Procurement · AI Orchestration
AI-assisted sourcing and contract intelligence at a European water utility
8%Savings realised through AI-assisted sourcing and contract intelligence, against the utility’s own baseline
- Client
- European water and wastewater utility
- Sector
- Energy & Utilities
- Region
- Western Europe
- Engagement
- Consulting engagement
- Scope
- Category strategy · Contract analysis · Sourcing automation · Supplier negotiation · Procurement data
01 — Situation
The utility operates dense regional networks under public-service obligations. Its procurement function had a modern platform and a competent team, but the contract base had grown for two decades: framework agreements, amendments, price-revision clauses and local addenda, filed but never analysed together.
Renewals were prepared by reading a sample of documents, and price revisions were accepted as suppliers proposed them. Finance suspected value was leaking; nobody could say where, or how much.
02 — Scope
- Categories in scope: maintenance services, network materials, laboratory consumables and professional services, together about a third of addressable spend.
- Read and structure the whole contract base for those categories: prices, revision formulas, volumes, service levels, termination and renewal terms.
- Rebuild category strategies on the evidence, and prepare the next sourcing events with AI doing the document work and buyers doing the decisions.
- Keep everything inside public-procurement rules and the utility’s audit trail.
- 01
Contract intelligence
Every contract in scope was extracted and classified by an AI reading layer: clauses, prices, indexation formulas and service levels, validated by buyers on a sample before being trusted at scale.
- 02
Evidence-based category strategies
Actual prices paid were compared with contracted prices, revision formulas were recomputed, and demand was consolidated across regions. Each category received a strategy with its sourcing wave and negotiation targets.
- 03
Sourcing automation
Requests for proposals, evaluation grids and clarification questions were drafted from the structured contract data and the category strategy, then reviewed and signed off by the category manager.
- 04
Negotiation and control
Buyers negotiated with the evidence in hand. Price-revision clauses were renegotiated, duplicate agreements merged, and a contract register with alerts became the standing control.
03 — Impact
The utility now knows what its contracts say. Renewals are prepared from the evidence, price revisions are recomputed before they are accepted, and the sourcing calendar is driven by the category strategies rather than by expiry dates.
The AI reading layer stays in place as a control: every new contract is structured on signature, and deviations from the agreed terms are flagged to the category manager.
04 — ROI and savings
- 8%Savings realisedOn the categories in scope, in the first sourcing wave, validated by Finance
- 100%Contracts structuredOf the contract base in scope, read and classified by the AI layer with buyer validation
- −50%Sourcing preparation timeFrom brief to published request for proposals, measured over the wave
- ≈ 5×Return on the engagementFirst-year savings compared with fees and tooling, order of magnitude
Rounded, conservative orders of magnitude, measured on the utility’s own baseline and validated with its finance function.
AI Orchestration
- The reading layer now flags price revisions that deviate from the contracted formula before they are approved.
- Next wave: automated preparation of supplier performance reviews from delivery and invoice data.
What this involved
- Category strategy
- Contract intelligence
- Sourcing
- Negotiation support
- Procurement data
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