Tobias Zwingmann

Author of AI-Powered BI (O'REILLY) | AI Coach & Mentor | I help organizations leverage AI and use data more intelligently to improve business performance. Sharing AI use cases & learnings.

My AI rule is simple. "If it doesn't cut costs, save time, increase throughput, remove risk or unlock revenue, it gets cut".In 5-continent wide enterprises and in the mid-sized companies finding $10k+ monthly savings in unexpected places I've helped, Profitable AI is about the same thing: Finding the overlap of what AI can do and what your bottom line needs it to do.Here's how I can help:→ Read the book: "The Profitable AI Advantage" – Frameworks for finding &amp; executing AI opportunities that pay off→ Use PAIGE: My AI strategy tool that helps you identify and evaluate opportunities for your specific business  → Join the Club: Direct access to me as you implement AI in your business (capped at 100 members)→ Work with me: When you're ready, my firm RAPYD.AI delivers AI strategy consulting and custom implementations that pay for themselves.I share what I'm learning every week in my Profitable AI newsletter – read by 10,000+ professionals who are tired of flashy demos and want real results instead.Follow along if you like. Or better: tell me what you're trying to figure out.

Managing Partner

We help you build amazing AI products faster.

In 2026, building a quick demo is effectively free. AI lets you pace through the Discovery phase at 10x speed. But look at the chart:
 
There is a cliff between Discovery and Delivery.
 
Where the "Exit Probability" spikes.
 
Where you realize the data isn’t ready.
Or the model isn’t reliable enough for a live customer.
Or the edge cases are too expensive to fix.
 
That high failure rate is actually fine. 
It’s a filter.

The problem is if the shipping rate is 0.

Because ROI only happens when you cross the blue line into Production.

Building the "shipping muscle" – to take the one surviving idea and deploy it as a real asset – is the most important skill that counts right now.

(Image adapted from Augmented Analytics, O'Reilly (2024)

Author: The Profitable AI Advantage. Created profits with AI from enterprise giants to 20-person teams.

Aaaaand that’s another wrap! It’s been super fun to record a new hands-on AI course, first time at the LinkedIn studios Graz. Huge thanks to the whole crew Jessica B. Violeta Puerta Cano Gregor Verdovsek Natalie Pao and more for a smooth experience and really cool live action setup. 

Looking forward to the release!

Can’t reveal the title yet, but it you want to build local AI automations without much technical overhead, this one’s for you.

Stay tuned.

PS: Back on the other side of the alps. Biggest benefit of small airports is you can just walk to your plane lol.

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PwC just released a new survey with 4,454 CEOs across 95 countries.

Take a look at this chart:

56% saw zero financial benefit from AI.
Only 12%(!) saw revenue AND cost impact.

What are these 12% doing differently? 

According to the report, the winners have:

✓ AI-ready tech stack and data environment
✓ A clear enterprise AI roadmap
✓ AI governance and risk management in place
✓ People and culture readiness for change

All of which are huge.

But 1 critical piece is still missing. 
(Tbh I think it's the most important.)

I'll break down what that is in Saturday’s newsletter.

Sign up before Saturday, 3:00 PM CET to get it in your inbox.
(Link under my name)

🎯 Mark Freeman II

onthemarkdata.com | Senior data engineer helping brands with GTM and content strategy.

I'm a community health advocate turned data engineer interested in the intersection of social impact, business, and technology. My life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
 
As the founder of On the Mark Data, I use this platform to help brands connect to data professionals through captivating content.
 
I received my M.S. from the Stanford School of Medicine and I'm also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. In addition, I have worked within numerous startups where I have put machine learning models into production, integrated data analytics into products, and led migrations to improve data infrastructure.
 
The culmination of my technical skills, content success, and experience within startups would make me an asset whether you are looking to create a successful content strategy, engage in influencer marketing campaigns, or develop a go-to-market strategy for your startup.

You can learn more about opportunities to collaborate, my media appearances, or my content at onthemarkdata.com.

