AI contracts introduce pricing models, IP provisions, and data rights that have no equivalent in traditional software agreements. Our advisors decode usage-based pricing, negotiate data governance protections, and secure the performance SLAs that AI vendors rarely volunteer.
The enterprise AI market has expanded from a handful of specialist vendors to an ecosystem of dozens of providers — from OpenAI and Anthropic to Microsoft Copilot, Google Gemini, Amazon Bedrock, and hundreds of specialist AI tools. Each brings a distinct pricing model, data use policy, and contractual risk profile.
Usage-based pricing creates budget unpredictability at enterprise scale. Data training provisions may allow vendors to use your confidential data to improve models that will be sold to your competitors. IP ownership clauses for AI-generated outputs remain legally unresolved and commercially contested. Model accuracy SLAs are often absent entirely, leaving you with no recourse when performance degrades.
Our advisors have reviewed AI contracts from the vendor side and the buyer side. We know which provisions are standard, which are negotiable, and which represent genuine legal and commercial exposure for your organisation.
Schedule an AI Contract ReviewFrom Copilot add-on evaluation through specialist AI platform procurement, our practice covers the full AI contract landscape.
Copilot adds $30 per user per month to your M365 spend. We assess whether your use cases justify that cost, negotiate pilot structures that gate the rollout on measured productivity outcomes, and ensure the commercial terms include flexibility provisions as Copilot's capabilities evolve — which they will, rapidly.
OpenAI Enterprise contracts are individually negotiated. Token pricing, data retention periods, zero-data-training provisions, and custom model fine-tuning rights are all on the table. We have reviewed OpenAI's standard enterprise agreement and know precisely which provisions require amendment before any large-scale deployment.
Many AI vendor agreements contain provisions that permit customer data to be used for model training unless explicitly opted out. The opt-out may require a specific request, a premium tier, or a separate contract amendment. We identify every data use provision in your AI contracts and negotiate comprehensive protections before deployment.
Token-based and API-call-based pricing is highly variable and can spike with user behaviour changes. We model your expected consumption, negotiate usage caps and overage protections, and structure enterprise agreements that convert unpredictable variable costs into budgetable committed rates — with discounts for volume commitments.
Many enterprises have accumulated AI tool subscriptions across departments without central oversight. We conduct an AI portfolio audit — mapping tools, costs, and overlapping capabilities — and consolidate to a rationalized set of vendors with negotiated enterprise terms. The average AI portfolio rationalisation exercise identifies 25–35% in duplicate and unused spend.
Who owns an AI-generated analysis, summary, or creative output? The answer differs between vendors, and in some agreements, the vendor claims a perpetual licence to use your outputs as training data. We review every IP provision, negotiate assignment of ownership to your organisation, and ensure that AI-generated work product belongs entirely to you.
We review your existing AI contracts and any proposed agreements, identifying pricing risks, data governance exposure, IP vulnerabilities, and missing SLA provisions. Where you are evaluating multiple vendors, we build a vendor-neutral comparison framework covering total cost of ownership, contract risk, data residency, and performance. This phase produces a written risk register for each contract and a prioritised set of negotiation objectives.
Using our assessment findings, we develop a negotiation strategy for each AI vendor. We engage in contract redlines — proposing specific amendment language for data use provisions, IP ownership clauses, performance SLAs, and usage cost controls. For new vendor agreements, we prepare the commercial model that converts from list pricing to enterprise rates. For existing agreements approaching renewal, we identify leverage and prepare the benchmarking evidence that supports a price reduction request.
AI contracts change more frequently than traditional software agreements — vendors update their terms of service, introduce new pricing tiers, and modify data use policies, sometimes unilaterally. We establish a contract governance framework that monitors these changes, triggers renegotiation when material amendments occur, and ensures your AI spend remains optimised as the vendor landscape evolves. Our clients with AI governance frameworks in place see 40% less contract-related cost creep than those without.
Expert advisory across the full enterprise AI ecosystem — from hyperscaler AI services to specialist providers.
A regional bank was deploying an enterprise AI platform for document processing and customer interaction analytics. The proposed contract contained broad data training rights, no model accuracy SLAs, token pricing that would scale unpredictably with document volume, and limited data residency protections. We renegotiated the agreement: secured zero-data-training provisions, added a 99.5% model availability SLA with credit provisions, converted token pricing to a fixed-rate enterprise tier, and added EU data residency guarantees. The total commercial saving was $1.8M over three years, and the risk profile of the contract was transformed.
Read Full Case Study →Before you sign any AI vendor agreement, read this guide. Covers the 15 provisions most commonly used to transfer risk from AI vendors to enterprise buyers — data training rights, IP assignment, hallucination liability, and more. Free download for enterprise IT and legal teams.
"We were about to sign an AI platform agreement that would have given the vendor the right to train on our client data. Atonement Licensing caught it in the first review and negotiated it out entirely. They also saved us $1.8M. Two problems solved at once."General Counsel — Regional Financial Services Group
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