Customer Story Manufacturing · Semiconductor

92% of work data, validated. Millions in consulting, saved.

How HR at a global semiconductor leader mapped and confirmed 53,000 people’s real work in-house — no consultants — and built an AI investment plan the CEO could stand behind.

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5 min read • Results verified with the customer

~92%

Of work data validated — gold-standard quality

53,000

Employees mapped at task level

Millions

Saved in consulting fees

3

Regions: Asia, North America, Europe

01 THE CUSTOMER

A global semiconductor manufacturer, with around 53,000 employees across Asia, North America and Europe. Its HR organization set out to build an AI investment plan on work data the executive team could actually trust — and to do it in-house, without a consulting engagement.

“Add the customer or analyst pull quote here — one sentence, in their words, about what changed.”

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02 THE CHALLENGE

Plenty of workforce data, little workforce truth

Roles, skills and responsibilities were fragmented and out of date — not clean enough to trust. You can’t build an AI strategy on data you don’t believe, and no one wanted to hand the question to consultants and get a static answer back.

03 THE APPROACH

One connected Work Context Graph

Using Reejig’s Work Operating System and proprietary Work Ontology®, work, people and AI agents were mapped as one connected Work Context Graph — then validated with leaders and SMEs against how work actually happened, region by region.

03 THE OUTCOME

Trusted work data, put to work

92% validation gave the executive team a task-level source of truth that stays current — replacing thousands of manual hours and millions in consulting fees. Leaders could see the business through an AI lens: where to reinvent, where AI returns most, and where to hold. Work was matched to approved AI agents, with a clear view of workforce impact at task level.

Keep reading — the challenge, the approach, and the outcome.

The challenge, the approach, and what trusted work data unlocked.

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Inside the story

What you’ll get

How a task-level source of truth was validated to 92% in-house, with no consultants

The AI lens leaders used to decide where to reinvent, where to invest, and where to hold

How validated work was matched to approved AI agents — and which tasks stayed with people

How workforce impact was read at task level to plan a people-first transition

How it works

One loop, seven stages, no dead ends.

Every engagement runs the same loop. Map the work, analyze it, build against it, run it, measure it, log it, and keep it current as the work moves.

Map

Build the Work Context Graph

Analyze

Your GPS for reinvention

Build

Workflows and agents

Run

Deliver the new way of working

Measure

Track board-ready ROl

Log

Source of truth and audit trail

Update

Keep the architecture current

The loop never stops. As roles, tasks and agents change, the architecture updates itself — which is why the numbers in this story kept holding after go-live.

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