Tobias Zwingmann

RAPYD.AI

Independent AI consulting to create profitable AI solutions to innovate and grow your business. Fast.

Managing Partner

O'Reilly Media

Author and Instructor

LinkedIn

LinkedIn Learning Instructor

D2A2

Chief of Research - Applied AI

Springboard

- Helping Springboard as a subject matter expert in AI, especially for data analytics
- Mentoring students who want to switch careers to data science / data analytics 
- Top Mentor Award in 2021 (Springboard Inspire)

AI SME & Data Science Mentor

Insurgence

Partner

Toptal

Independent Consultant

Data Science Dojo

ML/AI Instructor

Deutsche Messe AG

Systematic retrieval, analysis and utilization of (unstructured) data from diverse sources to build new products and processes.

- People movement analytics 
- Computer vision with focus on text retrieval

Senior Data Scientist

Building & maintaining predictive models. Generating and communicating insights about customer trends and behavior to product owners and senior management.

- Churn Prediction
- Next Best Offer Recommendations 
- Customer Segmentation
- Visual Analytics

Data Scientist

Leading various international trade show projects in e.g. China, India, Indonesia, Mexico, Myanmar, South Africa, South Korea, Turkey.

Project Director

Deutsche Messe Dubai Branch

Management Trainee

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.

I didn’t start my AI/ML journey doing “Profitable AI”.

I started it as a Data Scientist who built models that looked impressive on paper:

– Customer churn prediction with 95% accuracy ✅
– Next-best-offer systems with 15% uplift ✅
– Data-driven B2B products generating 6-figure revenues from day one ✅

But the hard lesson I’ve learned after 10+ years at the intersection of business and technology is that none of this matters if you can’t show results on the bottom line.

I’ve spent the last 5+ years in applied AI consulting where my work has taken me inside global enterprises in automotive and insurance, mid-sized companies with 500–1,000 employees, solopreneurs aiming to double their output, and small business owners uncovering $10k+ monthly savings in unexpected places. 

The contexts differ, but the patterns of what makes AI succeed (and fail) remain remarkably consistent.

Success is not about:
- Chasing the next shiny AI tool
- Hiring data scientists from Silicon Valley
- Hoping IT will figure it out

It’s about applying the principles of Profitable AI to deliver outcomes that matter: doing things faster, at lower cost, with higher quality — or scaling what you do to entirely new levels.

Along the way, I’ve shared many of these lessons in my books AI-Powered Business Intelligence (O’Reilly, 2022) and Augmented Analytics (O’Reilly, 2024), as well as in my weekly newsletter that is now read by 10,000+ professionals at companies like Google, Amazon, Gucci, H&M, Santander, and Mercedes-Benz.

So If you’re tired of the shiny AI demos and want to see it deliver real results instead — I think you found a good place.

We help you build amazing AI products faster.

Gemini 3 is making a lot of noise. And it's a really great model. "Is it better than GPT-5?!" My take: Use whatever works for you. Both are LLMs, both have their quirks. But if you want to know where each shines, here's what I found:

Gemini 3 = Messy Inputs → Clean Outputs
Throw it chaos: screenshots, transcripts, logs, mixed documents. It's great at compressing that mess into structure. Best when you're very specific about the output format you want. Seems like if you need to organize messy information, call Google.

Examples:
- Summarizing multiple meeting recordings into structured notes
- Extracting data from a messy pile of PDFs, screenshots, and spreadsheets

GPT-5.1 = Clean Inputs → Complex Thinking
Give it organized information and let it reason. It's great at planning, narrative, and multi-step decisions. Best when the task itself is complex, not the input.

Examples:
- Writing code (though Claude still beats it here)
- Building a strategy doc from a clean brief

Scenarios to combine both:

1/ Competitive analysis
Gemini 3 gathers data from competitor sites, pricing, and reviews; GPT 5.1 delivers the strategy

2/ Client onboarding
Gemini 3 cleans and organizes intake docs; GPT 5.1 produces the project plan

3/ Sales pipeline review
Gemini 3 compiles CRM data, calls, and emails; GPT 5.1 spots trends and recommends actions.

Don't try to find "the" best. 
Find whatever works best for you.

Fun fact: The infographic was done by me with Gemini-3 image gen ("Nano Banana"), first shot, Prompt in comments.

AI Advisor | O’Reilly Author | Instructor @ LinkedIn Learning | I find & realize profitable AI opportunities – here to share wins, fails, examples & frameworks.

I see a lot of confusion around AI workflows, AI agents, workflow automation, and agentic workflows. Most people can't even tell the difference. Which isn't a problem until you want to build an AI solution that is neither over-engineered nor insufficient.

So I put together a quick guide that breaks down each type and when to use what, along with a few examples.

