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

Your AI ethics principles are about to be tested. 
Literally.

The EU AI Act moved from paper to enforcement. Courts are defining boundaries faster than regulators can write rules. And companies treating governance as PR theater are being called out.

RAGN's new 2025 Responsible AI Governance report calls it the end of the "ethics debate era" and the start of the "governance execution era."

For mid-sized companies, three shifts matter:

→ Governance is now a business function, not a research initiative
→ The most in-demand role is the "Governance Translator" who bridges legal, technical, and business
→ Regulators prefer good-faith effort over checkbox compliance

The good news? Good governance accelerates innovation. Not slows it down.

I've joined RAGN as their Ambassador for Switzerland to help bring this practical perspective to our market.

Is AI governance on your 2026 agenda yet?

Stuck on AI? I help mid-sized companies go from paralyzed to pilot in 5 days | Speaker & Advisor

The 2025 responsible AI governance landscape

Everyone's talking about AI agents.

But they miss a critical point.

Autonomous systems. Multi-step reasoning. The future of work.

The demos are compelling. The vision is exciting.

But here's what nobody's saying:

If you can't prove value from a simple AI workflow in 7 days, building an agent isn't going to save you. It's going to bury you.

Because agents require:
→ Clear success criteria (you don't have those yet)
→ Reliable evaluation/measurement systems (you haven't built them)
→ Failure recovery processes (you haven't tested them)
→ User trust (you haven't earned it)

All of that comes from the groundwork: small, structured, proven experiments.

I've watched this pattern repeat across dozens of companies. They skip the basics. They chase the shiny new thing. Six months later, they still can't tell you if it worked.

Master the basics. Then build the agents.

Not the other way around.

I wrote about this—and the 5-question framework that turns random trying into structured proving—on my Substack this week. Go check it out for a deeper dive. Link in comments.

Experiments before agents

A company spent 6 months on an AI pilot.

Six. Months.

At the end, I asked them: "Did it work?"

They looked at each other. Paused.

"We think so? People seem to like it."

No metrics. No baseline. No decision criteria.

Just... vibes.

This is what happens when you skip the groundwork.

They weren't running an experiment. They were just trying something and hoping for the best.

Here's what they didn't have:
→ A hypothesis (what specifically should improve?)
→ A metric (how would you measure it?)
→ A baseline (what was the starting point?)
→ A timeframe (when would you decide?)

Six months. Hundreds of hours. And they still couldn't tell me if it worked.

That's not a pilot. That's pilot paralysis.

The fix? Five questions. Seven days. Clear answers.

This is for the bold ones: What's the longest you've run a pilot without knowing if it worked?

I watched a company spend 8 months "getting ready" for AI. They never started.

Executive workshops. Strategy documents. Vendor evaluations. Capability assessments.

Eight months of preparation.

Zero pilots. Zero experiments. Zero actual movement.

Here's what happened: They thought they needed to understand everything before they could do anything.

So they hired experts who went deep. Really deep.

The experts delivered exactly what was asked for. Comprehensive analysis. Technical specifications. Implementation roadmaps spanning eighteen months.

Beautiful documents. Perfect strategy.

And complete paralysis.

Meanwhile, a competitor ran a 7-day pilot. Messy. Imperfect. But real.

They learned more in one week of doing than the first company learned in eight months of planning.

The gap between these two companies? Not knowledge. Pace.

One learned by studying. The other learned by moving.

Guess which one has working AI systems today?

What's your experience? Planning or doing?

Consultants want you to understand everything before you start with AI. That's the trap.

You don't need to become an AI expert. 

You need just enough to decide competently.

There's a massive difference.

The 4 Things Executives Actually Need to Know About AI:

→ The Basics - What's automation? (Rules-based, repeatable tasks) What's AI? (Pattern recognition, prediction) What's agentic AI? (AI that takes actions based on goals) What's an AI agent? (Autonomous systems that make decisions and act)

One to two hours of clear explanation beats three months of technical deep-dives.

→ What AI Can Actually Do for Your Business - Not theoretical possibilities. Real capabilities. Where can it save time without sacrificing quality? Where can it surface insights you're currently missing? Where can it handle volume you can't scale manually?

→ Where to Start - Not a comprehensive roadmap. A first experiment. One painful workflow. One metric you can measure. One week to see if it helps.

→ How to Tell If It's Working - Not ROI projections for 2027. Clear success criteria. Did the thing you hoped would improve actually improve? By how much? What do you do next based on what you learned?

