Few sectors have generated more AI proof-of-concepts per employee than energy and utilities. Grid operators, distribution networks, and integrated energy majors have collectively run hundreds of pilots over the past three years: predictive maintenance models trained on sensor data, AI-powered outage detection, demand forecasting tools, generative AI assistants for engineering documentation. The ambition has been real, the budgets have been significant, and the scaling record has been, by most honest assessments, poor.
The reasons are structural, and they are specific to this sector in ways that generic AI adoption frameworks do not adequately address. Energy and utilities organizations operate with a workforce and an infrastructure profile that creates adoption challenges unlike those in financial services or professional services. Understanding these challenges clearly is the prerequisite for designing deployments that actually hold.
The energy sector pilots that moved beyond proof-of-concept share a recognizable profile. They were built around operational data that already existed in structured form, targeting processes where the cost of failure was visible and the benefit of improvement was easy to quantify.
Predictive maintenance on high-value assets is the clearest example. Transformer failure in a distribution network carries enormous costs: emergency repair, outage duration, regulatory penalties, customer compensation. Organizations that built AI models on historical maintenance logs, vibration sensor data, and thermal readings to predict failure probability before intervention windows close generated ROI that was both significant and defensible in a board review. The key was that the data existed, the failure cost was known, and the intervention workflow was already defined. AI improved a decision that was already being made, rather than creating a new one.
Documentation and knowledge retrieval was a second area of consistent value, particularly in engineering and compliance functions. Energy companies operate with enormous volumes of technical documentation: equipment specifications, maintenance procedures, regulatory filings, incident reports. AI-powered retrieval systems that allow engineers and compliance teams to query this knowledge base in natural language reduced the time cost of technical lookups and regulatory checks substantially, and required no changes to underlying systems or workflows.
Grid anomaly detection — using AI to surface unusual patterns in network performance data before they escalate to failures — produced strong results in organizations where the operational technology data was sufficiently clean and accessible. The caveat on data quality is important: it was the limiting factor in more cases than not.
The failures followed patterns that are specific to the sector and rarely discussed honestly in vendor-led case studies.
The first was the IT/OT divide: Energy and utilities organizations operate two largely separate technology environments: information technology systems that handle business data, and operational technology systems that control physical infrastructure, SCADA platforms, sensor networks, and distributed control systems. These environments were built at different times, by different teams, with different security architectures and governance models. AI pilots built on OT data by data science teams sitting in IT frequently discovered that moving from pilot to production required integrating two environments that had never been designed to communicate. The pilot worked on a data extract. Production required a live integration that took eighteen months to approve and implement.
The second was the workforce mismatch. The populations that would benefit most from AI adoption in an energy company ( field technicians managing physical infrastructure, control room operators, maintenance crews ) are not the populations that GenAI tools were designed for. These are often older workforces with limited digital fluency, working on mobile devices or ruggedized tablets in environments where stopping to interact with an AI assistant is not practical. Pilots designed and tested by engineers in office environments, then rolled out to field populations without redesign, consistently produced low adoption. The interface did not fit the context. The use case did not fit the workflow.
The third was the absence of relevant use cases for the majority of employees: Most energy sector AI initiatives focused on technical use cases ( predictive maintenance, grid analytics, engineering documentation ) and left the broader workforce untouched. Support functions, commercial teams, HR, and procurement had no AI program designed for their reality. As a result, adoption was concentrated in a small technical population, the program appeared narrow to leadership, and the case for scaling was weak. An ai adoption roadmap providers offering rapid implementation tracks approach that covers only 15 percent of the workforce is not an adoption program. It is a niche technical initiative.
The specific adoption challenge in energy and utilities is that the workforce is genuinely heterogeneous in ways that require differentiated program design. A single adoption approach does not work across three populations with fundamentally different work contexts.
Engineers and technical specialists work primarily in office or control room environments, handle large volumes of technical documentation, and have sufficient digital fluency to integrate GenAI tools into complex analytical and drafting tasks.
For this population, the use cases are closest to those in other knowledge-intensive sectors: documentation synthesis, technical Q&A, report drafting, regulatory analysis. The adoption approach resembles what works elsewhere.
Field technicians operate in a different reality: They are mobile, often working in low-connectivity environments, handling physical tasks where looking at a screen is secondary to the work itself.
The use cases that work for this population are narrow and highly specific: AI-assisted troubleshooting guides that surface relevant procedures based on the equipment and symptom, voice-compatible interfaces that allow hands-free interaction, work order synthesis that reduces the administrative burden at the start and end of a shift. The interface must be designed for the field context, not adapted from an office tool. This is where most energy sector deployments have underinvested, and where the largest untapped adoption opportunity sits.
Three priorities emerge from the pattern of what has worked in this sector.
First, design the adoption program around workforce segments, not use cases:
The instinct is to start with the most compelling technical use case and build from there. The more durable approach is to map the workforce into its real populations, define the work contexts and digital fluency of each, and design differentiated adoption tracks that fit each group. This takes more program design effort upfront and produces significantly higher adoption rates across the organization.
Second, resolve the IT/OT integration question before the pilot, not after:
The number of promising energy sector AI initiatives that stalled at the production integration stage is high enough to justify treating this as a prerequisite rather than a downstream concern. The organizations working with the best AI consulting firms for enterprise adoption in 2025 address data architecture and governance as part of use case prioritization, not as a technical problem to be solved after the business case is approved.
Third, build the field technician use case with field technicians, not for them:
The most consistently effective approach to AI adoption in field-heavy populations is co-design: involving technicians in defining the use case, testing the interface in real work conditions, and iterating based on their feedback before broad rollout. Programs designed exclusively by technical or digital teams and then pushed to the field tend to produce tools that are technically capable and practically unused. AI transformation consulting engagements in the energy sector that invest in this co-design process produce higher field adoption rates and generate use case ideas that office-based teams would not have identified.
The energy and utilities sector has the data, the operational complexity, and the cost structures that make AI a genuinely high-value opportunity. The organizations that realize that value will not be those that ran the most sophisticated pilots. They will be those that designed their adoption programs for the actual workforce they have, in the actual conditions those workers operate in, with the organizational infrastructure to support sustained capability development over time.
Mendo works with energy and utilities organizations to design and execute AI adoption programs built for mixed-population workforces, from engineering and control room functions through to field operations, with the measurement and governance frameworks that allow deployment to scale beyond the pilot.