By Amarpal Nanda, Co-Founder & COO, Equipt.ai
The views expressed by the author are their own and do not represent the views of Energy Workforce & Technology Council.
Dr. Amarpal Nanda is a co-founder and COO of Equipt.ai, with deep expertise in oil and gas operations, digital transformation, and supply chain optimization. He has led large-scale initiatives driving efficiency, innovation, and cost reduction, and is passionate about applying AI to modernize field services and asset management.
For many energy companies, Artificial Intelligence still lives in slide decks and conference keynotes. Strategy papers, roadmaps, and “AI centers of excellence” are everywhere, yet field teams continue to wrestle with manual dispatching, paper tickets, and reactive maintenance. The real gap is not a lack of AI strategy; it is a lack of AI in daily Operations.
Industry research shows that when AI is embedded into core operational workflows—such as maintenance, production, and field execution—companies can cut unplanned downtime by more than a third and significantly reduce maintenance costs. In other words, Operations is where AI starts paying for itself. [1]
Strategy Is Easy. Operational Change Is Hard.
It is now common for energy firms to publish AI roadmaps, announce pilots, and run hackathons. Most organizations can also experiment quickly using third‑party AI tools, low‑code platforms, or cloud services. The barrier is no longer access to algorithms; it is converting those algorithms into reliable, repeatable processes in the field.
This is why so many AI initiatives stall after the proof‑of‑concept stage. Strategy teams may identify dozens of potential use cases, but only a few ever move into day‑to‑day Operations. Without clear ownership, clean operational data, and simple ways for supervisors and technicians to use AI outputs, even the best strategy remains theoretical.
Why Operations Automation Delivers Faster ROI
Operations automation focuses AI on work that happens thousands of times a month: dispatch decisions, work order routing, inspection workflows, and asset monitoring. Automating these repetitive, rules‑driven tasks reduces human intervention and error while freeing people for higher‑value decisions.
Across the energy sector, AI‑driven maintenance and Operations automation have already demonstrated concrete benefits. Analyses of predictive maintenance deployments cite reductions of roughly 35% in unplanned downtime, 20% in maintenance costs, and measurable increases in asset uptime when AI is used to flag issues before equipment fails. Other studies on AI in Operations report payback periods shrinking to well under a year as organizations focus on high‑frequency operational use cases rather than broad, abstract programs. [2]
These are not theoretical gains; they are the compounding result of thousands of small decisions handled correctly and consistently by software instead of by over‑stretched teams.
The Role of Third‑Party AI Platforms
Fortunately, energy companies no longer need to build every AI capability from scratch. Third‑party platforms now package proven models, workflows, and integrations that can be configured around existing Operations rather than requiring a complete system overhaul.
For Operations leaders, this means:
- Rapid deployment using pre‑built workflows for scheduling, field ticketing, inspections, and maintenance.
- Consistent automation of routine tasks with guardrails that limit human error.
- Improved visibility across jobs, assets, and crews without rebuilding the underlying ERP or control systems.
Instead of spending years defining an enterprise AI strategy, organizations can start by automating a handful of high‑impact operational workflows and then expanding from there.
Less Human Intervention, Fewer Errors, Safer Work
In complex field environments, every manual handoff is a chance for something to go wrong. A missed radio call leads to a delayed crew. A mis‑keyed value in a spreadsheet hides an equipment alarm. A lost paper checklist turns into a compliance gap. Operations automation allows AI to watch for these failure points and handle them consistently.
Examples include:
- Automatically assigning crews based on skills, certifications, and location so the right people arrive with the right equipment the first time.
- Using AI to scan maintenance and sensor data for early signs of failure, triggering planned interventions rather than emergency callouts.
- Generating digital, time‑stamped audit trails for work orders, inspections, and handovers without extra effort from field teams.
The impact is practical and immediate: fewer repeat visits, fewer safety incidents driven by rushed decisions, and stronger documentation when regulators or partners ask for proof.
Putting Operations at the Center of AI
For energy firms under pressure to increase throughput, reduce emissions, and manage costs, the fastest path to AI ROI is not another strategy document—it is embedding AI into the operational heartbeat of the business. That means:
- Starting with real workflows that supervisors and technicians touch every day.
- Using third‑party AI platforms that integrate with existing systems and data sources.
- Measuring success with operational metrics—downtime avoided, jobs completed on first visit, schedule adherence—rather than abstract innovation KPIs.
When Operations leads the conversation, AI stops being a distant vision and becomes a practical tool: automating decisions, reducing human error, and delivering measurable returns in weeks, not years.
- https://energiesmedia.com/ai-in-oil-and-gas-preventing-equipment-failures-before-they-cost-millions/
- https://www.mckinsey.com/capabilities/operations/our-insights/bold-accelerators-how-operations-leaders-are-pulling-ahead-using-ai
Energy Workforce partner Pickering Energy Partners provides insights on ESG due diligence, disclosures and reporting. Melanie Vujovich, Vice President, Pickering Energy Partners.
