Should a small business be forced to diminish just because its small?

Everyone knows that Artificial Intelligence brings serious competitive advantage. Think Uber vs. local taxis or Amazon vs. local stores. Uber started small and quickly took a huge share out of the local taxi market. Amazon has the same story – it now dominates the market and local stores are not selling as much anymore. Get the picture?

This is a David and Goliath story. Goliath is big and strong. It seems difficult to fight someone who has such a serious advantage.

Could a small business become smarter? Why not use AI?

Machine Learning development is cumbersome. It is a multi-step-multi-skill process that makes predictive models from data and connects them to software applications. A large company can hire a team of business analysts, data engineers, cloud developers, QA/QC, and data scientists who bring their method and practice with them. A model is like any software. New data comes in and models have to be tuned and improvements added over time. Each model deployed to production also creates the baggage that is expensive to deal with later. The models also live in the minds of the developers. And of course, it creates job security. What happens if the person leaves? The next person has to start all over again for the model to continue to make sense.

Small companies have limited resources – it is IMPOSSIBLE for them to run this cumbersome process that big companies can afford to run. They want a simpler way. They need results in weeks, and not in a year!

That’s what pushed us to figure out a solution. Braintoy made it our mission to make AI accessible.

We believe that AI shouldn’t be complicated or expensive. The playing field between big and small ought to be levelled. AI should produce results for a business and not take the time and money away. We made a production machine learning platform called mlOS that does that.

The question – how do I start?

Try small stuff that has real impact.

These could be problems that you normally react to but it would be better if you could be proactive. For instance, if you are a store, what if the returns of your products could be reduced? You already have data for product sales and returns. Why not let machine learning make sense of it? If you are trucker, would it not make sense for you to predict empty miles. Or if you are a farmer, would it help if you were to reduce the cost of testing plant hybrids by predicting yields. Or what if you are a financial brokerage that recommends products, would it not be nice to know what products could work better for your customers? Your customers will like it – and so will you!

Think about it. AI is no more difficult that you believed it is.

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