From Contract to Close: Enterprise Sales for AI Startups

Enterprise sales look attractive from the outside. Big logos, long contracts, and the promise of predictable revenue. But for AI startups, closing those deals often has less to do with polished product demos and far more to do with what happens after the handshake: contracts, compliance, risk management, and trust.

In a recent Swiss Startup Association webinar, Thomas Kuster, Partner at LEXR, unpacked what really stands between AI startups and enterprise customers. Selling to corporates requires more than a strong product. It demands an understanding of how enterprise teams think, how they assess risk, and how a solid legal setup can either accelerate a deal or bring it to a halt.

Why AI contracts are different

AI contracts are not just SaaS contracts with extra buzzwords. They sit in a much denser regulatory environment, shaped by data protection rules, sector specific regulation, and now AI specific laws like the EU AI Act.

For startups, this often feels overwhelming. But Thomas reframed the issue. Compliance is not only about avoiding fines. It is about risk mitigation and reputation management. This is what enterprise legal and compliance teams care about most. Even if your internal champion loves your product, the deal will stall if legal teams do not feel safe signing.

This is one of the reasons enterprise sales cycles are slow. Corporates move carefully, while startups move fast, and bridging that gap becomes a core part of the job.

The reality of selling AI to enterprises

AI startups face a few recurring tensions. On one side, technology evolves quickly as models improve and features change. On the other, enterprises prioritize stability, predictability, and clear accountability.

Legal uncertainty adds another layer of complexity. Many AI rules are still evolving, especially in Switzerland. While the EU AI Act is already largely in force, Swiss regulation is moving more gradually. This does not remove uncertainty. Instead, it shifts it into contract negotiations, where customers expect startups to clearly explain their role, their risk category, and the safeguards they have in place.

Trust is the final hurdle. AI is often perceived as high risk, even when it functions much like a standard SaaS product. Startups must be ready to explain, calmly and clearly, why their solution is safe to adopt.

Understanding the EU AI Act without panic

For Swiss startups selling into the EU, the same misunderstanding comes up again and again: “We are Swiss, so the EU AI Act does not apply to us.” That assumption is incorrect. If your AI system has an impact on the EU market through customers, users, or deployment, the Act may apply. In sales conversations, what matters most is knowing which risk category your system falls into and being able to demonstrate that you have assessed this professionally.

In practice, most AI systems fall into the low or minimal risk categories. Only specific use cases, such as certain HR, health, or government applications, are classified as high risk. Still, enterprises will ask the question. And if you cannot answer it clearly, they may push you to sign warranties you cannot safely give.

Privacy and information security take the most time

In practice, most negotiation time is spent on privacy and information security. As soon as personal data is involved, data protection laws apply, whether that data includes names, IP addresses, or metadata.

Startups need to understand their role in the data supply chain. In B2B settings, you are often acting as a processor on behalf of your customer, who remains the controller. This distinction matters because it determines who is responsible for what. Enterprises expect clear documentation. This includes Data Processing Agreements, technical and organizational measures, and a transparent list of subprocessors. Perfection is not required, but clarity is.

Cross border data transfers are another sensitive area. Swiss and EU data storage is usually acceptable, while US based providers raise more questions, especially in health, education, or public sector contexts. These discussions are becoming more common, not less.

Intellectual property is never a side topic

Questions around ownership always come up in enterprise negotiations. Who owns what, who can use which data, and for what purpose all need clear answers. European law does not recognize ownership of data as such, but contracts do. Input data, output data, metadata, and model improvements must all be clearly allocated. One red line Thomas emphasized repeatedly was simple: never give away ownership of your algorithm. If your business depends on training or fine tuning models, this must be addressed explicitly in the contract. Vague language will only lead to longer negotiations or unpleasant surprises later.

What enterprises expect you to have ready

Before stepping into serious enterprise discussions, startups should have a basic legal setup in place. Not dozens of documents, but the right ones. This usually includes a main contract or framework agreement, an order form covering commercial terms, a Data Processing Agreement, documented security measures, and a transparent list of subprocessors. Certifications like ISO 27001 or SOC 2 can help significantly, but they are not mandatory at early stages. What matters most is being able to clearly explain how your setup works. Corporates know you are a startup. They do not expect perfection, but they do expect professionalism.

Negotiating without losing control

Enterprise negotiations can feel intimidating, with long email threads, legal ping pong, and constant procurement pressure. But startups often have more leverage than they think. Thomas’ advice was practical. Address legal topics early, not after the commercial deal already feels closed. Make sure what was promised during sales is reflected in the contract, and whenever possible, speak directly with the customer’s legal team. A single call can save weeks of back and forth.

Using the enterprise template early on can speed things up, but it should never be accepted blindly. Push back where it matters, especially on IP, liability, unrealistic service levels, and exclusivity. When requests are reasonable, even large companies are usually open to compromise.

Red lines founders should not ignore

Some topics deserve extra caution. Liability should be limited, payment terms can affect cash flow more than most founders expect, and marketing rights need to be clearly defined when special pricing is involved. Auto renewals also matter if you want predictable revenue.

In the AI context, inference costs and infrastructure choices can quietly destroy margins if they are not addressed upfront. In some cases, letting customers use their own LLM keys can solve both cost and trust issues.

Above all, contracts should reflect how your product actually works. When they do not, problems tend to surface later, usually at the worst possible moment.

Final thought

Enterprise customers do not buy AI because it is exciting. They buy it because it feels safe to adopt. Contracts are where that feeling is either built or destroyed.

For AI startups, winning enterprise deals is not about becoming risk free. It is about showing that you understand the risks, manage them consciously, and take your customers’ concerns seriously. Do that well, and contracts stop being a blocker and start becoming a growth lever. 

Catch the full webinar replay in the Swiss Startup Association Education Library, free for members.

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