Read the full blog post here: https://lnkd.in/erYqeVPi

AI Workflows vs. AI Agents

ChatGPT launched today 3 years ago. ONLY three years I should say. Feels like a decade compressed. To catch you up on what’s happened, I generated this little timeline with the help of ChatGPT Deep Research, had Gemini 3 double-check it, and Claude 4.5 generated the PDF.

Tasks like this would have taken a whole day in the "old" world.
Now done in less than 60 minutes with AI.

We're still far away from AGI.
But I don't care.
We're living in the most exciting timeline.
And I can’t wait to see what this timeline looks like 3 years from now.

3 Years Since ChatGPT

A while ago, I shared the AI Profit Stack – a simple framework to assess whether your AI projects are actually creating profit. People loved it, clients used it. Then someone asked me:

“How do I apply this to my Copilot licenses?”

Short answer:
You don’t.

Long answer:
Trying to measure all AI with the same ROI formula is exactly how you end up with fictional numbers, cynical finance teams, and AI steering committees that debate themselves into extinction.

Today I've shared the fix I'm using now in every client engagement – and the one I want you to have too.

Find it here here:

Open article: The Two Tracks of AI ROI by blog.tobiaszwingmann.com

How to separate 'AI that helps' from 'AI that pays'

Machine Learning @ Call of Duty | Rebel Data Scientist | Click follow to join a community of data, gaming, and tech professionals 🎮

Databricks

As a Field CTO in the Databricks gaming vertical, I am an external technical strategist helping game studios unlock the full potential of their data. 

As a hands-on solutions architect, I design and implement scalable, future-ready systems that empower developers to build next-generation player experiences, run efficient operations, and turn innovation into impact.

My role blends technical strategy, architectural design, and industry evangelism. I work directly with game studio leadership to architect the infrastructure that enables smarter game design and better live operations.

Ultimately, I help studios create leverage with their data so they can focus on what matters most: building great games that players love.

Field CTO, Gaming

ggAI

We are the next generation of future-ready game data geeks - engineers, analysts, ML practitioners, and technical leaders - united by a belief that collaboration and openness are the keys to unlocking better player experiences. 

Whether you're scaling infrastructure, tuning personalization models, or wrangling event data, GGAI is where the gaming industry's sharpest minds come together to share best practices, solve hard problems, and build what’s next.

Founder

Rebel Data Science

Rebel Data Science brings a refreshingly authentic approach to educating both people and brands working on AI. By making data science and AI more accessible, we hope to open up doors for women to find fulfilling careers in data. We are excited about partnering with brands who share our commitment to our communities.

Microsoft

Director, Engineering

Activision

As a Director for Call of Duty, I led a team focused on game security. 

We utilized a suite of technology to make decisions in real-time and strengthened the technology that delivers low-latency concurrent experiences to hundreds of millions of players.

Senior Manager, Machine Learning Engineering

As an expert machine learning engineer, I implement and oversee machine learning projects to detect and minimize disruptive behavior in online gaming.

Staff Machine Learning Engineer

As a Senior Data Scientist on Call of Duty’s behavioral machine learning team, I worked at the intersection of big data engineering, real-time data analytics, and behavioral modeling to drive business value in both paid and F2P online games. 

I balanced hands-on model development while collaborating cross-functionally with my stakeholders across Activision.

•	Managed a team of data scientists developing distributed machine learning models to support behavioral machine learning initiatives
•	Built, refined, and executed a roadmap for a behavioral machine learning approach to anti-disruptive behavior
•	Outlined team best-practices including library packaging, versioning, containerization, CI/CD and task-scheduling
•	Triaged and shaped incoming requests and supervise team deliverables

Game Security Group (GSG)

The Game Security Group (GSG), is the video game industry's preeminent information-sharing and analysis center that servers as a central resource for exchanging information on cyber threats. The GSG is comprised of 23 companies and facilitates in-person and virtual meetings for members to discuss security challenges and approaches to defending their corporate and game infrastructure.

Governing Council Member

Well Data Labs, Inc

As a Data Scientist on the Well Data Labs Research and Development team, I focused on how data science and machine learning can impact operational efficiencies in the oilfield and increase the ROI of unconventional wells. 

•	Developed, deployed and patented static and real-time machine learning models to automate the recognition of events in time-series metered data
•	Conducted customer discovery and authored studies that addressed key market needs while proposing solutions and strategies to business challenges
•	Prepared and delivered internal and external industry presentations to effectively demonstrate the value of data science in oil and gas
•	Worked collaboratively in an agile environment with Product and Engineering teams on prototyping, designing and implementing robust production-ready business solutions
•	Created documentation and project artifacts for maintaining production code for machine learning platform
•	Explored and visualized data to find actionable insights and developed robust pipelines to automate reporting for Professional Services

Data Scientist - R&D

Associate Data Scientist

AirDNA

As a Product Analyst at AirDNA, I leveraged, visualized and transformed short-term rental data into strategic, actionable analytics to inform decisions resulting in 120% YOY growth and a projected $6.8 million run rate.
 
