

Here’s a practical guide to help IT leaders in financial services assess readiness; build responsible frameworks; and train employees for secure, compliant AI adoption.

• Audit current use of AI
  Map where AI and generative tools are already being used across departments, including customer service, fraud prevention and compliance. This creates a baseline for adoption and risk management strategies.
• Migrate strategically to the cloud
  Legacy systems in banking and insurance often limit scalability. Moving to secure cloud platforms supports AI-driven workloads while meeting data residency and privacy laws.
• Create use case hypotheses
  Identify areas where AI could deliver tangible business value, such as reducing false positives in fraud detection or speeding up loan approvals. Test through pilot programs and track outcomes.
• Invest in employee training
  Equip teams with AI literacy and compliance skills to ensure proper tool use and adherence to regulations such as the Gramm-Leach-Bliley Act, the Financial Industry Regulatory Authority and the European Union’s General Data Protection Regulation.
• Implement strong governance
  Build robust frameworks to secure sensitive data, define responsible AI usage policies and stay ahead of regulatory updates.
• Build an AI center of excellence
  Collaborate with internal stakeholders and innovation partners such as CDW Technology Services, Microsoft Azure AI Foundry and Qualcomm’s AI Stack to continually evolve your AI roadmap.

“Early wins and proven ROI can help align stakeholders and build confidence,” says Jonathan Rosenberg, CTO at Five9, in a recent interview with BizTech.

Generative AI analytics can help financial services teams continuously improve their AI agents, ensuring models stay accurate and effective while meeting evolving compliance standards.

EXPLORE: Learn how artificial intelligence is empowering employees and IT leaders in the workplace.

Building Trust Through Responsible AI

In financial services, trust is everything. AI agents must not only perform well but also self-correct when errors occur.

“If an AI agent makes a wrong decision, it should be able to self-correct,” says Vinesh Sukumar, vice president of generative AI and machine learning product management at Qualcomm.

This feedback loop is critical for refining systems and ensuring they evolve to meet complex organizational and regulatory demands.

“We’re working with a lot of enterprises to understand how we can scale agentic AI and optimize it for their workflows,” Sukumar says. “We are going to learn a lot.”