I’m a tech-savvy problem solver with 6+ years of experience across SaaS, e-commerce and AI-driven tools.
My background in software engineering and no-code automation allows me to bridge technical and strategic work - designing systems that scale, save time and improve user experience.

I thrive in environments where I can simplify complex processes and deliver measurable results.

With hands-on experience spanning frontend engineering, process optimisation and no-code development, I build solutions that connect data, people and technology into seamless workflows.



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JavaScript • React • TypeScript • Node.js • REST APIs • SOP • process automation • Make.com • Zapier • Airtable • Google Workspace • Notion • Trello • Asana • OpenAI • CRM automation • AI workflows


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Kasia Wrześniak

AI Adoption Specialist | Fast results, human-centered, no fluff | Keynotes & Advisory

Your team knows they should use AI. They just don't know where to start.

I help mid-sized companies (50-200 employees) go from stuck to pilot in 5 days.

Most AI consultants start with months of analysis. I start with building. Working tools in your team's hands, not slides in a drawer.

What I do:
 → AI Adoption Sprints: Zero to working pilot in 5 days
 → Done-for-You Automation: I build it, you keep the time savings
 → Executive Advisory: Get your leadership aligned on AI strategy
 → Keynotes: Wake up your team to what's possible

Why this approach works:
The technical part of AI has become simple. The human part, getting your people on board, is where companies struggle. Speed creates space for that human work.

Who I serve best:
 CEOs and COOs at the beginning of their AI journey. Leaders who value their people and want results, not reports.

Based near Zurich. Serving Switzerland, DACH, and Europe.

Ready to stop wasting your people on work that doesn't matter?
Let's talk.

Damian Nomura

Most companies don't have an AI adoption problem.
They have a confusion problem.

When leaders say "AI adoption," they almost always mean one thing: deploying AI features. Chatbots. Prediction engines. Recommendation systems.

But that's only half the picture.

BCG found that 78% of managers use generative AI regularly. Frontline workers? 51%. Stuck. That number hasn't moved in two years.

The gap isn't about training budgets or tool access. It's about a confusion baked into how we talk about AI.

"AI adoption" has been quietly conflated with "AI solutions." And that conflation is why most initiatives stall.

There's a whole other side nobody names: using AI to build better things faster. Not AI as the thing your team interacts with. AI as how you create what your team uses.

A better onboarding flow. A cleaner data pipeline. A dashboard that answers the right question.

None of these are "AI solutions." All of them can be built in days instead of months.

The distinction matters. Because only one of these requires your entire team to change how they work.

I go deeper on this in my Substack. Link in comments.

Your team should be building product. I automate the non-product work that’s capping your growth. In a week.

Confusion rather than an AI adoption problem

Building an app without security training is like building a house without permits.

Over the past year, I've run multiple hackathons where non-technical teams build real software in hours.

An applicant screening tool. A client qualification system. Fully automated video avatars reaching out to prospects in their own language. Built by people who had never written a line of code.

Every time, the same thing happens. The energy in the room is electric.

And then it hits me.

Not one team asks about data protection. Not one asks who's liable when the screening tool discriminates. Not one asks what happens if the video avatar says something wrong.

They don't skip these questions on purpose. They don't know the questions exist.

That's the gap nobody's talking about.

I'll launch office hour sessions where you can ask me any question that comes up in the AI adoption journey of your company. And hearing the questions of others are super inspiring: to find out what you didn't know that you didn't know.

Follow me to not miss the post where I'll invite you to join this session.

Most builders think security review takes weeks. It takes four checks.

Everyone can build apps now. Most people skip the safety part. Not because they don't care. Because they don't know it exists.

If you've never worked in software, you don't know it needs security review. The same way someone who's never built a house doesn't know about load-bearing walls.

So what do you actually need to check before shipping?

Not four hundred things. Four things.

-> Does your app handle personal data? If yes, you're likely subject to GDPR. Consent, encryption, right to deletion.

-> What happens when your app is wrong? If it gives advice on health, finance, or legal matters, bad output has real consequences.

-> Who can access what? Excessive permissions sit near the top of OWASP's security risk list for no-code platforms.

-> Have you tested it like someone who wants to break it? I asked Claude to pen-test my client portal. It found issues I wouldn't have thought to look for.

That's just enough. Everything else is noise. For now.

My Sprint program teaches teams to build fast AND build responsibly. DM me if you want both.

