About Me

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Hi, I’m PradeepKumar Vasudevan, a Vice President at Barclays UK, where I represent the Corporate Data Services function. Prior to joining Barclays, I worked with NatWest as a Data Leader, leading the delivery of large-scale data engineering, ETL and machine learning solutions. Earlier in my career, I also worked with global consulting and technology firms including Cognizant, Wipro Technologies and Keane Inc., which gave me experience across a wide range of organisations, technologies and transformation programmes

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I have spent more than 20 years working with data and technology, and one of the things that continues to fascinate me is how quickly the role of data keeps evolving.

 

Over the years, I have seen several waves of technological transformation. There was a time when organisations were focused primarily on collecting and processing data. That evolved into the era of big data, cloud platforms, advanced analytics and machine learning.

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We often heard the phrase "data is the new oil", reflecting the idea that data had enormous value if organisations could refine and use it effectively.

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I think we are entering another important phase.

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For me, data remains the raw material, but AI is becoming the engine that turns it into understanding, decisions and increasingly, action.

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That evolution is what makes this such an interesting time to be working in technology.

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And while industries can be very different, I believe the underlying discipline of working with data and AI remains remarkably consistent.

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You could be working in banking, healthcare, aerospace, manufacturing or almost any other domain. The business context, regulations, risks and terminology may change, but the core principles remain the same: understand the problem, understand the data, establish trust in it, apply the right analytical or AI technique, and make the outcome useful, explainable and responsible.

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Data tells us more than we sometimes realize

 

My interest in data has always gone beyond simply moving it from one system to another.

Good exploratory data analysis, combined with a strong understanding of statistics, can reveal what is really happening beneath the surface. Distributions, relationships, anomalies, correlations and changes over time can often tell a story long before a sophisticated model is introduced.

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Machine learning takes this further by allowing us to identify patterns at scale, make predictions and help businesses make better-informed decisions. But I believe the most valuable applications of machine learning are not necessarily the most complex ones. They are the ones that connect data, analytical thinking and technology to a genuine business problem.

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Making data understandable

 

Another area I have always been interested in is data visualisation.

There is something powerful about taking thousands or millions of rows of information and turning them into something that a person can understand within seconds.

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And now, AI

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"There are weeks where decades happen" 

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AI is entering another important phase, and Agentic AI is the shift that interests me most.

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Just think about how often many of us now turn to tools like ChatGPT to ask a question, explore an idea, summarise something, write, analyse or simply think through a problem. That change has happened remarkably quickly.

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For me, the next natural question is: what happens when AI moves from simply responding to us, to doing more on our behalf?

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That is where Agentic AI becomes interesting. Instead of waiting for every instruction, AI systems could increasingly understand an objective, decide the next steps, use tools, work with data and carry out parts of a task with greater autonomy.

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I see this as a significant pivot : From AI as an assistant we continually prompt, towards AI as an intelligent participant in the way work gets done.

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Of course, greater autonomy also brings greater responsibility. Trust, governance, security, good data and human oversight will remain essential.

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But it is precisely that transition that keeps me curious: how far can we allow AI to act independently, where should humans remain firmly in control, and what will the relationship between the two eventually look like?

That curiosity is a big part of what keeps me interested in where AI goes next.

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Why I write

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This blog is where I explore my thoughts on Data, AI and Generative AI and how these technologies are evolving, how organizations are putting them to work, and what they could mean for the way we make decisions, build products and solve problems.

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I’m particularly interested in the journey from data to intelligence: how good data creates the foundation for analytics and machine learning, how Generative AI is changing the way we interact with information, and how Agentic AI could take us from simply generating answers to taking meaningful action.

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Some posts will explore emerging ideas and technologies, while others will look at practical applications, lessons from the industry or simply questions that I find worth thinking about.

After more than two decades in technology, I’ve learned that the landscape never stops changing, and that is exactly what keeps it interesting.

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I use this space to learn, question, share perspectives and explore where Data and AI might take us next.

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