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Rationalizing Contract Coding to Restore Cost Visibility Using AI

At a Glance

Rationalized more than 30,000 unmanaged Maximo contract codes into a clear, scalable framework, using AI-driven structuring

Restored reliable spend visibility across equipment, labor, materials, and other key contracting cost drivers

Designed a sustainable governance model and prototyped an AI agent to prevent future code sprawl and support automation

Challenge

A utility client’s procurement contracts team was managing more than 30,000 Maximo master codes with no standards or governance. We streamlined the structure, using AI to establish a clear, sustainable coding framework. The work positions our client to regain visibility into contracting costs and adopt an AI agent to keep the system clean going forward.

Over the years, the contract code list expanded into an unwieldy set of tens of thousands of entries. Creating a new code was easier than finding the right one, so duplicates and inconsistencies piled up. Category managers couldn’t rely on reports, leaving them without a reliable view of equipment, labor, materials, and other cost drivers. Without a coherent structure, our client couldn’t confidently manage contracting spend or support future automation.

Process

  • Applied AI-driven logic and structuring to rationalize the code set
  • Rebuilt the list structure around clear definitions, practical grouping, and future governance needs
  • Developed scalable, fit-for-purpose framework without reintroducing complexity
  • Prototyped an AI agent that reads contract line-item descriptions and identifies correct codes or recommends new codes aligned with existing framework

Result

  • Streamlined coding structure restored meaningful reporting
  • Empowered category managers to monitor and control contracting costs
  • Set foundation for ongoing improvements and additional automation
  • Reduced risk of future sprawl
  • Provided favorable conditions for substantial future cost savings by equipping teams with clean, reliable data

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