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.

The Paradox of Progress: Why we're underwhelmed when the future finally shows up

By Nathan Waterhouse · 2025-08-25

The other week, my mum decided to give ChatGPT another try. She'd read a couple of headlines about the latest AI breakthroughs and, curiosity piqued, opened her laptop.

"Play me some Elton John," she typed.

When the screen politely explained it could make her a playlist but not stream the music itself, she tutted: "I thought these things did everything now."

In her mind, the curve of capability had already raced far ahead of reality. The fact that this system could instantly compile a customised playlist from a lifetime of Elton's catalogue wasn't even on the radar. Instead of wonder, she felt mild disappointment.

That's the paradox of progress.

The Expectation Escalator

Humans are famously bad at judging exponential change. In the early stages, we underestimate it, assuming growth will be slow and steady. Then, as the curve steepens, our expectations jump on the same trajectory. We start assuming each leap will be as dramatic as the last, and that the next breakthrough will arrive without delay or extra effort.

Consider generative AI. Barely two years ago, the 'wow moment' was getting ChatGPT to write a wedding toast or a plausible sales email. Now we frown if it can't storyboard a marketing video, translate it into seven languages, and add just the right emojis, all in one go. GPT-5 is more capable than GPT-4, but because our expectations have compounded even faster, many people call it "underwhelming."

We're wired to think in lines, not curves. The rice-on-the-chessboard paradox shows how exponential growth looks trivial until the very end, when it explodes. Even experts misjudge it: in the 1980s McKinsey forecast fewer than a million U.S. mobile subscribers by 2000. The reality was 100 million.

Part of the problem is that technology rarely moves in straight lines anyway. Studies of dozens of technologies show they tend to follow S-curves: long periods of build-up, then rapid leaps, then plateaus. From inside the curve, each phase feels underwhelming. Only later does the shape of the change become obvious.

Take commercial aviation. After the Wright brothers' 1903 flight, progress exploded: within a decade, planes were crossing the English Channel. But then came the plateau. For nearly twenty years, aviation crawled forward with incremental improvements to engines and materials. Passengers in 1935 might have wondered why they weren't already flying to the moon. It wasn't until the jet engine arrived in the 1940s that the next S-curve began, transforming global travel in ways those disappointed 1930s passengers could never have imagined.

Why Organisations Can't Keep Up

The same expectation gap is now playing out inside organisations. The capability curve of AI is rising, but the adoption curve is far flatter. McKinsey research shows that while nearly every company is investing in AI, just 1 percent consider themselves truly mature, meaning AI is fully embedded in workflows and consistently delivering value. In other words, the technology is sprinting ahead, but organisations are still lacing up their shoes.

We also underestimate the real effort it takes to embed these tools in ways that genuinely add value: aligning with existing processes, building employee confidence, and shifting the culture. A recent HP/YouGov study found 72 percent of UK employees using AI tools save time weekly, with one in ten saving more than five hours. Yet more than a quarter of UK businesses still have no formal AI strategy, evidence of a disconnect between grassroots experimentation and leadership intent.

The Hidden Friction of Progress

But even when organisations do push for adoption, hidden friction emerges in unexpected places. The gender dynamics are particularly revealing. BCG research shows a fascinating split: whilst junior women lag behind their male counterparts in AI adoption, senior women are actually ahead, with 87% of women leaders regularly using GenAI compared to 79% of men at the same level.

This creates a powerful opportunity. The same study found that when senior women model AI use openly, it significantly increases adoption among junior women in their organisations. It's a reminder that progress doesn't affect everyone equally, but also that visible leadership can bridge these gaps.

The challenge is real for junior women: a Harvard Business Review study found male non-adopters judged female engineers who did use AI 26% more harshly. But when senior women demonstrate that AI use is not just acceptable but expected at the highest levels, it changes the entire dynamic. This normalises adoption, reduces stigma, and creates permission for experimentation without fear of judgement.

Progress rarely feels smooth from the inside. Each leap is followed by a plateau, a period of refinement before the next burst. Those pauses can feel like stagnation if you expect constant acceleration.

Working with the S-Curve

History is full of examples. The first iPhone felt like magic, but the next few models were mostly better cameras and faster chips. It's only with hindsight that we see the true shape of the change. Understanding this pattern can help leaders navigate their own S-curves more effectively.

Recommendations for Leaders

1. Skills and culture, not just tools

Invest in building AI fluency across all levels, not just in technical teams. People don't need to be coders to use AI, but they do need confidence, context, and permission to experiment. Create "AI office hours" where employees can bring their real workflow challenges and get hands-on support.

2. Model the behaviour

Leaders should use AI openly in their own work. Share your prompts, your failures, your breakthroughs. When the CEO of a major UK retailer started opening board meetings by showing an AI-generated summary of customer feedback, it sent a clear signal: this isn't just IT's problem to solve.

3. Work with the S-curve, not against it

Expect periods of refinement between leaps. Use those plateaus to consolidate value: tighten processes, capture learnings, and prepare for the next acceleration. During aviation's 1920s plateau, airlines didn't stop; they built airports, trained pilots, and created route networks. When the jet engine arrived, they were ready.

4. Close the adoption gap deliberately

Pair grassroots experimentation with a top-down strategy. Encourage pilots at the edge of the organisation, but don't leave them stranded: connect them back to core workflows and goals. Create clear pathways for successful experiments to scale

Perhaps my mum was right to be disappointed. Not because ChatGPT couldn't play Elton John, but because we've already imagined a world where it can, and much more besides. The future rarely feels as remarkable as it truly is, because by the time it arrives, we've already moved the goalposts.

That's the paradox of progress: we're always living in yesterday's future, wondering why it doesn't feel like tomorrow's.