10 signs you’re ready for AI — but might not succeed

Not every problem can be solved by machine learning, and not every company is poised to apply AI. Here’s how to know whether your IT organization is ready to reap the benefits of artificial intelligence.

10 signs you’re ready for AI — but might not succeed

Since machine learning is a panacea, your company should be able to use it profitably, right? Perhaps; perhaps not. OK, I’m just kidding about the panacea: that’s just marketing hype. Let’s discuss whether you have what it takes to harness artificial intelligence — and how you could get to that point if you’re not yet there.

To begin with, do you know what you want to predict or detect? Do you have enough data to analyze to build predictive models? Do you have the people and tools you need to define and train models? Do you already have statistical or physical models to give you a baseline for predictions?

Here, we’ll break down what you need for your AI and Ml projects to succeed, discussing their ramifications to help you ascertain whether your organization is truly ready to leverage machine learning, deep learning, and artificial intelligence.

You have plenty of data

Sufficient relevant data is the sine qua non of predictions and feature identification. With it, you might succeed; without it, you can’t. How much data do you need? The more factors you’re trying to take into account, the more data you require, whether you’re doing ordinary statistical forecasting, machine learning or deep learning.

Machine learning regression methods Microsoft

Take the common problem of predicting sales, such as how many pairs of navy blue short-sleeved blouses you will sell next month in Miami, and how many of those you need to have in stock in your Miami store and your Atlanta warehouse to avoid back-orders without tying up too much money and shelf space in stock. Retail sales are highly seasonal, so you need statistically significant monthly data from multiple years to be able to correct for month-to-month variations and establish an annualized trend — and that’s just for standard time-series analysis. Machine learning needs even more data than statistical models, and deep learning models need multiples more than that.

One statistical model you might build would analyze your chain’s monthly blouse sales nationally over 5 years, and use that aggregate to predict total blouse sales for next month. That number might be in the hundreds of thousands (let’s say it’s 300,000). Then you could predict blouse sales in Miami as a percentage of national sales (let’s say it’s 3%), and independently predict blue short-sleeved blouse sales as a percentage of total blouse sales (let’s say it’s 1%). That model points to approximately 90 sales of blue short-sleeved blouses in Miami next month. You can do sanity checks on that prediction by looking at year-over-year same-store sales for a variety of products with special attention to how much they vary from the model predictions.

Now, suppose you want to take into account external factors such as weather and fashion trends. Do short-sleeved blouses sell better when it is hotter or sunnier than when it is cooler or rainier? Probably. You can test that by including historical weather data in your model, although it might be a little unwieldy to do so with a time-series statistical model, so you might try decision forest regression, and while you’re at it try the other 7 kinds of machine learning models for regression (see screenshot above), and then compare the “cost” (a normalized error function) for each model when tested against last year’s actual results, to find the best model.

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