AI and the Operating Model: Designing the Organisation of the Future

AI is not just another technology trend, it redefines how organisations function as systems. While most conversations focus on impressive tools or productivity gains, the real question is more fundamental: What does AI mean for the design, governance, and evolution of your Operating Model?

Introduction: Why AI Forces Us to Rethink the Operating Model

Every organisation already has an Operating Model, whether designed intentionally or not (see my previous posts). It is the blueprint that describes how people, processes, systems, and governance work together to deliver value. The arrival of AI challenges this blueprint at its core. We now also have to define how we work together with AI. Decisions once made by people may now be taken by AI agents. Entire processes can be automated. Roles and responsibilities shift, sometimes subtly, sometimes dramatically.

And with this shift comes a reality leaders must accept: introducing AI has consequences. Some jobs will evolve, new skills will be required, and in some cases human roles will be replaced by agents. These consequences must be overseen, anticipated, and accepted, not ignored. Without that awareness, AI adoption risks creating resistance, confusion, or even ethical blind spots.

That’s why AI cannot be treated as a bolt-on tool. It needs to be integrated into the Operating Model itself. This means updating guiding principles, adjusting building blocks, and ensuring that governance mechanisms, such as the Design Authority and Enterprise Architecture (see the post; "Who Owns the Operating Model? And Who Guards It?"), are ready to steer AI’s role in the organisation.

Guiding Principles for AI in the Operating Model

AI doesn’t just add new capabilities, it changes the rules of the game. If the Operating Model is the blueprint of how the organisation works, then its guiding principles must be revisited in light of AI. Some examples:

• Data as a first-class asset. With AI, data moves from being a by-product of processes to the very fuel of value creation. This means stricter stewardship, clearer ownership, and potentially even a dedicated Data building block as part of the Operating Model.

• Human + machine collaboration. Principles should clarify where AI augments human decision-making, and where humans remain fully accountable. The line between automation and judgment must be explicit.

• Transparency and ethics. AI systems can be opaque, so principles must guarantee explainability, fairness, and accountability. Bias management should be non-negotiable.

Without these principles, organisations risk letting AI evolve organically and inconsistently. With them, AI adoption becomes coherent and sustainable.

The Impact of AI on the Operating Model Building Blocks

AI has systemic impact: it doesn’t just affect IT, it reshapes the organisation as a whole. Thought leaders highlight different aspects of this transformation:

Thought Leader / Author	Impact on Operating Model
BCG	“The AI‑first operating model rewires how organizations work. Hierarchies will flatten as AI agents—overseen by humans—operate back‑office processes.”
Thomas H. Davenport	“Generative AI appears to be the technology that is finally making it possible… to easily access important knowledge … to enhance productivity and innovation.”
Harvard Business Review (Lakhani)	“AI won’t replace humans—but humans with AI will replace humans without AI.”
Kay Firth-Butterfield	“Using AI wisely allows us to make more effective use of it.” (on the essential role of governance and sensible use of AI tools)

So AI touches every building block of the Operating Model:

• Organisation: line organisations must prepare for shifting roles and skills, while network organisations need to integrate AI into value streams and decision flows. Decision rights must be revisited when some decisions are delegated to agents.

• Processes: routine tasks can be automated, but new processes emerge around AI model training, monitoring, and compliance.

• Systems & Technology: AI platforms and services join the core IT landscape, creating integration and governance challenges.

• People & Capabilities: AI literacy becomes essential. Leaders must prepare for reskilling and for roles that may disappear or be augmented.

• Performance & Metrics: New KPIs are needed, from automation ROI to fairness and bias indicators. AI models themselves must be measured and monitored.

• Governance & Policies: clear policies must be set for responsible AI, aligned with regulatory frameworks such as the EU AI Act. The Design Authority plays a key role in ensuring coherence and oversight.

Tailoring AI in Your Operating Model

Not every organisation needs the same AI journey. For some, AI may mean automating back-office tasks. For others, it becomes central to products and strategy. Tailoring the Operating Model is about finding the right fit.

• Keep it simple. Start from the standard building blocks. Only add or extend if there is a clear business case.

• Design Authority involvement. Ensure all AI tailoring decisions are reviewed against guiding principles and strategic priorities.

• Involve subject-matter experts. Bring in people with knowledge of the area being tailored; they become ambassadors during testing and deployment.

• Use structured methods. Systems mapping, scenario workshops, and small experiments help visualise impacts, test assumptions, and build confidence before scaling.

This keeps AI adoption both agile and coherent, avoiding “big bang” overhauls or fragmented pilots.

Implement & Evolve: Keeping AI Alive in the Operating Model

Implementing AI is not the end game, it’s the starting point. Organisations need to think about both implementation and evolution:

• Implement AI in the Operating Model. Business and functional leaders take responsibility for adoption within their domains. Change & Transformation Leads design the change journey. Ambassadors help colleagues test and adopt new ways of working. The goal: AI embedded in daily practice.

• Evolve the impact of AI on the Operating Model. The Design Authority and Enterprise Architect continue to monitor the Operating Model as a system, ensuring AI remains aligned with strategy, regulation, and risk appetite. Adjustments are made incrementally, learning from experiments before scaling.

This combination ensures the Operating Model doesn’t freeze after implementation. Instead, it becomes a living system, capable of adapting as AI technology, regulations, and organisational needs evolve.

Conclusion: AI as a System-Level Transformation

AI is not just a tool for productivity. It is a system-level transformation that reshapes how organisations are designed, governed, and evolve.

The lesson is simple: don’t treat AI as a bolt-on. Embed it into the Operating Model. Update guiding principles, align building blocks, and make governance ready for the consequences. Accept that some roles will be augmented or replaced by agents and manage that transition consciously.

The winners will be organisations that don’t just use AI, but weave it into their Operating Model, keeping it coherent, ethical, and alive. The challenge is not adopting AI tools, but redesigning the Operating Model so humans and AI can thrive together.

”AI won’t replace humans, but humans with AI will replace humans without AI."

We always welcome your experiences and feedback.