2026 will not be remembered as the year AI got better.
It will be remembered as the year organizations were forced to confront whether they could actually change how work runs.
The last few years were about possibility.
2026 is about consequences.
Here is what I believe will be true.
By 2026, insight will be expected.
Most large organizations will have ways to analyse work, tasks, skills, and AI opportunity. Dashboards will be common. Reports will be polished. Heatmaps will look convincing.
That will no longer be enough.
Insight that does not lead to work running differently will lose credibility quickly. Executives will stop funding visibility efforts that cannot point to real change in production.
Pilots have been a socially acceptable way to delay commitment.
That tolerance disappears in 2026.
Boards and CEOs will stop asking where something was tested or demonstrated. The only question that matters is whether it is running in production as the default way work gets done.
Organizations that cannot answer that question will struggle to justify continued AI spend. The language of “we’re still learning” will stop working.
Execution replaces experimentation as the baseline expectation.
Large transformation programs were designed for a world where change was episodic.
ai pilotTraditional transformation programs were designed for episodic change.
AI breaks that model.
In 2026, work will change continuously. Tasks will be added, removed, and recombined. Roles will evolve in months. Agents will enter workflows incrementally rather than through large, coordinated launches.
Programs built around multi-year roadmaps and heavy upfront design will increasingly slow organizations down instead of helping them adapt.
Authority will move away from transformation offices and toward systems that allow work to change safely, continuously, and in production. Change stops being something organizations run and becomes something they operate.
Employees will no longer be periodically “taken through” change.
In 2026, change will become part of the environment. It is ongoing and expected.
As AI is introduced, work updates quietly. Tasks appear and disappear. Workflows evolve inside the tools people already use.
This makes traditional change management models ineffective. Organizations that succeed will stop treating change as an event and instead design systems where change is visible, normal, and absorbed through daily work.
Change stops being a moment and becomes a property of how work runs.
Throwing AI out to all employees and calling it a strategy will continue to slow organizations down.
If every team is experimenting independently, velocity collapses.
It’s like putting a company in a canoe and asking everyone to row in their own direction. You don’t move faster. You go nowhere.
Tools do not change organizations. Workflows do.
By 2026, serious organizations will stop measuring AI progress by licenses activated or tools deployed. They will look at what is actually running differently.
Which workflows changed, who is impacted, what value is being created, and what is now locked in will matter more than any adoption metric.
This shift exposes a hard truth: most AI investments never touched the work itself. Only workflows do.
AI ROI will stop being a consumption dashboard.
By 2026, executives will expect a monthly view of real impact, not usage metrics. Logins, prompts sent, or licenses activated will no longer be acceptable proxies for value.
Real ROI will be measured through observable changes to work, including:
If AI has not changed how work runs, there is no ROI to report.
This will fundamentally change which AI initiatives survive scrutiny.
Job architectures were built for a static world.
That world is gone.
In 2026, companies will realise that jobs are an output, not a stable foundation. Tasks change. Workflows change. Agents enter and exit. Skills evolve continuously.
Every serious organization will need a Work Architecture as critical infrastructure. A living system that shows how work actually runs across humans and AI, and updates as that work changes.
Without it, companies will not be able to govern AI, redesign roles, or protect people as work evolves.
Work Architecture will become as fundamental as financial architecture.
As AI becomes embedded in day-to-day work, companies will realise they need more than tools and policies.
They will need a Work Operating System.
AI is infrastructure. It sits underneath how work runs. Without a system that can see work, govern it, change it, and measure it, AI adoption will remain fragile.
The Work Operating System becomes the control layer where:
Without this infrastructure, organizations will remain reactive. With it, they can operate change deliberately and at speed.
Sending your teams “prompt training” and calling it AI skills is not capability building.
It teaches people how to talk to a tool, not how to redesign work.
In 2026, this will be obvious.
Real AI capability will come from understanding work, redesigning workflows, orchestrating humans and agents together, and measuring impact in production.
Strategy will remain important.
But it will no longer be sufficient.
These builders will not sit in innovation labs.
They will sit inside teams.
Their impact will compound because they change real work, create evidence, and reduce fear for the next change.
Organizations without builders will move slowly, no matter how strong their strategy decks are.
AI adoption will move beyond cost reduction.
In 2026, the focus will broaden to:
As AI narratives intensify, employee fear will increase.
There will be more noise. More headlines. More speculation about job loss and disruption.
In 2026, there has never been a more important moment for HR leadership.
This is how fear is reduced.
Not through reassurance.
Through clarity, agency, and real pathways forward.
The strongest HR leaders will not slow change. They will help people navigate it by making work visible, clarifying what is changing and what is not, and showing where evolution is possible.
Organizations that ignore this will lose trust and talent at the same time.
We will see more companies publicly celebrating job cuts “because of AI”.
That will not age well.
In 2026, leaders will increasingly be held to account for how they handle AI-driven change. Not just whether they are bold, but whether they are responsible.
By the end of 2026, the gap between organizations will be obvious.
Not between those who believe in AI and those who don’t.
Not between those who invested early and those who waited.
The divide will be between organizations that can act and those that cannot.
Some will have work running differently every quarter, with value compounding quietly over time.
Others will still be talking about potential.
That gap will be hard to close.
My focus is simple.
I will be obsessed with closing the gap between insight and action.
Ensuring work actually runs differently in production.
Driving low-risk, real change quickly.
Creating builders inside organizations.
Making value visible, measurable, and locked in.
Not ideas.
Not pilots.
Not theatre.
Just work, redesigned and running better.
2026 will reward organizations that act.
And expose those that cannot.
That is the year we are entering.
If your organization is sitting on insight but struggling to change how work runs, this is the conversation we’re having with leaders right now.