How to Build an Effective AI Operating Model

AI’s potential to revolutionise business is clear. But without a well-structured governance model and operational framework, even the most promising AI ambitions will stutter. Organisations that want to get the most out of AI must treat it not just as a set of tools, but as a strategic capability - one that spans leadership, technology, talent, and execution.

The Gartner model for building an AI strategy presents a compelling, interconnected view of how to move from ambition to action. In this article, I focus on one of its most critical components: the operational and governance layer that enables AI at scale.

1. Anchor AI Strategy in Business Strategy

The starting point is alignment. AI should not live in a silo, it must be aligned with (and continuously realigned to) the broader business strategy. This includes R&D, IT, data and analytics, and other departmental strategies.

Best practice tip:

Establish a cross-functional AI governance board that includes senior leaders from technology, data, operations, and line-of-business units. Their role is to ensure AI investments and initiatives support strategic goals and remain aligned as those goals evolve.

2. Create a Clear Operating Model for AI

The AI operating model defines how AI is governed, delivered, and sustained across the organisation. Gartner highlights six pillars essential to this model:

• Governance: Who approves and oversees AI projects? What are the ethical guardrails?

• Engineering: How are models developed, tested, and deployed?

• Data: Is the data trustworthy, accessible, and fit for purpose?

• Technology: What platforms and infrastructure support AI?

• Organisation: What roles, responsibilities, and skills are needed?

• Literacy: How well do stakeholders understand AI’s potential and risks?

Best practice tip:

Embed AI governance into existing enterprise governance structures, rather than duplicating them. This ensures consistency with broader compliance, audit, and risk frameworks.

3. Mature Governance Structures to Drive Trust and Control

As organisations move from experimentation to production AI, governance needs to mature from reactive to proactive. This means putting in place clear policies and controls around:

• Model transparency and explainability

• Bias detection and mitigation

• Regulatory compliance (e.g. GDPR, AI Act)

• Security and risk controls

• Approval workflows for new use cases

Best practice tip:

Use frameworks like Model Cards or AI Fact Sheets to document key metadata about each AI model: purpose, training data, performance, known risks, and human oversight mechanisms.

4. Prioritise Use Cases Through an AI Portfolio Approach

A strong operating model depends on clear priorities. That’s why Gartner advocates managing AI initiatives as a portfolio - balancing short-term wins with longer-term bets, and operational efficiency with innovation.

This involves:

• Identifying and prioritising high-value use cases

• Balancing “buy vs build” decisions

• Managing change effectively across business units

• Quantifying the cost and value of each AI initiative

Best practice tip:

Start with a portfolio discovery exercise. Map current initiatives, identify gaps, and assess each use case on feasibility, strategic fit, data availability, and expected business impact.

5. Drive Execution with Clarity and Confidence

The centre of the Gartner model - AI Strategy Goal Setting - serves as a compass. Key elements include:

• Drivers: What’s motivating your AI investment?

• Vision: What future are you trying to create?

• Value: What does success look like in concrete terms?

• Adoption: How will you ensure people trust and use AI?

• Risks: What could go wrong, and how will you mitigate it?

• Alignment: How do you maintain coherence across initiatives?

Best practice tip:

For each goal, define success metrics - not just technical performance (like accuracy) but business outcomes (like cost savings, CSAT improvement, or revenue impact). This helps bridge the gap between data science and enterprise value.

6. Enable Continuous Planning and Adaptation

AI is not static. Business environments evolve, regulations change, models drift. That’s why the final best practice is to embed continuous review and adjustment into the governance cycle.

This includes:

• Quarterly roadmap reviews

• Maturity assessments

• Feedback loops from model monitoring

• Adaptive prioritisation of use cases

Best practice tip:

Treat your AI operating model like a product - iterate on it. Measure its effectiveness, collect feedback, and evolve how you govern and execute AI work.

To close out, an effective AI governance and operating model is not about bureaucracy, it’s about enablement. Done right, it gives leaders confidence to scale AI safely, employees the tools to work effectively with AI, and customers the trust that AI is being used responsibly.

Gartner’s model reminds us that AI success doesn’t come from the technology alone, it comes from how the organisation wraps its strategy, culture, and operations around that technology.