As organisations invest in AI, they need to redesign their operating model.

In practice, this creates friction. Processes usually sit across multiple teams. AI changes how work is distributed, how decisions are made, and how accountability is shared.

This means that strategy execution is challenging, and different parts of a business need to collaborate in order to agree on the changed processes and approvals. Without this, the strategy execution fails, and Return on Investment (ROI) is not delivered on AI investments.

 

Why AI puts pressure on existing operating models

AI reshapes work.

Tasks that were once manual become automated.
Analytical work shifts closer to real-time decision-making.
Boundaries between functions begin to blur.

Despite this, many organisations leave their operating model largely untouched. It is easier to leave many things unchanged. Roles, responsibilities, and governance structures remain as they were before AI was introduced.

Gartner’s 2025 research on AI-enabled organisations highlights that misalignment between AI capabilities and existing operating models is one of the primary reasons AI initiatives fail to scale, particularly when roles and accountabilities are unclear (Gartner, 2025).

The issue is operating model design.

 

Role ambiguity undermines execution

One of the most common operating model problems I see is role ambiguity.

AI introduces new questions:
• Who owns AI-supported decisions?
• Who is accountable for outcomes influenced by AI?
• Where does responsibility sit when humans and AI collaborate?

When these questions are left unresolved, deployment becomes problematic. Teams duplicate effort, decisions are delayed, and accountability becomes blurred.

AI accelerates work, but it also exposes ambiguity. Deployment slows when people are unsure where their authority begins and ends.

 

Operating models must reflect how work actually happens

Traditional operating models often reflect organisational structure rather than real workflows.
AI highlights this gap.

Work increasingly cuts across functions. Data flows do not respect reporting lines. Decisions often require input from multiple domains.

McKinsey’s 2025 research on AI at scale found that organisations achieving stronger AI-driven performance are more likely to redesign operating models around value streams and decision flows, rather than preserving rigid functional silos (McKinsey, 2025).

A shift needs to happen in mindset and the way work gets done. This means aligning roles with outcomes rather than hierarchy.

 

Clarifying human and AI responsibilities

Effective operating models clearly distinguish between:
• Tasks automated by AI
• Tasks augmented by AI
• Tasks that remain human-led

This clarity supports confidence and accountability.

MIT Sloan research warns that when organisations fail to clearly define human versus AI responsibilities, decision quality and ownership suffer, particularly in complex environments where judgement is required (MIT Sloan, 2025). This date is not correct?????

Operating model design must make these boundaries explicit. Assumptions create risk.

 

Governance connects operating models to execution

Operating models do not function in isolation. They are reinforced through governance.

Governance clarifies:
• Who has decision authority
• How exceptions are handled
• How performance is monitored
• How the operating model evolves over time

Without governance, operating models exist only on paper.

Strong governance ensures that role clarity is maintained as AI capabilities expand and strategies evolve.

 

Designing operating models for adaptability

AI will continue to change how work is done.

Operating models designed for static environments struggle to keep up. Those designed for adaptability perform better.

This means:
• Periodic review of roles and responsibilities
• Clear mechanisms for updating decision rights
• Ongoing involvement of operational leaders

Adaptability does not require constant restructuring. It requires deliberate design choices and disciplined governance.

 

From capability to coordinated execution

Deployment of AI capability alone does not mean your strategy execution will be successful. To improve efficiency and effectiveness of business processes, operating models must clearly define:
• Who does the work
• Who decides
• Who is accountable for outcomes

Organisations that invest in operating model re-design and clarity are the ones who are more likely to achieve successful ROI with AI.

Strategy execution depends on more than technology. It depends on how work is organised and governed.

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