That's just enough. Everything else is noise.

Follow for guidance through the jungle of AI. Simple. Clear. Applicable.

74% of companies fail at AI. 

Here's what they have in common.

They're drowning in information before they even start.

Consultants teach you transformer architectures. Whitepapers explain supervised vs. unsupervised learning. Webinars promise seventeen use cases from different industries.

And you get lost.

Here's the uncomfortable truth: 72% of business leaders admit the sheer volume of data prevents them from making ANY decision.

Not just AI decisions. Any decision.

So what do consultants do when you're drowning? They add more water.

More frameworks. More case studies. More technical specifications. More vendor comparisons.

It's well-intentioned. But it's paralyzing.

Meanwhile, your competitors aren't learning. They're experimenting. They're running messy pilots and learning what actually works.

You don't need expertise. You need just enough to decide competently.

Four things. Not four hundred:

→ The basics (automation vs. AI vs. agentic vs. agent)
→ What AI can actually do for YOUR business
→ Where to start without betting the company
→ How to tell if it's working

That's it. Everything else is noise. For now.

The AI Maturity Index shows you exactly where you stand. Fifteen minutes. Free. Anonymous. Benchmarked against thousands of executives worldwide.

Before you hire another consultant. Before you read another whitepaper.

Take the assessment → Link in comments

Start right.

The best AI training I've seen wasn't training at all.

No slides. No briefings.

Just teams, real data, and a few hours to build something that works.

Pew found 52% of workers worried about AI, only 29% excited.
HBR found a 45-point gap between what executives think employees feel and what they actually feel.

Inside your organization, that same concern is quietly stalling real adoption.

You don't reverse that with more town halls.

You change the way people learn.

Pull a cross-functional group into a focused build sprint. Give each team governed access to data and a few specific workflows. Set a fixed deadline and require a working demo at the end, reviewed by senior leaders.

I run AI adoption hackathons for the entire company. Just recently with a service provider. CEO, CTO, department heads, team leaders, subject matter experts. All in the same room.

They built working pilots in two hours. But that wasn't the real win.

The real win was what happened after.

One subject matter expert told me: "I was super scared of losing what I have built. Loosing it to AI. My knowledge, my skills, my career. But after this session, I see what's possible. It's like a whole new world opened up."

That's how you build trust. That's how you move closer. That's how you lead.

Leaders who are serious about AI adoption design builds like this.
They invest in coaching the teams.
They protect the data.
They make the demos visible to the whole organization.

That's how AI shifts from a distant worry to practical operating leverage.

Follow for practical AI adoption. Simple. Clear. Applicable.

Lead through the AI adoption. Stop managing.

Your AI "experiments" aren't experiments.

They're just trying things.

Most companies launch an AI pilot, run it for months, then can't tell you if it worked.

Not because the AI failed. Because they never defined what success looked like.

Here's the uncomfortable truth: trying things isn't the same as proving things.

Experimentation is a scientific word. Most companies aren't using it scientifically.

And the data shows it:

→ Only 5% of AI pilots achieve rapid revenue acceleration (MIT)
→ 46% of POCs get scrapped before production (S&P Global)
→ 30% of GenAI projects abandoned after POC by end of 2025 (Gartner)

This isn't a technology problem. It's a methodology problem.

Real experiments have:
→ A clear hypothesis (what you believe will improve)
→ A measurable metric (how you'll know)
→ A baseline (where you started)
→ A timeframe (7 days, not 7 months)
→ Decision criteria (what defines success)

Without these, you're not experimenting. You're hoping.

Hope doesn't scale. Science does.

Follow me for human-centered AI adoption. Simple. Clear. Applicable.

What AI experiments really mean

Everyone's talking about agents. Almost no one should be building them yet.

The hype is real. The technology is coming.

But 90% of executives claim they're confident making AI decisions while only 8% actually possess substantial AI knowledge.

That 82-point gap is filled with buzzwords.

Agent. Agentic. Automation. Most people think they're the same thing.

They're not.

An agent operates autonomously. Perceiving its environment, making rational decisions, pursuing goals without constant oversight.

What most companies need? Automation. Rules-based workflows that run the same way every time.

Not sexy. But it works.

And here's the thing: if you can't automate your processes effectively, you're not ready for agentic systems. And you're definitely not ready for true agents.

