HubSpot AI contract — TermScout contract intelligence report

HubSpot Product Specific Terms AI Review & Analysis

HubSpot TermScout AI certified contract badge

AI Certification (Beta) Overview:

When TermScout certifies a contract, it means that agreement has met our rigorous standards and made the below commitments to their customers as it relates to their AI product or services.

Receiving Termscout's AI Certification (Beta) means that the product is on the cutting edge of AI development and shows they're committed to establishing trust and transparency with their customers and being a responsible corporate citizen when it comes to their AI development and functionalities.

Termscout acknowledges that presently AI is something of a moving target, but at this point, we believe responsible vendors make these minimum commitments and are continuously evaluating their practices.

AI Commitments Overview:

Review the AI-related commitments and view source language citations below.

HubSpot has established guardrails related to the use of their AI offering.
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HubSpot addresses how they use Customer data related to their AI offering.
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HubSpot addresses how they retain Customer data as it relates to their AI offering.
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Customer retains ownership of input data submitted to HubSpot as it relates to their AI offering.
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HubSpot commits to notifying Customer of a security breach impacting the Customer's data.
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HubSpot commits to complying with applicable AI laws
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In conversations between TermScout and HubSpot, HubSpot has indicated their ongoing commitment to adopting contractual best practices related to AI as they're developed.
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AI Certification (Beta) Guiding Principles:

Termscout's AI Certification (beta) covers five major categories outlined below. Termscout believes while there are other important principles related to AI, these five categories cover the major obligations related to AI found in vendor contracts today and expect over time this will expand to fully realized compliance index.

1. Use Restrictions: Limiting harmful or unintended uses of AI Systems.
2. Transparency and Explainability: Ensuring users understand how AI works and its limitations.
3. Data Usage and Ownership: Clarifying rights around training data and generated content.
4. Security and Incident Response: Protecting data and systems from breaches or misuse.
5. Regulatory Compliance: Ensuring alignment with evolving laws.

AI Certification (Beta) Program:

If you are interested in being part of the AI Certification (Beta) Program and joining a standing group of select customers who has ongoing conversations with us about these standards and how they should evolve over time, please contact us at sales@termscout.com

Frequently Asked Questions

Find quick answers to the most common questions about our platform, process, and agreements.

Enterprise buyers frequently push back on agreements that combine broad AI-related disclaimers with narrow vendor accountability. Friction increases when vendors exclude responsibility for model outputs, security incidents, or downstream operational impacts while also maintaining low liability caps. Buyers generally expect liability structures to reflect the practical dependence placed on the platform, especially when AI systems influence customer workflows, decision-making, or sensitive data processing. Agreements that appear structurally imbalanced relative to operational risk often require extended legal and procurement review.

Buyers typically compare liability structures against vendors with similar data sensitivity, operational importance, and AI functionality. Agreements may appear out of market when caps remain unusually low despite broad vendor access to customer information or mission-critical integrations. Enterprises also examine whether certain exposures, such as confidentiality breaches, data misuse, or infringement claims, receive separate treatment from general commercial liability. Benchmarking increasingly focuses on whether contractual accountability aligns with the real-world operational consequences of platform failure or misuse.

Competitive pricing rarely offsets concern when the agreement shifts disproportionate operational or governance risk onto the customer. Enterprise review teams often focus on whether the vendor’s contractual posture reflects confidence in the product and maturity in handling AI-related obligations. Agreements that aggressively disclaim warranties, limit remedies, or avoid responsibility for model behavior can create trust concerns independent of commercial terms. Buyers may interpret these positions as indicators of unresolved operational risk, immature governance controls, or future dispute complexity.

Enterprise buyers are becoming more cautious about agreements that disclaim nearly all responsibility for AI-generated outputs while simultaneously allowing broad use of customer data to improve models. Review friction also increases when vendors attempt to exclude obligations tied to confidentiality, security failures, or infringement issues from meaningful remedy structures. Buyers generally expect some proportional accountability when AI functionality materially affects business operations. Agreements that avoid balanced risk allocation often require escalation because they diverge from evolving enterprise procurement and governance expectations.