From autonomous finance and Agentic AI to predictive analytics and intelligent automation, finance leaders are being inundated with promises about what AI can do. Yet despite the excitement, many organizations continue to struggle with a fundamental question:
How do you move from AI hype to meaningful business results?
That question was the focus of a recent episode of the Emagia AI for Finance Podcast featuring Ranil DeSilva, Chief Financial and Operating Officer of CARE, a global humanitarian organization operating in more than 50 countries.
His answer was surprisingly simple:
Think big. Start small.
The Biggest AI Mistake Finance Leaders Make
DeSilva, told AI For Finance Host Brian Shappell that many organizations become focused on the technology before they address the foundations needed for success.
Finance leaders often feel pressure from boards, executive teams, and the marketplace to move quickly. New AI vendors emerge almost daily. Conferences are filled with promises of transformational results. Every platform suddenly claims to be AI-powered.
But before organizations can realize the value of autonomous finance or AI agents, they must address a much more basic challenge:
Data.
“If you don’t have the fundamentals of getting the data right, we can’t talk about autonomous finance and we can’t talk about AI,” DeSilva explained.
In other words, sophisticated AI operating on poor-quality data simply produces poor-quality outcomes faster.
Why Data, People, and Governance Matters
When discussing successful AI adoption, DeSilva repeatedly returned to three priorities:
1. Data Readiness
Organizations must establish trusted, accessible, and accurate data before expecting meaningful AI outcomes.
Without that foundation, even the most advanced AI models will struggle to deliver reliable insights or recommendations.
2. Workforce Transformation
AI is changing the nature of finance work.
As repetitive transactional activities become increasingly automated, finance teams will spend less time processing information and more time analyzing, interpreting, and acting on insights.
Rather than focusing on job replacement, organizations should focus on upskilling and reskilling employees to thrive in AI-enabled environments.
3. Governance
AI cannot operate without oversight.
Organizations need clear policies, controls, accountability structures, and human-in-the-loop governance models to ensure AI-driven decisions remain aligned with business objectives and regulatory requirements.
From Hype to Practical Progress
For finance leaders evaluating autonomous finance solutions, AI agents, or broader digital transformation initiatives, the lesson is clear.
Do not begin with the largest possible deployment. Begin with a well-defined business challenge.
- Establish the right data foundation.
- Create governance structures.
- Develop workforce readiness.
- Measure outcomes.
- Then scale.
The organizations that succeed with AI over the next several years will not necessarily be those that move fastest.
They will be the organizations that build the strongest foundations first.
As DeSilva summarized:
“Think big. Start small.”
It’s simple yet practical if not poignant advice for finance leaders.
Want to hear the full Emagia AI For Finance Episode with Ranil DeSilva, CARE’s Chief Financial and Operating Officer. Click here to watch listen.
