Energy and Resources CEOs Bet on AI, Talent, and Sustainability as Growth Confidence Rebounds-Even With Cyber and Data Hurdles

Energy, Natural Resources and Chemicals CEOs Bet on AI, Talent, and Sustainability as Confidence Rebounds

ENRC leaders are getting pragmatic. Confidence in mid-term industry growth is up to 84 percent (from 72 percent), while AI budgets, talent retraining, and sustainability execution are moving from concept to plan.

The message is clear: invest where returns are visible, fix data and cyber risks early, and tie every initiative to revenue, resilience, and emissions outcomes.

Confidence is back-paired with measured M&A and capital discipline

Despite inflation and regulatory friction, 84 percent of CEOs are optimistic about mid-term industry growth, helped by demand for both fossil fuels and renewables, plus advances in storage, smart grids, and carbon capture. Confidence in company-level growth remains high at 78 percent, though slightly lower than last year due to regulatory shifts, trade volatility, and chemicals pricing pressure.

M&A strategies are more cautious. Only 36 percent expect to chase "high-impact" deals in 2025 (down from 58 percent), while 55 percent expect "moderate" activity (up from 38 percent). Translation: more bolt-ons, fewer headline bets.

AI gets real budgets-and near-term ROI expectations

AI has crossed from pilots to planning. 65 percent of CEOs now rank generative AI as a top investment area. 72 percent plan to allocate 10-20 percent of budgets to AI over the next year, and 66 percent expect ROI in 1-3 years-up sharply from last year's expectations.

Agentic AI is on the radar too, with 51 percent expecting it to change operations and workforce efficiency. Yet barriers are real: ethics (55 percent), fragmented data (49 percent), and regulatory complexity (47 percent). Cyber risk tops the list, with fraud (64 percent), identity and data privacy risks (59 percent), and cyber-attacks (51 percent) leading concerns.

• Data first: consolidate critical operational and market data into governed platforms; reduce fragmentation before scaling models.
• Security by design: apply zero-trust principles, identity controls, model access policies, and incident playbooks for AI-enabled workflows.
• Accountability: stand up an executive AI governance forum with clear guardrails, testing standards, and audit trails.
• Value focus: prioritize 3-5 use cases with clear P&L impact (e.g., predictive maintenance, grid optimization, trading risk analytics).

For an executive-level view on AI strategy, governance, and ROI, see AI for Executives & Strategy.

Talent: reskill early, retain hard-to-replace expertise

The skills gap is widening, especially across engineering roles in oil and gas, mining, and metals. CEOs are responding: 40 percent are reskilling and upskilling roles impacted by AI, 31 percent are tailoring training to reduce generational gaps, yet only 18 percent offer AI education across the whole company.

Retention is the priority. 72 percent are focused on keeping and retraining high-potential talent. The biggest blockers: skills shortages (43 percent) and pay competition from tech (22 percent).

• Run a skills inventory tied to AI use cases; fund targeted upskilling sprints for operators, engineers, and analysts.
• Create T-shaped roles that blend domain expertise with data and software skills; formalize internal mobility paths.
• Shift incentives from job titles to outcomes: reliability gains, cost per MWh, unplanned downtime, emissions intensity.
• Buy-build-borrow mix: selective hiring, vendor partnerships, and contractor benches to cover near-term gaps.

Need a structured path for leadership teams? Explore the AI Learning Path for CEOs.

Sustainability: AI as an accelerator-governance must catch up

82 percent of CEOs believe AI can help reduce emissions and optimize energy use, and 74 percent see it improving climate risk analytics. Confidence in hitting 2030 net-zero goals stands at 62 percent-but only 38 percent fully integrate ESG into capital decisions, and just 26 percent feel very confident in ESG governance systems.

Data maturity is the unlock. 79 percent support using AI to improve sustainability data and disclosures, from grid monitoring to process-level energy management.

• Tie ESG targets to capex approval and OPEX plans; set clear hurdle rates that include carbon and reliability impacts.
• Operationalize AI for load forecasting, grid balancing, and process optimization; measure with energy intensity and uptime KPIs.
• Build trust: document data lineage, model assumptions, and audit trails for sustainability reporting.
• Scenario-test transition risks and physical risks; link insights to portfolio strategy, hedging, and insurance.

Regional focus: Thailand moves from ambition to execution

Thailand's energy sector is shifting from long-term ambition to delivery. CEOs are translating policy updates, faster renewable procurement, and regional interconnections into investable plans.

Priorities include grid resilience, large-scale storage, and preparing for SMRs, hydrogen, and carbon capture. AI and digital systems are being applied to predictive maintenance, renewable forecasting, and energy trading optimization, supported by investment in smart grids and interoperability standards. The talent agenda-reskilling and upskilling to run these systems-remains central.

What leaders should do in the next 90 days

• Select 3-5 AI use cases with quantified value (reliability, cost, emissions). Assign owners, budgets, and target KPIs.
• Allocate 10-20 percent of next-year spend to AI with a 60/40 split: data and security foundations vs. production pilots.
• Stand up AI governance with clear guardrails: privacy, model testing, change control, and vendor risk standards.
• Launch a reskilling cohort for high-impact roles; pair training with on-the-job application and outcome metrics.
• Link every AI project to emissions and energy efficiency targets; instrument reporting from day one.

Metrics to track over the next 12 months

• AI ROI window: percent of AI projects delivering within 1-3 years.
• Data readiness: reduction in critical data silos and time to model deployment.
• Security posture: mean time to detect/respond for AI-related incidents; third-party risk coverage.
• Talent: percent of workforce AI-trained in priority roles; internal mobility rate for upskilled staff.
• Sustainability: energy intensity per unit output; Scope 1/2 reductions tied to AI-enabled operations.

The throughline is discipline. Fund what works, fix the data and security layers early, train your best people, and connect AI to measurable reliability, cost, and emissions outcomes. That's how ENRC leaders turn optimism into durable performance.