Industry
Power, gas and water utilities and energy retailers: billing, outage communication, field work and grid operations.
14 organizations in 6 countries run these use cases in public. 86% of those 14 deployments disclose a measurable outcome.
A voice and messaging agent that contacts customers in early arrears and answers their inbound calls, takes payments and sets up payment arrangements within preapproved rules, and recognises signs of hardship or vulnerability so those customers go straight to a trained person.
An AI agent for energy and water customers that explains bills and tariffs, takes meter readings, sets up or changes payments, and handles move in and move out (final reads, closing one account and opening the next), across phone, messaging, email and the app, while anyone in payment difficulty, in a vulnerable situation or with a complaint is handed to a person.
AI that turns the flood of readings from smart electricity, gas and water meters into usable information: it monitors meter and network health at scale, estimates which appliances drive a household's usage from the meter signal alone, flags unusual consumption, and targets efficiency and electrification programmes at the customers who will benefit most, instead of a utility treating every meter and every customer the same way.
AI that captures supplier invoices from any format, extracts header and line data, matches them to purchase orders and goods receipts, proposes tax and cost centre coding, flags duplicates and suspected fraud, and routes them for approval and posting, leaving only exceptions to accounts payable staff.
Machine learning that learns the normal behaviour of industrial and energy equipment from sensor and process data, flags early signs of degradation weeks or months before a failure, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.
Automated quality assurance that transcribes and scores every customer interaction, voice and chat, against the organization's own rubric, checking required disclosures and script adherence, flagging conduct and mis selling risk, and surfacing coaching opportunities, instead of the small sample a human QA team can review.
AI that turns what a customer sends, such as a product list, a drawing, a roof photo or a request for quotation, into a draft quote: it reads the input, matches items to the catalog, calculates quantities and applies the organization's price rules, and hands the draft to a seller or estimator who checks and sends it.
AI that analyses satellite, aerial or lidar imagery of the land along power lines to estimate where and how fast vegetation will grow into the lines or fall onto them, and turns that into a risk based trimming and hazard tree removal plan, replacing fixed trimming cycles and manual patrols.
Free PDF playbook
8 use cases with deployments, value, playbooks and governance, in one document for your team.
Computed from the 14 public deployments behind these use cases at energy and utilities organizations. Numbers update with every checked record.
Aydem Energy, British Gas, Consolidated Edison (Con Edison), Dubai Electricity and Water Authority, Duke Energy, EDF, Enpal, Entergy, National Grid, Octopus Energy, Pacific Gas and Electric Company, Shell, Southern California Gas Company (SoCalGas), Veolia.
12 public deployments at 12 organizations outside energy and utilities run these use cases too. They are not counted in the numbers above; each use case page shows them.
