07-06-2026
As organizations accelerate their adoption of artificial intelligence, they face a critical challenge: learning to understand and trust AI outputs so they can move beyond experimentation into value creation. According to Purdue University alumnus Surya Gundavarapu, success depends less on chasing the latest technology and more on mastering the fundamentals of learning, adaptation and operational excellence.
Today, Gundavarapu serves as a business support engineer at Meta, where he works at the intersection of engineering and operations. His responsibilities include designing support and integrity workflows, leading incident response efforts and developing platform features that improve user experiences. Before joining Meta, he held analytics and business intelligence roles in healthcare, helping automate reporting, supply chain management and operational processes.
Gundavarapu’s perspective is informed both by his time at Meta and by his independent research on AI reliability, including award-winning work on automated systems for detecting when language models generate false information.
He credits the Daniels School’s Master of Science in Business Analytics and Information Management (MSBAIM) program, which is celebrating its 10th anniversary this year, with teaching him a skill that remains invaluable in today's rapidly changing technology landscape: the ability to learn continuously.
"The analytics landscape I trained on has already been reshaped several times over," Gundavarapu says. Rather than focusing on any single technical tool, the program emphasized core analytical principles and adaptability — capabilities that remain relevant even as technologies evolve.
That mindset is increasingly important as organizations accelerate AI adoption. Gundavarapu believes many of the concerns dominating discussions around AI — including trust, workforce disruption and unrealistic expectations — stem from a single underlying challenge: alignment.
To fully trust AI outputs, organizations need to understand how systems generate results or whether those systems consistently perform as intended. As AI capabilities expand, leaders must focus on transparency, governance and reliability alongside innovation. Understanding the mechanics behind AI systems helps bridge the gap between hype and practical business applications.
He also observes a significant shift in how organizations are using AI. Rather than treating AI as a standalone product or attention-grabbing feature, leading companies are embedding it into everyday workflows, automation platforms and decision-support systems.
"The meaningful shift right now is from AI as a novelty to AI as core infrastructure," he says. "The organizations extracting real value aren't the ones with the loudest demos; they are the ones treating AI as a system to be measured, verified and held to the exact same engineering reliability standards as anything else in production."
