Is Your Company Using AI? Why This Question Misses the Mark in B2B Contracts
As organizations increasingly adopt artificial intelligence (AI) across core operations, many B2B service agreements, data-sharing contracts, and cybersecurity checklists have begun to include a now-familiar line of inquiry: Is your company using AI? While seemingly straightforward, this question is deceptively vague and deeply problematic in its construction and practical implications. The challenge lies not in answering the question per se, but in the ambiguity surrounding what the question means, and what risks or assurances are presumed to follow based on the response.
At its surface, the question fails to define “AI”. This term spans a broad spectrum of technologies, from foundational machine learning algorithms embedded in productivity platforms to generative AI tools, predictive analytics engines, natural language interfaces, and fully autonomous systems. A company that uses Microsoft Teams, Zoom, Salesforce, or Google Workspace inherently uses AI, even passively. These platforms leverage AI for background functions like noise suppression, sentiment analysis, grammar correction, scheduling optimization, and threat detection. Does that constitute “using AI”? In a literal sense, yes. However, the answer becomes far less clear in a contractual or risk context.
When vendors or partners pose this question in due diligence documents or security reviews, it is often without a nuanced understanding of the layers of AI embedded within enterprise software ecosystems. More critically, the question rarely distinguishes between consumer-facing generative AI models like ChatGPT, internal experimentation with proprietary models, or AI usage entirely within sandboxed, non-production environments. For instance, a company’s R&D team may prototype solutions with agentic AI systems capable of autonomous decision-making. However, should those solutions be disclosed under such a broad prompt if they are not customer-facing, not connected to client data, and not deployed within operational infrastructure?
B2B relationships, particularly those involving the exchange of sensitive information or regulated data, require specificity and transparency. However, the responsibility for clarity lies in how the questions are framed, not simply how they are answered. Contracts that address AI use must go beyond the binary and explore the purpose, scope, governance, and controls surrounding AI applications. Is the AI system making decisions autonomously? Is it processing third-party data? Are outputs being audited or validated by a human before being shared externally? These questions matter, not whether the term “AI” can be applied to a tool or system somewhere in the workflow.
Additionally, this imprecise language introduces compliance concerns. Vague AI declarations may either overexpose a company to legal scrutiny or create the false perception that no AI risks exist. In either case, the result is a misalignment between actual AI usage and perceived obligations under the contract. This can create negotiation friction, delay onboarding, and weaken trust between partners. It also reveals a broader immaturity in how organizations understand and govern AI technologies, which will grow more consequential as regulatory frameworks evolve.
Organizations on both sides of a B2B relationship must begin adopting more structured AI governance protocols to address this gap. These should include formal AI usage policies, inventory logs of AI-powered systems, clear delineation between experimental and production deployments, and standardized language for contract disclosures. Just as data privacy has evolved to necessitate detailed mapping of how personal data is collected, stored, processed, and transferred, the same level of granularity must now be applied to artificial intelligence.
Ultimately, the question is not whether a company uses AI but how, why, and under what safeguards. Until we shift from asking simplistic yes-or-no questions to engaging in more contextual, operationally informed dialogue, AI-related clauses in B2B agreements will remain a source of confusion, not clarity. As the business landscape grows more automated and algorithmically driven, the sophistication of our contractual language must evolve in tandem. Precision matters, not only in code but in compliance.