AI Operating Model
Design and implement the operating model that lets your enterprise deliver AI at scale — with the governance, platform, FinOps and skills your regulators, board and CFO will accept.
A six-pillar framework
Every pillar is opinionated, vendor-neutral and pressure-tested in regulated enterprises.
Target operating model
Federated, centralised or hub-and-spoke. We map your organisation, current AI activity, and ambition to the model that actually fits — not the one in last quarter's analyst report.
Roles & RACI
AI product owner, ML engineer, MLOps, data steward, model risk officer, AI ethics lead. Clear accountabilities for who decides, who delivers, who reviews, and who signs.
Governance & risk
Model lifecycle gates, EU AI Act risk classification, DORA ICT-risk integration, GDPR/DPIA workflows, NIS2 sectoral controls. Auditable, evidenced, no theatre.
Platform & tooling
Shared MLOps platform vs. team-by-team. Build vs. buy for feature store, model registry, evaluation, observability. Vendor-neutral recommendations.
FinOps for AI
Showback, chargeback, GPU cost allocation, token economics for LLMs, budget guardrails. Stops cloud bills running ahead of business value.
Skills & ways of working
Hiring profiles, internal upskilling paths, partner ecosystem, agile/product operating rituals, definition of done for AI workloads.
Engagement phases
Diagnose (weeks 1–2)
Stakeholder interviews, current-state mapping of teams, platforms, governance, and active AI initiatives. Output: gap report against target ambition.
Design (weeks 3–6)
Target operating model, RACI, governance framework, platform reference architecture, FinOps model, hiring plan. Output: AI operating model blueprint.
Validate (weeks 7–8)
Pressure-test design with execs, risk, audit, security, finance and IT leadership. Output: signed-off operating model and 12-month transition roadmap.
Implement (weeks 9–12+)
Stand up the platform foundations, governance forums and first cross-functional AI product teams. Optional retainer support thereafter.
Typical use cases
- →Insurance group consolidating 14 fragmented AI teams into a federated operating model with central platform
- →Tier-1 bank introducing model risk governance ahead of EU AI Act high-risk deadlines
- →Energy utility designing FinOps controls before scaling generative AI internally
- →Healthcare network defining clinical AI governance under MDR and GDPR Article 35
- →Public-sector body building an AI operating model aligned with national AI strategy and NIS2
Platform & tooling we evaluate
We are vendor-neutral. The right answer depends on your existing platform investments, data residency needs, and team maturity.
- ·MLOps platforms — vanilla Kubeflow, MLflow, AWS SageMaker, Azure ML, Vertex AI, Databricks, Iguazio
- ·Model & data governance — Collibra, Alation, OneTrust AI Governance, Credo AI, Holistic AI
- ·Observability & evaluation — Arize, Fiddler, WhyLabs, LangSmith, Langfuse, Phoenix
- ·Feature store & registry — Feast, Tecton, Hopsworks, native cloud-vendor registries
- ·FinOps — native cloud billing, Apptio Cloudability, Vantage, CAST AI, ScaleOps for GPU efficiency
Frequently asked questions
What is an AI operating model?
An AI operating model defines how an organisation structures, governs, funds, staffs and operates its AI work end-to-end. It covers the target organisation design, role definitions and RACI, governance and risk gates, platform and tooling decisions, FinOps controls, and the skills and ways of working required to deliver AI safely at scale.
How long does AI operating model design take?
Design alone takes 6–8 weeks. Implementation of the foundations typically adds another 4–8 weeks. Most clients run design and the first wave of implementation as a single 12-week engagement, then move into a lighter advisory retainer for the remaining transition.
How much does AI operating model consulting cost?
Design-only engagements (weeks 1–8) are fixed-fee €60,000–€95,000 depending on scope and number of business units. Design plus first-wave implementation runs €120,000–€220,000. We quote a fixed price after a scoping workshop — no time-and-materials.
Federated, centralised or hub-and-spoke?
There is no universally correct answer. Centralised models work for organisations early in their AI journey or with strong centralised IT. Federated models suit mature, product-led organisations with strong domain teams. Hub-and-spoke is the most common end-state in regulated enterprises — central governance and platform, federated delivery. The diagnose phase determines which fits.
How does this fit with our existing data operating model?
AI operating models extend, not replace, data operating models. We deliberately reuse existing data governance, lineage, stewardship and quality structures wherever possible, and only add new constructs for model lifecycle, evaluation, drift, and AI-specific risk classification.
Do you cover regulated industries specifically?
Yes — financial services (DORA, EBA model risk guidance), insurance (Solvency II model governance), healthcare (MDR, GDPR Article 35), energy and utilities (NIS2, sector-specific cybersecurity), and public sector. The governance and risk pillar is calibrated per industry.
What AI operating model framework do you use?
We use a six-pillar reference framework — operating model, roles & RACI, governance & risk, platform & tooling, FinOps, skills & ways of working — refined across multiple regulated-enterprise engagements. It is opinionated but vendor-neutral and adapts to whether an organisation is centralising, federating or running hub-and-spoke.
Ready to design an AI operating model that scales?
30-minute scoping call. We will tell you whether full operating-model design or a focused workstream is what you actually need.
Book a Scoping Call