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Episode 13: Live from the Glastonbury of AI — Our Gartner Debrief 🍺

Week 13. Unlucky for some — but not for two people who've just spent three days at the Gartner Data and Analytics Summit, AKA the Glastonbury of AI. Neil says he was nearly as exhausted after three days sitting down as after five days at the actual Glastonbury. What goes on at Glastonbury stays at Glastonbury. But what goes on at Gartner comes out on this podcast!

The emotional rollercoaster 🎢 Monday evening beer: both of them felt reassured. Everything they'd heard confirmed Leading AI was on the right track. Tuesday: Kieron doubted everything — tech bro language, acronym soup, imposter syndrome at full volume. By Wednesday: unpack the jargon, and it turns out they already knew most of it, and in some cases were ahead of it. Neil's summary: two separate meetings with Gartner specialists, both said we were on the right track. Donald's response to Neil's email was along the lines of: "I'm on it, stop hassling me"... only ruder.

Context is king 👑 Kieron's biggest takeaway. The context layer — telling your AI what your organisation is, what your data means, and how different teams need to use it — is the difference between good retrieval and bad retrieval. The example: ask an AI "how many sales did we have this quarter?" Without context, it doesn't know what "sales" means (invoiced? agreed? handshake?), what your financial quarter is, or which column in your MIS system to look at. KnowledgeFlow already builds data dictionaries automatically when it loads data — but there's more to do. Knowledge graphs are the next step: storing context so the AI can pick up the right layer depending on who's asking.

Ontology, knowledge graphs and semantic layers — explained for humans 🧩 Neil was confused by the three terms being used interchangeably at Gartner. He asked Perplexity to explain the difference like a 15-year-old. The answer: think of a school. The ontology is the rule book (what is a teacher, what is a pupil). The knowledge graph is the directory (Bob is a teacher, Alice is a student). The semantic layer is the notice board (how many pupils are in Year 10?). Get all three in place and your retrieval gets dramatically better. Turns our they're already doing a lot of it — they just didn't know it had a name.

Feedback loops — the missing piece 🔄 Kieron's second big theme. The agentic email system works — it reads inboxes, triages, drafts responses, handles routine inquiries automatically. The next challenge: capturing what happens when a human looks at the draft. Did they send it unchanged? Edit it slightly? Rewrite it entirely? That data, captured over hundreds of interactions, tells you which types of email to fully automate and which ones still need a human. For a Housing Association, if 297 out of 300 pet policy inquiries sent unchanged are sent unchanged, automate your pet policy. The challenge: you only capture that feedback if the human stays in the platform rather than copying and pasting out of it. Which leads neatly to...

How do you make KnowledgeFlow so good people feel stupid going anywhere else? 💡 Neil's challenge to the team. Inspired partly by Gartner's focus on designing solutions that disappear — like the GP recording consultation tool that lets doctors look at patients instead of screens. And inspired partly by the stat that doctors interrupt patients after an average of 18 seconds. If the technology is invisible, the human interaction improves. Ibby and Donald are already building something. Watch this space.

Human in the lead, not human in the loop 🧠 One of Gartner's sharpest lines. Don't just put humans in the loop to click okay, okay, okay — they'll stop paying attention and let everything through. Use humans where human judgment actually matters. Pet inquiry? Automate it. Mould report or smell of gas? Human in the lead, immediately.

The Gartner stats (cos Neil's a stato at heart) 📊 Only 6% of AI leaders surveyed believed their organisations and people were AI ready. Only 12% felt their data was properly secured and governed. And fewer than 50% of organisations currently track their AI costs. Gartner's framework: are you AI cautious, AI plus, or AI first? Because if you're cautious while your competitors are AI first, you're already losing ground you may not get back.

The bonkers corner 🤪 The futurist with purple shoes was very entertaining. Neural prosthetics already exist that let you move a hand by thought. But would you take a cheaper version if it played ads? (There's a Black Mirror episode about this.) Would you let your employer connect to your neural network and pay you for time spent thinking about work? A woman in the audience laughed so hard she got the microphone. Her response: "If that happens, I'm screwed. I don't think about work all that much." Final thought from purple-shoes: would you want your wife to connect to your neural network? Neil's verdict: the doghouse would be permanent.

Product of the week 🎵 (hum the jingle) KnowledgeFlow now has memory. Kieron tested it by telling it to start every response with "Hey dude." Forgot he'd done it. Later asked it to analyse some data. It said: "Hey dude, here's the analysis." The serious version: memory means KnowledgeFlow can remember your role, your preferences, your output formats — securely, inside your own Azure tenancy. Something Claude can do publicly, but not securely. KnowledgeFlow now can.

Neil is in Scotland in the sunshine. He's knocking the top off a beer and going into the garden. Neil's wife Helen should probably (definitely) not connect to his neural network.

Two mates. A bar. Thirty years of business between them. And all they want to talk about is AI.

Pull up a stool — we'll get the beers in. 🍺