AI Energy M&A Due Diligence

The Problem

“Slow, fragmented energy M&A due diligence risk”

Organizations face these key challenges:

Unstructured, inconsistent data rooms: thousands of PDFs, emails, and spreadsheets with missing metadata, making it hard to find critical clauses, permit conditions, and cost obligations

Cross-discipline blind spots: engineering, commercial, regulatory, and ESG findings are siloed, causing missed interactions (e.g., curtailment impacts on covenant compliance, methane rules affecting netbacks)

Compressed timelines and subjective judgment: manual reviews rely on a few SMEs, increasing error rates and inconsistency in risk materiality, especially across multiple assets and jurisdictions

Impact When Solved

Automated extraction and clause-level summaries for PPAs, JOAs, leases, transportation, and service contracts with evidence links and change-of-control/assignment alertsAsset-level risk scoring and anomaly detection across production, downtime, OPEX, emissions, and reliability/availability metrics to validate management forecasts and identify outliersScenario-based valuation adjustments incorporating curtailment risk, basis/price exposure, regulatory and carbon cost sensitivities, and decommissioning/remediation liabilities

Real-World Use Cases

AI Applications in the Energy Sector (from multiresearchjournal.com article)

Think of this as giving power plants and grids a smart brain that constantly watches operations, predicts future demand and equipment issues, and suggests optimal ways to run everything more safely and cheaply.

time-seriesEmerging Standard

9.0

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5