•	Acted as the point-person on all things related to user data and analytics, from data ingestion, maintenance and tracking, to understanding and reporting
•	Prepared and delivered executive presentations to strategically meet growth objectives
•	Explored and visualized data to find actionable insights and make product recommendations through funnels, churn risk analyses, cohort analyses, customer lifetime value, and behavioral segments
•	Utilized a comprehensive understanding of the full-stack framework to implement and integrate front-end and back-end analytics solutions 
•	 Created documentation and project artifacts such as analytics schema, user stories, process diagrams, work-flows, and technical requirements
•	Prepared specialized market analyses for content creation in high-level publications such as the New York Times and Wall Street Journal

Product Analyst

Analytic Partners

As a Marketing Analytics Consultant at Analytic Partners, I built predictive models to improve marketing ROI and executed communication strategies across five divisions at two Fortune 100 companies. 

•	Developed predictive attribution and media mixed models
•	Outlined the impact of advertising for five individual divisions across two Fortune 100 companies
•	Addressed client specific business challenges while executing communication strategies and presentations to convey actionable insights
•	Extracted and synthesized information from multiple data sources while proactively performing data validation to ensure accuracy, quality and integrity of data
•	Work collaboratively as a consultant with colleagues in sales, marketing, and finance to provide data-driven recommendations for client resource allocation

Marketing Analyst

University of Colorado

As a Computational Researcher, I worked at the intersection of theoretical physics, chemistry, and computational modeling to simulate the behavior of electrons in photochemical reactions.

•	Utilized quantum simulation packages on massively parallel super-computing systems to model the behavior of electrons in photochemical reactions
•	Explored systems which had previously been limited to a mixed QM/MM approach
•	Author of 3 published articles, one of which was in a Top 10 journal
•	Earned grant worth over 1 million computational hours on the 8th largest supercomputer in the world
•	Worked with large, complex data sets generated by massively parallel super-computing systems
•	Spearheaded successful and ongoing summer internship program for at-risk high school students focusing on computer science, chemistry and biotechnology

Computational Chemist

- All views shared are my own opinion and do not reflect the stance of my employer

🎰 Vegas born
🧪 Ex-chemist, current data scientist, lifelong gamer. 
👩‍🔬 Advocate for women in STEM, tech, and gaming.
🚨 Security strategist at Activision. 

Have you ever wondered “what comes after breaking into data science?” I share tips on how to excel in your career - advocate for yourself to get promoted, navigate situations with leadership, and mindfully leverage your strengths to optimize your career. 

Carly is a data scientist, computational chemist and machine learning engineer. She obtained her M.S. in chemistry from the University of Colorado focusing on computational quantum dynamics. She has authored multiple peer-reviewed publications and holds two non-provisional machine learning patents. 

When she isn't writing about herself in the third person, building mechanical keyboards or neglecting the oxford comma, she works as a public speaker, author and content creator focusing on AI. 

More specifically, she applies Python-based machine learning and best practices in statistical analysis to creatively solve novel problems. This includes harnessing various toolkits to model real-world structured and unstructured data and drive business value to stakeholders at all levels of the organization. 

Areas of Expertise: data science, machine learning, fraud detection, trust & safety, distributed systems, leadership, team-building, time-series and dynamic modeling, anomaly detection, biotechnology, high-frequency signal processing

Languages:
- Python (NumPy, Pandas, PySpark)
- SQL
- Scala

ML Platforms:
- TensorFlow
- Keras
- Scikit-learn

Engineering:
- AWS
- GCP
- Docker
- Apache Spark
- Flask
- Airflow
- CI/CD (Jenkins, Kubernetes)

Carly Taylor, M.Sc.

If you want to read a happy Thanksgiving post, just scroll right on by this one…

Things are not okay right now for a lot of people. 

I’m sure you’ve noticed too - viral posts about fellow professionals who are about to lose everything. They’ve been laid off, unable to find work, and some are about to be homeless. 

I don’t want this to become the new normal. 

Being so lost and hopeless that you are begging for work on LinkedIn is, in fact, not normal. 

And it flies in the face of everything you’re told about people who struggle.

“Oh they don’t have the right degree.” 

“They must be addicts.” 

Nope. Just regular people like you and me. 

The astounding wealth transfer in the U.S. driven by layoffs, AI, and tied up with a pretty little bow we call “shareholder value” is not normal. 

There is an armed guard at my local grocery store checking receipts because of theft. And instead of addressing issues like inflation and price gouging by the grocery store, we accept this as normal. 

We accept that the cashier who checked us out is on food stamps. 

Corporate greed at this magnitude is not normal. 

I have a lot to be thankful for. I could spout some happy platitudes as I sit here in the airport writing this note. 

But that’s not why I’m here. And I don’t think it’s why you’re here either. 

I am using my platform to say none of this is normal or okay.

And I’m tired of it.

Carly Taylor

Senior Leader in Engineering and Gaming 🎮

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