Maybe this is a familiar scene: a founding team has a promising technology, a polished pitch deck, and a product roadmap that grows more ambitious every week. The prototype works. Advisors are encouraging. A few potential customers have said, “Interesting.”

Then comes the difficult part: nobody buys, pilots stall, and the team discovers that “interesting” is not a business model, but often just a polite way of ending a meeting.

 

Customer discovery matters

 

Customer discovery means checking whether your key assumptions about the customer, the problem, how buying works, and your value proposition are actually true.

In the NSF I‑Corps program, teams are encouraged to “get out of the building” and talk to potential customers, partners, and other stakeholders to see whether their technology could support a real, sustainable business model.[1]

For deep‑tech startups, this step is especially important because it shapes both what you build and how you plan to launch.

Bare in mind:

 

Good customer discovery does not validate your idea.
It investigates your risk.

 

If you treat it as a search for compliments, you will collect compliments.
If you treat it as a search for evidence, you may find a business.

 

Start with assumptions, not questions

 

Many founders begin customer discovery by writing an interview script. That is usually one step too late.

Before you ask anyone anything, write down what you believe to be true. For example:

• Who has the problem?
• When does the problem occur?
• How often does it happen?
• What does it cost in time, money, risk, frustration, or missed opportunity?
• How do people solve it today?
• Who would pay for a better solution?
• What would make switching worthwhile?

These are your hypotheses.
And you should test whether they are true or not.

Steve Blank’s customer development approach frames customer discovery as the process of turning a founder’s vision into business model hypotheses and testing customer reactions to those hypotheses.[3]

The practical implication:

your goal is not to “do interviews.”
Your goal is to reduce the uncertainty around the assumptions that could kill the company.

 

Speak to the right people,

 

not the easiest people

Early customer discovery goes wrong if founders interview people who are available rather than people who are relevant.

A useful interviewee is someone who

• has direct experience with the problem,
• influences the decision,
• owns the budget,
• uses the current workaround,
• OR feels the operational pain.

In B2B and deep-tech contexts, these may be different people. The user, buyer, technical evaluator, compliance stakeholder, and economic decision-maker may all have different definitions of “value”.

This is why customer discovery should include more than one stakeholder type. So, do not only speak with potential customers but also with partners and other business stakeholders.

For founders, this means that “we talked to ten users” may be useful, but incomplete if no one has spoken to the person who signs the purchase order.

A practical approach is to map the ecosystem:

1. Users: Who experiences the problem directly?
2. Buyers: Who controls budget?
3. Influencers: Who shapes the decision?
4. Blockers: Who can say no?
5. Current alternatives: What tools, services, spreadsheets, habits, or manual processes are already in place?

Customer discovery does not look for the most enthusiastic voice
but for the most informative pattern.

 

Ask about behaviour, not opinions

 

The weakest customer discovery question is also the most tempting:

“Would you use this?”

Most people want to be helpful. Some want to be nice.
A few want the meeting to end before their next call.
Future-intent questions invite all three.

Better questions focus on past and present behaviour:

• “When was the last time this problem occurred?”
• “What did you do?”
• “Who was involved?”
• “What did it cost?”
• “What happens if this is not solved?”
• “What have you already tried?”
• “Did you pay for any of those solutions?”
• “What would need to be true for you to switch?”

This principle is also central to Rob Fitzpatrick’s The Mom Test, a practical customer interview framework that advises founders to ask about the customer’s life and past behaviour rather than pitching the idea or asking for compliments.[4]

While this is an expert practitioner source rather than peer-reviewed research, the advice is consistent with a broader research logic:

Observed behaviour is usually stronger evidence than stated preference.

That does not mean interviews are perfect.
They are not.

People forget, rationalise, exaggerate, and simplify.
Think of the customer interview as a field sensor.
Useful, but occasionally muddy.

That is why strong discovery combines interviews with other evidence:
workflow observation, usage data, landing page tests, letters of intent, paid pilots, procurement conversations, or manual concierge tests.

 

Listen for pain, workarounds, and consequences

 

A real problem usually leaves traces.

If a customer has a painful problem, they may already be spending money, time, political capital, or emotional energy on it.

They may have built an ugly spreadsheet, hired someone, delayed a project, accepted regulatory risk, or created a workaround so strange it deserves its own museum label.

In customer discovery, these traces matter.

A useful signal sounds like:

• “We spend two days every month fixing this manually.”
• “We tried three tools, but none worked with our workflow.”
• “If this fails, the project is delayed.”
• “We already have budget for solving this.”

A weak signal sounds like:

• “That is interesting.”
• “Maybe we would use it.”
• “Send me something when it is ready.”
• “I can imagine this being useful for someone.”

The difference is evidence of urgency.

 

Do not confuse discovery with selling

 

Customer discovery and sales are related,
but they are not the same activity.

In discovery, your job is to learn.
In sales, your job is to persuade.

Mixing the two too early creates bad data.

If you pitch for twenty minutes and then ask for feedback, you are no longer studying the customer’s problem. You are studying their politeness under social pressure.

That does not mean you should hide what you are building forever.
At the beginning, stay problem-focused.
Once you understand the customer’s world, you can introduce a concept, prototype, or workflow and test reactions.

The goal is to understand what changes, what confuses, what creates value, and what blocks adoption.

A useful structure:

1. Context: Understand their role and environment.
2. Problem: Explore when and how the problem appears.
3. Current behaviour: Learn what they do today.
4. Cost: Understand consequences and urgency.
5. Decision process: Identify who is involved and what would need to happen.
6. Concept test: Only then introduce the solution direction.
7. Commitment: Ask for the next concrete step, if appropriate.

The commitment does not always have to be money. In early discovery, a strong signal might be an introduction to a buyer, access to data, a follow-up with the technical team, participation in a pilot, or permission to observe a workflow. The key is that it costs the customer something more than a compliment.

 

Look for patterns before you pivot

 

One interview rarely proves anything. Ten vague interviews do not prove much either. The power comes from patterns.

After each conversation, document customer segment, role, problem described, current workaround, perceived cost, urgency, buying process, objections, exact phrases used, next commitment, if any.

Then compare across interviews.

