Questions Companies Should Ask When Evaluating AI Startups

As the number of AI startups grows, so does the volume of proposals, demos and partnership requests reaching corporations. Almost every presentation promises higher efficiency, lower costs and rapid results. Yet this abundance makes decisions harder rather than easier, because the real challenge is not finding options; it is telling an impressive presentation apart from a solution that can create value at enterprise scale.

Evaluating AI startups calls for a different lens than conventional software vendor selection. Product performance depends on the company’s own data, the underlying models change rapidly, costs move with usage volume, and a faulty output can reach the customer directly. The capacity of an early-stage team to meet enterprise expectations, meanwhile, is a risk area of its own. For this reason, standard procurement questions often fail to bring the critical uncertainties to light.

The right questions move an evaluation away from personal impressions and onto comparable decision-making ground. A good question set is not designed to corner the startup; it is designed to clarify the expectations of both parties early on. The questions below, ranging from problem fit to financial resilience, help distinguish which answers inspire confidence and which call for further verification.

1. Which of the company’s priority problems does the startup address?

The first question concerns need, not technology. When a company enters a meeting without having clarified the problem it wants to solve, who owns that problem and what it costs today, the startup’s product features end up setting the agenda. Teams may then be persuaded of capabilities they do not need. What should be expected from the startup is not a general product overview, but the ability to connect its solution to the company’s concrete problem.

The quality of the answer is also a signal. A team that can restate the problem in its own words and state openly which areas its solution does not cover inspires confidence. A team that says yes to every need, on the other hand, may be signaling a lack of focus. A startup that knows its limits is a more predictable partner than one that promises everything.

2. Is AI at the core of the product, or merely window dressing?

The term artificial intelligence appears in many presentations, but it does not carry the same weight in every product. In some solutions, most of the value comes from workflow design or conventional automation, while AI remains a limited component. This is not a flaw; the problem arises when the company does not know what it is investing in. The question to ask is which function of the product would disappear if the AI were switched off.

This distinction is easier to make when comparison is possible. Entrepreneurship Demo Day Events offer the chance to hear teams working on the same problem area back to back, revealing which claims are commonplace and which are genuinely distinctive. A single presentation can be convincing; presentations seen side by side show where real differentiation begins.

3. Does the solution rely on its own model or on third-party infrastructure?

Many startups build their products on top of foundation models offered by large technology providers. This approach brings speed, but it places control over pricing, terms of use and model versions outside the startup. The company should ask which models are used, how quickly the solution could be adapted if the provider changed, and at which layer the startup’s own contribution begins.

Dependency is not negative in itself; what matters is how the startup manages this risk. Are alternative models being tested, and is output quality re-measured after model updates? In a field changing this quickly, the questions themselves must also stay current. Innovation and Entrepreneurship Newsletters regularly convey developments in technology and the ecosystem, preventing teams from making decisions based on yesterday’s assumptions.

4. What data and how much preparation are needed to create value?

In AI solutions, results depend largely on the data the company provides. The startup should be asked which data is required, in what format, covering how much history and at what frequency. If the data needs to be cleaned, labeled or consolidated from different systems, it should also be clear who will take on that effort. Data preparation left undefined is one of the items that quietly inflate both timeline and budget.

The second half of the question is directed at the company itself. A Digital Maturity Assessment makes the current state visible in terms of data access, data quality and consistency across systems, revealing whether the conditions requested by the startup can be met today. In this way, the expected value is assessed not against an ideal data environment, but against the company’s actual starting conditions.

5. Who retains ownership of the data and the outputs generated?

Once data sharing begins, ownership and usage rights cease to be a technical detail. The company should ask explicitly where its data is stored, who has access to it, how it will be deleted when the collaboration ends and, most importantly, whether this data will be used to train the startup’s models. The possibility that a model improved with the company’s data could later serve competitors as well should be discussed from the outset.

The same clarity applies to outputs. When rights over models customized for the company, the rules developed and the content generated are left to the contract stage, negotiations become harder. Asking these questions early is not a sign of distrust; it is a professional starting point that protects both parties.

6. Under what conditions and with which data were the performance claims proven?

Indicators such as accuracy rate, level of automation or time savings carry no meaning when detached from their context. The company should ask with which data, in which customer environment and against what baseline the result was achieved. Performance in a controlled demo environment may not be reproducible with the company’s own fragmented and incomplete data. The real measure is how much difference the solution makes under the company’s own conditions compared with the current method.

Setting realistic expectations is also part of this question. Sector Reports and Case Analyses provide context on which results have been achieved under which conditions in comparable use cases, making it easier to judge whether the claims are reasonable. The startup should be asked to share not only its success stories but also the situations in which its solution struggled.

7. How does the process work when the model makes a mistake?

No AI model produces the right result under every condition. The decisive question is not whether errors will occur, but who will notice them, when and how. Can the solution flag low-confidence outputs, does human approval come into play for critical decisions, and are user corrections fed back into the system? These answers reveal the maturity of the product.

The cost of an error varies by use case. A minor deviation in internal reporting does not carry the same risk as incorrect information delivered to a customer. The company should define in advance which types of error it can tolerate and evaluate the startup’s monitoring mechanisms against that threshold. How the startup tracks whether performance degrades over time is a continuation of the same question.

