We’re All Experimenting: The Quiet Middle Ground of AI Adoption

TL;DR:

  • Most organisations occupy the middle ground between pilots and full AI integration
  • Success comes from disciplined experimentation as operating habit, not moonshots
  • Architecture—how you connect people, partners, and machines—defines competitive advantage
  • HR becomes outcome integrator, owning skills development and culture change
  • Real progress emerges from thousands of small, well-run experiments

When a Fortune 100 manufacturer’s CIO recently asked where organisations truly stand in AI adoption, the answer proved both simple and revealing: we’re all in the same place. Despite headlines suggesting otherwise, most organisations today occupy the quiet, unglamorous middle ground of experimentation—and that’s precisely where real progress begins.

Beyond Pilots, Before Integration

The current phase sits distinctly between two extremes. The pilot era, when every company needed an “AI strategy” to signal relevance, has passed. Yet few organisations have reached full-scale integration. What’s happening instead proves more important: organisations are learning what it means when human intelligence and synthetic intelligence actually work together.

Across manufacturing, logistics, healthcare, and education, the pattern remains strikingly consistent. Organisations have stopped treating AI as novelty and started treating it as operating habit. They’re building experimentation muscles through assessment, testing, learning, building, and scaling. No moonshots. No hype presentations. Just disciplined iteration practice.

Architecture as Competitive Advantage

John Winsor, writing at the intersection of digital transformation and AI, argues that intelligence—whether human or synthetic—isn’t what’s scarce anymore. Architecture is. The way organisations connect people, partners, and machines now defines competitive advantage.

This echoes the Open Talent framework developed years earlier: brilliance is abundant but opportunity is scarce. In the AI era, that principle still holds. The organisations succeeding aren’t necessarily those with the smartest models or largest datasets. They’re the ones that have built effective coordination layers—what Winsor calls Work Operating Systems—enabling humans and AI agents to operate from the same playbook.

Deliberate Experimentation

The best organisations don’t experiment everywhere. They experiment deliberately, selecting meaningful workflows, running structured tests, measuring results, and building reusable patterns others can learn from.

Coursera exemplifies this approach. When CEO Jeff Maggioncalda began experimenting with ChatGPT in late 2022, he didn’t greenlight dozens of flashy initiatives. Instead, he launched Project Genesis—a disciplined portfolio organised around three metrics: value, cost, and ease.

The results speak clearly. Translations that once cost nearly $10,000 per course now cost around $20, opening 4,400 courses in 21 languages. Coach, an AI-powered learning assistant, improved student quiz pass rates by approximately 10 percent. Course Builder enables educators to assemble new curricula in hours instead of weeks.

None of these wins came from breakthrough moments. They emerged from small, structured experiments—each modest enough to fail safely but rigorous enough to generate learning. That’s what effective experimentation looks like: steady, cumulative progress.

Human Plus Synthetic Systems

This learning loop—test, measure, improve, repeat—appears across sectors. At Mayo Clinic, radiology teams now operate hundreds of AI models whilst employing 55 percent more radiologists than in 2016. AI didn’t replace expertise; it scaled it. By instrumenting workflows and embedding AI as collaborator, Mayo transformed synthetic intelligence into teammate rather than threat.

The architecture of adoption isn’t purely technical. It’s social—about how humans and machines share context and responsibility. Organisations treating experimentation as infrastructure rather than one-off project are the ones gaining traction.

HR as Outcome Integrator

One surprising development: the leaders aren’t always CTOs. Increasingly, it’s CHROs. Human Resources is becoming the outcome integrator of the AI era, owning skills taxonomy, incentive systems, and culture change that make human-agent collaboration sustainable.

When HR treats experimentation as learning engine rather than compliance exercise, adoption accelerates. People stop fearing AI and start shaping how it’s used. In the Human plus Synthetic era, HR doesn’t just manage people—it designs systems of learning.

Making the Ordinary Heroic

What stands out in this phase isn’t flash. It’s steadiness. The real heroes aren’t teams chasing viral demos or billion-dollar valuations. They’re teams wiring organisations for continuous learning—building feedback loops, adjusting incentives, refining workflows one experiment at a time.

This connects to the Open Talent movement’s core premise: turning work into living, adaptive system that evolves as fast as the world does. Now, with AI in the mix, that system includes teammates we cannot see but can measure.

Where We Really Are

When asked where we are in AI adoption, the answer remains consistent: right where we should be. In the middle. Tinkering, testing, learning, and redesigning our architectures so human and synthetic intelligence can flow together.

It’s not glamorous. It’s not headline-worthy. But it’s the foundation of what comes next. When the history of this era is written, the defining story won’t be about the first AI that passed professional examinations or the fastest model to reach a trillion parameters.

It will be about the moment when work itself became legible—when every process could be read, improved, and shared by both humans and machines. That’s when AI becomes truly useful. That’s when intelligence, human or synthetic, turns into progress.

And it starts here, in the quiet middle ground, with the simple, unglamorous habit of experimentation.

Source Attribution:

Share this article