• Are the same problems appearing?
• Are they strongest in one segment?
• Are buyers and users aligned or in conflict?
• Does the problem occur often enough?
• Is the budget real?
• Are people already trying to solve it?

One outcome of customer discovery can be described as substantial first-hand evidence for OR against product-market fit, including clearer customer segments and value propositions.[1]

Good discovery may tell you to continue.
It may also tell you to stop, narrow the segment, change the value proposition, or rethink the route to market.

A pivot discovered through evidence is cheaper than a pivot discovered after twelve months of product development and a website with tasteful gradients.

 

Common mistakes founders should avoid

 

The first mistake is asking leading questions: “Wouldn’t it be useful if…?” This turns the interview into a courtroom where the founder is both lawyer and defendant.

The second is interviewing only friendly contacts. Friends, advisors, and innovation managers can be helpful, but they may not represent the buyer with budget pressure.

The third is stopping at problem confirmation. A problem can be real and still not become a business. You also need to understand willingness to change, ability to pay, procurement barriers, and the competitive alternative.

The fourth is outsourcing discovery too early. Agencies can support research, but founders need direct exposure to customer reality. For early-stage teams, the discomfort is part of the data.

The fifth is treating discovery as a one-time phase. Customer discovery is most intense early, but the underlying discipline continues as the market, product, and customer segment evolve.

 

Conclusion

 

Customer discovery done well helps you discover

• whether the problem is painful,
• whether the customer is specific,
• whether current alternatives are inadequate,
• whether the buying process is realistic,
• and whether your product direction deserves more investment.

The uncomfortable truth is that good customer discovery may slow you down at the beginning. The useful truth is that it can prevent you from accelerating in the wrong direction.

If you are building a tech-driven startup and want structured support with validation, business model development, market access, and growth, take a closer look at the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup Incubation Program→ inits.at/en/scaleup

 

The Big Takeaways

 

Write down your riskiest assumptions before you interview anyone.
If you do not know what you are testing, every conversation will feel useful and very few will be decisive.

Ask about past behaviour, not future interest.
What did you do last time this happened?” is stronger than “Would you use our product?”

Look for costly signals.
A pilot, budget discussion, data access, internal introduction, or workflow observation is more meaningful than “sounds interesting.”

---

Source list

 

1. U.S. National Science FoundationI-Corps Report2019

   https://nsf-gov-resources.nsf.gov/2022-06/I-CorpsReport–6_4_19FINAL_508_0.pdf

2. U.S. National Science Foundation2025 NSF Innovation Corps (NSF I-Corps) Biennial Report2025

   https://www.nsf.gov/funding/initiatives/i-corps/2025-biennial-report

3. Steve BlankCustomer Development for Web Startups2010

   https://steveblank.com/2010/02/25/customer-development-for-web-startups

4. Rob FitzpatrickThe Mom Test: How to Talk to Customers and Learn If Your Business Is a Good Idea When Everyone Is Lying to You2013

   http://momtestbook.com/

The most expensive startup mistakes are often the ones someone could have warned you about.

 

A wrong assumption about the market. A product nobody really needs. A decision that sets you back months.

Founders need to make mistakes to learn.

But do they always have to be their own?

This is where mentoring comes in.

 

 

 

 

Mentoring for Startups

 

Why the right mentor can make all the difference, how to find the right match, and when it’s time for a fresh perspective.

Between the first spark of an idea and a growing startup lie countless decisions.

• Which problems are actually worth solving?
• When is a product ready for the market?
• Who do you need on your team?
• And how does your role change when a handful of people suddenly becomes an organisation?

There are no universal answers to these questions.

And often, it’s precisely when the stakes are highest that founders lack the experience to fully anticipate the consequences of their decisions.

That’s where mentoring can make a real difference.

Simply because experienced mentors have faced similar challenges before. They can recognise patterns more quickly and ask questions you might never have thought to ask yourself.

 

1. Why Mentoring Is So Valuable for Founders

 

Building a startup means making decisions in the face of uncertainty.

In the early stages, reliable data is often scarce, resources are limited, and many assumptions about customers, products, or business models still need to be tested. At the same time, founders carry the responsibility for whatever comes out of those decisions.

Experienced mentors can help uncover blind spots, sharpen priorities, and identify common mistakes before they become costly.

That doesn’t mean they’re always right. Even highly successful entrepreneurs can get things wrong.

The point isn’t to blindly follow their advice. It’s to learn from their experience and draw the right conclusions for your own business.

Research also suggests that mentoring can play a meaningful role in entrepreneurial development. A study by Eesley and Wang on entrepreneurial mentorship found that mentors can influence entrepreneurial career decisions, highlighting the potential impact of these relationships.

The greatest value of a mentor isn’t telling you what to do. It’s helping you see what you might be missing.

 

2. Sometimes, the Most Valuable Perspective Comes from Outside

 

The value of a fresh perspective became particularly clear at the latest INiTS Mentors Day.

The event brings together founders from the SCALEup incubation programme and experienced mentors, giving them the opportunity to connect, exchange experiences, and explore potential mentoring relationships.

This time, Marcus Ihlenfeld, co-founder of Austrian children’s bicycle brand woom, joined us to share some of the lessons he has learned throughout his entrepreneurial journey.

One of his key messages was something that sounds obvious to most founders but is surprisingly easy to forget in practice:

 

Start with the problem. Think from the customer’s perspective.

 

Technology-driven startups, in particular, often invest enormous energy into developing their solutions. The team knows every technical detail, is excited about what’s possible, and keeps working to make the product better and better.

But somewhere along the way, one crucial question can get lost:

Is the problem we’re solving actually important enough to our customers?

Instead of spending months perfecting a product, it’s often better to build prototypes quickly and start collecting feedback early.

Because the longer you work on what you believe is the perfect solution, the more time you risk investing in assumptions that might not hold up in the real world.

 

So don’t be afraid of negative feedback! The earlier you invite it, the sooner you can identify what’s not working, make improvements, and correct mistakes while they’re still relatively inexpensive to fix.

 

 

This is exactly where good mentors can add tremendous value.

