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Case study

Spend under intelligence

A top 10 US technology company reviewed every purchase request by hand. We replaced that with a system that learns from every decision it makes.

$1B+indirect spend under AI
6% → 3%avoidable spend target
Every batchaccuracy improves
Onestandard on every request

Top 10 US technology company · 2023–2024

The situation

A billion dollars a year, reviewed inconsistently.

Indirect procurement covers software licences, external services and supplies: high volume and hard to control. Different reviewers applied different standards, different parts of the organisation used different criteria, and nothing learned from decisions already made. Around 6% of spend was avoidable.

How it works

A system that starts smart and gets smarter.

Each request is reviewed against policy, past decisions and category benchmarks. Clear cases are processed; edge cases go to a human with a recommendation. Every confirmation or override becomes a training signal, and after each batch the model updates.

Autonomy on evidence

Thresholds rise as accuracy does.

As measured accuracy improved, procurement leadership raised the thresholds for autonomous handling. Reviewers focus on the genuinely complex. The system running today is materially smarter than the one deployed on day one.

What we learned

Four things worth carrying forward.

The learning loop is the product

A model that stays fixed decays. One with a good feedback loop improves every day it runs.

Humans make the system stronger

Keeping people on edge cases is how the system learns.

Consistency at scale is a superpower

One standard applied to every request is what moved the avoidable-spend number.

Autonomy has to be earned

Recommendations first, decisions by people, autonomy expanding with accuracy.

Let's talk

Tell us the number you need to move.

A 45-minute working session with an operator who has run the kind of work you are describing. You will get an honest read on where your programme stands and what it would take to move it.