The foundation has to come first.

Honest question: Do you know the difference between automation, agentic systems, and agents? Or does it all blur together?

You've already wasted 6 months on AI. And you're about to waste six more.

Not because you're lazy. Because you're trying to be thorough.

Hiring consultants. Reading whitepapers. Attending webinars. Building task forces.

All the "right" things.

And every day you're not moving, the gap widens.

Here's what's actually happening: You're waiting to understand everything before you do anything. But understanding doesn't come from study. It comes from movement.

Your competitors aren't smarter. They're faster. They started before they were ready. They're running experiments while you're perfecting strategy documents.

They're learning what works. In your business. With your people. With your workflows.

And they're building momentum you can't catch by reading one more report.

Before you spend another dollar on expertise you don't need.

Before you hire another consultant to make you more dependent, not more capable.

Take the AI Maturity Index. Fifteen minutes. See exactly where you stand. No consultant filter. No vendor agenda. Just clear data on what you actually need to do next.

Benchmark yourself now → Link in comments.

Stop learning. Start doing.

Stop overthinking. Get active.

Still in planning meetings about AI?

While you're getting ready, your competitors are learning.

Here's the math that should worry you:
If they ship and test 4 ideas per month while you're still in committees, they're learning 12 times faster than you per quarter. Every week you spend "getting ready," they're iterating. Every month in planning, they've built and discarded 5 approaches that didn't work and found 3 that did.

Speed isn't about being sloppy.

Speed is about compressing the learning cycle.

The companies that win aren't the ones with the best strategy document. They're the ones who figure out what works before everyone else does.

How much longer can you afford to wait?

Follow for practical AI adoption. Simple. Clear. Applicable.

Enterprises should be crushing it with AI.
Most are getting marginal gains.

The problem is not the models.
It is how your work is wired.

AI amplifies whoever can own the whole loop.
Not the person who writes the prompt.
The person who can take an outcome from start to finish.

In a large org, that loop is usually shredded before AI even shows up.
Handoffs.
Approvals.
Silos.
Process debt.

So we deploy AI into a broken workflow.
And we call the result "AI adoption."

→ AI is fastest when the decision, the execution, and the feedback are in one place
→ Enterprises separate those three across teams, tools, and governance layers
→ Every separation adds latency, translation loss, and turf wars
→ By the time you "scale," the advantage is gone

Small teams don't win despite being small.
They win because they have fewer seams.

It's like hiring a world-class chef but requiring sign-off on every ingredient. By the time the dish is approved, dinner is cold.

One capable operator with AI can now do what used to require a mini-department.

I watched a founder connect Claude to live SEO data last week. He qualifies and researches leads in real-time. No clicks. No handoffs. No waiting.

If one person can run that loop, AI becomes a multiplier.
If 12 people each own a slice, AI becomes another ticket in the queue.

Do you want a beautiful strategy deck? Or do you want one person who can run the whole play?

If you're responsible for AI outcomes, try this audit:
Pick one workflow that touches revenue or cost.
Name the single accountable owner.
Then remove one approval layer for a 30-day window.

If you can't do that, you don't have an AI constraint.
You have an operating model constraint.

Which loop in your company is currently shredded by handoffs? Drop the workflow below and I'll tell you if it's ready for an AI pilot.

Saving 10 hours a week means nothing.

Not if those hours fill with meetings. Not if they disappear into email. Not if your team works just as hard with nothing new to show for it.

I see this pattern constantly. Company implements AI. Time savings appear. Everyone toasts. Six months later, nobody can point to what actually changed. The efficiency gains evaporated. They got absorbed back into the noise.

This is the celebration trap.

BCG found 74% of companies struggle to see value from AI. Not because they can't save time. Because they don't know what to do with it.

The ROI of AI isn't in the savings. It's in what the savings enable.

Four paths to convert saved time into actual outcomes:

-> Growth: More outreach. More pipeline. Revenue leverage.
-> Quality: Deeper work on complex cases. Excellence leverage.
-> Expansion: Time to launch something new. Opportunity leverage.
-> Retention: Best people stop working nights. Human leverage.

Same saved time. Completely different outcomes. But you have to choose one. Deliberately. Before you start.

I help companies go from efficiency gains to actual business outcomes. Sprint week or Executive Sparring. DM me.

The celebration trap, where is your ROI?

74% of companies fail at AI.

Not because they're doing it wrong. Because they never actually started.