They ask the uncomfortable questions, challenge assumptions, and encourage teams to look at their ideas through the eyes of their customers.

 

3. How Do You Find the Right Mentor?

 

Ideally, your search shouldn’t start with:

„Who’s the most successful person I know?“

Instead, ask yourself:

„What’s the biggest challenge I need to solve right now?“

 

Because an impressive CV doesn’t automatically make someone the right mentor for your startup.

A founder developing their first product might benefit most from someone with hands-on experience in product development, customer discovery, and go-to-market strategies.

A startup already generating revenue and preparing for international expansion, on the other hand, might need expertise in scaling, sales, organisational development, or fundraising.

Three questions can help you find the right match:

• Relevant experience: Has this person faced challenges similar to the ones you’re dealing with right now?
• Personal fit: Do you feel comfortable openly discussing uncertainties, mistakes, and difficult decisions?
• Time and commitment: Does this person have the capacity and genuine interest to engage with you regularly?

Personal chemistry deserves particular attention.

After all, the most valuable conversations often happen when founders feel comfortable talking about the things that aren’t going well.

So how do you find out whether there’s a good match?

At INiTS, here’s how we approach it:

We bring founders from our SCALEup incubation programme together with experienced mentors for an evening in a relaxed atmosphere.

Through personal conversations, both sides get the chance to discover whether their expertise, expectations, and personalities are a good fit.

Because a successful mentoring relationship needs more than relevant experience. It also needs trust.

 

4. When Is It Time to Change Mentors?

 

A mentoring relationship doesn’t have to last forever to be successful.

Startups evolve. And as they grow, the questions founders need to answer change too.

In the early stages, the focus is often on understanding a real customer problem, developing an initial solution, and finding a viable business model.

Later, entirely different challenges take centre stage: processes, leadership, team structures, financing, growth, and international markets.

At the INiTS Mentors Day, Marcus Ihlenfeld also emphasised that different stages of a company’s development require different leadership styles and team structures.

In the beginning, a strong hands-on mentality is often essential. People need to move quickly, take responsibility, and find pragmatic solutions.

But as the company grows, it needs more structure, clearer responsibilities, and people who can manage increasing complexity.

The same principle applies to mentoring.

Someone who was incredibly helpful during your initial product development might not be the best person to guide you through international expansion.

That doesn’t mean the relationship has failed.

It might simply mean that it has fulfilled its purpose at a particular stage of your journey.

A good time to reassess your mentoring relationship is whenever your startup’s main challenges begin to change.

Other signs that it might be time for a fresh perspective include:

• Your conversations no longer lead to meaningful new insights.
• Your mentor’s experience is no longer aligned with your current challenges.
• The relationship lacks openness, trust, or constructive criticism.
• There’s no longer a clear purpose or shared goal for your conversations.

There’s no fixed timeline for mentoring. Some relationships are valuable for just a few months, while others support founders for years.

What matters is setting clear expectations from the beginning and regularly reflecting on whether the relationship is still creating value.

Here’s something worth remembering: Your mentor doesn’t necessarily have to grow with your startup. But your mentoring should.

 

5. What Even the Best Mentors Can’t Replace

 

As valuable as mentoring can be, the responsibility for the business ultimately stays with the founders.

This was another key message Marcus Ihlenfeld shared at the INiTS Mentors Day.

He spoke about the importance of setting ambitious goals, staying true to your values, and continuously improving.

He also emphasised that product development and sales must go hand in hand.

A great product isn’t enough if nobody knows how to bring it to market.

And even the strongest sales team will struggle in the long run if the product doesn’t solve a meaningful customer problem.

For founders, this means bringing different strengths and competencies together within the team and taking responsibility for the bigger picture.

Mentors can help prepare for critical decisions, challenge your thinking, and offer fresh perspectives.

But ultimately, it’s the founders who have to make those decisions.

 

Conclusion: You Don’t Have to Learn Everything the Hard Way

 

Entrepreneurship will always be a learning process. Mistakes, uncertainty, and difficult decisions are part of the journey.

But not every lesson has to come from a mistake you’ve made yourself.

Sometimes, all it takes is a conversation with someone who’s faced a similar situation before.

An uncomfortable question. A different perspective. Or one piece of advice that helps you spend the next few months moving in the right direction instead of the wrong one.

The right mentor won’t take the challenges of building a startup off your shoulders.

But they can help you approach those challenges with greater clarity, experience, and confidence.

 

Mentoring at INiTS: Part of the SCALEup Incubation Programme

 

At INiTS, we support innovative startups through our SCALEup incubation programme, providing not only expertise and access to a strong network but also opportunities to work with experienced mentors.

Our Mentors Day creates a space for meaningful connections. Founders and mentors get to know each other, exchange perspectives, and explore where working together could create real value.

Because sometimes, the next big step in a startup’s journey begins with just one good conversation.

 

Find out more about the INiTS SCALEup incubation programme: inits.at/scaleup

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Sources and Further Reading

 

1. Eesley, C. E. & Wang, Y. (2017): Social Influence in Career Choice: Evidence from a Randomized Field Experiment on Entrepreneurial Mentorship. Research Policy, 46(3), 636–650.
2. Blank, S. (2013): Why the Lean Start-Up Changes Everything. Harvard Business Review.
3. Kram, K. E. (1985): Mentoring at Work: Developmental Relationships in Organizational Life. Scott, Foresman.
4. St-Jean, É. & Audet, J. (2012): The Role of Mentoring in the Learning Development of the Novice Entrepreneur. International Entrepreneurship and Management Journal, 8, 119–140.

 

Building a startup in a crowded market can feel a bit like arriving late to a dinner party where everyone is already talking at once. The instinct is to speak louder.

That is usually a mistake.

Startups do not lose because the market is full. They lose because their difference is vague, cosmetic, or impossible to notice without a twelve minute explanation and a slide deck no one asked for.

Real differentiation is rarely about doing everything better. It is about doing one important thing so clearly and so convincingly that the right customers immediately understand why you matter.

That may sound simple, but it is not. In many cases, figuring out how to stand out takes almost as much work as building the product itself.

Still, there are patterns. And fortunately, markets have a habit of leaving clues behind.

