AI in ITSM: Does size matter?

Every organisation gets one of two advantages and never both. Small organisations can move. Large ones can absorb. The trick is knowing which you have and compensating for the one you do not. Most assume their governance comes along for the ride. It does not. Enlarged, it just becomes coarse, and everything you were quietly getting away with is suddenly there for the board to see. Governance has an aspect ratio, and most AI incidents are what happens when somebody ignored it.

The session sets out a test that holds at any size: can you explain it, test it, monitor it, and stop it. Four questions, no new committee. Then what each costs at ten people, at five hundred, and at fifty thousand.

This session sets out how RELEX built a measurable AI programme by treating AI as an operational layer in Support and Service Delivery rather than a series of technology experiments. Every initiative runs through a structured Jira-based lifecycle, from intake through build, deployment and measurement, so that adoption and time savings are tracked from day one. Drawing on 18 months of production experience, it covers how usage data becomes a management signal, how ROI evidence informs resourcing decisions, and why the operating model, not the tooling, drives adoption.

The session will cover to key stages of the project:

• The project lifecycle (idea to ROI)
• Turning usage and metrics into measurable ROI
• From ROI data to resourcing decisions
• Why the model works (repeatable at scale)

Universities are under growing pressure to respond to generative AI—balancing innovation with governance, security, equity of access, and escalating concerns about environmental impact. At the University of Edinburgh, these tensions were felt across the community: professional services worried about automation, academics faced uncertainty about how learning and teaching would change, and students were unsure what was permitted and how AI use would be viewed.

In this session, EDINA will share how we moved from that “dilemma” to practical direction through ELM®: the University of Edinburgh’s institution-wide, secure gateway to generative AI. We will outline the drivers behind ELM® (senior leadership sponsorship and ethics review), and the design principles that shaped the platform—safety, security, responsible use, inclusivity, responsiveness, and climate sensitivity.

We will also describe what ELM® provides in practice: equitable access for staff and students; a secure web platform offering both commercial models and locally hosted open-source LLMs (including a greener default option); direct API access to enable local innovation; and the deployment of custom chatbots for real university services.

We’ll cover how we are building organisational capability alongside the technology, including our training and support programme, and how we are beginning to quantify usage and environmental impact through platform defaults, awareness measures, and an impact dashboard in development.

Finally, we’ll share what’s next on the ELM® roadmap—expanding local and commercial model options, improving UX, and developing sector collaboration—along with practical takeaways for IT and service leaders looking to deliver secure, responsible GenAI at institutional scale.

Most IT organizations know how many AI licences they have and how often tools like Microsoft Copilot are being used. But adoption is not the same as value.

In this session, Mark Bewick will explore how IT teams can use Experience Intelligence to understand what AI tools are actually doing for employees: who is saving time, who needs more training, where AI is improving work, and where it may actually be adding friction.

Using HappySignals research data and practical examples, Mark will show how to move from AI adoption metrics to measurable employee impact through three areas:

Ask the people using AI
Measure employees’ AI skills, usage, attitudes, happiness, and lost time. Then connect those signals with employee data such as role, team, and location to understand why the same AI tool can create very different experiences.

Find where AI already creates value
Put usage next to experience. Identify the teams and roles getting real value from AI, where training could unlock more value, and where adding more licences may not be the answer.

Turn experience data into answers
Use HappySignals Ask to question experience data in plain language, move from a broad problem to its likely drivers, and decide what to investigate first. Rather than generic AI, the answers are grounded in your experience data and HappySignals’ ITXM domain intelligence.

The session will also explore a simple but important question: Are your employees actually getting value from AI? Because the people using the tools are the ones who can tell you the difference between adoption and impact.