Experience

Independent Slate Supplies could see that AI might have real value for the business, but they did not know where to start. Put simply, they didn’t know what they didn’t know.

To tackle this, I designed and delivered an exploration session with the leadership team. It combined clear explanation and practical demonstrations with a simple framework for asking the right questions about how the business worked, where people were losing time or missing information, and where AI might genuinely have something to add.

Beforehand, I spent time understanding ISS through earlier conversations and AI-assisted deep research using publicly available information. That helped me build demonstrations grounded in the company’s specific context rather than generic examples. In the room, the team could compare what the research suggested with what they knew from running the company. That contrast was revealing: AI could surface a remarkable amount very quickly, while their experience showed where the picture was incomplete, superficial or simply wrong.

Together, we surfaced possibilities across communications, sales, customer relationships, strategic planning and business data. The useful part was not producing a longer list of things AI could do. It was deciding which ideas were genuinely relevant, which were worth pursuing, and where to start.

That first piece of work did not end with a list of ideas. The collaboration has since moved into building and testing AI-driven solutions around real business processes, including reporting and analysis. What began as uncertainty about where to start has become applied work shaped around the business and the people who know it best.

JensonR+ is a specialist pharmaceutical consultancy working across areas including regulatory compliance, quality assurance and pharmacovigilance. As AI began to affect those fields, the question was how to build useful capability without compromising the accuracy, confidentiality and professional judgement the work depends on.

The two-part programme was part-funded through the Government’s Flexible AI Upskilling Fund pilot. I surveyed colleagues beforehand about how they were already using AI and what they most wanted to understand. That brought out concerns around data privacy, reliability, bias and appropriate use, alongside practical questions about where AI could help when time or specialist capacity was constrained, including marketing and content development.

The sessions moved from foundations into practical demonstrations using tools and approaches including customised GPTs, NotebookLM and the Generative AI Canvas. Examples were built around Jenson’s own context, from communications and content creation to quality and regulatory work. The focus kept returning to judgement: where AI was useful, where it could be unreliable, and where human verification remained essential. Participants particularly valued the customisation, real examples and demonstrations.

One of the things participants particularly valued was the responsiveness of the programme. The second session was reshaped around what had emerged from the first, and people explicitly recognised that their feedback had been listened to and acted on. The JensonR+ team later reflected that the training had given them the tools and confidence to begin testing where AI could support quality assurance, regulatory compliance and decision-making, while keeping human oversight central.

The MTU programme brought together academic researchers alongside professional services teams that support research and innovation. The mix of roles, disciplines, different starting points with AI and perspectives made it unusually demanding to design — and hugely rewarding to experience.

Several months of planning with MTU gave us the space to design it as a progression rather than a single generic workshop. The first day gave the two communities their own starting points, followed by time to reflect and experiment before we came back together the next morning for recap, reflection and recalibration: sharing what had landed and what people had discovered, including some surprisingly inventive things they had already tried outside work, before moving into hands-on problem-solving.

The warmth and openness of the MTU culture were a big part of what made the programme work so well. Using the Generative AI Canvas and live AI tools, groups took on actual problems and began building AI-supported prototype workflows. Around the tables, people questioned assumptions, combined ideas and found approaches they might not have reached alone. The feedback repeatedly singled out the experimentation, collaboration and value of hearing how others had explored possibilities.

Feedback described a sense of achievement, reduced apprehension about using AI, and a desire to continue developing the ideas with colleagues. Participants described having their sense of what was possible opened up, and leaving energised to put new ideas into practice.

The NCACE Bridging Worlds webinar brought together people working across universities, arts and cultural organisations, creative practice and public engagement. Participants arrived with a striking spread of reactions to AI: curiosity and excitement alongside caution, discomfort and strong concerns about authorship, ownership, bias, environmental impact and unequal access. The cultural context put many of these issues sharply into focus.

The pre-session responses shaped the webinar around the interests, concerns and different starting points in the group. Alongside explaining how generative AI works, the session used live demonstrations and examples drawn from cultural knowledge exchange: anticipating tensions in partnerships, translating research for different audiences, testing ideas from different perspectives and strengthening grant and project development. There was plenty of space for questions and for participants to test the examples against their own experience.

Feedback highlighted the clarity, relevance and balance between technical understanding and practical application. One participant described the session as “clear, accessible & not patronising to those new to AI”, while others picked out the emphasis on working with AI through dialogue while keeping direction and judgement with the person doing the work.

There was also a clear appetite to go further. That has helped shape a follow-on NCACE workshop in autumn 2026, moving from an introductory online webinar into more sustained, hands-on work together in person.

This session brought together professional services colleagues from Scottish universities working across academic consultancy, business engagement and knowledge exchange. Almost everyone who responded beforehand was already using AI to some degree, and their questions were specific: due diligence, market and competitor research, bids and contracts, project management, workflow automation, communications and identifying new opportunities for collaboration. Accuracy, confidentiality, data privacy and responsible use were also firmly in the mix.

The session moved quickly into practical ways of working. We looked at prompting in some depth, worked through live examples and explored tools including NotebookLM, connecting them back to the kinds of research, analysis and professional tasks people had raised beforehand. That gave participants concrete material to test against their own roles and institutional contexts.

Feedback particularly highlighted the prompting language, detailed walkthroughs, new tools and applicability beyond the immediate focus of the session. People also valued hearing how colleagues at other universities approached similar work, and several responses pointed to ideas they wanted to start using or experimenting with afterwards.

A recurring request was simply for more time. Participants wanted space to experiment, revisit the slides and tools, and explore how AI could fit into existing workflows and procedures, including task management, project management and review processes.