AirPilot

Founder / CEO

HY Partnerships

Founding Partner

Commonwealth of Massachusetts

Head of Data

Wayfair

Technical Lead

Saylent

Director Of Engineering

Next Step Living

Senior Software Engineer

Optaros

Part of the Platform Engineering Team at Macys. Building a RESTful web service api  which helps other teams to build their own web services. Other services can be built on top of this platform which gives them a unified structure across the organization.

Majesco

Built an UI framework which enables Business Analysts create their own forms without learning HTML, Javascript or CSS.
Developed an integration layer which helps other applications to talk to the product by sending requests or receiving responses. Integration layer is acts like a lightweight service bus without much of overhead.
Other Responsibilities includes:
1. Working with Sales and Marketing team to come up with next generation product features. It is an interesting part of the job. Meeting sales team's expectation with product's existing technology structure and resource limitation is always challenging.

2. Changing the products screens, database tables or configuration settings as per client specific requirements.

3. Working with Sales and Marketing team to come up with next generation product features.

Sr. Product Engineer

Bank of America

Part of the team as a lead developer which has developed Bank's first in-house mobile application. GFO mobile is a blackberry application for Sales Associates. It is a mobile version of original GFO web application. It delivers real-time status updates regarding client’s recent setup. It provides the flexibility to know how far along the client’s setup is, no matter where the Sales associate is physically located.
App Store URL: http://BBApps  App Name: GFO Blackberry
	
Role & Responsibilities:

•	Developed the front end of the mobile app using Blackberry Webworks SDK. This includes building ‘cod’ file out of the front end code and deploying it on bank approved devices for end to end testing
•	I was lead developer for a team of 10 developers. It include (2 onsite and 8 offshore).
•	Onsite and offshore coordination: It includes assigning daily tasks to offshore team, resolving all their requirement/design related queries and validating their end of the day delivery.

Technologies - Blackberry Webworks 2.0.0, Spring RESTful service, AJAX, HTML, CSS.

With a strong background in engineering and experience honing my marketing analytics skills at industry leaders like Wayfair, CVS, and Kum & Go, I specialize in applying Machine Learning & AI to drive customer segmentation, channel attribution, and future-proof forecasting. This unique perspective enables me to leverage data-driven insights to fuel growth and innovation.

Currently, I'm leading Airpilot, a game-changing startup offering customized client reporting and analytics services. 
Airpilot's solutions are designed to transform marketing and advertising agencies by streamlining their client reporting processes, significantly reducing manual efforts, and delivering high-quality, real-time insights. 
It's an exciting time as we help businesses make data-driven decisions to shape their future strategies. Let's connect to explore how Airpilot can revolutionize your operations and marketing strategies.

Himanshu Yadav

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'

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