Energy projects are ballooning in size and regulatory complexity, so energy & utilities teams increasingly rely on AI contract review software to keep transactions moving.
The energy sector's contract landscape has transformed dramatically. With 18.5 GWAC of new utility-scale PV capacity coming online in 2023 alone, bringing cumulative capacity to more than 80 GWAC across 47 states, the volume and complexity of agreements have reached unprecedented levels. Traditional contract management methods are buckling under this pressure, as efficient contract management remains manual and error-prone, leading to delays, financial risks, and compliance challenges.
This shift isn't just about volume. Energy contracts now span multiple jurisdictions, involve intricate regulatory requirements, and require sophisticated risk assessment across power purchase agreements, EPC contracts, and operations agreements. 42% of organizations are currently implementing AI in their contracting process, up from 30% just a year ago. For energy companies managing multi-year project timelines and complex stakeholder networks, this adoption has become essential rather than optional.
Generative AI is transforming procurement and contract management by delivering actionable insights for improved efficiency, with 70% of CPOs prioritizing digital transformation in their strategic plans. The technology addresses a critical gap: converting unstructured contract text into structured, actionable data that legal teams can use to accelerate deal velocity while maintaining compliance standards.
Without standardized playbooks, energy companies need AI tools that can adapt to diverse contract types while maintaining security and accuracy. The stakes are particularly high given recent security trends: 21 firms filed data breach reports in the first half of 2024 alone, compared to 28 firms throughout the entirety of 2023.
Key capabilities to prioritize include:
Clause Extraction Flexibility: Look for platforms offering customizable extraction options. Leading solutions provide selection from 75 clause types and 55 fact types, allowing teams to configure AI data-extraction on a per-field basis without rigid templates.
Risk Scoring Intelligence: Generic AI must identify non-standard terms across diverse agreement types. The best platforms assign risk grades based on configurable parameters, adapting to your organization's risk tolerance rather than forcing predetermined frameworks.
Security Certifications: Clients increasingly pressure firms to pursue SOC 2 or ISO 27001 certifications. These frameworks help identify gaps in data security programs that could create access points for cyber criminals.
Accuracy Benchmarks: While playbook-specific tools might promise higher precision, generic platforms achieving 90-95% accuracy rates provide sufficient reliability for initial review phases, especially when combined with human oversight.
Integration Capabilities: Energy companies typically manage contracts across multiple systems. Prioritize solutions that integrate seamlessly with existing workflows rather than requiring wholesale platform migrations.
The market offers several robust options for energy companies seeking generic AI contract review capabilities. Each platform brings unique strengths, from specialized energy features to enterprise-grade security.
"ThoughtRiver's platform reviewed complex supply agreements in less than 3 minutes with >90% accuracy. The same contract took qualified lawyers 4 hours to review at an average accuracy rate of 86%." This speed advantage proves crucial for energy deals with tight timelines.
Lexible® reads and understands contracts, flagging key risks and obligations before you've finished your coffee. The platform leverages training on "Trained on 4,150+ lawyer-built concepts," ensuring comprehensive coverage across energy-specific provisions. Integration options include email upload, drag-and-drop, and Word add-ins, allowing teams to work within familiar tools.
DiliTrust brings specialized energy sector expertise, centralizing PPAs including annexes and price schedules in one secure repository. The platform uses AI to flag deviations from standard terms like withdrawal periods, indexation formulas, and liability caps.
For EPC agreements, DiliTrust manages the full lifecycle while tracking contractual KPIs such as uptime guarantees and HSE requirements in real time. The platform achieves "10x Faster Contract Processing with automation and templates" while maintaining "100% Documentation Traceability for audit readiness."
AI-powered contract analysis fits squarely in the post-signature phase of the CLM lifecycle, where focus shifts from drafting to managing rights, obligations, and risks. LawVu Lens transforms static contract data into practical, business-ready intelligence.
The platform addresses a critical gap where "Only 37 percent feel adequately resourced to meet rising demand, with inefficiencies costing millions each year in wasted time, outsourcing, lost revenue, and reputational damage." LawVu maintains ISO27001, SOC2 and GDPR compliance, ensuring data protection meets energy sector requirements. Currently in early access, the platform represents the next evolution in post-execution contract analytics.
CounselLink+ offers AI-powered tools that extract key facts and clauses from contracts, saving substantial time during the contracting process. Users can opt into AI data-extraction on a per-field basis, selecting from 75 clause types and 55 fact types.
The platform eliminates manual data entry of routine information, reducing human error while speeding up the contracting process. "CounselLink customers achieve an ROI of 13x their CounselLink spend and 100% of surveyed organizations experienced an increase in productivity compared to before they implemented CounselLink."
Zuva's provision extraction finds clauses like term, termination, indemnification, and default provisions across diverse contract types. The platform includes clustering capabilities for grouping related information, defined term detection, and document comparison showing differences between agreements.
Contract risk scoring assigns grades based on predetermined risk parameters, while signature detection helps determine execution status. The platform's optical character recognition converts images into text, enabling analysis of scanned documents common in legacy energy agreements.
"Legal Twin® Contract Insights is more than just a tool - it 's a legal co-pilot for effortless contract analysis and intelligent risk assessment." The solution adapts to all contract types, whether for M&A, supplier contracts, NDAs, or general business agreements.
