AI Spend is Outrunning Procurement. Here’s What Sourcing Leaders Are Actually Doing About It.

2026-05-07 · Source: NPI · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Consulting & Professional Services · Depth: Intermediate, short

Summary

IT sourcing leaders in large enterprises face significant challenges managing AI spend, with 56% citing unpredictable usage and scaling costs due to the shift from per-seat to consumption- and token-based pricing models. Traditional procurement playbooks are ill-equipped for this new "tokenomics," where vendors define credits inconsistently and bundle AI features. While 50% of large enterprises have an AI Center of Excellence, its mere existence doesn't guarantee a coherent strategy. Key cost pressures for the next 12 months include token/API usage growth (27%), new vendor pricing (23%), increased employee AI tool adoption (22%), and expanding AI into production (19%). Emerging best practices involve pilot-first, shorter-term contracts, negotiating "super caps" and price-change clauses, accounting for expensive forward-deployed engineers, addressing data training legal exposure, and framing AI ROI as a portfolio-level conversation.

Key takeaway

For IT sourcing leaders managing escalating AI costs, your traditional per-seat procurement models are obsolete for AI's consumption-based pricing. You must proactively implement internal budget allocation for tokens and negotiate specific contractual protections. Prioritize shorter contract terms (e.g., one-year renewals) and demand clauses for pricing changes and consumption caps to maintain flexibility and control. Additionally, ensure forward-deployed engineers' costs are accurately modeled into your total cost of ownership, as they represent a significant, often hidden, expense.

Key insights

AI's consumption-based pricing creates unpredictable spend, necessitating new procurement strategies and internal cost governance.

Principles

• Consumption-based pricing demands new procurement playbooks.
• Optionality often outweighs deep discounts for AI tooling.
• AI ROI is a portfolio, not per-tool, conversation.

Method

Treat tokens and credits as internal budgets, allocating them to business units and requiring requests for more to enforce cost discipline.

In practice

• Negotiate one-year or shorter AI tooling contracts.
• Demand super caps and price-change clauses in AI contracts.
• Model forward-deployed engineers into total cost of ownership.

Topics

• AI Cost Management
• IT Procurement
• Consumption-Based Pricing
• Tokenomics
• AI Centers of Excellence
• Vendor Contract Negotiation
• Forward-Deployed Engineers

Best for: CTO, Executive, Consultant, Director of AI/ML, VP of Engineering/Data

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Editorial summary, takeaway, and curation by AIssential. Original article published by NPI.