À PROPOS DE CE PODCAST 🔗
Professional Services Tech Talks is about what actually happens when you put technology to work in a professional services firm.
Garwood Growth’s Adam Maze and George White share what they’re trying, why they’ve made particular choices, what’s worked, what hasn’t and what they’d do differently. They’re starting with AI, before getting into the wider technology questions facing professional services firms.
Practical, honest and based on what they’re actually doing.
Fréquence de mise à jour:
weekly
Durée audio moyenne:
26 minutes
Anglais
Royaume-Uni
5 épisodes
depuis 22 septembre 2026
episodic
DANS CE PODCAST 🔗
DERNIER ÉPISODE 🔗
There's a lot of valuable expertise sitting within consulting firms, often captured in frameworks, methodologies and other IP. But what if you could use AI to turn some of that knowledge into interactive tools that clients and prospects can use for themselves?
In this episode of Professional S…
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ÉPISODES PRÉCÉDENTS 🔗
Meeting transcription tools are hardly new. But automatically producing a set of notes after a call is only part of what they can do.
In this episode of Professional Services Tech Talks, Adam Maze and George White talk through how Garwood chose its meeting transcription platform, Fellow, and what t…
Creating a client deck often starts the same way: find an old one, strip out the content and spend far too much time getting fonts, colours, logos and layouts where they’re supposed to be.
We’ve been experimenting with a different approach.
In this episode, Adam and George go behind the scenes of t…
You’ve chosen your AI tools. Now comes the bit that probably didn’t feature in the demo: policies, data protection, security and working out what people should actually be allowed to do with them.
In this episode, Adam and George talk through the AI governance Garwood has been putting in place, fro…
ChatGPT? Claude? Copilot? Something else entirely?
When we started looking at which AI platform to use across Garwood, we didn’t begin with a comparison of models and features. We started by looking at how our team actually works.
Where were people losing time? What were they already using AI for? …
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