Contracting for AI Model Deployment: Key Challenges & Proven Solutions > 자유게시판

AI deployment isn't just about hosting code—it's a complex operational and contractual undertaking.

Success hinges on contracting with reliable partners—whether internal departments, external suppliers, or SaaS platforms—who commit to reliability, elasticity, and regulatory compliance.

Too many organizations mistakenly treat AI deployment like conventional software rollouts, leading to costly oversights.

Defining clear, measurable expectations for model behavior remains a frequent blind spot.

AI systems decay silently—driven by changes in input distributions, behavioral shifts, or external context.

Vendors can’t be held responsible if success criteria aren’t codified in the contract.

The fix? Embed service level agreements (SLAs) with KPIs directly linked to business impact, not just technical metrics.

The opaque handling of training and inference data creates serious compliance vulnerabilities.

Vendors often receive unrestricted data flows without clear retention, access, or deletion policies.

Insist that vendors use synthetic, aggregated, or pseudonymized datasets unless absolutely necessary.

Integration complexity is routinely underestimated in AI contracts.

Without documented interfaces, test cases, or compatibility requirements, integration efforts collapse.

The solution? Require sandboxed integration proofs before contract signing.

Others demand large upfront fees with zero performance guarantees.

Avoid open-ended pricing—negotiate tiered, аренда персонала usage-capped structures with refund mechanisms.

Proprietary model formats, custom APIs, and closed training pipelines make switching prohibitively expensive.

Ensure exit clauses mandate full data portability and knowledge transfer to internal teams.

Communication, transparency, and enforceable safeguards are non-negotiable.

Those who rush into AI contracts without precision will pay the price in operational chaos, financial loss, and broken trust