An Overview of Generative AI for the Electrical Industry

The Electrical Association's Annual Industry Summit. Mystic Lake Casino, Prior Lake, Minnesota. January 2026. Two days of sessions for electrical contractors and business leaders covering paid family medical leave, 2026 code updates, government contract bidding, and, for the first time, generative AI.

The audience was not technologists. They were business owners, estimators, project managers, and field supervisors running electrical contracting companies across Minnesota. The question they came with was practical: what does this technology actually mean for my business?

The first section of the talk established urgency without hype. The numbers are striking: 800 million people using ChatGPT weekly, adoption outpacing PCs and the internet at the same stage, and AI model intelligence jumping from an IQ equivalent of roughly 30 to over 140 in under two years. But the data point that landed hardest with this audience was energy: global data center electricity demand is projected to double by 2030 to 945 TWh.

Client data grounded the numbers further. After four months of regular use at one client organization, over 90 percent of users reported saving time every week. Nearly 40 percent reported saving five or more hours. One in five reported saving ten or more hours weekly.

The second section demystified the technology using a human-brain analogy that made the concepts accessible. General Pre-Trained Transformers are the brain. Embeddings are long-term memories. Training is education. The context window is short-term memory. Alignment is the model's sense of values. Then the same framework mapped directly to limitations: the brain hallucinates, long-term memories have cutoff dates, education has gaps in local knowledge, attention span is finite, and the heart carries OEM bias.

From there, the talk bridged to workflow. A detailed matrix mapped AI use cases across the full electrical project lifecycle: bidding and preconstruction (takeoff support, scope checklists, draft assumptions), build and execution (RFIs, daily logs from dictated notes, change order descriptions), closeout and turnover (punch lists, O&M documentation, lessons learned), and maintenance and service (visit summaries, follow-up recommendations, standardized templates). The point was not theoretical. These were tasks the audience recognized from their own weekly calendars.

Local examples made it concrete. One Minnesota company reported a 70 percent reduction in takeoff time on a large project. Another contractor freed about 10 hours per week previously spent shuffling dispatch schedules. A third company cut end-of-day field reporting from an hour of typing to about five minutes of dictation.

The talk included two live demonstrations designed to show, not tell. The first tackled field safety: generating a Job Hazard Analysis for a realistic electrical scenario (new 120V branch circuit in an occupied office with suspected asbestos). The demonstration walked through a three-step process: ask the model what information it needs, provide the specifics, then validate the output. The audience watched a comprehensive, site-specific JHA materialize in minutes.

The second demonstration addressed administrative work: turning messy service call notes into two polished outputs (a professional customer email and an internal job log) using a reusable prompt template. The scenario was familiar to everyone in the room: you just finished a three-hour service call, you have rough notes, and you need documentation before you can move on. The demonstration showed how to build the prompt once and reuse it across every service call going forward.

Both demos reinforced the same principles: talk to it like a coworker, treat the first output as a draft, audit responses, and use your voice for speed.

The final section shifted from "what AI can do" to "what you should do Monday morning." It began with the future state of their customers. In two to three years, customers will record every call and site visit with AI summarization, paste estimates into ChatGPT to find gaps and ambiguities, compare proposals side-by-side in seconds, and expect response times measured in hours rather than days. The competitive landscape is not just about price anymore. It is about clarity, speed, and documentation quality.

The practical playbook was deliberately simple. Start with business-tier AI assistants (ChatGPT Business, Claude for Teams, or Gemini Enterprise) at $20 to $30 per user per month with no contract and no integration needed. Pick one or two real workflows you already do every week. Assign one person to drive the pilot. Define success in measurable terms: time saved, fewer edits, faster turnaround, better consistency. Run a two to three week test, tune the prompts and templates, then broaden to the next workflow. Pilot, learn, expand.