5 Reasons AI Terms Are Becoming the New Deal Breaker (and What You Can Do About It)

AI innovation is outpacing AI contracts. That's the uncomfortable reality facing B2B companies in 2025. While product teams ship new AI features every quarter, legal departments struggle to articulate what those features mean for data ownership, liability, and regulatory compliance.

Not long ago, contract negotiations focused on pricing, liability caps, and termination rights. Today's deals stall on questions about training data usage, model transparency, and algorithmic accountability—questions that many standard vendor agreements simply don't address.

Companies like Webflow have experienced this friction firsthand. As their platform incorporated more AI capabilities, prospective customers began asking detailed questions about how AI contract terms handle data privacy and model training. Without clear contractual language, each deal required custom negotiations that delayed revenue and frustrated everyone involved.

1. Training Data Ownership: The Question That Kills Deals

The single most friction-inducing question in modern B2B software deals: "Will you use our data to train your AI models?" The answer should be straightforward, but it rarely is. Many vendors don't have clear policies about training data usage. Others have policies but haven't incorporated them into contractual commitments.

Why This Matters More Than You Think

Models improve through exposure to more data. Vendors want flexibility to use customer data for model enhancement. Customers want assurance that their proprietary information won't train models that competitors might also access.

When AI terms remain vague on training data—using phrases like "may use data to improve services"—buyers interpret that ambiguity as risk. Procurement teams assume the worst: their sensitive business data flowing into models that benefit competitors.

Key issues that create friction:

• Vague contractual language about "service improvement" that could include AI training
• No clear distinction between operational data use and model training use
• Lack of customer control over whether their data trains AI models
• Uncertainty about whether trained models become vendor IP or contain customer IP

Traditional confidentiality clauses don't resolve these concerns. A typical NDA prevents disclosure to third parties. But training an AI model on customer data doesn't necessarily "disclose" that data—it transforms it into model weights that may not reveal the original information.

TermScout's AI certification evaluates how clearly vendors address training data usage, giving buyers confidence before negotiations even begin.

2. Output Liability: When AI Creates Problems, Who Pays?

AI systems produce outputs: text, images, code, recommendations, and predictions. Who owns those outputs? More pressingly, who's liable when those outputs cause problems?

The Unpredictability Problem

If an AI-powered contract analysis tool misses a critical clause that leads to financial loss, can the customer sue? If an AI content generator produces text that infringes copyright, does liability fall on the vendor or the customer?

Traditional applications produce predictable results. AI systems produce variable outputs influenced by training data and contextual factors that neither party fully controls. This unpredictability makes liability allocation significantly more complex.

When AI contract terms fail to address output ownership and liability explicitly, risk-averse legal teams negotiate extensively or walk away entirely. Nobody wants to sign a contract that might assign them liability for AI behaviors they can't predict or control.

TermScout helps vendors benchmark their output liability provisions against industry standards, reducing negotiation friction.

3. Regulatory Compliance: The Moving Target Problem

The regulatory environment for AI is changing month by month. The EU AI Act introduces new requirements for high-risk AI systems. Various US states are proposing their own AI regulations. Industry-specific frameworks are emerging for healthcare, financial services, and other sectors.

Why Vague Compliance Language Doesn't Work

Buyers want assurance that vendors will remain compliant as regulations evolve. But how can a vendor commit to complying with regulations that haven't been finalized yet?

When AI terms include vague compliance commitments like "will comply with applicable AI laws," buyers question what that actually means:

• Which laws apply across different jurisdictions?
• What happens if compliance requirements conflict?
• Does the vendor commit to updating practices proactively or only after regulations take effect?
• Who bears the cost of compliance updates?

Non-compliance with AI regulations can carry severe penalties. The EU AI Act imposes fines up to €35 million or 7% of global annual turnover for serious violations. Buyers naturally want to shift compliance risk to vendors. Vendors resist taking on unlimited liability for undefined regulations. This tension creates a negotiation deadlock.

TermScout's certification process evaluates vendor compliance commitments against evolving regulatory frameworks, providing independent validation that accelerates procurement decisions.