 

1. Differentiate Through Extreme Focus, Not Broad Appeal

 

One of the oldest mistakes is also one of the most persistent:
trying to build for everyone.

Trying to appeal to everyone from the start can make a product feel ordinary. People may like the idea but still choose something else.

It’s better to start with a specific group and solve a real problem for them. This can help a startup become valuable to those customers, rather than only somewhat useful to many people.

WHOOP is a good example. The company was founded in 2012 and built its early credibility by focusing on serious performance and recovery tracking for elite athletes. Its early adoption among high level athletes, including Michael Phelps, helped it build trust in a demanding niche before expanding into a much broader wellness market.[1][2]

That is the strategic lesson. A niche is a testing ground where a startup can become excellent, credible, and hard to replace.

 

2. Focus on Customer Experience, Not Feature Accumulation

 

Another common trap is assuming that competitive markets are won by building more.

More features. More options. More customization. More buttons, which is apparently how some companies define innovation.

Customers, however, are not always looking for more.
Quite often, they are looking for less friction.

In crowded markets, products often become more alike over time. Competitors copy useful features, and what once felt unique soon becomes common. Then the customer experience matters just as much as the product itself.

The better question is not always, What can we add?

It is often, What can we remove that currently makes this annoying, slow, expensive, or oddly bureaucratic?

Customers want the problem solved with as little fuss as possible.

 

3. Differentiate Through Pricing Model Innovation

 

Startups can stand out by changing how they charge, not just what they offer. This can work when prices feel outdated or do not match how customers want to pay. A startup may not need a new product. It may only need a new pricing model.

Spotify is a well-known example. Launched in 2008, it helped make freemium streaming popular: people could listen for free, while paid subscribers got extra features and convenience.[3][4] Spotify did not invent music streaming. It made legal music easy to access when existing options did not fit what listeners wanted to pay.

That is the key point.

For startups, pricing innovation can take many forms. Subscription instead of one time payment. Usage based pricing instead of flat fees. Entry level access that reduces adoption risk. Premium layers for customers who want more depth, speed, or support.

The point is to remove the awkwardness between what customers value and what the market is asking them to pay for.

 

4. Win by Simplifying Complexity

 

Busy markets often become too complicated.

Over time, products collect jargon, extra steps, and confusing designs. People in the industry may accept these things, but customers find them frustrating.

That creates an opportunity: make the product easier to understand and use.

Revolut is one example. Traditional European banking, especially spending across borders, was often slow, unclear, and expensive. Revolut made the experience much simpler:

• Instant account setup in-app (no branch, minimal paperwork)
• Transparent FX + multi-currency accounts (so travel and international payments feel easy)
• One clean interface for cards, budgeting, transfers, and subscriptions

In other words, they didn’t “invent money”, but they made an old, complicated category feel modern and accessible.

Things like that are often enough to change a market.

 

The Big Takeaways

 

1. Start narrow enough to matter.

A niche gives you the chance to become essential before trying to become universal.

1. Remove friction before adding features.

In mature markets, the smoother experience often beats the more elaborate product.

1. Question the pricing logic.

If an industry prices value badly, there may be room to compete without reinventing the core offer.

1. Make complicated things feel clear.

Customers are not rewarding complexity. They are rewarding relief.

 

Conclusion

 

To stand out in a crowded market, find what customers dislike and make it better.

That might mean focusing on a specific group, making it easier to buy, offering fairer prices, or making a complicated product simple.

The best startups often succeed by doing one important thing better than others—and making sure customers notice.

That can feel like magic.

 

Source:

 

[1] https://www.cnbc.com/2026/06/09/whoop-founder-how-i-built-a-10-billion-fitness-wearable-company.html

[2] https://www.whoop.com/at/de/thelocker/

[3] https://newsroom.spotify.com/company-info/

[4] https://www.businessofapps.com/data/spotify-statistics/

[5] https://www.revolut.com/

Adaptronics, COSMOKE and TECHiFAB took the top three places at the Infineon Startup Challenge 2026 Demo Day on October 6 at EBSCON in Graz. Their technologies address three practical challenges in robotics: gripping objects, sensing touch and controlling movement.

INiTS supported the challenge as one of its business coaching and pitch training partners. The program brought startup teams together with Infineon’s technology and expertise to develop their robotics applications.

 

The winners

 

First place: Adaptronics s.r.l., Italy — gripping with electrostatic adhesion

Adaptronics makes grippers that use static electricity to hold objects. They do not need suction cups or mechanical clamps.

The grippers can handle many types of objects and could be used in robotics, logistics, packaging and even space. The jury praised the technology for being innovative, versatile and useful in industry.

 

Second place: COSMOKE, South Korea — giving robots a sense of touch

COSMOKE develops flexible magnetic robotic skin that detects touch, pressure and interaction across large surfaces. For humanoid robots, this means a way to sense physical contact with their surroundings.

The technology is designed to support safer, more intuitive interaction between people and robots. Its relevance to humanoid robotics and its potential to improve safety and interaction quality were key strengths highlighted in the winner information.

 

Third place: TECHiFAB, Germany — improving how robots move

TECHiFAB uses novel semiconductor technology for AI-supported motor control in robotic actuators: the components that turn control signals into physical movement.

Its approach aims to make control faster, more energy-efficient and safer by improving performance directly within the robot’s drive system.

 

How Infineon works with startups

 

The challenge combines prototype development with business preparation. Teams gain access to prototyping kits, technical guidance, business coaching and pitch training. INiTS is part of the partner network supporting the business side of that process.

Infineon works with startups to find new uses for its semiconductor technology. Startups gain access to this technology and industry expertise as they develop their products. Together, they can test new ideas in a growing market. Business success will depend on the next steps.

The challenge is part of Infineon’s wider program for working with startups. Through joint projects, Infineon may become a customer, supply technology, help develop products or support startups in bringing them to market.

 

Beyond Demo Day

 

Infineon’s published program provides for invitations to a one-year innovation program for selected teams. The winners are also due to be invited to pitch at Infineon Startup Night in Munich on November 25, 2026.

Learn more about the Infineon Startup Challenge.

