Data lessons that a $100K AI mistake taught me...
How to ensure your AI investments don't fail due to process or data challenges.
Opening Insight: Invest into data solutions before investing into AI projects.
I nearly made a $100K mistake with an AI project - the same one I now see across multiple orgs & AI initiatives. If you’re planning to launch an AI initiative, here’s what to remember…
Last quarter, we took a very complex problem for our production team. Our plants were seeing productivity and leakage issues related to raw material and we wanted a tech solution for the same.
We started building a video-based AI system that would capture all material that got pushed into the conveyers, identify the bad items in real time and improve downstream productivity. It sounded obvious and doable.
We got the budgets approved, hired a product manager, and onboarded an international partner to work on the problem statement. The MVP was supposed to come in 3 months for 1 plant. Everything seemed on track.
During a weekly review, about 7 weeks into the project, I started seeing the results not aligning. The tests kept failing. The results were poor even with our regular scenarios.
I asked the team how many images they processed, and the answer was about 2000.
Not an answer I expected!
To give you an idea, on a realistic basis, we would be processing over 1 million images per day from across our 10 warehouses. And we had processed only 2000 images for training.
That’s when the team realized they did not have data. Neither the volume, nor the format. We were nowhere close to making a successful product.
We got into a war mode for the next 10 days. We built a data collection tool, deployed cameras in the conveyer areas, and aligned ops teams to manually tag the data for the project.
It took us about 20 days to get enough data to be able to build something substantial. Then too, it was not a production ready project.
We eventually succeeded but not before almost failing.
and that’s not just us.
The same happened at a large D2C brand trying to automate their customer support. Their team didn’t have both the historical data and the access to live data. No wonder the chatbot gave bad customer experience.
Across companies, data is the biggest challenge. AI projects not delivering results. Not because of poor tech or team, but because of lack of data.
But what’s the way out?
Today, I get my teams to spend over 50% energy into getting the right data. Here’s the COAT framework that we follow while designing AI solutions:
The COAT framework to get the AI data into shape:
Collect Data (current data sources, new streams, etc)
Organize data (clean, label, structure it)
Analyse the Data (identify patterns, gaps, edge cases)
Train the models (build intelligence & automation)
Remember, in any AI development, training the models and building solution is the 4th step. Skip any of the first three, and the fourth step will fail. No matter how good your team is.
💬 Leadership Signal
“The people closest to the problems have the best ideas to solve the problems. If coding is the main thing holding them back, Just start building (using AI).”
Amjad Masad, CEO - Replit
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