To truly unlock the potential of AI at work, we need more than new systems or software—we need better infrastructure.
Not technical infrastructure, but the architecture of work itself: how it’s defined, structured, and deployed across an organization.
Without that, AI can’t deliver real value.
Here’s what that architecture looks like.
You still need clear roles, role groups, role hierarchy and standardization.
This creates a consistent foundation across the business—supporting clarity, equity, and alignment at scale.
But that’s just the start.
Tasks and subtasks bring clarity to what’s actually being done—not what we think is being done.
This is essential for automation, human-AI collaboration, and smarter org design.
It’s not enough to assign duties.
Outcomes and responsibilities clarify what success looks like; connects roles to business impact and performance expectations.
AI can’t do everything—and neither can people.
That’s why we need to map skills to tasks. It drives accurate hiring, targeted learning, and mobility.
This links skills directly to the value-crating work.
As roles evolve, reward systems must evolve too.
It’s time to move past static levels and align pay with real contribution—especially in hybrid human+AI roles to ensure fairness.
The career ladder is now a career lattice.
We need clear career paths and pivot pathways so people can shift, grow, and stay relevant as the nature of work evolves.
And without it, we’re trying to power a next-gen workforce on last-gen foundations.
Let’s build smarter
Siobhan 💜