Enterprises are learning to turn messy data into meaningful AI, fight financial crime with digital FTEs, & drive real transformation—reveals Abhishek Mittal.

You require strong digital engineering skills to stitch various systems together with the foundational models of enterprises.”
— Abhishek Mittal

WASHINGTON, DC, UNITED STATES, November 14, 2025 /EINPresswire.com/ -- In the CAIO Podcast’s latest episode, host Sanjay Puri sits down with Abhishek Mittal, EVP and Chief Product & AI Officer at AML RightSource, to explore how enterprises can turn imperfect data into actionable AI systems and why the next frontier lies in agentic AI and digital FTEs. Abhishek, who previously led innovation at Wolters Kluwer, brings a rare blend of domain, product, and AI expertise, now focused on fighting financial crime through technology.

Fighting Financial Crime with AI
At AML RightSource, Abhishek leads a mission with a moral edge: to use AI to combat money laundering and financial fraud. He notes that if financial crime were a country, it would rank as the seventh-largest GDP in the world — a staggering reality that demands innovation. His approach combines domain expertise, AI-driven analytics, and human oversight to detect complex patterns of illicit activity hidden in massive data flows.
“The intersection of finance and technology isn’t just about efficiency,” Abhishek explains. “It’s about protecting trust in the system.”

Start with Imperfect Data
When it comes to building AI systems, many organizations say, “We’ll start once our data is perfect.” Abhishek disagrees. His message is simple: start anyway.
He advocates a “run in laps” approach—beginning with high-value use cases, improving data iteratively, and learning through execution. “You can’t wait for perfect data because the real world is never perfect,” he says. “Each use case you deploy improves the data that follows.”
This mindset shift—from perfection to pragmatism—turns AI from a theoretical initiative into a measurable business driver.

Synthetic Data: From Skepticism to Scale
Abhishek admits he was once a synthetic data skeptic, but experience changed his mind. With enterprises struggling to access high-quality, labeled data, synthetic data became a powerful tool to train, stress-test, and simulate AI models safely.
He recalls using synthetic datasets to prepare for rare financial crime scenarios or recession-like events—conditions that real data might not yet reflect. “Synthetic data lets us test resilience,” he explains. “But it must always be validated with real data and human oversight.”

Experience as the Real Competitive Moat
As AI models become commoditized, Abhishek argues that experience—not algorithms—becomes the differentiator. The real advantage lies in teams trained to use AI effectively, integrating machine intelligence with human expertise.
Enterprises that blend workflow wisdom with modern AI systems can outperform even the most agile startups. “It’s not about who builds the best model,” he says. “It’s about who knows how to apply it best.”

Agentic AI and Digital FTEs
Looking ahead, Abhishek sees the rise of agentic AI—systems that don’t just predict or recommend but act autonomously. He describes this as the age of the “digital FTE” — AI agents functioning as part of human teams.
But agentic AI success requires three enablers:
1. Process reimagination, not automation of old workflows.
2. Empowered domain experts who guide and evaluate outcomes.
3. Engineering excellence to connect AI seamlessly across legacy systems.

Pragmatism Over Perfection
As the conversation wraps up, Sanjay Puri highlights Abhishek’s defining philosophy—pragmatism over perfection. AI, Mittal insists, is most powerful when it’s embedded into real workflows, improving both efficiency and accountability.
“Start small. Scale fast. Stay accountable,” he advises. “That’s how you turn AI from a lab experiment into enterprise transformation.”
Through this compelling discussion, CAIO Podcast captures the essence of modern AI leadership—not just building smarter machines, but creating smarter systems that work hand-in-hand with people to solve meaningful problems.

Upasana Das
Knowledge Networks
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