Six months ago, we launched a new community to extend our AI scope into the area of professional services. It’s a broad canvas – admissions, student support, marketing, estates, finance, legal, compliance and more – but beneath all of that complexity sit three institutional priorities that rarely change: improving student experience, strengthening staff capacity, and sustaining the financial health of the organisation.
Those priorities matter because they should be the basis of every AI decision. It’s tempting to dive straight into tools and pilots in such a fast-moving market, but AI only delivers lasting value when governance, people and technology are treated as a balanced ecosystem from the outset. When institutions skip straight to the latest tech, they often end up with fragmented pilots, mismatched tools or costly experiments that collapse as soon as their enterprise platforms deliver the same features natively.
By bringing the “governance–people–technology” triad to the front of the conversation, AI becomes less a trawl through the marketplace and more a steady development of organisational capability.
Making sense of the marketplace (without letting the marketplace drive you)
So, what does the AI in professional services marketplace actually look like? There’s certainly no shortage of platforms, and there’s a myriad of new companies and associated websites. But institutions shouldn’t begin with the hyperlinks. The real decision drivers are data readiness, integration, sustainability, risk evaluation and the alignment to your individual institutional purpose.
Broadly, you’re choosing between:
Data & AI foundations – platforms that turn scattered institutional data into something AI can reliably work with.
Front-door AI services – tools that automate high-volume student and staff interactions.
Domain-specific solutions – ones for content and engagement to those for policy and governance workflow automation.
Ecosystem partners – organisations that help institutions extract real value from any existing Microsoft/Google-centric stacks.
Those categories are useful, but the critical question is not what’s out there? but what is my institution ready for? Data quality, system integration, sustainability, scalability and risk tolerance usually decide the answer long before a sales pitch does.
Where to start? The power of simple but significant
The most sustainable approaches emerging across FE and HE share a simple pattern:
Start with small, simple but significant use cases: policy management, triage workflows, finance and HR operations, or other back-office processes where measurable improvement is easiest to achieve. These aren’t just automation wins; they build staff confidence, strengthen digital capability and create the cultural readiness for later more ambitious AI projects.
Pick only a small number of strategic data and AI platforms. This provides the foundations for consistent governance, reuse, security and budgetary control.
Layer specialist tools only where they solve a sharply defined problem and integrate cleanly into your ecosystem.
Each of these choices should link directly back to those opening priorities: improving student experience, freeing staff for human-centred work and delivering long-term financial sustainability.
Current market trends – and what they actually mean in practice
The major trends shaping the sector right now are well established, but what matters is what they require from institutions:
Data consolidation – the move from siloed systems to cloud platforms gives AI the access to the coherent data it needs, but it also demands solid governance, classification, stewardship and clear ownership. When data quality is weak, AI simply amplifies existing inconsistencies rather than resolving them.
Agentic AI – tools that not only answer questions but take autonomous actions. They’re powerful, but they bring a requirement for safeguards, human-in-the-loop design, transparent audit trails and mature institution-wide conversations about risk.
Microsoft/Google-centric stacks – with many institutions already deep into Microsoft/Google the gravitational pull towards Copilot/Gemini-based ecosystems is strong. That requires a clear ecosystem strategy: clear roles, defined boundaries.
These trends don’t dictate a path, but they do help shape what AI “readiness” looks like.
Returning to the foundations: governance, people, technology
Circling back to my initial premise: balanced governance, people readiness and technology choices can make or break AI adoption. Governance needs to be considered early – GDPR, DPIAs, data governance, AI safety, sustainability, risk management and trade union expectations all sit firmly along the pathway.
People need clarity, confidence and literacy. This isn’t optional scaffolding; it’s the structure that stops pilots collapsing under their own weight of expectation. Jisc already offers extensive training and support in this area, available to all members.
And technology needs to be purposeful, not simply en vogue.
AI in professional services will continue to evolve at pace, which is why the community exists: a place to share challenges, surface lessons learnt and build capability collectively.
Conclusion: purpose, capability and a measured path to success
Successful AI adoption in professional services won’t come from racing toward the newest tools but from clear purpose, balanced governance, strong data foundations, and purposeful piloting. When institutions build capability gradually – starting with high-value mundane tasks and growing staff confidence along the way – they set themselves up not just to adopt AI, but to absorb it sustainably.
If you have a story of piloting, implementation or stakeholder experience, we’d love to work with you on a case study. The community is open, active and built for exactly these conversations.
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One reply on “AI in Professional Services: Purpose first, product second”
This is a well-articulated and grounded perspective on AI adoption that cuts through much of the current hype. I especially appreciate the emphasis on starting with institutional priorities rather than tools, and the consistent return to the governance–people–technology triad as the foundation for sustainable progress. The focus on small but significant use cases is particularly compelling—it acknowledges real operational pressures while building confidence and capability in a pragmatic way.