They're still "getting ready." Still planning. Still in committee meetings. But the world moved on.

Here's what changed:
Traditional IT projects were slow for real reasons. Waterfall methodology. Fear of costly rework. Physical servers. Long procurement cycles.

Those constraints are mostly gone.

AI-assisted development: 92% of developers now use AI coding tools. Stack Overflow reports 76% save at least 30% of their time. And if you ask me, that's a very conservative take.

Modular systems: What took months of custom development now takes days of configuration.

Iteration as default: You don't need to be right the first time. You need to be fast enough to learn.

McKinsey reports a 55% reduction in development time through AI-assisted coding.

Y Combinator startups deliver MVPs in 6 weeks that used to take 6 months.

The technology changed. The question is whether your mindset has. Stop treating AI like a 2-year IT project. Start. Build. Test. Ship. Learn.

Ready to move faster? I run Sprint weeks where we build working prototypes for your business. DM me.

Speed is the asset you miss out on.

My friend in Spain was nowhere near AI in June.

Then 2025 happened. A brutal year.

Market pressure. A sophisticated cyber attack that nearly took his company down. Yesterday, he told me he's deploying two AI solutions tomorrow. Not "exploring." Not "piloting." Deploying. Into production. Then he asked me something surprising:

"Can you help me get an overview of everything in AI?"

My answer: 
"I could, but you would lose focus. The way you're doing it is exactly how it works. Just keep going like that."

He doesn't need a comprehensive AI strategy document.

He doesn't need to understand the full landscape.

He needs to keep doing what he's doing: picking problems, building solutions, testing them, iterating. That's the speed lane. And he's on it.

What's stopping you from getting on it too?

Follow for practical AI adoption. Simple. Clear. Applicable.

A client saved 40 hours a month with AI.

Customer service operations. Automated responses. Faster ticket routing. The efficiency gains looked great on the quarterly deck. Leadership was thrilled.

I asked what they were doing with those hours.

Silence.

They hadn't thought about it. The hours just got absorbed. More tickets. More meetings. More of the same work that used to fill the day. Nobody had a plan for what those 40 hours would become.

That's not ROI, redistribution is what this is.

The hard part isn't finding where AI can save time. Any decent consultant can audit your processes and find automation opportunities. That's the easy part.

The hard part is the question that comes next: "What will we do with the freed capacity?"

This is the leverage question. And almost nobody asks it.

Your AI initiative either converts saved time into something that moves the business forward. Or it just rearranges the calendar.

Which one is yours?

Curious: Have you defined what your AI savings actually become?

Why does a 7-day experiment beat a 7-month pilot?

Every company I work with asks: "How long should we test this?"

My answer: As short as possible while still being valid.

For most AI workflows, that's 7 days. Maybe 14. Why do short time frames work? That's why:

- Urgency forces clarity:
When you only have a week, you can't afford vague goals. You define success upfront.

- Feedback loops are faster:
Problems surface immediately. You fix them in real-time instead of discovering them in month 5.

- The opportunity cost is lower:
If it doesn't work, you've lost a week—not a quarter.

- Momentum doesn't die:
Three-month pilots lose steam. People forget why they started.

A week? Everyone stays focused. If it doesn't show value in 7 days, extending it to 90 won't save it.

Run it fast. Decide fast. Move on.

What's your take: How long should an AI pilot run?

Most AI "experiments" aren't experiments at all.

But I got an easy fix for you.

Real experimentation is science. And science has structure. But it does not have to be complex.

Here are the 5 questions that turn random trying into real experimentation:

1. HYPOTHESIS: What do you believe will improve?
Not "let's see what happens." What specifically do you think will get better? Name it.

2. METRIC: How will you measure it?
"Productivity" isn't a metric. "Time to first draft" is. If you can't measure it, you can't prove it.

3. BASELINE: What's the current state?You can't know if something improved if you don't know where you started. Measure before you change anything.

4. TIMEFRAME: How long will you test?
Seven days beats seven months. Most AI experiments don't need months. They need days.

5. DECISION CRITERIA: What makes this a success?
Set the threshold before you start. 20% time savings? 40%? 90% accuracy? Decide upfront. Then stick to it.

Answer these five questions in 30 minutes. Run it for a week. You'll know.

Speed without sloppiness. That's the balance.

This post may be forgotten in a few minutes of on-scrolling. To make sure you step out of the vicious circle and start applying it now, I have created a short deck.

