Build · Deploy · Assure
AI Assurance Operating Model Sprint
Design and implement the governance, monitoring, and accountability framework required for production AI.
Operating method
- 01
Build
Scope and construct the workflow
- 02
Deploy
Install it in real operations
- 03
Assure
Prove it keeps working
What the Operating Model Includes
Nine core components that together form the governance, monitoring, and accountability infrastructure for production AI.
Governance Workflow
A defined process for how AI use cases are proposed, evaluated, approved, and monitored throughout their lifecycle.
Intake Process
A structured mechanism for capturing new AI use case requests with the information required for risk assessment and prioritization.
Review Gates
Stage-gate checkpoints that ensure AI systems meet defined criteria before advancing from development to staging to production.
Risk Classification Model
A tiered framework for classifying AI use cases by risk level, determining the governance rigor applied to each.
Evaluation Criteria
Defined, measurable criteria for assessing whether an AI system is ready for production deployment.
Monitoring Cadence
A structured schedule for ongoing review of deployed AI systems including performance, drift, bias, and operational health.
Decision Rights
Clear assignment of who can approve, escalate, pause, or retire AI systems at each stage of the lifecycle.
Escalation Model
Defined protocols for how AI incidents, risks, and exceptions are escalated through the organization.
Documentation Templates
Standardized templates for AI system documentation, risk assessments, compliance evidence, and governance records.
Sprint Structure
A four-phase engagement designed to move from assessment to operational deployment within 8-16 weeks.
Discovery & Assessment
Weeks 1-3
- Stakeholder interviews and organizational mapping
- Current-state governance assessment
- AI portfolio inventory and risk tiering
- Regulatory and compliance landscape review
- Gap analysis against production requirements
Design
Weeks 4-7
- Governance workflow architecture
- Risk classification model design
- Review gate and evaluation criteria definition
- Decision rights and RACI development
- Monitoring and escalation framework design
Build
Weeks 8-12
- Documentation templates and tooling
- Process implementation and integration
- Compliance mapping and evidence frameworks
- Incident response playbook development
- Dashboard and reporting configuration
Deploy & Train
Weeks 13-16
- Team training and enablement sessions
- Pilot deployment with initial use cases
- Stakeholder alignment and executive briefing
- Operational handoff and support transition
- 30-day post-deployment review planning
Deliverables
A complete, operational AI assurance framework — implemented, not just documented.
AI Assurance Operating Model Sprint
Contact for pricing
A structured engagement to design and implement an AI assurance operating model — the governance, monitoring, and accountability framework required for production AI in regulated environments.
- 01Governance workflow
- 02Intake process
- 03Review gates
- 04Risk classification model
- 05Evaluation criteria
- 06Monitoring cadence
- 07Decision rights
- 08Escalation model
- 09Documentation templates
- 10Compliance mapping to applicable standards
- 11Incident response playbook
- 12Executive briefing and implementation roadmap
Timeline
8-16 Weeks
Duration depends on organizational complexity, number of AI use cases in scope, regulatory landscape, and existing governance maturity. Every engagement is scoped to deliver an operational framework — not a theoretical one.
Next step
Build the governance infrastructure your AI requires.
Start with a conversation about your AI deployment landscape, compliance requirements, and governance gaps. We will scope an engagement that delivers an operational assurance model.
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