Until recently, technology’s history fit a single phrase: humanity’s quest to manipulate atoms.
Fire to stone tools to metal. Agriculture to the Industrial Revolution. Horse-drawn power to steam, combustion, electricity.
Then something shifted.
The Abstraction Jump
By mid-twentieth century, technology moved to a higher level. Data and information became recognised as core properties of the universe.
We stopped just moving atoms. We started processing signals.
VUCA emerged when conflict changed character. Volatile, Uncertain, Complex, Ambiguous. It proved useful for describing similar shifts in business.
But the world kept accelerating. Linear progression steepened into an exponential curve.
BANI came to reflect newer dynamics. More brittle. More anxious. More nonlinear. More incomprehensible.
Better vocabulary. Same gap.
Description without direction.
The Tidal Event
Now we face something different. Not just a coming wave — a tidal event.
Two general-purpose technologies capable of operating at both the grandest and most granular levels: AI and synthetic biology.
Technology is undergoing a phase transition. No longer merely a tool, but an augmentation of intelligence. AI replicates speech, vision, reasoning. It may soon rival our own capability.
Then surpass it.
The Gap Nobody Talks About
Here’s what bothers me.
VUCA named the storm. BANI intensified the warning. Neither tells you what to do Monday morning when your roadmap hits dependencies you mapped but couldn’t predict.
Right now, I’m working on an AI pilot at BJSS. As an Organisational Agility Strategist, my focus differs from most colleagues on the pilot. They’re exploring software engineering acceleration, project delivery optimisation, service design efficiency. All valuable.
I’m asking a different question: how can Copilot and generative AI increase agility in client organisations through impactful integrations?
The use cases I’m testing: AI-assisted client diagnostics that surface patterns across previous engagements. Knowledge retrieval that makes institutional memory searchable. Proposal development that draws on proven approaches rather than starting from scratch. Engagement scoping that identifies risks earlier.
Early indicators are promising. Discovery phases that took 2-3 weeks appear to be compressing to 4-5 days. Knowledge retrieval — previously hours of searching across documents and colleagues — now takes minutes. Proposal development seems to be accelerating by 40-60%, though we’re still validating.
The irony isn’t lost on me. I’m using AI to navigate the turbulence that AI is creating.
And here’s what I’m learning: the technology works. The bottleneck isn’t capability. It’s diagnosis. Teams reach for AI tools without first understanding what problem they’re solving. They automate the wrong things faster. The firms seeing real gains aren’t the ones with the most sophisticated AI — they’re the ones who diagnosed their friction points first, then applied AI to the right problems.
We’ve become excellent at describing turbulence. We remain terrible at navigating it.
I’ve watched this pattern across 40 years of transformations. Leadership teams gather. Someone presents a framework. Everyone nods. Nothing changes.
Why?
Because knowing you’re in turbulent conditions doesn’t tell you which lever to pull. “We’re in a VUCA environment” is observation, not action. “We need to be more agile” is aspiration, not intervention.
The gap isn’t vocabulary. It’s the operational bridge between naming what’s happening and choosing what to do about it.
The Skill That Actually Matters
The leadership capability that matters most now isn’t better forecasting.
It’s better noticing.
Not predicting what will happen. Perceiving what’s happening right now — fast enough to respond before the window closes.
Most leaders I work with aren’t failing because they lack frameworks. They’re failing because they’re applying yesterday’s logic and diagnosis to today’s conditions.
The symptoms look similar. The root causes differ. The interventions required are often opposite.
Same velocity decline. Three different teams. Three different causes. Three different responses needed.
Apply the wrong intervention and you make it worse. In a system, you can compound the severity.
The Question Worth Asking
When your transformation stalls — and it will — what’s your first move?
Most leaders reach for more alignment. More communication. More meetings. More emails. More dashboards.
Sometimes that’s right. Often it’s exactly wrong.
The differentiator isn’t knowing more frameworks. It’s diagnosing which condition you’re actually facing before you pick your response.
Here’s the uncomfortable question:
When did you last pause to diagnose conditions before reaching for your favourite solution?
I’ve spent three decades documenting patterns across hundreds of transformations and engagements. The hardest lesson: the same symptom can have opposite causes. I’ve seen what transpires when a leader gets the diagnosis wrong — it costs months of effort and time, and sinks millions in spend.
What patterns are you seeing in your organisation right now? What’s your diagnosis?
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