This is the “superpower moment” for the energy sector. The convergence of artificial intelligence (AI), drones, and robotics is not only reshaping how energy companies operate but also setting the stage for a future where autonomous systems, predictive analytics, and real-time decision-making become the norm. According to Nitin Gupta, Founder and CEO of FlytBase, who provided the opening keynote at last week’s 9th Annual Energy Drone and Robotics Coalition Summit, “The question is no longer whether these innovations will transform energy operations, but rather who will harness their power first and best.”
The Perfect Combination: Edge AI, Autonomy, and Domain Expertise
Gupta set the stage by explaining how for the first time, machines can understand, reason, and act autonomously. Generative AI, in particular, continues to unlock unprecedented value.
According to McKinsey research, AI could generate up to $550 billion in annual value for the energy and materials sectors alone. These numbers represent new value creation, above and beyond traditional operational improvements. Companies that move swiftly to adopt these technologies have the potential to gain compounding advantages and leave slower competitors struggling to catch up, he emphasized.
The energy industry’s digital transformation, in particular, has accelerated rapidly over the past decade. From 2018 to 2025, the sector has witnessed the rise of edge AI, which enables real-time data processing directly on devices in the field. This eliminates the need for constant cloud connectivity. This shift supports instant decision-making and greater operational resilience.
Autonomous systems, including drones and robots, are now capable of operating in real-world environments and adapting to changing conditions without human intervention. The integration of domain expertise—combining deep operational knowledge with AI—means these systems can understand and address industry-specific challenges with remarkable precision.
From Traditional to AI-Native Operations
The difference between traditional and AI-native operations is stark. In the past, energy companies relied on reactive maintenance, human inspections, and manual optimization. Expertise was limited to what could be learned and retained by individuals. Today, AI-driven predictive intelligence enables proactive detection of issues, such as gas leaks or equipment failures, before they escalate into costly problems.
Autonomous monitoring and self-optimizing systems are rapidly replacing manual processes. Infinite knowledge scaling—where AI systems continuously learn from vast datasets—ensures that even the most complex operations benefit from the collective intelligence of the entire industry.
Real-World Impact: Predictive Maintenance in Oil & Gas
The impact of AI is already being felt on the ground. Gupta described how a major oil and gas operator recently deployed AI-powered predictive analytics across its critical infrastructure, revolutionizing maintenance operations. The results were striking: equipment failure prediction improved by 75 percent, advance warning time increased to nine days, issues resolved before failure reached 85 percent, and maintenance costs dropped by over 30%.
One notable example involved the detection of a subtle temperature anomaly in an export compressor—a problem that traditional monitoring would have missed. The early warning provided by AI prevented a catastrophic failure and avoided two days of unplanned downtime. This demonstrates the tangible benefits of intelligent automation.
Forces Driving Urgency: Workforce, Regulation, and Economics
Several forces continue to accelerate the adoption of AI, drones, and robotics in energy:
- Workforce crisis: Half of the energy sector’s skilled employees are expected to retire within the next five to ten years. This demographic shift threatens to create a knowledge gap just as operations become more complex.
- Regulatory pressure: Compliance with standards such as NERC CIP, NIST, and ISO 27001 is non-negotiable, even as the frequency of cyberattacks on utilities continues to rise.
- Economic squeeze: Renewable energy competition has compressed margins and forced companies to seek efficiencies wherever possible.
Overcoming Enterprise Barriers
Despite the clear benefits, energy companies face significant barriers to AI adoption. Concerns about cybersecurity, workforce reskilling, job displacement, and integration with legacy systems remain top of mind for industry leaders. Many wonder whether AI will compromise critical infrastructure or require a costly overhaul of proven systems.
Gupta said that answer lies in a measured, security-first approach. Edge-first processing ensures that sensitive data never leaves company premises. Compliance-native design means every AI decision is explainable, reversible, and fully auditable. Air-gapped operations isolate critical systems from the internet, reducing the risk of external threats.
Elevating the Workforce, Not Replacing It
Rather than eliminating jobs, AI and robotics should elevate the workforce. By automating dangerous and repetitive tasks, these technologies free human operators to focus on strategic decision-making and innovation. Gupta recommended a “co-design approach,” where workers help shape the AI systems they will use. This, he said, turns employees into “force multipliers” by amplifying their impact across the organization.
Augmentation Over Replacement: Integrating AI with Existing Systems
The most successful energy companies do not “rip and replace” their existing infrastructure, Gupta noted. Instead, they leverage modern AI platforms that integrate seamlessly with legacy systems. A “phase-gate implementation approach” allows organizations to identify and validate specific use cases, deploy solutions in controlled environments, and scale proven innovations across the enterprise.
This method ensures a controlled transition from human-in-the-loop to human-on-the-loop operations, with clear success metrics and continuous improvement at every stage.
Collaboration: The Key to Unlocking AI’s Potential
The future of energy operations depends on close collaboration between domain experts and AI specialists. Operational knowledge, industry-specific understanding, and real-world constraints must be combined with advanced AI technology and deployment expertise. This partnership remains essential to design systems that are both powerful and practical.
The Current Window of Opportunity: Catch the Vision Before It’s Too Late
Imagine an energy operation that prevents problems before they occur, responds to emergencies in minutes rather than hours, and continuously optimizes itself. In this future, the workforce is liberated from hazardous environments and empowered to drive innovation and strategy. This vision is not science fiction—it is being built today by those who recognize the transformative power of AI, drones, and robotics.
The opportunities are vast, but so are the challenges. Success will require a security-first mindset, a commitment to workforce elevation, and a willingness to collaborate across disciplines.
The next 24 to 36 months represent a critical window for early movers in the energy sector. Companies that act now will enjoy a lasting competitive advantage, while late followers risk falling irreversibly behind. The compounding benefits of AI, drones, and robotics will only accelerate as more organizations embrace these technologies.
As the industry stands at this pivotal moment, the question is simple: Who will seize the superpower of AI and shape the future of energy? The race is on, and the winners will define the next generation of energy operations.
This article draws on insights from FlytBase CEO Nitin Gupta’s keynote at the 2025 Energy Drone and Robotics Coalition Summit and industry research to provide a comprehensive look at how AI, drones, and robotics are revolutionizing the energy sector.