There’s a considerable distance between „I have interesting technology“ and „I want to start a company.“ Many researchers know it well: you have a promising research result. And then what?

Too often, good ideas stay in the drawer, not because they’re bad, but because nobody shows what the next step could look like.

 

AI:SPiN guides researchers from the question „Does this have potential?“ to a possible spin-off. Applications open until 18 October 2026.

With AI:SPiN, INiTS, AI Factory Austria AI:AT and the University of Vienna are closing that gap together. The new program is aimed at researchers whose work builds on AI, and helps them find out whether and how it could become a company.

 

The Real Problem: Everything at Once

 

Commercialising AI research has a particular quirk: business questions and technical questions arrive at the same time, and they depend on each other.

• You want to know whether there’s a market for the application while you’re still working out whether the technology performs well enough.
• You need customer feedback to decide what to prototype in the first place.
• You need serious computing infrastructure before you can test whether the prototype works at all.

Under these conditions, „validate the market“ isn’t helpful advice. That’s why AI:SPiN tackles both at once: technical work and market exploration run side by side and shape each other.

 

What You Get

 

The program is free of charge and combines two worlds that are often kept apart:

• Tech: AI expertise, access to supercomputer infrastructure and hands-on prototyping support through AI Factory AI:AT. With their help, you can build your own prototype.
• Business: Coaching from INiTS, drawing on more than 20 years of experience with spin-off and incubation programs.
• Network: Exchange with other founding teams, including a coworking space.
• Structure: A clearly organised, step-by-step path with workshops, practical assignments, and personal business and tech mentoring.
• Flexibility: The format is modular. You join at the stage that fits the maturity of your project.

The Philosophy: Learning by Doing

 

No theory lectures, but practical implementation, iterative feedback and personal commitment. From the initial orientation through an on-site bootcamp to prototype support, every stage has the same goal: learn enough to make the next decision with confidence.

 

A „No“ Is Also a Result

 

Let’s be upfront: AI:SPiN doesn’t aim to push every good AI project into a company. If, after structured exploration, you find that a spin-off isn’t the right path, that’s a valuable outcome. Better to learn it now than after months spent heading in the wrong direction.

And if the answer is yes?

Then you have considerably more than an idea.
You have evidence to build on.

 

Who Is This For?

 

Researchers from all disciplines, from pre-docs through PhDs and postdocs to senior-level scientists, whose work uses AI as a central, enabling component of the technology.

 

Who’s Behind It

 

AI:SPiN is the result of a joint ecosystem: INiTS | Vienna’s High-Tech Incubator is responsible for incubation management, AI Factory Austria AI:AT provides the technological framework, and the University of Vienna and other Austrian academic institutions handle outreach and technology transfer.

 

Apply Now

 

The application deadline is 18 October 2026.

• Full Program Information: www.inits.at/joinaispin

 

Some startups do not fail because the idea is bad.
They fail because every week produces motion, but not enough progress.

Meetings happen. Tasks multiply. Decisions circle back. The product roadmap expands. Nobody is quite sure who owns what. The team is working hard, but the company has started to resemble a hamster wheel: constant motion, considerable effort, and remarkably little distance.

This is execution risk: the possibility that unclear ownership, weak prioritisation, slow decisions, or inconsistent delivery will become the startup’s main bottleneck.

For an early-stage tech company, strong execution does not simply mean working faster.
It means focusing limited time, capital, and talent on the most important next proof point, and turning that focus into measurable progress.

 

Execution starts with strategic focus

 

A startup is not a smaller version of a large company.
According to Steve Blank it is a temporary organisation searching for a repeatable and scalable business model (Ed. note: under conditions of extreme uncertainty). [1]

That search requires focus. If the team tries to validate the product, build ten features, prepare investor materials, attend every event, redesign the brand, and explore three customer segments at the same time, ambition is not the problem. The problem is physics.

Execution improves when the team can answer:

• What is the single most important milestone now?
• What evidence do we need next?
• What work directly supports that milestone?
• What should we stop doing for now?

Clarify roles before speed increases

 

In early teams, ambiguity often feels natural. Everyone helps everywhere. That can work for a while. Then complexity grows, and “everyone owns it” begins to mean “nobody really owns it”.

Entrepreneurial teams are interdependent social units, not simply collections of talented individuals. [2] The team performance depends not only on individual skills, but also on how people coordinate.

Clarify:

• Who owns product decisions?
• Who owns customer discovery?
• Who owns fundraising?
• Who owns technical delivery?
• Who owns sales or partnerships?
• Who makes the final call when opinions differ?

This will reduce confusion before confusion becomes culture.

 

Build an operating rhythm

 

Execution improves when the team has a simple rhythm for priorities, decisions, and learning.

Here is an example for a useful early-stage rhythm:

 

Weekly

1. define the top 1–3 priorities,
2. review customer evidence,
3. check progress against milestones,
4. identify blockers,
5. make decisions.

Monthly

1. review assumptions,
2. assess runway and resources,
3. update the roadmap,
4. decide what to stop.

Quarterly

1. revisit strategy,
2. evaluate team gaps,
3. define the next major proof point.

The goal is to prevent the company from being managed by whoever spoke last.

 

Make decisions explicit

 

Startups lose enormous time to invisible decisions.

Everyone assumes something was agreed, until two weeks later it becomes clear that three different versions of reality were quietly operating in parallel.

Use a lightweight decision log:

• What decision was made?
• Why?
• Based on what evidence?
• Who owns it?
• When will it be reviewed?

This helps teams move faster because decisions stop evaporating.

It also helps founders distinguish between reversible and irreversible decisions.
Some decisions are reversible: landing page copy, outreach segment, interview script, prototype flow.
Some are much harder to reverse: co-founder equity, regulatory pathway, architecture, hiring commitments, investor terms.

Treating every decision as equally heavy slows the company down.
Treating every decision as equally light creates expensive chaos.

 

Strengthen the team around gaps, not titles

 

When it comes to hiring the right people, begin with what the startup must prove over the next six months, determine which skills that requires, and compare them with the capabilities already available.

Ask:

• What must we prove in the next six months?
• What skills are required to prove it?
• Which skills do we already have?
• Which are missing?

