AI is redistributing tasks faster than organisations are redesigning roles, decisions, and learning systems.

Last year I kept hearing the same two sentences in the same meeting. Everyone is using AI now. Nobody has changed how the work is organised. Both were said with confidence, and neither was measured, so I built a workforce intelligence platform to measure it.

There are two corporate phrases I hate more than anything: "be more strategic" and "use more AI." These phrases reveal a fundamental misunderstanding of what both strategy and AI are.

For the past couple of years everyone has been using AI, and companies aren’t yet seeing the results, because the work around it hasn’t changed.

Verellax reads the hiring record: 43,967 live job specifications from 283 employers across seventy-two markets, as at 2 October 2026. Each role is read on whether it involves AI, whether AI sits at the centre of the work, and whether the role is accountable for what the model produces. Every AI-central role is then read on four tests. Accountable is one of them.

1. 01Works with the model.
2. 02Can overrule it.
3. 03Is accountable for what it produces.
4. 04Keeps decisions over it.

What the data shows is a distance, and the distance has a direction. Adoption arrives at the top of an organisation and stops before it reaches role design.

This is not a forecast. It is the hiring record, written by the companies and hiring managers themselves, with a date on every line.

I am not the first to say that roles lag tools. What I can do that the last person couldn’t is measure it.

AI has fundamentally changed knowledge work, and tasks have been redistributed from humans to machines.

AI isn't transforming work by wiping out whole jobs. It's changing work at the task level. Cognitive work that used to span different roles is being automated, accelerated, or reshaped, so most jobs won't vanish outright, but the composition of each role is changing.

Execution work is the layer a model absorbs first, and the layer that goes next is the one middle management was built to run: the handoffs, the sequencing, the approvals. Jack Dorsey said the same from the top. He cut Block from over ten thousand people to under six thousand in February, and in March wrote with Sequoia's Roelof Botha that hierarchy has only ever been a way of routing information and a system can now do the routing.

And the roles that use AI are arriving. Of roles advertised in Q3 2026 that involve AI, 60.5% are designed for people who use AI (all collected specs, posting-date basis · read week ending 2 October 2026). Companies are hiring people to use tools the specification makes no one accountable for. The job that changed is the job that uses AI, and nobody rewrote it.

Companies need to redesign how they operate. The role didn't move with the task.

The seat that governs it is the one hired least.

Companies write AI policy far more often than they write about who is accountable for what the model produces. The minority that has designed the work furthest is in seven of eight sectors, so the deficit is a choice rather than a property of the technology.

That is a design failure rather than a technology failure, and the organisations that own it are the ones writing the specifications, not the market they hire in. Adoption is not the question any more. Design is, and almost nobody is hiring for it.

People need to learn new skills.

Roles are changing shape, careers are losing their old ladders, and businesses are realising that many of their speed and efficiency problems are system design problems, not people problems.

Graduates used to learn judgement inside execution work. Employers are removing that apprenticeship without designing its replacement: of 874 live Australian entry-level roles, 40 involve AI, and 11 of those have AI at the centre of the work (live specs, week ending 2 October 2026). About one in five roles that involve AI, 21.7%, are designed for people who build it (all collected specs, posting-date basis · read week ending 2 October 2026).

The work changed faster than the organisation, and faster than the people inside it were taught. That is the premise. The thesis beneath it is the part I can measure:

Where this came from

I've spent my career inside big agencies and global companies, Disney and M&S among them, and I see technology changing how work gets done every day. I had been building with AI a lot in my spare time, and I started to notice that it was fundamentally changing the way I worked. What improved my adoption of AI was redesigning how I worked, down to the task level.

I don't have a technical background. I spent countless hours after the kids went to bed learning, through AI, about engineering, systems, workflows, databases, agents, agentic workflows, all the buzzwords. I first built CareerOS, a career-context platform to help people apply for jobs and learn new skills, and that's when I switched to building intelligence first, not tools. Like any strategy work I've done before, I was thinking about a problem and reverse-engineering the data points I would need to understand it.

This led me to something I'm now calling workforce intelligence: a new way to measure, redesign and build the businesses, operations and workflows of the future. I wrote the argument down in January, under the name Future Skills Intelligent Work, before the instrument existed. At the end of July, I quit my full-time job to build it properly. I couldn't shake the desire to get as close as possible to redesigning how work is done.

What I think it means

People expected AI to save them time. It redistributed the work. People are saving time with AI and don't know where to put it, because nobody has redesigned how the time should be allocated.

AI provides the foundation for the process; it's not the output. The output remains the same. The work is to redesign the process and workflows, down to the task level, and to write down the decisions before the workflow rather than after.

Always start with the work, not the tools. I learned that by building the tool first.

This technology is not going away and is becoming a fundamental commodity. Businesses need to completely redesign how they operate, and continue to do so, and people need to learn, and relearn, new skills. If redesign and relearning don't move faster than the tools, the cost lands on the people inside the roles. It's less another wave of tools and more a structural reset: closer to the Industrial Revolution than a software update.

The path to agentification is the journey. Agents aren't the destination. People are overselling now because everyone is racing to the finish, and many are overlooking the work still to be done. There is so much work to be done.

So Verellax measures where a company sits, redesigns the roles, decision rights, and workflows to close the gap, and builds the systems and governance to keep it closed. My goal is to lower that cost by supporting businesses in redesigning how they work and helping people learn the skills they need for the work that's coming, including standing in front of universities with the Skills Index data.

The smallest proof I have

The platform that produced every figure on this page is run by agents. Before any of them could act, I documented the frameworks and the thought process, built the context files they decide from, and codified the decisions: what each one may promote, what it must discard and why, and where I hold the gate. I wrote the decision rights for my own agents before I measured whether any company had written them for its people. That's why the tools were worth anything.

So read your own specifications back. Not the values page, the specifications. Whichever role first names who is accountable for what the model produces is where the redesign has already started.