AI Operating Model: A guide to building an enterprise AI operating model

Artificial intelligence is moving from being a technology used in isolated experiments to becoming a strategic capability for businesses. As organizations implement generative AI, AI agents, predictive models, and intelligent automation across different areas of the business, technology alone is no longer enough.

Businesses need an AI Operating Model that establishes how artificial intelligence is developed, governed, deployed, measured, and scaled across the organization.

An enterprise AI operating model integrates the strategy, governance, technology, data, people, and processes required to turn AI investments into repeatable and scalable business outcomes.

According to McKinsey's The State of AI study, published in 2025, more than three-quarters of respondents said their organizations were using AI in at least one business function. The study also identifies a trend toward redesigning workflows, establishing governance structures, and developing new AI-related capabilities.

What is an AI Operating Model?

An AI Operating Model is a framework that defines how an organization manages and operationalizes artificial intelligence across the business.

An enterprise AI operating model connects five key dimensions:

• AI Strategy: establishes business objectives, priorities, use cases, and expected outcomes.
• AI Governance: defines policies, responsibilities, risk management, compliance, and controls.
• Technology and Data: provides the platforms, models, infrastructure, integrations, and data required to operate AI solutions.
• People and Capabilities: determines roles, skills, responsibilities, and collaboration between business and technology teams.
• AI Operations: establishes how solutions are deployed, monitored, measured, maintained, and improved.

In practical terms, an AI Operating Model answers questions such as: who is responsible for AI? Which use cases should be prioritized? Who approves an implementation? How are models monitored? How is ROI measured? And how can successful solutions be replicated across other areas of the organization?

Why do businesses need an AI Operating Model?

Many organizations begin their AI strategy with AI use cases, proofs of concept, or experiments developed by specific departments. The real challenge emerges when these initiatives need to move into production and scale.

Without a common operating model, businesses can end up with duplicated technology, fragmented data, inconsistent governance structures, disconnected AI teams, and difficulties measuring business impact.

Research by McKinsey on AI operating models indicates that the challenge is becoming increasingly organizational rather than purely technological. Companies can access similar models and infrastructure, but their ability to redesign processes, decision-making, governance, and capabilities determines how they capture value from AI.

For this reason, an operating model for AI transformation is essential for organizations seeking to move from experimentation to AI at scale.

Key components of an enterprise AI Operating Model

1. AI strategy and business alignment

The first component is an AI strategy connected to business objectives.

Instead of starting by asking "where can we use AI?", organizations should identify business problems where the technology can generate measurable impact.

An enterprise AI strategy should define:

• Strategic AI objectives
• Priority functions
• AI use case portfolio
• Expected business outcomes
• Investment priorities
• AI roadmap
• KPIs and success metrics

This prevents AI initiatives from becoming technology projects disconnected from business priorities.

2. AI governance and risk management

AI Governance establishes the policies and controls required to develop and use artificial intelligence responsibly.

This includes:

• Model lifecycle approval and management
• Data governance
• Security and privacy
• Regulatory compliance
• Human oversight
• Model monitoring
• Auditability and traceability
• Risk classification
• Responsible AI policies

IBM describes AI governance as the ability to oversee and manage AI-related activities within an organization, including model and data lineage, validation, monitoring, compliance, and operational controls.

As AI agents become more autonomous, governance also needs to evolve. McKinsey notes that agent-based organizations require integrated governance mechanisms and continuous oversight capabilities while maintaining human accountability for critical decisions.

3. Technology architecture and data for AI

An AI Operating Model architecture requires a scalable technology foundation.

This may include:

• Cloud and hybrid infrastructure
• Enterprise data platforms
• Machine learning platforms
• Large Language Models (LLMs)
• Generative AI applications
• AI agents
• APIs and enterprise integrations
• Model management
• AI observability
• Security and access controls

An important principle is to avoid unnecessary dependence on a single model or technology provider. A modular architecture allows businesses to incorporate new models, tools, or infrastructure as the market evolves.

