Over the past three weeks, the OneValley team has spoken to about 20 startup founders building AI-focused products. This blog is part one of a series that we’ll be publishing on the challenges of growing an AI-focused startup in 2026.

Last week I spoke to an AI fintech founder who had spent eighteen months pursuing a single enterprise customer. During that time the client added new security requirements, introduced multiple approval layers, and repeatedly delayed the decision.

Meanwhile the startup pivoted its product and shipped several new releases. The company’s technology was evolving faster than the enterprise contract.

As AI startups build at an ever-increasing velocity, the mismatch between enterprise expectations and startup speed is only going to grow.

The (False?) Promise of Enterprise

The $300B enterprise software market offers something consumer apps rarely do: large, durable contracts and predictable ARR.

But selling to enterprise has never been easy. Security requirements, compliance reviews, and demanding IT teams have historically protected incumbents like Adobe and ServiceNow.

AI introduces a new dynamic. Small teams now have dramatically more engineering leverage, and (in theory, at least) AI-native products could break through the switching costs that have long protected enterprise SaaS companies.

At the same time, AI also introduces new risks. Probabilistic systems behave very differently from traditional deterministic software. Consumers might tolerate a product that occasionally produces strange outputs. Enterprise buyers won’t.

If an AI system inserts a hallucinated fact into a compliance report or a supply-chain audit, the consequences can be immediate.

So what does it actually take for an AI startup to win enterprise customers?

In conversations with founders building AI products for enterprise environments, three themes came up repeatedly: data discipline, evaluation infrastructure, and model portability.

Table Stakes: Lock Down Data

Enterprise buyers will quickly disqualify vendors with weak data handling practices.

In fact, according to a December 2024 Bain survey, privacy concerns have overtaken accuracy concerns as the deal-killer for enterprise AI:

“Security and privacy concerns have grown, especially among firms leading the charge on generative AI. Meanwhile, accuracy concerns are beginning to ease, suggesting that companies are gaining more confidence in generative AI’s outputs.”

For AI startups relying on external model APIs, this usually starts with ensuring that personally identifiable information (PII) is removed before sending data to the model. One medtech founder described their approach:

“We strip out patient-identifiable information. The model sees ‘27-year-old female’ rather than a real patient record. When the response comes back, we reattach it to the correct user internally.”

Startups also need to ensure that the models they rely on do not retain customer data. Most major providers—OpenAI, Anthropic, and Google—offer zero data retention (ZDR) options, which ensure prompts are discarded rather than stored for training.

For regulated industries like healthcare or education, these practices are often the starting point for HIPAA or FERPA compliance.

But while data discipline is essential, it’s just the baseline for enterprise readiness.

The Real Differentiator: Evaluation

As one AI leader told us:

“Evaluation is the differentiator for enterprise.”

In traditional software, reliability comes from deterministic code and automated tests. With generative AI, reliability must come from evaluation systems that monitor and test probabilistic outputs. This becomes especially important when companies deploy agentic workflows.

Changes to prompts, model versions, or configuration can quietly degrade system behavior. Without evaluation infrastructure, those regressions are difficult to detect.

Despite this, many founders acknowledge the importance of evaluation while delaying investment in it.

Only one of the companies we spoke with had built a comprehensive automated evaluation framework. Their system tested model outputs for tone, accuracy, and policy compliance before shipping updates.

In this way, the startups we spoke with reflect a greater trend in AI software development. As of February 2026, Gartner reports that only 18% of software engineering teams use AI evaluation tools, a number they expect to grow to 60% by 2028.

Tools like LangSmith and Braintrust are rushing in to fill this gap by enabling both offline testing and online evaluation. Some platforms also generate synthetic test datasets when real user data is scarce.

What many startups have invested in is observability. Founders frequently mentioned using tracing and monitoring tools like Grafana and Sentry to track agent behavior and investigate failures.

But observability alone doesn’t guarantee reliability. For enterprise AI products, evaluation infrastructure increasingly functions as the equivalent of automated testing in traditional software. 

Prepare for Portability

Another pattern that repeatedly surfaced in founder interviews was the importance of model and infrastructure portability.

Enterprise buyers often impose strict requirements on how software must be deployed. One client might require deployment inside their VPC, another may insist on a particular cloud provider, and others may demand the use of a specific model or open-weights alternative.

This is an area where the ground is still shifting. A 2025 survey from Broadcom found organizations are choosing private cloud for AI workloads at nearly the same rate as public cloud, and 66% of IT leaders were “very” or “extremely” concerned about storing data on public cloud environments.

Early-stage startups can’t realistically support every possible configuration. But founders emphasized the importance of designing systems that can adapt when necessary.

One fintech founder described landing a client who required:

• deployment inside the client’s VPC
• a Google open-weights model

Fortunately, the team had already experimented with several model providers and hosting configurations. Because their system was modular and their migration processes well-documented, they were able to migrate without rewriting large portions of the product.

Another founder who currently relies on the OpenAI API for their SMB product has begun experimenting with OpenAI’s open-weights model, gpt-oss, to prepare for future enterprise requests.

Of course, even without enterprise customers, churn is inevitable in AI development. With new models, capabilities, and frameworks released daily, you’ll need to be comfortable switching pieces out regularly just to stay current. So, take advantage of that chaos: fastidiously document your switching processes, explore multiple models, and prepare for the day when enterprise requirements come knocking.

Trust Is Also a Product Decision

Enterprise adoption is not driven by architecture alone. In my conversations, I saw several examples of small, user-centered choices to increase the perceived trustworthiness of a product.

One medtech founder said they de-emphasize AI in their marketing, because the label alone sometimes reduces trust among clinical buyers.

Another founder underlined the importance of letting enterprise customers run their own output evaluations:

“We built tools so customers can see the outputs, run tests, and provide their own feedback. That transparency helps them trust the system.”

In another case, a founder considered switching away from a high-performing general model toward one fine-tuned for medical tasks—not because performance demanded it, but because customers trusted the specialized model more.

Selling Enterprise AI in 2026

To be clear, most of the founders I spoke with aren’t charging at enterprise yet. They’re waiting. They’re building credibility with smaller customers first, quietly preparing the architecture that enterprise will eventually demand. 

That might be the right call. As the example I opened this piece with illustrates, the sales cycles alone can bleed a startup dry.

But the opportunity is there for whoever figures out how to make probabilistic software reliable enough for a Fortune 500 procurement team.