Best Practices for Executive Teams Deploying AI in Financial Services
The AI revolution feels like humanity just discovered fire—and everyone is racing to see what they can ignite.
That means a rush of AI pilots and proofs-of-concept across all industries, many of which launched without evaluating each use case against actual business value.
As I meet with CEOs and executive teams from leading mortgage lenders and financial institutions, the conversation has shifted from “What can AI do?” to “How do we deploy AI responsibly, at speed, and with measurable impact?”
The market leaders I work with are outpacing competitors by following a remarkably consistent playbook. They’re not just testing AI, they’re embedding it across their organizations with purpose, speed, and discipline.
Below, I’ve distilled the best practices I’ve observed from the institutions getting the most from AI today.
Anchor AI strategy to business outcomes
Tie every AI initiative to a clear business priority—whether it’s loan growth, customer retention, or operational efficiency.
Define KPIs, ROI targets, and adoption metrics before a project begins. No project should exist without a measurable path to value.
Start with high-impact, low-friction wins
Focus first on areas where a proof of concept or pilot is feasible within 30-60 days. Conversational and Voice AI solutions provide many options for pilot use cases. Other common use cases involve document classification, predictive churn modeling, or intelligent lead scoring. These early wins build momentum, prove ROI, and prepare teams for more complex deployments.
Invest in data quality and governance early
AI is only as good as the data feeding it.
Start by creating a single source of truth for customer and loan data. Then, anticipate obstacles to deploying AI with your data, such as consumer consent and preference management, and start addressing these things ASAP. Investing in tools like Customer Intelligence will help enrich your data and increase its value.
Embed compliance and risk management from day one
Regulations such the Gramm-Leach-Bliley Act (GLBA), TCPA (Telephone Consumer Protection Act), and UDAP (Unfair, Deceptive, or Abusive Acts or Practices) will be a few key areas where regulators dig in and look for companies cutting corners.
Create a cross-functional AI task force
Bring together leaders from product, compliance, data science, operations, and customer experience. Avoid siloed pilots—alignment ensures every initiative supports the broader business strategy. Include change management expertise to drive adoption, not just deployment.
Prioritize customer experience and trust
Every organization has gaps in their customer journey and can benefit from leveraging AI to provide human-like touch points throughout the experience. Use AI to remove friction, improve transparency, and deliver personalization at scale. Keep humans informed about high-stakes decisions and be transparent with customers about how AI is used and how their data is protected.
Build for integration, not isolation
Select AI solutions that integrate seamlessly with your CRM, LOS, core banking systems, and data lakes. Use APIs and modular architectures to avoid “AI silos” that slow scale and ROI.
Focus on talent and change management
Embracing AI with a growth mindset should be table stakes. Incentivize adoption so teams see AI as an enabler—not a threat to their roles. Upskill executives and frontline teams in AI literacy. When needed, recruit or partner for deep ML and data science expertise.
Measure, monitor, and iterate
AI is not a one-and-done project—it’s a living product. Track performance, user adoption, and ROI continuously, and refine models quarterly to maintain accuracy and relevance.
Choose the right tech partners: favor vertical specialists
Partner with vendors who understand financial services—especially your unique customer journeys or workflows. Deep domain understanding on core systems, database schemas, compliance, and other nuances will be a key factor in the results you achieve.
Benefits of vertical-focused partners:
- Deep understand of unique data sets and customer profiles
- Faster implementation with industry-specific models
- Built-in regulatory and risk controls
- Product roadmaps aligned to lending and banking trends
Horizontal AI tools have their place, but without deep domain expertise, they often require heavy internal customization and a slower time to value.
The future is here
AI today is not the same as the project in 2018 that failed to deliver those operational efficiencies in the back office everyone was promised. Its potential to transform nearly every part of our businesses is becoming increasingly clear. Every day you delay, competitors are building up their capabilities and you will struggle to catch up. As one of my investors put it bluntly, “Every day you fail to execute a comprehensive AI strategy, the value of your business goes down.”
To learn more about how Total Expert is working with our customers on high-impact AI initiatives, please reach out to our team.