Apply, don't forget, so you get the best out of your next experiment.

Tell me in the comments and I'll send it your way. You can also simply comment "QUICKWIN" if you want to enjoy a lazy Friday.

From random trying to structured proving. Go get it!

Your AI program looks successful.

The number that actually matters is still missing.

Executives talking about: "We saved 20,000 hours with AI this year."

Then they look at the P&L and nothing moved.

The problem is not the models.
It is that we lump everything into "AI productivity" and never separate tools that help people work faster from systems that actually change how money moves.

There are really two different beasts:

Productivity AI: copilots, chat, assistants that help individuals draft, research, summarize. The work feels smoother. The calendar does not get lighter.

Engineered automation: AI wired into workflows, systems of record, and approval paths. Steps disappear. Queues shrink. Unit costs change.

Only the second one reliably hits the P&L.

So you need a profit stack, not just an adoption report.

Here is a simple way to stack ROI for any AI initiative:

→ Layer 1 - Activity: prompts, usage, adoption, satisfaction.
→ Layer 2 - Flow: cycle time, throughput, error rate, hours removed from a process.
→ Layer 3 - Financial: cost per unit, margin, revenue per employee, cash collected.

Most enterprises stop at Layer 1.
Some measure Layer 2 in a slide deck.
Very few tie Layer 2 to Layer 3 in a way the CFO will defend.

A quick example:
- Productivity AI: an agent drafts customer emails for your support team. Tickets per agent rise, but service policies, staffing, and pricing stay the same.
- Engineered automation: an AI triage system classifies, routes, and resolves low complexity tickets end to end. You redesign staffing, SLAs, and pricing on top of that.

In the first case, time saved becomes more work.
In the second, the process changes and the profit stack moves.

For every serious AI rollout, I look for three things:

 1. A clear declaration: is this primarily productivity AI or engineered automation?

 2. A profit stack: one Activity metric, one Flow metric, one Financial metric, each with a baseline and a target.

 3. Owners: a product owner for the workflow and a finance partner who agrees how impact hits the P&L.

If you cannot point from a specific workflow to a specific line item, you do not have an AI strategy.
You have AI experiments.

The technology is ready.
The question is whether your profit stacks are.

Where are your AI investments sitting today - productivity gains or real profit stacks?

Follow for more practical frameworks on AI ROI at scale.

Your biggest cyber threat isn't a human hacker anymore.

It's a tireless AI agent chain that never logs off.

We trained AI agents to help our teams ship faster.

We also taught them how to behave like perfect attackers.

An autonomous agent with tool access is not just "another script." It can:

→ Map your external surface in minutes
→ Chain exploits others have published
→ Quietly exfiltrate data through "legitimate" tools and APIs

All at machine speed. 24/7. Without fatigue, ego, or fear of getting caught.

Why does this matter?

Because most security programs are still optimized for human-paced threats: credential stuffing, phishing, manual lateral movement.

The problem isn't the technology.

It's that we're giving agents the keys before we redesign the locks.

Here is what AI-native offense already looks like:

- An agent spins up cloud resources, runs scanners, and tunes them based on your responses.
- Another watches logs and error messages, learning which defenses are noisy vs real.
- A third focuses only on data movement, routing "normal" looking exports through business systems.

No single action trips a classic alarm.

The pattern does.

So we need AI-native defense to match:

→ Threat models that treat internal agents as semi-trusted actors, not magic helpers
→ Fine-grained scopes for each tool - smallest possible permission, shortest possible lifetime
→ Behavior analytics on agent toolchains, not just network flows
→ Sandboxed "cyber ranges" where your own red-team agents attack your systems before someone else's do
→ Hard human-in-the-loop checkpoints for any high-impact action: config changes, mass downloads, cross-tenant moves

The technology isn't the bottleneck.

Our governance patterns are.

If you are experimenting with autonomous agents today, build one habit into your culture:

Every time you design a new capability, design the misuse case and the kill switch in the same meeting.

Machine-speed offense is here.

Machine-speed resilience has to be built - intentionally.

Where are you seeing the first real signs of AI-agent threats in your world?

If you're responsible for AI adoption or AI governance and want to design for agent-safe architectures instead of playing catch-up, DM me.

Follow me for clear, practical takes on AI adoption, risk, and resilience.

taplio

The Battle

Kasia Wrześniak

Damian Nomura

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Damian Nomura

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