Your team spends three hours every week coordinating schedules. Nobody calls that an AI problem.

But what if you used AI to build a custom scheduling system in two days? Clean interface. No AI visible. The team just opens it and it works.

They never touch AI. They never learn a prompt. They just get three hours back. Every week.

That's AI adoption too.

We've narrowed "AI adoption" to mean chatbots and copilots. The flashy stuff. But some of the biggest wins happen when AI is the builder, not the product.

The 90% who resist AI aren't resisting better tools. They're resisting being told to learn yet another thing.

Give them the result. Skip the lecture.

Sometimes the best AI adoption happens when nobody even realizes AI was involved.

Want to find out, how to actually amplify your workforce? Leave a follow behind.

Most AI adoption advice only covers half the picture. The other half is where the real wins are.

There are two kinds of AI adoption. Most companies only talk about one.

**AI as solution**
You deploy a chatbot. Your team manages it, trains it, handles escalations. The end user interacts with AI directly.

**AI as development tool**
You use AI to build a custom app that eliminates a bottleneck. The end user sees a clean interface. They never touch AI. They just get better results.

Both create value. But they require completely different change management.

The first demands your entire team learn something new, trust something unfamiliar, change their habits. No wonder there's resistance.

The second? The team just gets better tools. Faster. Their workflow improves without them needing to become AI-literate.

Gartner predicted 30% of GenAI projects would be abandoned after proof of concept. Most of those were AI-as-solution plays that couldn't cross the adoption gap.

The question to bring to your next meeting: is the answer an AI-powered feature, or a solution built faster with AI?

Follow for the real side of scaling with AI. No slide decks. Working software.

"We know where we need to go. We can't get there with what we have."

I hear some version of this in almost every discovery call with scaling founders.

The company hits product-market fit. Revenue grows. The team doubles from 10 to 25. Then the machine around the product starts breaking.

Not the product. Not the market. The operations.

I call it the scaling wall. It shows up somewhere between 15 and 35 people.

The instinct is to hire. Stretched? Hire. CTO can't keep up? Hire a second one.

Premature scaling kills 74% of high-growth startups. Not because the market wasn't there. Because the internal systems couldn't support the growth.

Worklytics' 2025 data: the median engineering team spends 28% of its workday in meetings. At the 75th percentile, 35%. Coding time drops to 22%.

Your most expensive people, doing their most valuable work, less than a quarter of the time.

The capability gap is real. Naming it is the first step to closing it.

I wrote about this in depth on my Substack this week. Link in comments.

Your CTO is spending 40% of their time on non-product fire-fighting.

Your people aren't the problem. The system they're trapped in is.

I see this pattern in every scaling company I work with. The team grows from 10 to 30 to 50. Revenue goes up. Complexity goes up faster. And somewhere along the way, your most technical leader stops building product and starts managing chaos.

HR tickets. Process gaps. Operational bottlenecks that nobody else can fix.

The instinct is to hire more people. But every new hire adds coordination overhead too. More Slack channels. More alignment meetings. More context sharing. Employment costs reach 1.4x the base salary before your existing team has finished onboarding them.

At 10 people, communication is ambient. Everyone knows everything.

At 30, it breaks. Tribal knowledge doesn't transfer.

At 50, it's a full-blown crisis disguised as growth.

Talent isn't the constraint. Architecture is.

Fix the operational bottleneck first. Then decide if you need the hire.

I help companies go from operational bottleneck to working software in 5 days. DM me if you're ready.

A Build Week costs less than one month of a senior AI engineer's salary.

And it ships on day 5, not month 6.

Let's do the math.

A senior AI engineer in Switzerland costs roughly CHF 160,000 per year in base salary. Factor in the 13th month, social contributions, equipment, and the 3-6 months before they ship anything meaningful. You're looking at a CHF 180,000+ commitment before you've seen a single result.

Or: CHF 15,000. Five days. One operational bottleneck. Working software by Friday.

The instinct to hire is real. But hiring as your first move is a six-figure bet on a problem you haven't validated yet.

A Build Week validates whether AI solves your bottleneck before you commit to the annual hire. Think of it as the rational first step before the headcount commitment.

Not instead of hiring. Before hiring.

I help companies go from operational bottleneck to working software in 5 days. DM me if you're ready.

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Kasia Wrześniak

Damian Nomura

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The Battle

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Damian Nomura

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Damian Nomura's LinkedIn Performance

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