Not every gap demands a full-time hire; advisors, freelancers, partners, or mentors may provide what is needed at this stage.

In technical startups, the missing capability is often not additional technical talent, but customer access, regulatory expertise, sales, business model design, clinical validation, manufacturing, or fundraising.

A strong team is one whose capabilities match the startup’s next stage of risk.

 

Protect learning speed

 

The NSF I-Corps program emphasises evidence-based decision-making and customer discovery for science and engineering teams exploring commercial viability. [3] This matters because early-stage execution should not only produce outputs. It should produce validated learning.

A team that ships features but does not learn from customers is not executing well.
It is manufacturing uncertainty in a more organised format.

Ask every week:

• What did we learn?
• What changed because of it?
• What evidence do we still lack?
• What is the next test?

Common execution mistakes

 

Execution problems often begin with familiar habits:

• confusing busyness with progress,
• avoiding difficult priorities,
• leaving responsibilities vague,
• hiring before the team has learned what it needs,
• and postponing important conversations between founders.

A full calendar is not a strategy, and if everything on the roadmap is important, the roadmap is lying.

Ambiguity may initially feel flexible, but it often conceals unresolved conflict.

Hiring can accelerate a system that already works; it rarely fixes one that lacks clarity.

Founders should therefore discuss motivation, risk tolerance, time commitment, salary needs, equity expectations, and decision rights before pressure rises and not after those differences have become operational problems.

 

Conclusion

 

Execution risk is not solved by telling the team to work harder.
Most founders are already working hard enough to make their calendars look medically concerning.

The real solution is focus, role clarity, decision discipline, and a rhythm that turns effort into evidence.

A strong team is not the one that does everything.
It is the one that knows what matters now, who owns it, and what evidence will guide the next move.

If you want structured support strengthening your team, execution, validation, business model, and growth, explore the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup → inits.at/en/scaleup

 

 

The Big Takeaways

Define the next proof point.
Execution improves when the team knows what evidence matters now.

Clarify ownership.
“Everyone helps” is good. “Nobody owns it” is dangerous.

Create a simple operating rhythm.
Weekly priorities, explicit decisions, and regular learning reviews prevent drift.

Sources

[1] Steve Blank — What’s a Startup? First Principles — 2010 — https://steveblank.com/2010/01/25/whats-a-startup-first-principles/

[2] Lazar et al. — Entrepreneurial Team Dynamics and New Venture Creation — SAGE Open — 2018 — https://journals.sagepub.com/doi/10.1177/2158244018781446

[3] U.S. National Science Foundation — I-Corps Report — 2019 — https://nsf-gov-resources.nsf.gov/2022-06/I-CorpsReport–6_4_19FINAL_508_0.pdf

 

A promising solution is not automatically a viable business. Pressure-testing your business model means examining whether a clearly identified buyer will pay, whether you can reach and convert customers through a realistic route to market, and whether the resulting revenue can eventually support the costs of acquiring, delivering to, and serving them.

A startup can have a real problem, a clever product, and enthusiastic users, and still not have a business.

That is the core of the Business Risk profile.

The product may create genuine value. The market may even care. But “useful” does not pay invoices on its own.

A business model describes how your startup creates, delivers, and captures value. The Business Model Canvas popularised this wider view by encouraging teams to look beyond the product and consider customer segments, channels, revenue streams, key activities, partners, and costs. [1]

For founders, the practical message is simple:

Your product is only one part of the business model.

If the economic buyer is unclear, willingness to pay remains hypothetical, the route to market is unrealistic, or the cost structure cannot support the expected revenue, even an excellent product may not become a sustainable company.

 

Start with the 4 money questions

 

To pressure-test your business model, begin with four questions:

• Who pays?
• Why do they pay?
• How do you reach them?
• Can you deliver profitably or sustainably?

Early-stage teams tend to answer the first question too vaguely: “companies”, “hospitals”, “universities”.

These are not customers. They are categories.

A better answer identifies the economic buyer: the person or unit with budget, urgency, and authority.

For example:

• Not “hospitals”, but “heads of radiology trying to reduce reporting backlog”.
• Not “manufacturers”, but “operations managers responsible for reducing downtime”.
• Not “research institutions”, but “technology transfer offices looking for licensing-ready proof”.

If you know who pays, your sales process becomes much easier.

 

Separate users from buyers

 

In B2B and deep-tech startups, the person who uses the product may not be the person who pays for it. This creates business model risk.

A lab technician may love your tool. The department head may worry about budget. IT may worry about integration. Procurement may worry about vendor risk. Legal may appear late in the process, carrying a very small hammer and a very large checklist.

This is why pressure-testing requires conversations with multiple stakeholders. It is important to interview potential customers, partners, and business stakeholders to assess whether an innovation could form the basis of a sustainable business model. [2]

Your goal should be to understand the buying system.

Ask:

• Who feels the pain?
• Who owns the budget?
• Who approves the purchase?
• Who can block adoption?
• What evidence is needed internally?
• What alternatives are already approved?

Test willingness to pay early

 

It is tempting to delay pricing because there is the fear it will scare customers away.
But avoiding price discussions hides business risk.

You do not need a perfect pricing model at the beginning.
But you do need evidence that the value is strong enough to support payment.

Ask customers:

• What does this problem cost today?
• Is there a budget for solving it?
• What category would this purchase fall into?
• What have you paid for similar solutions?
• What would make this worth paying for?
• Who would need to approve it?

And test behaviour:

• paid pilot,
• letter of intent,
• budget owner meeting,
• procurement introduction,
• pre-order,
• implementation fee,
• design partner agreement.

You can distinguish between stronger evidence, such as actual behaviour, purchases, usage, and retention, and weaker evidence, such as opinions and hypothetical responses. [4] Look for the stronger one.

 

Pressure-test your channel

 

A business model can fail because the product is too hard or expensive to sell.

This is especially relevant for startups selling to enterprises, public institutions, healthcare systems, universities, or regulated industries.

Long sales cycles, tender processes, integration requirements, and trust barriers can turn a theoretically attractive market into a slow and expensive one.