Data is equally important. AI applications depend on reliable, accessible, secure, and governed information. Without strong data governance, even the most advanced AI systems can produce unreliable or difficult-to-audit results.

4. People, roles, and AI Capabilities

An AI Operating Model for enterprises must also establish who is responsible for artificial intelligence.

Depending on the organization's size and maturity, the following roles may be involved:

• AI or transformation leaders
• AI Product Managers
• Data Scientists
• Machine Learning Engineers
• AI Engineers
• Data Engineers
• AI governance specialists
• Cybersecurity teams
• Business domain experts

The operating model must also define how central AI teams collaborate with different business units.

For example, an AI Center of Excellence can establish standards, reusable technology, governance, and specialized knowledge, while business teams identify use cases and are responsible for adoption.

There is no universal organizational structure. McKinsey research on financial institutions indicates that the appropriate operating model depends on factors such as the company's structure, culture, technological maturity, and specific AI objectives.

5. AI Operations and continuous improvement

Implementing an AI solution does not mean the process is complete.

Organizations need AI Operations, also known as AI Ops, to continuously monitor system performance, costs, security, model behavior, adoption, and business outcomes.

An AI Operating Model should establish a lifecycle such as:

Build → Test → Deploy → Monitor → Measure → Improve

This process is especially important for generative AI and agentic AI systems, as models, data, prompts, integrations, and business requirements can change constantly.

Deloitte proposes an AI operating model consisting of three interconnected layers: AI Strategy, AI Foundry, and AI Control Tower. This approach connects strategy with the industrialization of AI solutions and the continuous management of their performance.

How to build an AI Operating Model

Organizations can develop an enterprise AI operating model through six steps:

1. Assess AI maturity: analyze existing technology, data, talent, governance, use cases, and capabilities.
2. Define strategic priorities: identify where AI can generate measurable business outcomes.
3. Design governance: establish responsibilities, policies, risk controls, and decision rights.
4. Build the technology foundation: develop the data, technology, integration, security, and infrastructure layers required to scale.
5. Create the operating structure: define AI teams, responsibilities, processes, and collaboration between business and technology.
6. Measure and scale: monitor business impact, operational performance, adoption, risks, and ROI, and then scale successful use cases.

AI Operating Model vs. AI Strategy: What is the difference?

An AI Strategy defines what an organization wants to achieve with artificial intelligence and why. An AI Operating Model defines how the organization will execute that strategy.

For example, an AI strategy may establish the goal of automating customer service through AI agents. The operating model determines which team will be responsible for the initiative, which technology it will use, how it will access customer data, which governance controls will be required, how performance will be measured, and how the solution can be deployed across other business units.

Both elements are necessary to move from AI experimentation to scalable enterprise implementation.

The future of enterprise AI Operating Models

The growth of AI agents is making operating models even more important. AI systems can increasingly execute multi-step workflows, interact with enterprise applications, make recommendations, and coordinate tasks.

McKinsey describes this evolution as the emergence of an agentic organization, where humans and AI agents collaborate across different business processes and where the operating model, governance, people, technology, and data must evolve together.

Research from Deloitte in 2026 also indicates that, although many organizations express confidence in their ability to implement and govern AI at scale, a large majority expect their operating models to require changes over the following 12 to 18 months.

The conclusion is that AI-driven business transformation is not only about implementing new technology. It also involves transforming how an organization works, makes decisions, governs its data, and develops new capabilities.

Build your AI Operating Model with Rootstack

Moving from AI pilots to scalable enterprise solutions requires much more than selecting an artificial intelligence model. Organizations need to combine AI strategy, governance, data, technology, integrations, AI engineering, and operational processes.

Rootstack helps businesses design and implement AI solutions aligned with their business objectives, technology ecosystem, and operational needs.

If your organization is developing an AI Operating Model, scaling artificial intelligence initiatives, or preparing to adopt generative AI and AI agents, contact Rootstack to explore an enterprise AI strategy and implementation approach tailored to your organization's needs.