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As financial services organizations accelerate their use of AI, many are learning that success depends less on algorithms and more on foundations, people, and trust. Speaking ahead of her keynote at CDAO Insurance in New York, Jeannie Furlan of Mutual of Omaha explains why data discipline and human change remain the true differentiators
Across financial services, organizations are expecting more from their AI initiatives. Leaders are under pressure to shorten financial close cycles, improve forecasting accuracy, detect fraud earlier, and deliver insights faster. But in a highly regulated environment, ambition alone is not enough.
For Jeannie Furlan, VP, Financial Data & Analytics Strategy at Mutual of Omaha, the gap between AI promise and AI impact is rarely technical. Instead, it is rooted in how organizations treat their data and the people responsible for using it.
Furlan describes AI enablement as a domino effect. If the foundations are wrong, the outcome is inevitable.
“If you want that beautiful cascade of dominoes, you have to start at the right point,” she says. “You can’t just push the last domino and expect it to work.”
Over the past four years, her team has focused on foundational data, curating financial and actuarial data so it is consistent, accurate, and easy to consume. That groundwork, she argues, is what now makes AI adoption realistic rather than risky.
Skipping those steps, she adds, leaves organizations unable to capitalize on emerging AI capabilities, no matter how advanced the tools appear.
Dashboards or Dialogue?
One area where Furlan sees clear momentum is self-service analytics. But self-service, she says, is evolving.
Historically, it meant giving users access to dashboards or data extracts without needing technical know-how. The next phase is conversational, where stakeholders can ask questions in natural language and receive trusted answers directly from curated data.
In the near term, she thinks financial firms are focused on relatively basic AI-powered chatbots operating on governed datasets. The long-term vision is more ambitious, but should be deliberately grounded in data discipline rather than experimentation.
No Black Boxes Allowed
In financial services, not all AI is created equal. For Furlan, explainability is non-negotiable.
“We can’t have a black box when it comes to AI,” she says. “Results have to tie back to audited systems of record.”
Whether supporting financial reporting, forecasting, or fraud detection, AI outputs must be transparent, traceable, and defensible. Approximation may be acceptable in other industries, but in regulated finance, trust is foundational.
This requirement is shaping how AI models are built, governed, and deployed, ensuring accountability at every step.
The Three-Legged Stool of AI Success
Furlan frames AI readiness as a three-legged stool: data, people, and technology.
Data must be accurate, consistent, and accessible. Technology must be capable. But people, she argues, are the hardest and most overlooked element
“Technical excellence alone is insufficient for adoption or success,” she says. “The other two legs matter far more.”
Extracting business context from stakeholders, embedding AI into existing workflows, and changing long-standing habits all require time and trust. That work is labor-intensive, but essential.
People Are the Hardest Part
Much of the friction in AI adoption comes from human behavior. Stakeholders naturally revert to familiar processes, even when new tools are available. Building AI that fits seamlessly into daily workflows requires deep collaboration with the business
This challenge is compounded by the pace of change. AI capabilities are advancing faster than organizations can modernize platforms or retrain teams. Traditional multi-year transformation programs no longer align with reality.
“The pace at which AI demands us to change is far faster than our ability to change,” Furlan says.
Four Bold Truths for Data Leaders
Furlan’s keynote at CDAO Financial Services is built around four “bold truths” for data leaders:
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Organizations are often in a toxic relationship with their data, blaming it for failures they helped create.
- Data is not the most important part of a data strategy, people are.
- Ready or not, AI is coming and resisting it only increases risk.
- Ultimately, it is not the data that holds organizations back, but leadership choices.
Together, these truths challenge leaders to stop treating AI as a technology problem and start treating it as an organizational one.
A Human-Centered Path Forward
As financial services leaders look ahead, Furlan believes the path forward is clear, if not easy. Invest in data foundations. Design AI for transparency. And above all, prepare people for change.
AI may be inevitable, but its success is not. That, she argues, is still firmly in human hands.
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Jeannie Furlan is a senior leader in financial data strategy and analytics, currently serving as Vice President, Financial Data Strategy at Mutual of Omaha, a Fortune 500 insurance and financial services company. In this role, Jeannie drives data-centric transformation—shaping how the organization leverages analytics, governance, and data strategy to generate business value and enhance stakeholder outcomes.
Jeannie holds professional credentials including Certified Fraud Examiner (CFE) and Certified Professional Category Analyst, reflecting her deep expertise in data ethics, risk, fraud analytics, and category management. She is a frequent speaker and thought leader in the data and analytics community, sharing insights on leading cultural and technological change to unlock the full potential of data and AI in complex enterprise environments.
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To explore how financial services leaders are building trusted, human-centered AI strategies in regulated environments, join the USA’s top financial data leaders at CDAO Insurance this February in New York.
