Skills
About
Venkata Kondepati is a seasoned technology leader with over 24 years of experience in cloud engineering, data platforms, and enterprise software development. He has held multiple Director-level roles at S&P Global, where he led Customer IAM, Cloud Operations, and Data Engineering teams to drive large-scale cloud migrations, build multi-region high-availability platforms, and modernize enterprise systems that supported more than $1.2B in revenue. With a career foundation in GIS and geospatial analytics, Venkat expanded his expertise into cloud architecture, platform engineering, and generative AI. He has successfully led teams across four countries, delivering secure, scalable solutions leveraging AWS, Azure, GCP, Kubernetes, Snowflake, Apache Spark, and advanced data engineering frameworks. Venkat is also a recognized contributor to the global technology community. He is a Senior Member of IEEE, a PMI member, an Esri ArcGIS MVP contributor, and an active Forbes Technology Council member. He is widely respected for his ability to align business strategy with technology investments, build high-performing global teams, and foster innovation through mentorship and collaboration. His leadership philosophy centers on empowering people, modernizing platforms, and delivering measurable business impact.
Venkata Kondepati
Published content
expert panel
Sep 25, 2026
The first generation of AI adoption asked a simple question: What work can we automate?The next question is harder: What happens to the people who were experts at doing it before?Expertise has traditionally been built through practice. Engineers write and review code. Analysts work through problems. Marketers study audiences and campaigns. Architects design systems. Leaders accumulate judgment by making decisions and seeing what happens next. If AI increasingly performs those activities, where does that leave the role of the expert?Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in machine learning, generative AI and enterprise AI applications—are thinking about that transition from inside the organizations and disciplines being reshaped by AI. Together, they raise an important question that reaches into the design of the organization itself: How do you build a company where AI can do more of the work without losing the human expertise that tells the company what work is worth doing?
expert panel
In the past, the easiest way to talk about a technology investment has been to put a number on it: How much did it cost, and what did the company get back? With AI, that calculation is becoming harder to take at face value.An AI system can save thousands of hours without reducing headcount. It can help employees make better decisions without generating an immediately measurable dollar value. It can turn a successful pilot into a new way of working—or quietly remain a one-off experiment that never scales. And it can change who holds critical expertise inside an organization, with consequences that may not appear on a balance sheet for years.That raises a more consequential question for CEOs: What should you measure if you want to know whether your AI strategy is actually making the organization better?Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, suggest that CEOs shouldn't abandon ROI—but here are the other metrics they recommend measuring to capture not only what AI delivers today, but what it enables the organization to become.
expert panel
The AI industry has been celebrating increasingly capable demonstrations: a model beats a benchmark, an autonomous vehicle completes a route, an AI agent handles a task that once required a person. But a successful demonstration answers only one question: Can the technology work?Commercialization demands much harder answers: Can it work every day? What happens when it fails? Who is responsible? Can the surrounding operation absorb those failures? And do the economics still work when real customers are paying for the result? Nevada’s recent approval of robotaxi networks for Tesla, Waymo and Uber opens the door to thousands of autonomous vehicles operating commercially, and brings those questions into sharp focus.Members of the Senior Executive AI Think Tank bring perspectives from machine learning, enterprise technology, product management, data architecture, financial services, infrastructure, healthcare and design to this next stage of the AI conversation. Below, they examine what it takes to move beyond a working model and build a system that can withstand real-world complexity—from managing failures and infrastructure to earning regulatory and public trust, establishing accountability and making the economics work at scale.
expert panel
For many executives, AI has already become part of the daily workflow. They use it to summarize reports, find information, draft emails and work through routine tasks. But familiarity with the tools can create a false sense of fluency—and make it harder to recognize the gap between using AI and knowing how to use it strategically.Microsoft research involving thousands of knowledge workers found that generative AI can reduce time spent on tasks such as writing, information retrieval and summarization. But those individual productivity gains do not automatically translate into changes in how organizations make decisions or operate. Research from McKinsey similarly finds that while AI adoption is widespread, most organizations are still struggling to turn it into significant enterprise-level impact.The challenge, then, is not simply getting leaders to use AI more. It is learning to recognize where AI can change the work itself—and developing the judgment to know when, where and how to make that change.So what does genuine AI fluency look like at the leadership level? Members of the Senior Executive AI Think Tank approach that question from different vantage points, spanning enterprise technology, research, finance, retail, design and AI implementation. Their experiences offer a closer look at what happens when leaders move beyond individual productivity and begin applying AI to decisions, workflows, business systems and organizational strategy.
expert panel
For all the talk about AI making customer service faster and more personal, there is still a harder question to answer: Does the customer actually feel better served? An instant answer or perfectly timed recommendation may be useful, but usefulness is not the same as feeling understood. When a customer has a complicated problem, is frustrated or simply wants to talk to someone, efficiency can quickly become the problem.That tension is becoming more important as companies bring AI into more customer interactions. Customers are increasingly comfortable with AI when it saves time or effort, but many still expect a human option when the stakes are higher. The challenge then is figuring out how technology can remove friction without taking away the judgment, reassurance and connection that make good service feel like good service.In this article, members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI and enterprise AI applications—explore that challenge from different angles. They discuss personalization and customer memory, where human involvement matters most, how AI can give employees more room for empathy, and how customer signals can shape better products. They also examine what it really means for AI to remember—and how leaders can tell whether an interaction leaves customers feeling understood, rather than simply processed.
expert panel
Artificial intelligence may be transforming the enterprise, but behind every successful AI initiative is something far less glamorous: disciplined data engineering.As organizations race to deploy generative AI, agentic systems and increasingly sophisticated analytics, many are pouring resources into new models, cloud platforms and AI applications. Yet time and again, ambitious projects fail to deliver meaningful business value—not because the technology falls short, but because the underlying data is inconsistent, poorly governed or difficult to trust.According to McKinsey's latest State of AI research, organizations seeing the strongest returns from AI distinguish themselves not by the models they choose, but by the maturity of the data, governance and operating foundations supporting those models. In other words, AI success begins long before a prompt is entered or an algorithm is deployed.Members of the Senior Executive AI Think Tank, a curated community of executives specializing in machine learning, generative AI and enterprise AI applications, have witnessed this firsthand across industries ranging from healthcare and financial services to manufacturing, retail and cloud computing. Below, they outline the foundational data engineering capabilities they believe consistently deliver the greatest business value and why leaders should take more notice.
Company details
Ascentt
Company bio
About Ascentt Enabling Enterprise Excellence, Ascentt strives to be a trusted partner to the modern enterprise by helping them realize value from their data assets with our innovative AI/Data products & solutions. Our Vision Empowering enterprises to lead in a tech-first future. Global Delivery Center Our Global Delivery Center located in Pune provides us the ability to churn new products, new features and new solutions at breakneck speed. Excellent project management capabilities, strong technical competency and proven best practices provide us the ability to deliver robust solutions at a competitive price, enabling higher ROI for our clients. AI Solutions Lab Our cutting-edge AI R&D Center in India pioneers breakthrough innovations, developing sophisticated AI solutions across machine learning, computer vision, NLP, and predictive analytics. We empower businesses worldwide to unlock unprecedented efficiency, intelligence, and growth.