AI Strategy in ENRC: From Experiments to Enterprise Value
ENRC leaders are putting AI at the center of strategy, not on the sidelines. KPMG's 2025 Global ENRC CEO Outlook reports sector confidence at 84%, up from 72% last year, despite inflation, geopolitics, and regulatory pressure. The message is clear: AI has moved from proofs of concept to a core lever for efficiency, resilience, and growth.
KPMG has set a net zero target by 2030 and a near-term science-based target to cut GHG emissions by 50% by 2030-mirroring the push for measurable outcomes across the industry. That focus on tangible results is shaping how executives fund, govern, and scale AI initiatives.
Investment Signals: Bigger Budgets, Faster Payback
AI spend is accelerating. 65% of CEOs now list generative AI as a top investment priority-up 12 points year over year. And 72% plan to allocate 10-20% of budgets to AI over the next 12 months.
Expectations are rising too. 66% anticipate returns within one to three years, a sharp jump from 15% in 2024. This ROI window is forcing tighter prioritization of use cases that move the P&L and emissions profile, not just pilots that look good on slides.
Where the Value Is Showing Up
"AI is fundamentally reshaping the oil and gas sector by unlocking gains in productivity, accuracy and efficiency across both core operations and enterprise functions," says Shreyansh Upadhyay, Lead of Global AI for ENRC, KPMG International.
He points to drilling optimization, reservoir performance, refining margins, renewable asset profitability, and supply chain and finance as near-term wins-areas where faster, data-led decisions reduce risk and create durable value.
From GenAI to Agentic AI
The shift is bigger than chat interfaces. More than half (51%) of ENRC CEOs expect agentic AI to have a significant or transformational impact-especially on operational and workforce efficiency. Think autonomous workflows across planning, scheduling, field operations, and maintenance, with humans setting guardrails and reviewing exceptions.
Execution Hurdles You Need to Manage
The appetite is there, but so are the blockers. CEOs cite ethics (55%), fragmented data (49%), and regulatory complexity (47%) as the main constraints. These aren't IT problems-they're enterprise issues that require clear ownership and board-level oversight.
Gillian Morris, Lead of Global Chemicals at KPMG International, advises companies to put a fit-for-purpose governance framework around AI agents and data so usage stays aligned with ethics and international law. A useful reference point is the NIST AI Risk Management Framework.
Regulatory differences across markets add friction. "Geopolitics has a major influence on ENRC companies who must deal with varying regulatory environments around the world with differing appetites for the energy transition," says Jonathon Peacock, Lead of Global Oil and Gas, KPMG International.
AI, ESG, and the Net Zero Mandate
ESG is now embedded strategy, not a side project. 72% of CEOs confirm ESG principles are built into corporate strategy. AI is a core enabler of that agenda.
82% believe AI can help cut emissions and optimize energy use. 74% say AI improves climate risk analytics and scenario modeling. Mike Hayes, Lead of Global Climate Change, Decarbonisation and Renewables at KPMG International, notes that energy and the transition remain central to the sustainability agenda-but emphasizes real-world constraints: data center demand, grid upgrades, and permitting delays (especially in Europe) require tighter collaboration between corporates and government.
Many leaders now see AI as the "operating system" for the transition-from real-time grid balancing to predictive maintenance at scale. The catch: results depend on secure, high-quality data and strong ESG governance, or projects stall at pilot.
For context on science-based targets, see the Science Based Targets initiative (SBTi).
What Executives Should Do Next
- Prioritize high-value use cases: Tie each to P&L impact and emissions reduction (e.g., fuel efficiency, yield, downtime, methane detection).
- Build the data backbone: Consolidate operational, market, and ESG data into governed layers; fix data quality before scaling agents.
- Operationalize AI governance: Define model risk, ethics, and review processes; align with frameworks like NIST; make someone accountable.
- Adopt agentic workflows carefully: Start with constrained, auditable agents in planning, maintenance, and scheduling; keep humans in the loop.
- Quantify ROI and ESG impact: Set baselines, measure uplift, and link to incentives; report both financial returns and emissions outcomes.
- Strengthen talent and change: Upskill engineers, operators, and finance teams on data and AI basics; embed AI PMO and change management.
- Secure the stack: Protect models, data, and interfaces; enforce access controls; test for drift and failure modes.
- Plan for market and policy shifts: Build scenarios across supply, prices, and regulation; keep your portfolio adaptable.
- Partner where it speeds outcomes: Work with OEMs, cloud providers, and regulators to move faster on compliance and interoperability.
Bottom Line
The ENRC sector is past the hype cycle. Budgets are set, ROI clocks are ticking, and boards expect measurable outcomes. Companies that align AI to value pools, secure the data foundation, and enforce governance will pull ahead-on margin, resilience, and emissions.
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