Pressure-test:

• How will customers first hear about you?
• Can you reach them directly?
• Do you need partners?
• How long does the sales cycle take?
• What proof is required before purchase?
• What does customer acquisition cost?
• Can your pricing support that cost?

If your expected contract value is small but the sales process requires six months, three committees, and a ceremonial procurement dance, the business model needs work.

Check the basic economics

 

At the pre-seed stage, startups do not need a perfect financial model,
but they do need credible economic logic.

Founders should estimate key variables such as pricing, gross margin, cost to serve, onboarding and sales effort, support requirements, retention, and expansion potential.

For hardware, biotech, climate tech, and other deep-tech ventures, this should also account for factors such as manufacturing costs, certification, regulatory timelines, maintenance, and partner margins.

The point is not precision, but to identify economically impossible assumptions before significant time and capital are invested.

 

Conclusion

 

Business model risk appears when the solution may be valuable, but the path to a viable company is still unclear.

Pressure-testing your business model means moving from attractive slides to uncomfortable specifics: who pays, why they pay, how you reach them, what adoption requires, and whether the numbers can eventually work.

A good business model gives the product a route into the world.

If you want structured support with business model development, validation, market access, and growth, explore the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup → inits.at/en/scaleup

 

The Big Takeaways

 

Identify the economic buyer, not just the user.
The person with the pain and the person with the budget may not be the same.

Test willingness to pay before scaling activity.
Budget conversations are uncomfortable, which is exactly why they are useful.

Pressure-test the route to market.
A valuable product can still fail if it is too expensive or slow to sell.

 

Sources

 

[1] Alexander Osterwalder / Harvard Business Review — A Better Way to Think About Your Business Model — 2013 — https://hbr.org/2013/05/a-better-way-to-think-about-yo

[2] U.S. National Science Foundation — I-Corps Report — 2019 — https://nsf-gov-resources.nsf.gov/2022-06/I-CorpsReport–6_4_19FINAL_508_0.pdf

[3] Strategyzer — Testing Business Ideas: Book Summary and Key Takeaways — https://www.strategyzer.com/library/testing-business-ideas-book-summary

[4] Strategyzer — Testing Business Ideas — https://www.strategyzer.com/library/testing-business-ideas-book

 

 

At the beginning, every startup is partly fiction.

The customer segment is a hypothesis. The problem is a hypothesis. The product is a hypothesis. The price, channel, adoption process, and market timing are hypotheses too.

Starting with assumptions is the nature of building something new.

The danger begins when assumptions quietly get promoted to facts because the team has repeated them often enough. A sentence on a pitch deck can become company doctrine surprisingly fast.

The Evidence Risk profile means your startup may have promising signals, but not enough proof from the market yet. The next step is to turn assumptions into evidence.

 

Evidence reduces uncertainty

 

The Lean Startup approach frames startup progress as a process of building, measuring, and learning through experiments. [1] A similar logic can be applied to science and engineering teams:

participants conduct customer discovery to assess whether a technological innovation may support a sustainable business model and to develop evidence-based decision-making. [2]

The important word is “decision”.
An experiment is useful only if it helps you decide what to do next.

 

Step 1: List your assumptions

 

Start by writing down what must be true for your startup to work.

Use categories:

 

Customer

• Who has the problem?
• How urgent is it?
• Who pays?

Problem

• How often does it happen?
• What does it cost?
• What happens if it remains unsolved?

Solution

• Does the product solve the problem?
• Can users adopt it?
• Does it fit the workflow?

Business model

• What will customers pay?
• How will you reach them?
• Can you deliver sustainably?

Execution

• Can the team build, sell, and support this?
• What capabilities are missing?

This exercise can be irritating because it reveals how much is unknown.
But that’s a good thing.

A startup that knows where it is guessing is already less dangerous to itself.

 

Step 2: Prioritise the assumptions that could kill you

 

Not all assumptions deserve equal attention.

A button color is an assumption.
Whether hospitals can legally use your product is also an assumption.

These should not receive the same calendar space.

Prioritise assumptions by:

Importance: If false, would this seriously damage the business?

Uncertainty: Do we actually know this?

Cost of learning later: Would it be expensive to discover this in six months?

The best experiments often target assumptions that are both highly important and highly uncertain.

 

Step 3: Choose the right evidence

 

Not all evidence is equally reliable.

Compliments, opinions, likes, and statements such as “I would use this” may indicate interest, but they reveal little about actual customer behaviour.

Repeated problem descriptions, past behaviour, qualified sign-ups, and prototype engagement provide stronger signals.

The most convincing evidence comes from commitment: customers paying, using and returning to a product, signing a pilot, involving budget owners, sharing data, entering procurement, or changing established workflows.

Weak evidence is not useless, it can help founders identify where to investigate next. However, it should not justify decisions that require significant time, capital, or product development.

 

Step 4: Design a small experiment

 

A useful experiment begins with a clear assumption and defines how it will be tested.

A good experiment has 5 parts:

Assumption: What are we testing?

Method: How will we test it?

Audience: Who must participate?

Success criterion: What result would count as evidence?

Decision: What will we do if the result is positive, negative, or unclear?

For example, a startup might test whether operations managers will share anonymised downtime data in exchange for an analysis of recurring bottlenecks. If at least three out of ten target managers provide data and agree to a follow-up, the team has a reason to develop the analysis workflow further. If they do not, the result points to questions about urgency, trust, or data-access barriers.

The experiment does not need to be elegant or scalable. Its purpose is to produce evidence that informs the next decision.

 

Step 5: Record what you learned

 

Capture your learnings!
Sometimes teams rely too much on memory, but memory is an unreliable colleague.

After each experiment, document:

• what you assumed,
• what you tested,
• who participated,
• what happened,
• what evidence was strong or weak,
• what surprised you,
• what decision follows.

This prevents “selective learning”, where teams remember the encouraging parts and forget the awkward ones.

 

Common mistakes

 

The first mistake is testing too late.
If an assumption is critical, test it before building the expensive version.

The second is testing too many things at once.
If a landing page fails, was it the segment, message, offer, channel, or call to action?
Nobody knows. The spreadsheet shrugs.

The third is confusing activity with evidence.
Fifty meetings are not automatically progress.
Ten relevant conversations with clear patterns may be more valuable.

