How it works

AI changes the work first. But most companies are redesigning everything except the work.

Leaders know names and titles. How work actually happens, how a deal moves from marketing to sales to onboarding, lives in people's heads. WorkGraph AI writes it down, then helps you decide what AI should do with it.

The org lens, before over after: the Business Systems Analyst role splits, two people become Pricing Product Owners under the Head of Revenue Operations, and five roles that ripple are listed with what changes for each.
Illustrative demo
The method

The WorkGraph Stack

We map the work, then design the workforce around it.

  1. 01Work

    We break down the work — the process, the steps, and the tools that deliver each outcome.

  2. 02Skills

    We identify the skills required to perform each step of that work.

  3. 03Roles

    We group work and skills into roles — often new groupings created by AI-enabled skills and output.

  4. 04Org

    We group the new roles into organizations — sometimes entirely new orgs emerge.

  5. 05Operating Model

    Orgs support the operating model — and new models become the catalyst for the next cycle.

A new or changing operating model triggers a fresh look at the work, skills, roles, and org.

46%

of 844 tasks are ones workers want AI agents to automate, mostly low-value, repetitive work. In 45% of occupations, the setup workers want most is an equal partnership between person and AI.

Stanford SALT (Shao, Brynjolfsson, Yang and others), Future of Work with AI Agents, June 2025 · 1,500 workers in 104 occupations and 52 AI experts; a preprint

Why it matters

The questions every AI plan has to answer

“Where's the AI-driven margin?”

Leaders want a credible AI value-creation plan this year.

“Will this break the workforce?”

A reckless rollout trades one P&L problem for another: retention, engagement, capability.

“Why do our AI pilots stall?”

Nobody redesigned the work the tools were dropped into.

“Can we explain these decisions?”

Role changes, automation, headcount: every consequential AI decision needs a record. Most companies don't have one.

The Blueprint, step by step

Map the work, make the call, then translate it to dollars.

A role's replay for a Logistics Coordinator: a timeline from first description to impacts, with what was known about the role's steps at each date set beside the first description.

Illustrative demo

Where the evidence comes from

Confidence improves with every layer of evidence.

People stay accountable for the interpretation and the decisions.

  1. Job data

    We start with a hypothesis from anonymized HR data and your job descriptions.

  2. Short surveys

    We refine it with short surveys that capture tasks, handoffs, and how long things really take.

  3. Interviews

    We finish by talking to the people doing the work and to leadership, and flag where their answers differ.

  4. Your plan

    A map of the work across roles, a decision on how each role changes, and the cost and evidence behind each one.

See the platform

We tell you what to build and who should build it

You get a plan for your company, and we have no agents or tools to sell you afterward.

Book a scoping callSee the Blueprint

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By design, not by default.