A national healthcare group operating 47 hospitals and 200+ outpatient facilities had selected an enterprise AI platform for clinical decision support, administrative automation, and predictive analytics across their patient management infrastructure. After a six-month evaluation process, they had identified their preferred AI vendor and were moving toward contract execution — with an assumption that the commercial and legal terms were largely non-negotiable for a relatively new category of enterprise software.
When the group's Chief Digital Officer reviewed the vendor's proposed agreement, she recognised immediately that the standard terms were structured in ways that were materially unfavourable to the organisation — but the internal legal and procurement teams lacked the specialist AI contract knowledge to identify precisely which provisions were problematic, let alone construct a credible alternative position. She retained us with four weeks before the planned signature date.
The AI contract contained a pattern of risks we encounter in virtually every first-time enterprise AI procurement:
Beyond the contractual risks, the vendor's initial pricing was structured at list rates with a modest loyalty discount — reflecting neither the client's scale, the competitive alternatives available, nor the significant development investment the client would make in customising the AI models for their clinical environment.
We began with a comprehensive assessment of the vendor's proposed agreement across four dimensions: commercial terms (pricing, escalation, and usage caps), IP and data rights, model performance and accountability, and exit and portability provisions. We prepared a risk-ranked assessment that distinguished between provisions that were immediately unacceptable, provisions that required amendment, and provisions where the client could accept the vendor's standard language with minor clarification. This assessment gave the client's legal and technology teams a shared framework for the negotiation.
We benchmarked the vendor's proposed pricing against comparable enterprise AI platform agreements in the healthcare sector. The client's scale — 47 hospitals, 200+ facilities, and a projected API call volume in the top 5% of the vendor's client base — justified pricing significantly below what the vendor had proposed. We also identified that the vendor's pricing model was structured to obscure the total cost of ownership: implementation, customisation, and model training costs had been excluded from the headline subscription and would have appeared as significant over-runs post-signature.
The data training rights provision was our first and most critical negotiating objective. Using our advisors' experience with AI vendor commercial structures, we proposed a specific amendment that permitted the vendor to use anonymised, aggregated usage data for model improvement but prohibited use of the client's clinical data — including derivative data and outputs — for any purpose beyond the contracted services. After three weeks of negotiation, the vendor accepted this amendment with minor modifications. The resulting provision became the template the client used for all subsequent AI vendor agreements.
We negotiated clear IP ownership provisions that assigned to the client all outputs generated using their clinical protocols, patient data, and proprietary workflows. We also introduced model performance SLAs — specific accuracy benchmarks for each clinical use case, quarterly performance reviews, a remediation process for benchmark failures, and a price reduction mechanism for sustained underperformance. These provisions were novel for the vendor's standard agreement structure but were accepted after we demonstrated precedent from comparable enterprise AI agreements in regulated industries.
The commercial negotiation restructured pricing from usage-based with discretionary escalation to a tiered subscription model with fixed annual increments of no more than CPI + 2%. We secured a usage cap for the first 18 months while the client's deployment scaled, eliminating the open-ended cost exposure the original model had created. Exit provisions were extended to a 36-month data retention obligation, with structured portability in machine-readable formats compatible with major competing platforms — provisions the vendor's team acknowledged had never been requested before but ultimately accepted.
The final contract delivered $1.8M in savings against the vendor's initial proposal — a 29% reduction achieved through pricing restructuring, the elimination of open-ended usage escalation, and the recharacterisation of several services the vendor had positioned as additions but which were, on analysis, core to the contracted use case.
The contractual protections secured were, in many respects, more valuable than the financial savings. The data governance provisions — limiting the vendor's use of patient data and clarifying IP ownership of AI-generated outputs — eliminated a regulatory exposure that would have been extraordinarily difficult and expensive to remediate post-contract. The model performance SLAs introduced accountability into an agreement type that historically has had none.
"We nearly signed a contract that would have given our AI vendor rights to our patients' data that we had no idea were in the standard terms. Atonement Licensing identified every risk, negotiated every provision that mattered, and delivered a contract our board and legal team were genuinely comfortable with."Chief Digital Officer — National Healthcare Group, 47 Hospitals
15 critical clauses that appear in standard AI vendor agreements — and what you should negotiate instead. Essential reading before signing any enterprise AI contract.
Weekly AI procurement intelligence, contract risk alerts, and vendor negotiation tactics for enterprise technology leaders.
Standard AI vendor terms contain critical risks that most buyers never see. We identify every risk and negotiate every provision that matters — before you sign.
We apply the same methodology to your engagement. Initial scoping call is confidential and obligation-free.