DynPro's CEO on Solving Corporate Knowledge Loss with AI‑Driven Data Modernization

The market may be fixated on fully autonomous AI built from the group up, but that’s often not where the fastest or most sustainable ROI is emerging. The real value comes from something far more fundamental: getting legacy data into a state where AI can actually do its job. Enterprises focused on durable returns have realized a simple truth that autonomous agents can’t deliver lasting value without a modern, governed data foundation beneath them. The organizations investing in that foundation now are positioning themselves to win long term, because AI’s biggest bottleneck isn’t capability, it’s access to clean, interpretable, high-quality data. The leaders who remove that bottleneck early are quietly building an advantage that compounds with every new AI capability that follows.

We spoke with Shivkumar Thiagarajan, the Co-Chief Executive Officer at DynPro Inc., about the challenge of accessing, cleaning, and transforming data to drive ROI and efficiency gains. For Thiagarajan, this challenge is familiar territory. With 25 years of experience in management and business development, he has focused his team on using AI to solve this core data problem first. Their answer is an internal accelerator they call the "Data Modernization Genie," which zeroes in on data migration and illustrates AI's power as a force multiplier.

"The real challenge with AI and data is often a human one: the people who originally understood the old systems are no longer there, leaving business analysts unable to make sense of what’s in the data," Thiagarajan said.

• Ancient data, new speed: The difference isn’t just speed with AI, it’s also predictability and scale. The Genie automates much of the heavy lifting, reduces human error, and surfaces insights along the way, turning what was once a painstaking, high-risk process into a repeatable, efficient workflow. For enterprises burdened with decades of legacy systems, this acceleration doesn’t just save time, it enhances the ability to leverage AI on data that was previously inaccessible or incomprehensible. "Traditionally, data migrations took six months, but now we do them in weeks. With our Data Modernization Genie, we moved a 99-year-old company’s data into Snowflake. A project that initially took three months was most recently completed in just three weeks," Thiagarajan said.