Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

Over the last 15+ years, I’ve built my career at the intersection of enterprise technology and industrial transformation. Early on, I moved into technical leadership roles where I was designing and scaling cloud-native, IoT, and data-driven platforms for global enterprises.

A significant shift in my career came when I moved deeper into the energy and Oil & Gas sector. In my current role as a Senior Solution Architect at Shell India Markets, I work in the downstream business with a focus on decarbonization, nature-based solutions, and low-carbon initiatives. My work is largely centered on applying AI and modern cloud architecture to solve real operational and commercial problems.

Before this, I spent several years leading Industry 4.0 and digital transformation programs across energy and industrial clients. That experience gave me a practical understanding of how industrial systems actually operate—from field devices and connected assets to enterprise-scale data and AI platforms.

 

Q2. How are advances in AI—particularly agentic or autonomous AI systems—reshaping the way energy companies approach operational decision-making and problem-solving?

What I’m seeing is a clear shift from reactive operations to more predictive and, in some cases, semi-autonomous decision-making.

AI systems today are not just analysing data—they are continuously ingesting inputs from SCADA systems, IoT sensors, maintenance logs, and even market signals. In some environments, they are already recommending actions, and in controlled scenarios, executing them.

In practical terms, this is showing up in areas like:

• Predictive maintenance and failure prevention
• Real-time production optimisation
• Faster incident detection and response
• More informed energy trading decisions

In more advanced setups, multiple AI systems are working together across supply chains, refineries, and renewable assets. The goal is not just efficiency, but balancing cost, safety, uptime, and emissions at the same time.
The biggest impact, in my experience, is reduced decision latency. Teams are spending less time diagnosing issues and more time acting on them.

 

Q3. How can AI-driven models contribute to better forecasting of geopolitical risks and their potential impact on energy supply, pricing, or infrastructure?

From what I’ve seen, the real value of AI here is in connecting signals that would otherwise remain fragmented.

Energy companies are now analysing a mix of data sources—shipping patterns, policy changes, satellite imagery, news flows, and market data—to identify early indicators of disruption. Individually, these signals don’t say much. But when combined, they start to show patterns.

For example, unusual tanker routes, changes in export policies, or instability near key infrastructure can be picked up early and translated into potential supply or pricing risks.

What this allows companies to do is move earlier than they typically would. Instead of reacting after an event unfolds, they can:

• Adjust procurement strategies
• Reroute supply chains
• Build inventory buffers
• Hedge exposure more effectively

It doesn’t eliminate uncertainty, but it gives decision-makers more time and better context.

 

Q4. How are shifting energy demand patterns and the rise of low-carbon solutions influencing technology priorities across the industry?

This is where I’ve seen the most visible shift in recent years.

Energy demand is no longer as predictable as it used to be. Electrification, distributed generation, EV adoption, and changing consumption patterns are making demand far more dynamic.

At the same time, decarbonization is forcing companies to expand beyond traditional operations.

As a result, technology priorities are changing in a few clear ways:

• Greater focus on real-time visibility across assets
• Investment in forecasting and demand modelling
• Integration of renewable and conventional energy systems
• Stronger emphasis on carbon tracking and reporting

Technologies like AI, digital twins, IoT, and cloud platforms are no longer optional—they are becoming core to how both conventional and low-carbon assets are managed.

In my view, the shift is quite fundamental. The objective is no longer just production efficiency. It’s about balancing cost, reliability, and sustainability in a much more complex system.

 

Q5. As geopolitical conflicts increasingly extend into the cyber domain, how are energy companies strengthening the resilience of their digital systems and operational technologies?

Cyber resilience is no longer treated as a separate IT concern. It’s now part of core operations.

What I see across the industry is a move toward an “assume breach” mindset. Instead of focusing only on prevention, companies are designing systems that can detect, isolate, and recover quickly.

Some of the more tangible shifts include:

• Stronger separation between IT and OT environments
• Adoption of Zero Trust and network segmentation
• 24/7 monitoring of industrial control systems and SCADA environments
• Use of AI to detect abnormal behaviour in real time

There’s also a growing focus on resilience, not just security:

• Redundant control systems
• Offline recovery capabilities
• Cyber-tested disaster recovery plans
• Use of digital twins to simulate attack scenarios

One area that’s getting a lot more attention is third-party risk. In many cases, vulnerabilities come through vendors or external partners, so companies are tightening controls across the entire ecosystem.

 

Q6. As large technology companies increasingly move into energy data platforms and digital infrastructure, how do you see the competitive landscape evolving for established energy firms?

The competitive landscape is definitely shifting, but not in a way that replaces traditional energy companies outright.

What’s changing is the basis of competition. It’s no longer just about physical assets—it’s increasingly about data, platforms, and customer insight.

Energy companies still have strong advantages:

• Deep operational experience
• Existing infrastructure
• Regulatory understanding
• Long-standing customer relationships

At the same time, technology players bring:

• Faster innovation cycles
• Advanced AI capabilities
• Scalable cloud platforms

From what I see, the outcome is likely to be a hybrid model.

The companies that do well will be the ones that combine domain expertise with strong digital capabilities. The risk is for those that treat digital as a support function—they may gradually lose control over data, customer interfaces, and future revenue streams.

 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

The question I would ask is:

“How are you building a defensible advantage as energy becomes increasingly data- and AI-driven—and what measurable value is coming out of that?”

What I would be looking for is clarity.

• Are they just adopting technology, or are they using it to create differentiation?
• Can they link digital investments to outcomes like margin improvement, risk reduction, or faster decision-making?
• Are they building capabilities that competitors will find hard to replicate?

In my experience, the companies that can clearly connect technology to business value—and demonstrate it consistently—are the ones that tend to stay ahead.