Operating Model for AI at Scale

Welcome to Edition 14 of AI at Scale Weekly – Making AI at Scale real for mid-sized companies.

Theme of the week: Digital Capability Deep Dive

Happened so far in AI at Scale Weekly:

We have gone through essential digital capability areas from Strategic Management to Computing, and from Data Engineering to Agent Engineering. Now it is time to put it all together by operationalizing everything discussed until now. That’s where Operating Model comes in.

What is Operating Model?

Overall

An operating model is the blueprint of how a company functions on a day-to-day basis. It defines the organizational structure (how roles, teams, and domains are organized), value-creating processes (how value is delivered through products, services, and operations), and governance mechanisms (how decisions are made, who owns what, and how accountability is distributed).

These three pillars – structure, processes, and governance – shape how work gets done, how resources are allocated, and how the organization adapts to change. In essence, the operating model operationalizes strategy into repeatable execution.

In relation to Business Model and Strategy

Operating Model is somewhat close to Business Model and Strategy but key differences remain.

Business Model defines how you create, deliver and monetize value. Value Proposition is in the heart of a Business Model.

Strategy outlines where you're going – the choices, direction, priorities, and positioning in the market.

Operating Model, then, is how you make it happen – the underlying system of structures, processes, people, and tools that enables the execution of strategy and the realization of the business model. Without a clear operating model, even the best strategy will struggle to materialize into results.

In relation to Enterprise Architecture

The Operating Model describes how the organization works – roles, processes, decision-making, and responsibilities. The Enterprise Architecture (EA), in turn, provides a structured view of the technical and information systems that support that operation – including data, applications, infrastructure, and integration.

EA brings coherence to IT investments, while the Operating Model ensures business alignment.

In AI at Scale context, Operating Model sets the “why” and “how” while EA provides the “with what”. Together, they ensure organizational and technical readiness for AI.

Why Operating Model is elementary for AI at Scale

AI won’t scale through tools alone.

Mid-sized companies eager to embrace AI often jump straight to use cases, data, and technologies. But something critical is missing: the glue that binds strategy, capabilities, and action – that is, the operating model.

The right operating model doesn’t just enable AI at Scale. It operationalizes it, makes it functional.

It defines the organizational structure and business processes enabling AI-driven value creation at scale, where and how decisions are made, and how digital capabilities are deployed – so that AI becomes everyone’s job.

Without well-designed and carefully implemented operating model, AI remains marginal and disconnected.

From experiments to scalable AI: What needs to change?

In many companies, digital capabilities are centralized leading to bottlenecks and lost context and semantic understanding. A data lake turned into a data swamp that nobody wants to touch is a prime example of such failure. It is clear that AI at Scale is not possible without sorting out fundamental problems like that.

At Stage 2 (Agentic AI) and Stage 3 (Embedded AI), poorly designed operating model collapses under its own weight. What’s needed is something radically more adaptive:

• Decentralized and distributed ownership of digital assets and capabilities
• Business Domains built as value creation and innovation engines
• Integrated XOps (DataOps, MLOps, DevOps, AgentOps) for continuous delivery
• Cognitive load reduction for domain teams
• Platform Engineering to offload and standardize business domain operations

This isn’t just about structure, processes and governance. It’s about creating Organizational Pull that matches the massive Technology Push created by rapid AI advancements.

Core principles of Operating Model for AI at Scale

Business Domains as Bounded Contexts

Each Business Domain (e.g. Marketing, Production, Customer Service) becomes a bounded context –  a focused unit of ownership for digital assets, data, AI models, AI agents, and outcomes.

• Clarity and shared semantic understanding of domain-specific concepts
• Cross-functional teams with deep domain knowledge
• Responsibility for AI use cases, innovation, development, and continuous delivery

XOps: The Engine of Digital Innovation

A fusion of DataOps, MLOps, DevOps, and AgentOps, the XOps team is the delivery mechanism for innovation within each Business Domain.

