The AI-First Company summary

Before you build an AI you need to ask whether your customer actually needs one. 

Think about the kind of decision your customer is trying to make. If it’s a one-off call with high stakes, using structured data from a known source combined with statistical analysis will probably serve them better than any machine learning model. AI earns its keep when decisions are frequent, data is messy, and the environment keeps shifting. In most real business situations, customers need a blend of both approaches – and a common mistake is jumping straight to the sophisticated solution before you’ve validated whether the simpler one does the job.

Not all data is worth pursuing, and confusing quantity for strategic value is one of the most expensive mistakes an AI-first company can make. 

The datasets worth fighting for are the ones that are genuinely hard to replicate. These might be physically difficult to access – they may require going to a local council office, or collecting paper files and photocopying them manually. Other datasets may be legally restricted and only harvestable at a slow, painstaking rate. Financial market data providers, for example, only allow you to quote consecutive stock prices at a predetermined time interval, unless you pay more. If you’re offering an AI product that makes predictions on profitable trades, having this data could make all the difference.

An AI-first company needs a genuinely different kind of team – one that spans data infrastructure engineering, machine learning research, statistics, econometrics, and deep domain expertise. That’s a wide range to hire for, and assembling it takes time. A good place to look is university departments, in fields including economics, actuarial science, biology, biostatistics, engineering, and physics. These kinds of backgrounds equip potential hires with a lot of fundamentals required for data handling. 

Once your team exists, you move on to the harder challenge: managing it. Data scientists don’t work like engineers – they work like researchers. An engineer gets a specification, builds to it, ships it, and moves on to the next ticket. A data scientist explores. They ask questions, follow threads, sometimes hit dead ends, and often backtrack before finding something useful.

Building your model involves training it to make predictions based on input data. More specifically, you need to instruct it what features it needs to look for in data to be able to identify things. 

There are several ways you can achieve this. Sometimes you can code directly. Say you’re building an AI model to identify eyes. You can write in a feature in the code that activates whenever the model comes across a circular, black group of pixels – a pupil. 

Learning is a loop. You observe something, act on what you’ve learned, observe the consequences, then act again with better information. Every scientist, every athlete, every manager worth their salary runs some version of this cycle. Data learning effects work the same way.

Take a simple example: an AI-First product predicts the pasta shelf in a store will be empty in 15 minutes and pings the stocker. He checks after lunch, finds it bare, restocks it, and logs it in his app. The in-store camera catches the whole sequence – pasta, no pasta, pasta again. The AI pulls in both signals, confirms the prediction was spot-on, and next time nudges the stocker a few hours earlier so the shelf never actually runs out.