STP.one, headquartered in Germany, "STP employs over 400 people across 10 locations and provides more than 8,000 clients with software and relevant services for their daily operations." This scale demonstrates the platform's enterprise readiness for large energy organizations managing global operations.
Implementing generic AI for energy contracts presents unique challenges that teams must navigate carefully. Data security concerns top the list, particularly in defense-adjacent energy projects with stringent data security requirements. Recent statistics paint a sobering picture: "9 out of 10 of companies have former employees who accessed assets stored in SaaS applications after they left the company," while "64% of active third-party OAuth apps are over-permissioned."
Budget constraints often derail AI initiatives before they start. Organizations cite difficulty obtaining budget as a primary adoption barrier, despite clear ROI potential. This challenge intensifies when teams lack concrete metrics to justify investment in generic AI tools versus specialized solutions.
Trust calibration represents another critical hurdle. Research reveals that "Users prefer seeking evidence over explanations, especially from shared knowledge bases." "Existing commercial AI contract review tools uniformly present AI's recommendations in a static manner, rather than actively engaging with users and providing feedback." This disconnect between AI capabilities and user expectations can undermine adoption, particularly when attorneys face surges in contract volume without corresponding confidence in AI accuracy.
Successful AI adoption in energy contract review requires a phased approach that balances innovation with risk management. "By reducing complexity and aiding strategic decisions, generative AI (GenAI) is revolutionizing the utility industry," but implementation must be deliberate.
Phase 1: Pilot Selection (Months 1-2)
Start with low-risk, high-volume contracts like NDAs or standard service agreements. "Iberdrola established a generative AI Centre of Excellence with AWS to develop more than 100 generative AI applications," beginning with "Iberdrola is building a generative AI application that will help its legal team quickly find and ask questions about corporate contracts."
Phase 2: Process Integration (Months 3-4)
Focus on integrating AI tools with existing contract repositories and workflows, ensuring data flows seamlessly between systems. "Naturgy prevé que el 99% de las facturas serán emitidas sin intervención manual," demonstrating the power of process automation.
Phase 3: Scaling & Optimization (Months 5-6)
HCLTech's Rate Case Normalizer shows what's possible at scale: "This reduces the typical rate case process timeline from years to just a few months, enabling utility companies to respond swiftly to regulatory demands." Expand AI usage to complex agreements like PPAs and EPC contracts, leveraging learnings from initial phases.
Measurement Framework:
"Utilities can optimize performance with GenAI, enhancing proposal development, compliance and discovery processes during this time of energy transition." Success depends on maintaining realistic expectations while building organizational confidence through demonstrated wins.
The energy sector's contract complexity isn't decreasing. With massive project pipelines and evolving regulatory landscapes, generic AI contract review software has shifted from nice-to-have to essential infrastructure. The platforms reviewed here demonstrate that achieving 90%+ accuracy without rigid playbooks is now table stakes.
For energy and utilities teams evaluating options, remember that perfect shouldn't be the enemy of good. While specialized tools might offer marginally higher accuracy for specific contract types, generic AI platforms provide the flexibility to handle diverse agreements across your portfolio. The key is selecting solutions that balance adaptability with security, speed with accuracy, and automation with human oversight.
Dioptra stands out in this landscape by offering customizable AI contract review that adapts to your specific needs without requiring predefined playbooks. "A review that would have taken me 2 hours of painful intellectual labor was done in 30 minutes!" according to Wilson Sonsini. "Dioptra flags non-market provisions so we can quickly situate ourselves and focus on what matters," reports CyberOne.
Whether you're managing power purchase agreements, EPC contracts, or complex multi-party arrangements, the right AI platform can transform your contract review process from bottleneck to competitive advantage. The technology exists; the question is how quickly you'll adopt it to keep pace with the energy transition's accelerating demands.
Prioritize flexible clause and fact extraction, configurable risk scoring, and strong security (SOC 2 or ISO 27001). Look for 90–95% accuracy with human oversight and seamless integrations with your existing repositories and workflows.
Generic platforms often reach 90–95% accuracy, which is sufficient for initial reviews across PPAs, EPC, and operations agreements. Playbook-specific tools may perform slightly better on narrow use cases, but generic AI offers broader adaptability.
Use a three-phase plan: pilot low-risk, high-volume contracts in months 1–2; integrate with repositories and workflows in months 3–4; and scale to PPAs and EPCs in months 5–6. Track review-time reduction, accuracy, cycle time, outside counsel spend, and compliance metrics.
Common pitfalls include data security gaps, budget constraints, and misaligned trust in AI outputs. Mitigate by enforcing least-privilege access, choosing certified vendors, building a clear ROI case, and pairing AI with transparent evidence and review workflows.
Dioptra provides customizable extraction, risk summaries, and PromptIQ to tune accuracy without predefined playbooks, and it maintains SOC 2 Type II compliance. Client feedback in the blog highlights faster reviews and effective non-market term flagging; see resources on dioptra.ai for more details.
Configurable risk scoring flags non-standard terms such as indexation, liability caps, and uptime guarantees, while clause extraction captures obligations and deadlines. This speeds negotiations and improves compliance tracking across large portfolios.
