Przewodniki
Podręczniki wdrażania
Ustrukturyzowane sekwencje bezpiecznego transportu sztucznej inteligencji od pilota po długoterminowe operacje.
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Pilot to production
A staged path from prototype workflow to governed production service.
- Operating owner assigned before rollout expansion.
- Release gates linked to measurable quality checks.
- Client-side handoff package complete before closeout.
- Scope workflowMap actors, systems, handoffs, and irreversible actions for one production-shaped workflow.
- Define quality gatesConvert expected behavior into measurable release checks tied to business risk.
- Deploy controlled pilotRun shadow, canary, and staged rollout with explicit escalation and observability controls.
- Operational handoffTransfer service ownership with runbooks, alerting norms, and change-management cadence.
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Retrieval hardening
Improve source quality, freshness, and citation confidence before broad rollout.
- Source freshness tracked as an SLO by source class.
- Citation confidence exposed in review and ops dashboards.
- Drift monitoring alerts operationalized before scale-up.
- Inventory sourcesBuild a source catalog with ownership, update cadence, permissions, and data criticality.
- Define freshness SLOsSet freshness targets per source class and business use case.
- Stress-test citationsProbe citation coverage on edge prompts and low-context requests.
- Monitor driftTrack retrieval drift and source regressions with escalation automation.
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Support automation rollout
Deploy ticket triage and drafting with risk controls and escalation policies.
- Queue segmentation implemented by impact and SLA risk.
- Draft acceptance and escalation rates tracked per queue.
- Support managers receive ticket packets with full context.
- Queue segmentationPartition support queues by complexity, urgency, and downstream blast radius.
- Draft + reviewDeploy automated drafts with reviewer controls and response-quality feedback loops.
- SLA escalation policiesEncode SLA breach signals and escalation trees as executable policy.
- Quality telemetryAttach operational metrics to queue health and resolution quality decisions.
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Governance by design
Embed approval and release controls directly in delivery workflow.
- Authority boundaries documented for every autonomous action class.
- Release and rollback policy treated as first-class artifacts.
- Incident response loops linked to evaluation improvements.
- Authority mappingDefine where autonomy is allowed, restricted, or blocked pending review.
- Approval packet designStandardize escalation packets so reviewers can act without reconstructing context.
- Release policiesTie model/prompt changes to release gates, approvals, and rollback plans.
- Incident loopClose incidents with postmortems that feed directly back into evaluations.
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Value realization operating model
A practical sequence for turning AI delivery into measurable economic outcomes.
- Baseline metrics are captured before implementation choices harden.
- ROI claims are tied to live operating telemetry and owner decisions.
- Scale decisions follow measured value, not demo enthusiasm.
- Baseline economicsMeasure volume, handling time, delay cost, error rate, escalation share, and labor mix before scope is finalized.
- Model rollout scenariosBuild conservative, expected, and stretch scenarios with adoption ramp and support overhead included.
- Instrument go-liveAttach production metrics to request cohorts, reviewer decisions, resolution time, and unit cost.
- Run value reviewsReview economic performance on a fixed cadence and decide whether to expand, tune, or stop.