Tech Lead, GTM Engineering

• Joined as employee one to lead GTM at Gable to help us reach our first customers.
• Worked with the CEO to establish our GTM strategy, content strategy, and messaging for technical audiences.
• Building internal data products to drive our GTM strategy and sales.

Hannes Mühleisen (co-founder of DuckDB) doing his slides like SQL queries is so damn cool.

The most powerful communication is often what's not said.

Dev 🤝🏼 GTM | O’Reilly Author | [in]structor (33k+ Learners)

Joe Reis recently inspired me to consider Claude Code as a writing tool when he challenged the notion of context graphs.

He essentially took the Foundation Capital article that has been going viral and ran it through Claude Code.

It instantly transformed a very theoretical article into an entire code repo you could explore on his public GitHub (then used that as a tool to critique).

A thought then popped into my head:

"What if my writing was so clear that you could throw an entire article into Claude Code and it would one shot a working POC of the presented ideas."

Welp, the implementation chapter of our book (Ch. 7) is public on GitHub and is stored as a README file.

So let's see if my writing is clear enough for AI to one shot it.

I downloaded the repo to my local machine, got rid of every file except the README, the JSON dataset, and a folder of diagrams from the chapter.

Then I prompted: "This is my technical chapter of the book. Implement it."

I couldn't believe it... In about 10 minutes it was spinning up a Docker image, loading data into Postgres, and then running my tests.

Keep in mind I spent a couple intense weeks building that codebase for the book.

To be clear, I'm not advocating for such code to be useable, but rather it's now a tool that I can use to further validate my writing.

Present your novel ideas.
But show the working POC.

It's the same as in academia where you have research papers that include code and data for repeatability.

The bar has risen for everyone now. Talk is cheap.

Crazy Idea: I use claude code on a live stream where I'm building an app from scratch.

But...

People on the live stream can enter prompts on my behalf while I'm building to either help or mess with the project.

I think the chaos would be both fun and hilarious.

You didn't hear it from me, but the coolest talk at Data Day Texas is happening at 2:55PM in room 204 👀

Live coding exercise for my conference talk at Data Day Texas? You bet!

Saturday - 2nd Floor - Ballroom 204

Code: The Untapped Metadata Source Driving Most Data Failures

Over the past three years, collaborating with organizations implementing data contracts and writing the O'Reilly book on the topic, it has become abundantly clear that data is surprisingly the wrong surface to address systemic data issues —we actually need to focus on code. Code not only defines what data is captured and moved through software systems, but it's also the ultimate source of truth in how a business operates and its documented changes over time. This is evident in how some of the most significant internet outages can be traced not only to code changes, but also to how those changes affected underlying data models. In this talk, I share case studies and lessons learned in extracting metadata from code and using it to enforce expectations across software systems and data products.

Remember back in the ancient internet times when you could use this cool thing called HTML to customize your Myspace profile.

Just randomly hacking away at sections to see what changed. Copy and pasting random code and hoping it works.

Vibe coding feels eerily similar.

What data scientist experienced in 2023 with the realease of ChatGPT is the same experience software engineers are experiencing today with Claude Code.

Back in 2022 I was transitioning from data science to data engineering, and a major project that led to that promotion was building and deploying a natural language processing pipeline.

Back then I was using SpaCy, which was an opinionated framework for implementing the latest production-ready NLP models.

Yeah... I open up ChatGPT and immediately see the "advanced" work I put into production was now antiquated.

While I wouldn't throw an LLM at my production project back in 2023, I was intimately aware of what was required to get business value out of text.

What ChatGPT showed me was that a large portion of the difficult parts of NLP were now just a simple API call... and more importantly, highlighted the inflection point if the momentum of change followed.

Fast forward to today, and so many software engineers built side projects over the holiday break and experienced the exact realization I had back in 2023.

"This LLM accomplished way more than I expected, and I can see an inflection point if this trend continues."

Imagine what will be accomplished in the next few years.

taplio

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The Battle

Tobias Zwingmann

🎯 Mark Freeman II

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Engagements

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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);}Tobias Zwingmann's LinkedIn Performance

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