Buyer glossary
Definitions that keep product claims, safety, enterprise operations, and market evidence in the same frame.
How to use it
These are working definitions for enterprise research, not legal, professional, or educational advice. Follow the linked category and market guides for context.
A system that helps a distribution operator monitor, analyse, and coordinate network operations, often combining outage, switching, distributed-energy, and field information.
Why it matters: AI added to an ADMS remains bounded by network models, operating procedures, safety rules, permissions, and control-room accountability.
A set of data, engineering, maintenance, and workflow practices used to understand asset health, risk, performance, and intervention priority.
Why it matters: A predictive score is useful only when asset identity, failure modes, work processes, inspection evidence, and responsible engineers are connected.
A change in electricity consumption in response to a price, reliability, or operational signal, usually within an agreed customer or asset programme.
Why it matters: Buyers need to test consent, customer impact, device control, opt-out, measurement, and the consequences of a wrong or late signal.
A smaller generation, storage, controllable load, or flexible asset connected near customers or within a distribution network.
Why it matters: DER orchestration depends on device telemetry, interoperability, customer permissions, network constraints, and safe fallback.
Software and operating processes used to monitor, forecast, optimise, and control energy resources or facilities.
Why it matters: An energy-management AI feature must be assessed against the authority it has, the constraints it enforces, and the operator who can override it.
The range of plausible outcomes around a forecast, including uncertainty from data, weather, model assumptions, rare events, and changing operating regimes.
Why it matters: A point forecast without confidence, scenarios, or tail-event testing can create false precision in planning, trading, and dispatch.
The authority, information, time, training, and workflow needed for a person to review, correct, override, escalate, or stop an AI-supported result.
Why it matters: A human-in-the-loop label is not enough if an operator cannot understand the evidence or safely intervene.
The hardware, software, communications, and processes used to monitor or control physical industrial operations.
Why it matters: AI connected to an ICS needs stronger identity, segmentation, monitoring, fallback, and change controls than a general office assistant.
A change in the relationship between inputs, operating conditions, and outcomes that makes prior model performance less reliable.
Why it matters: Weather, assets, markets, customers, regulations, and technology change; buyers need monitoring and a revalidation trigger.
The connection of operational technology environments with information technology, cloud, enterprise data, or security systems.
Why it matters: Convergence can improve visibility and analytics but changes the threat boundary, access model, recovery plan, and accountability.
A coordinated portfolio of distributed generation, storage, flexible demand, and other assets operated as a combined resource.
Why it matters: The commercial and reliability case depends on accurate telemetry, customer consent, dispatch constraints, market rules, and fair measurement.
AI used in or around services whose failure could affect essential energy, safety, economic, or public functions.
Why it matters: The risk assessment must cover the physical consequence, not only model accuracy or software security.
Why read
The short answer: shared definitions help a buyer separate a product's stated purpose from evidence, safety, market, and procurement questions.
Next step: choose a category, open its buyer questions, and ask the vendor to define any term that still means something different in practice.
A practical next step
Enterprise AI Group describes a 6–8 week path for a defined business process, with governance, policy management, enterprise security, and Microsoft-tenant deployment considered from the start.
Enterprise AI Group describes a 6–8 week path for a defined workflow. Timing and cost depend on scope, users, integrations, security, governance, data, operational risk, and support. These research pages are published by Enterprise AI Group. The implementation links describe optional Enterprise AI Group services; they are not product endorsements or a replacement for local energy, safety, cyber, privacy, or procurement diligence.
Explore workflow automationDo not include operational technology details, customer records, vulnerability information, credentials, commercial secrets, or other sensitive data in an enquiry.
Keep the useful part
Send the asset, grid, market, customer, safety, cyber, or engineering workflow you are assessing. We will use it to shape the next practical buyer brief.
Useful detail: include the market, workflow, or category behind a energy AI term or category.
Please do not send operational technology details, customer records, vulnerability information, credentials, commercial secrets, or other sensitive data.