4. Transparency vs. Trade Secrets: The Impossible Balance

Many regulated industries now require some level of AI explainability—the ability to understand how an AI system reached a particular decision. This matters for compliance in financial services, healthcare, hiring, and other domains where algorithmic accountability is becoming legally required.

The Vendor's Dilemma

AI vendors often consider their model architectures and algorithmic methods to be proprietary trade secrets. They're reluctant to commit to detailed transparency that might expose competitive advantages.

This creates a conflict: buyers need explainability for compliance, vendors need confidentiality for competitive protection.

What "reasonable transparency" could mean:

• A general description of how the model works
• Access to training data sources and composition
• The ability to audit specific model decisions
• Documentation of known limitations and biases

Providing meaningful transparency requires documentation that many vendors haven't created: model cards describing training data, impact assessments evaluating potential biases, and security documentation addressing AI-specific vulnerabilities.

Creating this documentation takes time and resources. Vendors hesitate to make contractual promises about deliverables they're not confident they can produce. But without those promises, buyers who need transparency for regulatory compliance can't sign the contract.

TermScout evaluates transparency commitments as part of AI certification, helping vendors understand what documentation buyers actually need.

5. AI-Specific Security Risks Nobody Talks About

AI systems face security threats that traditional software doesn't encounter. Model inversion attacks can extract training data from deployed models. Adversarial inputs can manipulate AI outputs in harmful ways. Data poisoning can corrupt model training to introduce biases or backdoors.

Why Standard Security Language Falls Short

When AI contract terms address security using standard IT security language, they often miss AI-specific concerns entirely. A commitment to "industry-standard security practices" doesn't necessarily mean the vendor is protecting against adversarial machine learning attacks or monitoring for data poisoning.

AI-specific security concerns that contracts should address:

• Protection against model inversion and data extraction attacks
• Adversarial robustness testing and monitoring
• Data poisoning prevention in training pipelines
• Incident response plans for AI-specific failures

What happens when an AI system fails in a way that causes customer harm? Traditional incident response plans address data breaches and service outages. They're less clear about how to respond when an AI model starts producing biased outputs or when adversarial attacks succeed.

What Smart Companies Are Doing About It

Stop Treating AI Terms Like Boilerplate

AI terms deserve the same attention as pricing and liability provisions. They're deal-critical provisions that require thoughtful drafting based on how the vendor's AI actually works and what risks it creates.

For vendors, this means getting product, legal, and security teams aligned on what AI commitments the company can realistically make. For buyers, it means developing clear requirements for what AI contract terms must address before a vendor makes the approved list.

Use Independent Certification to Cut Through Uncertainty

One reason AI terms create so much friction is that buyers don't trust vendor assurances. When a vendor claims their AI terms are "industry-standard," procurement teams have no easy way to verify those claims.

Independent certification changes this dynamic. TermScout's AI certification evaluates vendor contracts across seven principles: use restrictions, transparency, data handling, security, communication, best practices, and compliance. The certification badge provides buyers with immediate assurance that AI terms have been independently validated.

This doesn't eliminate due diligence entirely, but it provides a foundation of trust that accelerates procurement. Buyers can focus their limited time on provisions that truly require custom negotiation.

Make Commitments Specific and Measurable

Vague AI terms create uncertainty. Specific commitments reduce it. Instead of "will comply with applicable AI laws," consider "will maintain compliance with EU AI Act requirements for high-risk AI systems and provide annual compliance attestation."

Specificity requires more work upfront, but saves time during negotiations. When vendors make clear, measurable commitments, buyers can evaluate whether those commitments meet their needs without extensive back-and-forth.

Moving Forward: Clarity Wins Deals

AI terms have become deal breakers because contracts haven't kept pace with technology. Training data usage, output liability, regulatory compliance, transparency requirements, and AI-specific security represent genuine risks that responsible procurement teams can't ignore.

Vendors who invest in clear, specific AI contract terms—and validate those terms through independent certification—differentiate themselves in competitive situations and close deals faster. TermScout's certification process helps vendors turn AI terms from friction points into competitive advantages, while giving buyers the confidence they need to move deals forward quickly.

The companies that address AI contractual risks proactively—with specific language and verified commitments—will find themselves closing deals while competitors remain stuck in endless negotiations over undefined terms.