The fourth is ignoring negative evidence.
Bad news early is useful. Bad news late is a budget event.

 

Conclusion

 

Evidence-driven founders do not wait until everything is certain. Startups never get that luxury. But they do create enough evidence to make better decisions under uncertainty.

You can never remove all risk. But you can stop pretending that guesses are facts.

If you want structured support turning assumptions into experiments, evidence, and market progress, explore the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup → www.inits.at/en/scaleup

 

 

The Big Takeaways

 

Write assumptions down explicitly.
Hidden assumptions cannot be tested.

Prioritise the assumptions that could kill the business.
Do not spend weeks testing cosmetic details while ignoring buying risk.

Define the decision before the experiment.
Evidence is useful only if it changes what you do next.

 

Sources

 

[1] Eric Ries / The Lean Startup — Methodology Principles — https://theleanstartup.com/principles

[2] U.S. National Science Foundation — I-Corps Report — 2019 — https://nsf-gov-resources.nsf.gov/2022-06/I-CorpsReport–6_4_19FINAL_508_0.pdf

 

 

If your product risk is high, the problem may be real.
But your solution may not yet be the right one.

 

Before adding features, validate whether users understand, use, and value the core solution enough to change their behaviour.

A common early-stage trap looks productive from the outside: the team keeps shipping.
New features appear. The roadmap gets longer. The demo improves. Everyone is busy.

 

But the uncomfortable question remains:

Are you building something customers actually need or just making the wrong solution more impressive?

This is the essence of Product Risk.

It does not necessarily mean your idea is weak.
It means your solution has not yet earned the right to become more complex.

For early-stage tech startups, product validation is about finding evidence that the solution helps a specific customer make progress on a real problem. The Lean Startup approach describes the minimum viable product (MVP) as a way to begin learning as quickly as possible through the build-measure-learn loop, not as a smaller version of the final product.[1, 2]

 

An MVP is not necessarily something you “build” in code.
It is the fastest way to test critical assumptions with the least effort.

That distinction matters.

Many founders use MVPs to start building.
Better founders use MVPs to start learning.

 

Start with the riskiest product assumption

 

Before building anything else, ask:

What must be true for this solution to work?

Examples:

• Customers understand the value proposition.
• The workflow fits into how they already work.
• The user can reach the desired outcome without heavy support.
• The buyer sees enough value to pay.
• The technical performance is good enough for the use case.

Do not test everything at once.
Choose the assumption that would most seriously damage the business if it were false.

For example, if you are building AI software for clinical documentation, the riskiest assumption may not be whether the model can generate summaries. It may be whether doctors trust the output enough to use it, whether hospitals allow the workflow, or whether the time saved justifies procurement effort.

 

Test the core value, not the full product

 

Founders often postpone validation until the product is “ready”.
However, this is often expensive procrastination wearing a lab coat.

The better question is:

What is the smallest test that can reveal whether customers value the solution?

Possible tests include:

• a clickable prototype,
• a landing page with a clear call to action,
• a concierge test where the team manually delivers the result,
• a pilot with a narrow use case,
• a workflow simulation,
• a paid discovery or implementation project.

Even lightweight coded MVPs require support, bug fixes, and infrastructure. Hence validating the value proposition before committing to code is a good idea.[3]

 

Watch what users do, not what they praise

 

Positive feedback is useful only when it is connected to behaviour.

“Looks great.” “This could be useful.” or “I can imagine using this.” are weak signals. You should not count on them.

Stronger signals:

• The user completes the task in the prototype.
• The user asks for access to try it with real data.
• The buyer introduces the team to procurement or IT.
• The customer agrees to a pilot with clear success criteria.
• The customer pays, even a small amount.

User research should not be reduced to a yes/no validation exercise. When research is used only to confirm an existing decision, teams may miss the nuance that makes the research valuable.[4]

The real goal is to understand what creates value, what creates friction, and what must change before adoption becomes realistic.

 

Define success before the test

 

Before you run a test, define what evidence would make you continue, change direction, or stop.

For example:

• At least 5 out of 8 target users complete the core workflow without help.
• Three qualified customers agree to a pilot.
• One customer pays for a limited implementation.
• Users repeatedly return to the product without reminders.

Avoid vague goals such as “get feedback” or “see if they like it”.
Look for real evidence.

 

Do not mistake feature requests for validation

 

Feature requests feel exciting because they sound like demand.
But they can also be a polite way for customers to postpone commitment.

When someone asks for a feature, ask:

• What would this allow you to do?
• How do you solve this today?
• How often does this matter?
• Who else needs this?
• Would this change your decision to use or buy?
• What would happen if this feature did not exist?

A feature request is only a roadmap item when it is connected to a valuable use case, repeated across relevant customers, and aligned with your strategic focus.

 

Conclusion

 

Product validation is not about building less.
It is about learning faster based on evidence.

If your biggest risk is the product, do not build more yet.
Test whether the core solution creates value, whether users can adopt it, and whether customers are willing to make a real commitment.

A product becomes stronger when every important feature has earned its place.

If you are building a tech-driven startup and want structured support with validation, product-market fit, and growth, explore the INiTS SCALEup Incubation Program.

Explore INiTS SCALEup → https://www.inits.at/en/scaleup/?src=wp

 

 

The Big Takeaways

 

Test the riskiest product assumption first.
Do not validate the easy parts while ignoring the adoption risk.

Use prototypes and pilots to learn before you build.
The goal of an MVP is learning, not feature delivery.

Treat behaviour as stronger evidence than praise.
Usage, payment, data access, and pilots matter more than compliments.

 

Sources

 

[1] Eric Ries / The Lean Startup — Methodology Principles — https://theleanstartup.com/principles

[2] Strategyzer — Don’t Build When You Build-Measure-Learn — https://www.strategyzer.com/library/dont-build-when-you-build-measure-learn

[3] Nielsen Norman Group — Minimum Viable Product: Definition — https://www.nngroup.com/articles/mvp-definition/

[4] Nielsen Norman Group — In User Research, Don’t Stop at “Yes” or “No” — https://www.nngroup.com/articles/research-yes-or-no/