• DataOps: Data product lifecycle management and quality pipelines
• MLOps: AI model training, deployment, monitoring
• DevOps: Continuous integration and software delivery
• AgentOps: Orchestration and lifecycle management of AI agents

When unified, XOps enables each domain to move fast, learn continuously, and scale AI with confidence.

Each engineering domain discussed in previous AI at Scale Weekly editions is deeply embedded into the Operating Model. Data Engineering (with DataOps) ensures that Business Domains have reliable, accessible, and high-quality data – the lifeblood of AI. AI Engineering (with MLOps) provides methods to design, train, deploy, and manage AI models throughout their lifecycle.

Software Engineering (with DevOps) integrates those models into digitalized workflows, services, and systems. Agent Engineering (with AgentOps) brings it all together by orchestrating intelligent agents into coherent systems.

The Operating Model makes these engineering domains collaborate effectively across organizational and technical boundaries – ensuring they function not in isolation, but as a coordinated engine of digital innovation and delivery.

Industrial-grade operations

AI at Scale is not just about doing AI – it's about doing it repeatedly, reliably, and with strategic impact. That requires industrial-grade scale: the ability to run tens or hundreds of AI use cases across domains, processes, and products.

But scale without quality leads to failure. That’s why robust delivery pipelines, governance, monitoring, and lifecycle management are essential.

At the same time, the competitive landscape changes fast – so agility and innovation are not luxuries, but necessities.

The Operating Model must enable both rigor and speed, supporting high-velocity experimentation while ensuring enterprise-grade reliability.

Platform Engineering to reduce cognitive load

To make XOps viable for mid-sized companies, Platform Engineering is essential.

• A Platform Team builds and maintains the internal tooling, environments, and workflows
• An Internal Developer Platform (IDP) provides self-service infrastructure, automation, and guardrails
• Cognitive load is reduced, enabling domain teams to focus on business logic – not infrastructure or engineering intricacies.

In short: Platform = leverage. It’s the key to empower Business Domains to emerge as digital innovation and value creation engines.

One Company principle: autonomy with alignment

While Business Domains enjoy high autonomy, company-wide standards ensure:

• Clarity on ownership and responsibilities at organizational demarcation lines
• Consistency and reuse of key assets and capabilities
• Smooth knowledge transfer
• Compliance and policy adherence

This balance – decentralized execution, centralized coordination – is the hallmark of a scalable operating model.

Mapping Operating Model across the AI Journey

Stage 1: Ad hoc AI

No AI related requirements on operating model. Ad hoc and isolated AI tool usage does not need anything specific from operating model.

Stage 2: Agentic AI

AI-First Operating System (AI-FOS) emerges. Business Domains own AI use cases and agents. Early XOps and Platform Engineering begin to take shape.

At Stage 2, AI-FOS introduces a transformative challenge: designing an Operating Model that includes not just humans, but AI Agents as part of the workforce.

This goes beyond technical orchestration (AgentOps) – it requires organizational answers to new questions: Who owns the agents? How are they evaluated? What is their scope of autonomy?

Hence, Operating Model must incorporate governance, accountability, and lifecycle management for agents, alongside human employees. This is unprecedented but extremely powerful. It enables hybrid teams of humans and AI agents to co-create value, making Agentic AI real.

Stage 3: Embedded AI

Stage 3 is the culmination point where AI is fully embedded into the company’s products, services, and operations.

At this stage, Business Domains operate as digital innovation and value creation engines – operating with high autonomy while continuously exploring new use cases, experimenting with AI, and delivering value at scale.

This does not happen by accident. Operating Model must provide infrastructure, autonomy, guardrails, and incentives for such innovation to thrive.

By now, XOps and Platform Engineering are fully established, cognitive load is actively managed, and AI is part of “how we work”.

Operating Model doesn’t just support AI at Scale – it embodies it.

Operating Model vs. Operating System

The distinction between Operating Model and Operating System is crucial – especially in the AI at Scale context, where we operationalize AI in both human and machine workflows.

