What does Adaptive Edge do?

Adaptive Edge is a UK strategy and innovation consultancy. We help organisations decide what to change, then make the change real: strategy built with the people who must carry it, AI adoption inside real work, and behaviour change that sticks.

How is Adaptive Edge different from a traditional strategy consultancy?

We do not hand over a deck and leave. Strategy is made in working sessions with your own teams, so the people who must deliver it own it from day one. We prove an approach in one team before asking the whole organisation to change.

Who does Adaptive Edge work with?

Global organisations across healthcare, industrials, media, retail technology and financial services, including ITAB Group, Freudenberg, Sky and Sandvik Coromant. Engagements run across Europe and beyond, remote and on site.

How does Adaptive Edge approach AI adoption?

We put AI to work inside real workflows rather than running abstract training. Teams learn on their own live work, we measure what actually changes, and we scale only what proves useful.

What does a typical engagement look like?

Most engagements combine focused research, co-created working sessions, and hands-on tools built for your context. They range from a single workshop to a multi-phase transformation programme.

How do we start a conversation?

Use the contact form on the site. We usually begin with a short call to understand the decision you are facing, and we will tell you plainly if we are not the right fit.

What Managing Taught Me About AI

By Nathan Waterhouse · 2026-01-27

It was late last week, and I was staring at a terminal window, stitching together outputs from three different prompts. I'd been at this for hours. My family had noticed I wasn't sleeping well — I kept going to bed with the laptop open, the blue light luring me back in for one more tweak.

Everything I'd built over the past month was consuming more and more of my time. It started to remind me of something I hadn't thought about in years: being a junior manager with too many direct reports, struggling to delegate properly.

Let me back up.

The Unblocking Phase

Before Christmas, I'd been on a journey with AI coding tools. I started with vibe coding platforms like Lovable, then moved to using Claude Code directly in the terminal. This unlocked ideas that had been sitting in a to-do list for years. I built 17 small apps, mostly things I could use in my consultancy work. It felt like a superpower.

Then reality set in. You have to maintain these things. Optimise them. Make them better. Every app I'd built created new work.

Agents seemed like the answer. If AI could work for me overnight — while I was asleep — maybe I could get ahead of the treadmill.

It didn't work out that way.

The Overnight Question

Jeremy Utley, a leading thinker on AI and innovation, asked a question in the AI community I'm part of: How could you get AI to do something for you overnight? So you wake up and the work's been done?

I started playing with that idea. My first agent built interview guides for a strategy project I'm running. I'd wake up, and there'd be a guide ready for whoever I was speaking to that day.

But instead of saving time, it created more. I'd review the output, correct it, re-prompt, refine. I was still doing all the thinking. I'd just moved the work from building to critiquing.

I felt underwhelmed. I kept thinking: isn't this just a bunch of LLM calls and prompts strung together? What actually is an agent?

What I'd built wasn't an agent. It was a cron job.

Then I watched an Anthropic video that reframed everything.

Goal-Driven, Not Task-Driven

They put it simply: real agents aren't task-driven. They're goal-driven.

"Make me an interview guide" is a task. "Ensure I'm ready for my interviews" is a goal.

The difference sounds subtle, but it changes everything. A task gets you a deliverable. A goal gets you an outcome — and leaves room for the agent to figure out how to get there.

That's when the management parallel clicked.

The Management Parallel

As a junior manager, I used to give people tasks. "Write this document." "Prepare this analysis." I'd check the work, correct it, send it back. I stayed busy because I was still doing all the thinking. I'd just outsourced the typing.

Over time, you learn that's not delegation. Real delegation is outcome-driven. You set a goal, some constraints, and let people figure out the path. You assess the outcome, not the task. That's when you stop being the bottleneck.

AI agents sit in exactly the same place. If you're task-driven, you're still the bottleneck — just at a higher level of abstraction. If you're goal-driven, you can actually let go.

Building Systems, Not Tools

This shift has changed how I approach everything I'm building now. I'm not building tools. I'm trying to build systems. Not things I prompt when I hit an obstacle, but infrastructure that runs whether I'm paying attention or not.

"Ensure I'm ready for my interviews" doesn't mean "make me a document." It means: check my calendar, pull relevant context from previous transcripts, flag if there's a gap in what we've learned so far, suggest questions worth probing — and only ping me if something genuinely needs my attention. The output might be a guide, or it might be a nudge, or it might be nothing at all. The agent decides.

I don't have this figured out. The tools may not be ready. I'm still fumbling. But the questions have changed, and that feels like progress.

How are you using AI? Has anything shifted for you recently? I'd love to hear if anyone else has had a similar reframe.