A shared assurance model for enterprise AI, designed to give leaders and delivery teams the release evidence, decision rights and operating controls required to get high-consequence AI workloads live.
Initial enterprise delivery scope across customer service, document processing and decision-support workflows.
Release evaluations run when a relevant model, prompt, retrieval, tool or policy change is proposed.
Each blocking failure is connected to a named decision owner, evidence and resolution path.
One portfolio view of model, retrieval, tool and handover assurance, linked to the underlying runs.
Metrics describe the delivery scope and assurance model on this page.
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A delivery package that turns assurance intent into a release-ready operating model.
Defines the task, users, consequence, failure classes, evidence sources and release authority for each priority workload.
Establishes versioned scenarios, expected behaviours and separate checks for model, retrieval, tool and handover behaviour.
Captures the configuration, evidence, material failures, accountable owner and residual-risk decision for every release.
Connects relevant changes to the stable release suite before promotion.
Gives leaders and delivery teams a shared view of readiness, unresolved risk and the evidence behind it.
Prioritise workloads, decision rights, existing evidence and the risk that matters first.
Implement the evaluation estate, release gates and decision record in the delivery workflow.
Use release evidence and reviewed production failures to strengthen the assurance rhythm over time.
Enterprise AI programmes often test each workload with its own checklist, data and owner. That can work during a pilot, but it weakens once several teams are changing models, retrieval sources, tools and customer-facing workflows at different speeds. The organisation needs a shared way to describe what was tested and who accepts a release, without forcing every workload into the same threshold.
A summarisation assistant, a customer-facing agent and a workflow that can change a record carry different consequences when they fail. The useful assurance model therefore keeps the operating language consistent whilst making the evidence, acceptance bar and decision rights specific to the workload.
The bottleneck is not the number of tests. It is the absence of an assurance model that can connect workload-specific evidence to an accountable release decision.
Designed, Built and Managed by Humint Labs. The delivery connects workload-specific evaluation, release evidence and accountable decision-making so enterprise teams can move from pilots to operating AI services with confidence.
The capability combines workload profiles, a versioned evaluation estate, change-triggered regression gates, an evidence and decision record, and post-release learning. It supports a shared assurance rhythm without treating every workload, scenario or failure as equally consequential.
Defines the task, users, consequence, named failure classes, evidence sources and approval threshold for one AI workload.
Maintains versioned cases, expected behaviours and separate checks for model, retrieval, tool and handover behaviour.
Records the configuration, evaluated scenarios, thresholds, material failures, owner and any accepted residual risk.
Runs the stable release suite when a relevant model, prompt, retrieval, policy, tool or handover change is proposed.
Shows readiness by workload and failure class whilst retaining the underlying run for engineering, operations and governance review.
Promotes reviewed production failures into the evaluation estate so future release decisions use current evidence.
A single score is attractive and usually misleading. Useful assurance keeps enough detail for a delivery team to diagnose a failure, and enough structure for leaders to see which workload is changing, what evidence exists and who owns the decision.
Industry: Technology
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Executive AI guide series
Start with Design Thinking and choose the right combination of generative AI, workflows, bounded agents and agentic services. The four-pillar framework connects practical service outcomes, authority, evidence and human oversight.
Read part 1: Exec Guide to Generative AI, LLMs & Agentic AIAn executive guide from Design Thinking to accountable enterprise AI delivery: departmental use cases, investment ranges, service design, decision rights, architecture, pilot selection, rollout, adoption and operating measures.
Read part 2: Executive Guide to Enterprise AI Implementation