Here’s a compact and actionable way to describe the relationship:

Operating Model defines how your company organizes and governs value creation

• What are the business functions (business domains) and support functions
• Who owns data, AI models, and other digital assets
• How AI agents are managed as Digital Workforce

AI-First Operating System is about how you run AI-driven processes at scale

• What agents exist and what are the workflows they are for
• What tasks do they perform
• How are they orchestrated and observed

Need to reduce Cognitive Load

One of the most overlooked aspects of Operating Model design is cognitive load reduction.

AI at Scale demands that business teams engage deeply with data, AI models, software, and AI agents. Without smart operating model design, the cognitive load on them becomes overwhelming.

Therefore, the Operating Model must:

• Offload complexity through platforms, advanced tools, and orchestration
• Provide clarity on roles, responsibilities and workloads across organizational demarcation lines
• Allow business teams focus on business outcomes, not engineering hurdles

This isn’t optional. For mid-sized companies, it’s the only way to sustainably scale AI.

Do we still need organizations?

Some argue that as AI agents grow more capable and autonomous – coordinating and performing complex tasks and enforcing rules – the traditional role of the firm may begin to erode. If AI can reduce transaction costs and distribute authority, do we still need organizations?

The answer, at least for the foreseeable future, is yes – absolutely.

Because firms still provide what networks can’t:

• Trust and accountability
• Reputation and brand equity
• Long-term coherence
• Reliable customer and regulatory interfaces

Even as AI enables more decentralized work, companies that combine human leadership with agentic automation inside a well-designed operating model will have a decisive advantage. They will be able to:

• Orchestrate complex work across hybrid teams
• Leverage brand trust in agent-enabled service delivery
• Ensure compliance and ethical alignment
• Create consistent value at scale

AI doesn’t eliminate the firm – it transforms what the firm must become: A flexible, responsive, trust-anchored organization that blends digital capacity with human judgment.

That transformation starts with Operating Model design. Because in the end, who owns the agents, who governs the outcomes, and who earns the trust, still matters.

Considerations for mid-sized companies

• Start small, think modular: You don’t need to transform whole company overnight. Start with one or two Business Domains as pilots.
• Don’t replicate large enterprises: Focus on fit-for-purpose operating practices, not complexity for its own sake.
• Prioritize Platform Engineering early: Even a small platform team or outsourced service can be a force multiplier.
• Treat AI Agents as first-class citizens: Who owns them? How are they governed? What workflows are they part of?

Bottom Line

Without the right Operating Model, AI efforts remain stuck in pilot mode.

With it, mid-sized companies can:

• Scale value creation across Business Domains
• Leverage XOps for continuous innovation and delivery
• Empower teams while staying aligned
• Build an AI Organization – one that learns faster than the competition

In the age of agentic automation and digital workers, Operating Model is no longer a background concern. It’s a strategic enabler.

Next Week

Next week in AI at Scale Weekly: We will finish exploration of digital capabilities by discussing the vital role of Data Culture as essential enabler for AI at Scale – complementing Operating Model in creating organizational pull for large-scale AI utilization.

Annex – Available services

AI Transformation service portfolio supports Operating Model design and deployment:

AI at Scale Workshop introduces Operating Model as an essential enabler for AI at Scale. Workshop provides a forum for leadership alignment on Operating Model’s crucial role in the overall AI Journey.

Constraints Assessment explores current state of Operating Model and reveals potential gaps and shortcomings that are prone to hinder scaling up AI utilization. Assessment creates a baseline for Operating Model update to enable Agentic AI and further evolution to Embedded AI.

Change Management and Execution Support comes with an option for highly tailored support for Operating Model design and deployment – adapted to current state and strategic objectives.

Picture: AI Transformation core services – focused, connected and coherent

Overall, AI Transformation core services recognize Operating Model as a cornerstone of successful AI Journey – also when Operating Model is left in the background as an enabling framework rather than directly worked on.

Start from where you are. Move with strategic clarity toward where you need to be.