ITIL AI Governance (Version 5) – highly relevant to AI and automation initiatives

Monica de Celis, Manager – Vodafone Network Operations Process House Team

Monica de Celis works for the Vodafone Network Operations Process House Team, which supports network services across numerous European markets.

Nine months ago, the team joined the digital operations teams, with a greater focus on automation and digitalization. This supports a business objective to become a level 4 autonomous network by 2030, which requires less human intervention in operations.

Here, Monica – a recent beta tester for the new ITIL AI Governance (Version 5) certification – explains its relevance to the company.

With more and more services connected to our network – e.g., Internet of Things – this means a greater need for automated controls. Therefore, the role of the human in this is changing.

And this is why governance of AI is so relevant: we need to be sure that automation is working to make autonomous decisions, for example, using AI/machine learning to filter alarms which help prevent incidents in the network and support post-incident recovery.

Applying governance to AI and, in fact, all automation enables us to understand what we’re automating and where this needs to escalate to a human, benefiting from the knowledge and expertise of our engineers. The ITIL AI Capability Model helps us understand the six capabilities that shape how AI is adopted and used, which in turn tells us how much oversight each use case needs and where a decision must escalate to an engineer.

This also involves recognizing risks such as cybersecurity, unintended bias, and the use of data in an ethical way.

ITIL AI Governance (Version 5) – going beyond compliance

AI is not just a digital tool, but part of the operations ecosystem. Therefore, scaling AI and automation requires more than technical capability; it needs a governance model to tie together strategy, roles and responsibilities, risk management and monitoring.

When increasing autonomy in the network, it’s important for humans to monitor and AI governance is a key part of that.

Training in ITIL AI Governance (Version 5) helps bring structure and a practical approach to governing AI-enabled products, services, operations and decision-making. It also places risk management in a broader context: not only legal compliance but also ethical, transparent use and operational impact.

This is important for our internal stakeholders, creating confidence among senior leaders about AI investment and alignment with business outcomes. They can see that AI/automation will support people while retaining human accountability.

For the customer, ultimately, this is about providing a better and more reliable service.

Supporting speedy innovation with AI governance

As my team acts as a bridge between operations, strategy and requirements, having the knowledge from ITIL AI Governance (Version 5) gives us a common language to recommend controls and improvements.

But this is about applying governance in a proportional, non-bureaucratic way. It should not slow innovation but provide guardrails so teams can innovate quickly and with confidence, giving teams a safe space to experiment.

With the right guardrails, governance should be an accelerator of innovation.

Scaling AI to create measurable value

AI and automation can create value only if moving beyond pilot projects and becoming embedded into operations.

Therefore, the vital first step when analyzing a new AI/automation opportunity or demand is to evaluate its potential value.

ITIL AI Governance (Version 5) guidance links proposed initiatives to measurable value co-creation and service outcomes. For example, this could be about improving operational efficiency, reducing repetitive tasks, minimizing incident volumes in the network, faster detection for network issues and improving mean time to detect and repair – each helping the customer experience.

Governance is an essential part of evaluating AI value and what benefits and outcomes it would provide to the company.

But AI value is not created by technology alone; value co-creation happens only when business teams, technology and users work together to address real operational pain points, such as accelerating root cause analysis.

The ITIL AI Governance Improvement Model provides the steps and structure to help us evaluate and monitor AI initiatives for their contribution to value. And, for teams that typically work in isolation, this guidance ensures that AI aligns with business objectives and has risk management in mind.

Enterprise-wide AI governance

The skills and knowledge in ITIL AI Governance (Version 5) are highly transferable, especially as AI adoption is happening across many parts of the organization, including customer care, security, finance and sales.

Along with embedding the same language – for example, covering value and risk – it creates a common approach to the use of AI and supports prioritization of AI investment; reducing duplication and fragmentation of AI initiatives.

Scaling AI responsibly across an enterprise requires both technical experimentation and the necessary and appropriate level of control and measurement.