Design how your organisation will work with AI — before agents are deployed at scale.
AI tools and agents do not automatically produce better outcomes. The operating model — roles, governance, workflows, KPIs, and human-AI division of labour — determines whether AI creates lasting value or remains a collection of isolated experiments.
The model that makes pilots scale.
Most organisations run successful AI pilots — then struggle to scale them. The reason is rarely the technology. It is the absence of a defined operating model: clear ownership, governance, KPI frameworks, and a human-AI workflow design that the whole organisation understands. AI Operating Model Design closes that gap before it becomes expensive.
Traditional model vs. AI-native operating model.
Business Operating View
The same transformation — seen from two perspectives.
Operating Model Redesign
How the business will function differently — processes, roles, and decision flows reshaped for AI-native operation.
Agent Workflow Architecture
The orchestration layer that executes redesigned processes — agent types, trigger logic, and handoff protocols.
Decision Authority & Governance
Who owns AI-driven decisions, what escalation paths exist, and how accountability is distributed across functions.
Human-in-the-Loop Control Points
Where human review, approval, and override capabilities are embedded within agent workflows.
Business Value & ROI Targets
The measurable outcomes the transformation must deliver — cost reduction, revenue impact, or efficiency gains.
Automation Scope & Measurable Outputs
The specific tasks automated, the output metrics captured, and how agent performance maps to business targets.
Data Strategy & Ownership
Which data assets underpin AI capability, who governs them, and what quality and access standards apply.
Data Layer & Integration Dependencies
The technical connections between agent systems and enterprise data sources — APIs, connectors, and sync protocols.
Risk Framework & Compliance
How AI-related risks are classified, monitored, and reported within existing regulatory and governance structures.
Governance Controls & Audit Trails
The technical guardrails, logging infrastructure, and audit records that make AI decisions traceable and explainable.
Stakeholder Adoption & Change Plan
How affected teams are prepared, trained, and supported through the transition to AI-augmented ways of working.
System Observability & Feedback Loops
Monitoring, alerting, and continuous improvement mechanisms that keep agent performance aligned with business expectations.
Five operating model design areas.
AI Organisation Model
Define AI ownership structure — centralised, federated, or hybrid. Identify the AI Transformation Lead, business owner roles, IT/data responsibilities, and governance committee membership.
Human + AI Workflow Design
Map which tasks are AI-supported, which decisions remain with humans, where human-in-the-loop approval is required, and how escalation flows when AI outputs are uncertain.
AI Governance Operating Model
Build the governance layer: AI committee structure, risk classification framework, policy architecture, logging and audit requirements, and model provider evaluation criteria.
AI KPI Framework
Define adoption KPIs, business impact KPIs, quality KPIs, risk KPIs, and operational KPIs — so progress is measurable from day one and scale decisions are evidence-based.
AI Adoption Model
Design the AI Champion Program, department-level playbooks, training plans, and usage guidelines that turn technology deployment into genuine organisational capability.
Assess → Design → Align → Operationalise.
Assess
Analyse current organisation structure, AI ownership, and existing governance gaps.
Operating Model Assessment
Design
Design roles, workflows, governance mechanisms, and KPI framework for AI operations.
Operating Model Draft
Align
Align leadership and departments on the model before it is operationalised.
Alignment Notes & Decisions
Operationalise
Produce the implementation plan, champion program structure, and measurement baseline.
90-Day Operating Plan
8 operating model documents.
- AI Operating Model Document — ownership, structure, and governance design
- AI Roles & Responsibilities Matrix — who owns what at every level
- Human + AI Workflow Model — task allocation and approval flow design
- AI Governance Operating Model — policy, risk, audit, and compliance framework
- AI KPI Framework — adoption, quality, impact, and risk measurement
- AI Champion Program Draft — internal capability building roadmap
- AI Center of Excellence Recommendation — organisational design for scale
- 90-Day Operating Model Roadmap — prioritised implementation plan
Two engagement formats.
3–5 weeks
Single business unit or organisation with clear scope and available stakeholders.
6–8 weeks
Multi-department, holding structure, or complex governance requirements.
Build the operating model before you need it.
The best time to design how your organisation works with AI is before agents are deployed across multiple departments. Start with a Discovery Session.