8. How are security, regulatory compliance and auditability ensured?

For corporations, information security and regulatory compliance are prerequisites, not matters for negotiation. Alongside access management, encryption and certifications such as ISO 27001, the company should also ask about AI-specific risks, such as the model being steered by malicious inputs or sensitive information leaking into outputs. How obligations under data protection regulations such as KVKK and GDPR are met should be confirmed in writing.

In regulated fields such as finance, healthcare or insurance, the rationale behind a decision must also be explainable. Is there a record of which input produced which output, and can decisions be reviewed retrospectively? A solution that cannot be audited cannot be moved into critical processes, no matter how well it performs. An early-stage startup may not be expected to hold every certification; what matters is that it has a concrete plan to close the gaps.

9. How will the solution integrate with existing systems and workflows?

A solution that works well on a standalone screen goes unused if it sits outside the systems employees rely on every day. The startup should be asked which integration methods it supports, whether it offers cloud or on-premises deployment, how it aligns with identity management and at what scale it has delivered similar integrations. How much time will be required from the company’s IT team should also be known from the start.

The solution’s place in the workflow matters as much as the technical connection. At which step, to whom and in what format will the output be delivered, and how will it change existing job descriptions? A Digital Transformation Program supports the consistent embedding of new solutions into processes and technology architecture, so that integration stops being a one-off technical task. A solution with no counterpart in the process creates no value, even if it is technically connected.

10. Does the founding team have the depth to deliver at enterprise scale?

Working with an early-stage startup largely means working with that team. The company should ask how many people hold the AI expertise, whether domain knowledge sits within the team or with advisors, what experience the team has with enterprise customers and how support processes are run. Technical knowledge that depends on a single person is a continuity risk for the company.

These qualities become apparent through working together rather than through presentations. Corporate-Founder Workshops (Founder Workshops) bring founders and company experts together around a real problem, making it possible to observe how the team approaches constraints, how it responds to difficult questions and whether it is able to say “we cannot do that” when necessary.

11. Can the startup’s financial resilience sustain a long-term collaboration?

The survival of the startup behind a solution embedded in a critical process matters at least as much as the quality of the product. How long the startup can operate with its current resources, whether its revenue depends on a handful of customers and the nature of its investor backing can all be raised in an appropriate tone. The aim is not to eliminate the startup, but to calibrate the scope and pace of the collaboration correctly.

Safeguards for possible scenarios should also be discussed. If the startup is acquired, changes direction or ceases operations, in what format can the company retrieve its data, and how long will the service continue? The teams making this assessment need to understand how startups grow. Entrepreneurship Training and Workshops strengthen employees’ competence in entrepreneurship and business models, making this reading easier.

12. How will the total cost change as usage grows?

In AI solutions, cost is rarely limited to the license fee. Model usage fees tied to transaction volume, cloud infrastructure, integration, data preparation, maintenance and the time allocated to human oversight should all be calculated together. A cost that looks reasonable at the pilot stage can turn into a very different picture as the number of users and the transaction volume grow.

The startup should therefore be asked which variables drive its pricing, how unit cost behaves as volume increases and how price changes by the underlying model provider would be passed on to the company. A sound comparison is made not on the first-year quote, but on the total cost of ownership at the targeted scale.

13. How will the answers received be verified?

Receiving answers to the questions is not the end of the evaluation; it is the beginning of verification. Not every answer can be confirmed in the same way, so grouping answers by how they can be verified speeds up the process.

• Verified through documents: Commitments that can be put in writing, such as certifications, data processing terms, and ownership and exit provisions.
• Verified through references: Experiences that only existing customers can convey, such as support quality, delivery discipline and the actual level of use.
• Verified only through testing: Performance on the company’s own data, integration effort and the extent to which users adopt the solution.

In reference calls, it is real-world use, not the logos in the presentation, that should be examined. How many users actively use the solution and for how long, has the contract been renewed, and what difficulties arose during rollout? A reference call measures not the startup’s promise, but its delivery discipline.

The third group, in turn, is the natural input for the scope of a PoC. Corporate-Startup Collaboration (Scouting & PoC) covers the process from identifying startups aligned with strategic objectives through to PoC design, ensuring that the uncertainties left open in discussions are turned into hypotheses to be tested. A well-designed PoC does not try everything; it tests only the uncertainties that questions alone cannot resolve.

From questions to trust, from trust to scale

In collaborations with AI startups, the outcome is often determined less by the technology selected than by how the selection was made. Companies that develop a discipline of asking questions advance on the strength of evidence rather than the impact of a presentation; they decide knowing which uncertainty they accept, which they will test and where they will step back. The evaluation thus stops being a personal preference and becomes a corporate decision whose rationale can be explained.

This discipline is valuable for startups as well. A company that knows what it expects, communicates its questions openly and assesses the answers fairly becomes a partner that high-caliber teams prefer to work with. The accumulated questions, answers and lessons, in turn, form an institutional memory that makes each new evaluation faster and more accurate.

Although comprehensive questions may appear to slow the process down, they in fact do the opposite. When surprises surface in the first meetings rather than at the contract or scaling stage, the path from trial to lasting use becomes shorter. In the age of artificial intelligence, competitive strength will accumulate not in the companies that meet with the most startups, but in those that build trust quickly by asking the right questions early and are able to carry that trust to scale.

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