AI first operating model

Designing AI operating models that deliver real value

AI isn’t a technology challenge anymore. It’s a systems challenge.

Every enterprise is experimenting with AI, yet few have built the operating models to turn pilots into performance.

At Valliance, we design those systems the frameworks that make AI work for people, not the other way around. Our approach helps leaders move from isolated success stories to scalable, measurable outcomes.

The compass for designing AI that works

We look at every AI operating model through five connected lenses. Together, they form a compass for decision-making.

This structure keeps organisations grounded. It connects vision to value and ensures AI becomes a living system not a series of disconnected initiatives.

Three models for building enterprise-ready AI

1. The pillar model – structure and clarity

Best for enterprises at the start of their AI journey.

This model mirrors traditional operating frameworks and creates clear ownership.

Core Pillars

• AI Mission – strategic intent and leadership

• AI Services – service catalogue and delivery model

• AI People & Organisation – roles, skills, and culture

• AI Processes – lifecycle and ways of working

• AI Technology & Tools – infrastructure and enablement

• AI Data & Knowledge – data and decision intelligence fabric

• AI Governance – guardrails, ethics, and accountability

• AI Change & Adoption – engagement, training, and trust

Why it works

Simple, intuitive mirrors TOGAF and easy to communicate across leadership teams. Every pillar has an owner, a mandate, and a set of measurable outcomes.

The Valliance view

When you are codifying a first-generation AI Operating Model, this is a great start as we strengthen this model by explicitly linking each pillar - governance informs data, services enable adoption, and every function contributes to real business impact.

2. The layered model - flow and feedback

Best for enterprises scaling AI into core operations.

This model visualises how value moves through the system from vision to adoption.

Layers AI Mission & Governance (Top Layer)

• Defines why and what good looks like

• Includes ethics, regulation, and enterprise alignment

AI Platform & Infrastructure (Foundation Layer)

• Technology, Tools, and Data & Knowledge

• Includes observability, agent mesh, and knowledge graph

AI Services & Processes (Middle Layer)

• Discovery, Design, Build, Operate, Improve

• Includes Risk & Assurance and Change Services

AI People & Organisation (Enabler Layer)

• Roles, capability maturity, and human–agent orchestration

Adoption & Impact (Outcome Layer)

• Training, adoption, measurement, and value realisation

Why it works

It’s systemic, transparent, and ideal for mapping feedback loops. It shows how every layer feeds the next, creating a continuous cycle of improvement.

The Valliance view

If you are integrating with enterprise architecture, AI mesh, or DevOps frameworks. We design these systems so data informs governance, people shape process, and insight continuously refines action. It’s how enterprises move from deployment to decision intelligence.

3. The Flywheel model – adaptive and agentic

For organisations ready to operate as AI-native.

This is where humans lead, agents learn, and the enterprise evolves continuously.

Phases

1. Vision & Governance

2. Discovery & Build

3. Deploy & Operate

4. Learn & Adapt

5. Upskill & Evolve

Powered by agentic loops:

• Critic Agents – uphold ethics and performance

• Curator Agents – maintain data and knowledge flows

• Coach Agents – evolve learning and change programmes

• Concierge Agents – enhance user experience and adoption

Why it works

Future-proof your Enterprise as it is built around continuous intelligence. It embeds continuous learning into the enterprise blending human oversight with adaptive intelligence.

The Valliance view

We use this approach for clients who want to lead the next generation of AI-first business - enterprises where trust, transparency, and autonomy coexist.

The balanced enterprise model

In practice, most organisations need a hybrid.

We combine the strengths of all three:

• Pillar for structure and ownership

• Layered for systemic flow

• Flywheel for adaptability and evolution

This creates what we call the Balanced Enterprise Model - an AI Operating Model that’s practical today and ready for agentic systems tomorrow.