Neelima Sharma is a dynamic, results-driven digital leader with deep expertise in emerging technologies and digital transformation. She drives enterprise-wide digital, data, and technology strategies, leveraging Industry 4.0 levers to enhance operations, unlock growth, and support CAIRN's vision for domestic crude production leadership.
In an engaging interaction with Woman Entrepreneurs Review Magazine, Neelima shares her insights on how digital transformation is reshaping traditional business models in the oil and gas sector. Drawing on her industry experience, she highlights the industry’s key trends, challenges, and the role of AI and automation in driving future efficiencies.
In the current landscape of the oil and gas sector, how is digital transformation reshaping traditional business models? What key trends should companies watch out for?
Digital transformation is fundamentally reshaping the oil and gas industry by driving greater efficiency, safety, and sustainability. Traditional asset-heavy, manually intensive operations are evolving into agile, data-driven ecosystems. Companies are leveraging cloud, AI, and IoT to optimize exploration, drilling, and production while enhancing real-time decision-making and predictive maintenance. A key disruptive solution is the use of drones—especially in remote and challenging regions like Northeast India—enabling rapid pipeline inspection, methane detection, and surveillance without risking human safety. Looking ahead, key trends include scaling AI for asset optimization, deploying industry-specific digital twins, and integrating sustainability metrics into digital strategies. These advancements are unlocking new business models and creating a smarter, safer, and more responsive energy sector.
Which significant challenges have you encountered in driving digital transformation? How can oil & gas organizations better prepare for them in the evolving market?
The global AI in oil and gas market is projected to grow from approximately USD 5.86 billion in 2023 to USD 19.36 billion by 2032, at a CAGR of around 14.2%.
Successful AI projects begin with clearly defined business goals, ensuring alignment with specific objectives like reducing downtime or optimizing processes. High-quality, well-labeled data is essential for accurate model training and performance. Cross-functional collaboration between data scientists, engineers, IT, and field operators is crucial for developing practical and actionable AI solutions. Starting with small proof-of-concept projects allows for testing and refining AI applications before scaling them enterprise-wide, minimizing risks.
Integrating AI into existing operational systems, such as SCADA or digital twins, ensures continuous value delivery and real-time applicability. Combining human expertise with AI enhances decision-making, as AI can flag anomalies while human experts validate and act on these insights. Prioritizing ethics, safety, and cybersecurity is vital, especially in safety-critical environments, to ensure robust governance and protection against cyber threats.
Addressing cultural resistance to AI adoption is as important as the technology itself. Champions at the plant level and executive buy-in can help bridge the gap and foster acceptance. Monetizing AI's return on investment (ROI) takes time, with some benefits like energy savings being quick to measure, while others, such as equipment longevity, may take longer to realize. Continuous learning and feedback loops are necessary to keep AI models accurate and relevant, adapting to operational changes and improving over time.
As AI and automation rise, how do you foresee these technologies optimizing operational efficiency in the oil and gas industry? What ethical considerations come into play in their implementation?
AI and robotics technologies like RIA (Robotic Inspection Assistant) are transforming the oil and gas industry by improving safety, enabling continuous monitoring, and driving remote, data-driven decision-making. Beyond physical automation, AI is also being leveraged to enhance safety behavior through predictive analytics. A Digital twin is used to optimize process efficiency example, by managing slug catcher pressure, reducing fuel gas consumption, minimizing flare rates, and predicting equipment failures such as seal breakdowns. These advancements significantly reduce downtime and operational costs while improving asset reliability. However, ethical considerations—like workforce displacement, data privacy, AI bias, accountability, and environmental impact—must be thoughtfully addressed to ensure responsible, inclusive, and sustainable adoption.
As a leader fostering digital culture, how do you align digital initiatives and organizational goals when working across geographies with varying digital maturity levels?
As a leader fostering a digital culture, ensuring alignment between digital initiatives and organizational goals—especially across diverse geographic locations—starts with the core belief that digital transformation is a collaboration of people, processes, and technology. We prioritize selecting digital initiatives that add business value, whether by improving volume, reducing cost, or enhancing ESG outcomes, all guided by well-defined KPIs to measure impact. Equally important is cultivating a digital mindset across the organization—built on trust, empowerment, and continuous learning. By enabling cross-functional teams, encouraging local ownership, and maintaining strong alignment with business objectives, we ensure that digital doesn’t operate in isolation but becomes an enabler of strategic goals across every location.
What role will data-driven decision-making play in the future of digital transformation for manufacturing within oil and gas? How can leaders effectively manage data challenges?
Data-driven decision-making will be the cornerstone of future digital transformation in manufacturing, particularly within oil and gas. With industries generating petabytes of data from sensors, control systems, and enterprise platforms, the ability to extract actionable insights is now a competitive differentiator. Studies show that data-driven organizations are 23 times more likely to acquire customers and 6 times more likely to retain them (McKinsey), and in manufacturing, AI and data analytics can improve asset uptime by 10–20% and reduce maintenance costs by up to 25%.
As one of the 100 Most Influential AI Leaders recognized by AIM under the Data-Driven CXO Awards, I firmly advocate that data is the new oil—but like crude, its value lies in how it’s refined and used. Leaders must focus on building strong data governance frameworks, investing in scalable data infrastructure, and fostering a culture where data literacy and trust are embedded across all levels. Managing challenges such as data silos, quality, and security requires a strategic blend of technology, talent, and trust, ensuring that data not only informs decisions but drives meaningful, measurable outcomes aligned with business goals.
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