Real-world AI, nuance, and why “AI for the Rest of Us” felt refreshingly grown-up

Last week I had the privilege of speaking at AI for the Rest of Us – a day packed full of thoughtful conversations, healthy scepticism, and, most importantly, a real sense of maturity about the topic of AI.

Instead of being dominated by buzzwords, product pitches, or binary debates about “AI good” or “AI bad,” the day was full of nuance. People talked about representation and the gender gap in AI (a topic my colleagues Christie and Liz wrote about recently), the long history of overblown AI promises (which, yes, existed before ChatGPT), and – perhaps most encouragingly – a real understanding of just how broad the AI landscape is.

That last point matters. Too often, everything gets collapsed down into a single “AI” conversation, creating pressure to treat it as one monolithic thing. But the reality – and what this event captured beautifully – is that AI is a collection of overlapping, evolving technologies, practices, processes, opportunities and trade-offs. 

It’s far messier, more interesting, and more human than the hype suggests.

My talk: Real-world AI Delivery

My session was called “Real-world AI Delivery”, and it’s based on an internal playbook we’re building at Equal Experts. This isn’t about being an AI evangelist or claiming to be a machine learning expert (I’m not either of those things). It’s about helping delivery teams get practical value from applying AI today – and when we say “AI” we’re really talking about GenAI and, more specifically, Large Language Models.

The core idea is simple:

The biggest benefits from AI in software delivery come from taking a whole-team view.

That means:

• Focusing on reducing time to value rather than chasing abstract productivity gains (see my last blog post for more on the “productivity” topic)
• Concentrating and amplifying shared human knowledge, rather than trying to replace it.

I shared six principles we’ve been using across real delivery teams:

1. AI is an amplifier, not magic – Understand how LLMs actually work, build on proven practices, and set realistic expectations.
2. Teams own intent and outcomes – AI is a tool, not a contractor. Keep accountability human.
3. Documentation is shared reasoning – Lower the cost of creating and maintaining living assets, and use AI to support shared understanding.
4. Focus on value delivery, not demonstration – Real learning happens in real work, not in lab experiments.
5. Actively reduce context load – Smaller, well-scoped tasks produce better outcomes.
6. Sustainable pace requires deliberate choice – Beware the hackathon deceit; pace must be balanced with sustainability.

These principles are supported by a growing set of patterns and anti-patterns – things like “Build throwaway tools”, “Yesterday’s Weather”, “The AI Gauntlet”, and “Automate the Automaton” – all grounded in what we’re actually seeing in delivery teams today, not what vendors promise.

(If you’re interested, follow me on LinkedIn and I’ll reshare the full talk video when it’s released)  

Thanks to Hannah and the team

A huge thank you to Hannah and the organising team for creating such an inclusive, thoughtful event. It was a space where conversations weren’t just about “what’s possible” with AI – but about how to use it responsibly, sustainably, and in ways that make humans better.

That’s the kind of conversation I want to keep having.

If any of these ideas resonate – or if you’ve got your own patterns and anti-patterns from using AI in delivery – I’d love to hear from you.

About the author

Phil Parker is Head of Technology Strategy at Equal Experts, where he helps organisations navigate the rapidly evolving technology landscape and deliver meaningful business outcomes. With more than two decades of experience spanning software product delivery, agile transformation, and strategic leadership, Phil specialises in shaping technology approaches that align with organisational goals and deliver lasting value.

He is passionate about applying emerging technologies – at the moment particularly AI in Delivery – in practical, outcome-focused ways, and about building collaborative, empowered teams that solve complex problems. Phil’s work is driven by a belief that great technology strategy is as much about people and culture as it is about tools and platforms.