AI has dominated boardroom conversations in higher education and student accommodation for the past two years, but the gap between talk and action remains stark. Universities and PBSA providers are being pulled in two directions at once, with pressure to adopt AI from vendors, students and competitors, and genuine uncertainty about where to start, how to do it safely and what would be the right time to do so.
When Mark Atkinson, VP of Innovation Acceleration at Kinetic, surveys the landscape of higher education and PBSA organisations he works with, he sees a sector full of potential rather than action when it comes to AI. He says that roughly 20 per cent of organisations are actively pursuing AI and seeing it as an opportunity. Another 30 per cent know it’s coming but haven’t really started. And around 40 per cent either believe it isn’t relevant to them right now, or genuinely don’t know where to begin.
“All of those answers are understandable,” Mark says. “But what they tell us is that the sector is largely at a starting line.”
Research from Google Cloud and Cisco confirms this: even among technology companies and software vendors, only around 13 per cent of organisations are “capturing real value from AI”. The reason, he says, comes back not to the tools but to the human and organisational factors around them.
For organisations that have concluded AI simply isn’t relevant to them right now, Mark offers a gentle challenge. The question isn’t whether leadership has decided to adopt AI; it’s whether AI has already arrived, regardless of that decision.
“AI is already there; it’s already arrived,” he says. “Students will be using AI tools for searching for accommodation, for their academic work, for how they communicate. Suppliers like us are embedding AI into our products.”
Perhaps the more useful question, he argues, isn’t about whether AI is a threat or an opportunity, but what the signal will be to tell you when AI becomes relevant for your business and how quickly you can react when that moment arrives. Organisations that have done nothing to build readiness will find that moment far harder to respond to.
Readiness, in Mark’s framework, is not about how sophisticated your current AI deployment is. It’s about whether you’ve built the foundations that make effective AI adoption possible, for which he sees three main pillars.
The first is data. Not whether the data exists (as most organisations have plenty), but whether it is clean, structured, governed and accessible.
“If I asked you to use AI to analyse 10 years’ worth of student accommodation trends, occupancy patterns, demographics, and pricing, could your data support that today?” he proposes. “For most organisations, the honest answer is probably not. The data exists, but it’s in silos, on paper, duplicated across systems, or locked in formats that AI can’t ingest.”
Henry Wadge, Kinetic’s Head of IA Engineering, adds an important caveat that readiness doesn’t mean doing a massive data clean-up project before anything else can happen.
“You do what you need, as you need it, on an ad-hoc basis,” he says. “But you go in with eyes open and build to do that as you go.”
The second pillar is governance. Staff need to know what’s encouraged, what’s permitted with care, and what’s off limits. A policy document that lives in a shared drive and gets presented at the annual away day does not constitute governance, Mark says.
Kinetic’s own AI usage policy, he continues, was deliberately kept simple: three buckets (allowed, permitted with guardrails, and prohibited) with explicit reassurance that good-faith experimentation would not be punished. That clarity, he says, drove more adoption than any technology investment.
The third pillar is culture. Does your organisation have the appetite for iterative, incremental change? AI works best in small, frequent experiments where people can try something, measure it, learn, adjust and iterate. Organisations accustomed to large, carefully planned project cycles often struggle, because AI is, by its nature, non-deterministic.
“The ready organisations are not necessarily the ones furthest along; they’re the ones building the right foundations,” Mark explains.
An organisation that leads with the technology – by rolling out Copilot to everyone before addressing data quality, governance, or people readiness, for example – is putting the cart before the horse, he continues. An organisation with an AI strategy document that nobody can describe in practical terms has aspiration, not a strategy. An organisation that measures AI adoption by licence utilisation (“85% of staff have logged in”) rather than by outcomes and behaviour change has mistaken access for use.
A genuinely ready organisation, on the other hand, is one making architectural decisions with AI in mind, even before it has deployed any AI features. It is asking, of every technology decision: will this make it easier or harder to use AI in 12 or 24 months? Its leadership is having honest conversations not just about automation, but about what AI means for the skills they need, the roles they design, and the way they serve their customers. And critically, someone specific is accountable for the AI journey; not a committee, but an individual who owns the assessment, the roadmap, and the outcomes.
“Committees discuss, individuals deliver,” Mark says. “Someone needs to own this.”
Henry is candid about how he expects this to play out over the next few years for organisations that don’t build genuine readiness.
“It’ll look like businesses that are doing the right things on the surface, making the right noises,” he says. “And then something comes out of the blue. Someone more nimble just gets around them. They get outmanoeuvred.”
The pattern, he suggests, will look like many technology transitions before it: a long period where nothing seems to be changing, and then rapid, visible divergence. Organisations that have been quietly building the foundations will accelerate. Those that haven’t will find themselves trying to catch up from a standing start.
Mark frames it in terms of the underlying question every organisation needs to answer honestly:
- Do your people know where you stand on AI? If you asked ten people across your organisation right now what’s encouraged, what’s permitted, and what’s off limits, would they give you consistent answers?
- Could your people describe specifically how AI changes their role? Not in generic terms, but what would it mean for a customer services advisor, an operations manager, a finance lead?
- When did your staff last have genuinely protected time to learn, explore, and experiment with no deliverable expected at the end? If the answer is never, that is a readiness gap that needs to be addressed.
- Is your data clean, structured, governed, and accessible enough to support AI-assisted analysis?
- Do you have one person accountable for your AI journey? Without that ownership, AI remains a conversation rather than a capability.
The goal isn’t to arrive at the perfect answer. The goal is to move from an implicit, unconsidered position to an explicit, honest one, and then to start building, incrementally, from where you actually are.
So the real question isn’t whether your organisation is ready for AI. It’s whether you are on the right path to get there. And when the moment comes that AI can no longer be ignored, will you be reacting to it, or will you be hitting the ground running?
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