AI transformation, where the work actually happens
Most companies have spent on AI and cannot point at what changed in the work.
We help you get the highest return on your AI investment, by starting where the work actually gets done.
An AI transformation partner for the CEOs and COOs deciding which work to redesign around AI first — and for the CFOs and CHROs who have to hold that decision with them.
Illustration — a function read task by task, each one scored for how much of it stays human.
The difficulty
AI transformation is one of the most important, and most difficult, jobs facing leaders right now.
Most organizations are trying to change the work with AI. But AI doesn't transform a business just because people have access to better tools.
Three things have to move together.
The work
Processes have to be redesigned around what AI can actually do.
The data
The information those new workflows depend on has to exist, connect and be trustworthy enough to act on.
The people
People need to understand what's changing, why it's changing, and how their work changes with it.
Transformation isn't just a technology problem. It is an organizational problem.
People are asking:
What does AI mean for my work?
What should I still do?
What changes about my role?
Does my role even exist?
What is leadership actually asking me to do?
And beneath all of that is a deeper question: can I trust where we're going?
Where we are different
We don't start with tools. We start with how the work actually gets done.
We read a function the way work is really produced — task by task, workflow by workflow — and we see four things.
The gold is the human share of the work. Illustration of the reading, not a client's numbers.
What you get
Then we turn that evidence into decisions.
- What changes first.
- What it's worth, in a range, with every assumption named.
- What needs to be in place before it can run.
- And what should wait.
Not a stack of analysis. A small set of resolved decisions, in the order a leader has to make them.
Four levels of evidence, resolved into four decisions in the order they have to be made.
Communication
Because we understand how the work is changing, we can help you say it clearly.
Not just what is changing. But why. What it means for people. What they can expect. What they need to learn. And where they can create more value.
We are not writing reassurance. We are telling people what the work actually becomes, from the same evidence the decisions rest on.
A roadmap handed over at the end of the analysis is not the product. The value is created months later, when the redesigned work is simply how the place operates.
Through delivery
We stay through delivery and adoption, so the redesigned work becomes the way the organization operates.
Four stages — diagnose, recommend, deploy, adopt — with one accountable party across all four. Most partners stop at the handover, and the handover is where value leaks.
How the engagement worksThe diagnosis is where you start. Staying through adoption is why it is worth starting with us.
The line does not stop at stage two, which is where a diagnostic engagement usually ends.
What this rests on
Four things you can check before you talk to us.
A published rubric
Every task-level score uses a written rubric with worked examples at each level. In a 164-task calibration study, independent raters agreed exactly on 89% of scores and within one level on 100%.
Samples you can read
Complete engagements built end to end with the same method we run with a client. Not a brochure — the actual readout, with the numbers and the assumptions behind them.
A named lineage
We productized an analytical tradition rather than inventing one. The Human Agency Scale is Stanford's. Task grounding is O*NET. Labour market calibration is Burning Glass. We say which parts are ours.
People who have done it
Four leaders who have built or scaled the disciplines this work runs on — enterprise AI product, transformation, people and change, experience design.
Meet themThe goal isn't to have more AI.
It's to improve organizational effectiveness. To help your people become better, more capable and more confident. To build a culture of trust and clarity.
And ultimately, to create an organization where people and AI do their best work together.
Effectv. AI transformation, where the work actually happens.