AI in business, are you too late to the party? | 7wData

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As far back as 2017, nearly three-quarters of executives believed that business advantage in the future would be driven by AI. There are companies and people who are using AI as a way to hype their businesses, as an important part of what they want to tell investors and so on. As business leaders observe this environment, there’s the nagging internal question:

Now of course, in full disclosure, we’d have to note that as a company that sells a unique AI solution we think that right now is always a good time to get started. That said, there are some things to consider.

To start off, let’s look at this table of factors to consider as a simple TLDR.

If you’re trying to keep up with competitors who are making AI announcements in the press or making splashy technical hires, you might be too late. There’s an awful lot of big project promises that aren’t coming to fruition because the companies doing the hires and initiating those “disruptive” projects don’t know what they’re doing. The big data scientist hire isn’t always so great. The company doing the hire often doesn’t know how to evaluate the job. Even more troubling, the company might not have the data or the culture to initiate effective AI projects.

If you’re doing AI just to get a hype bump then it’s probably too late. There isn’t much value in having an AI announcement in a crowded sea of AI announcements. You’re going to have to earn it the hard way or the smart way.

If, on the other hand, you want to make an improvement to your business it’s a great time to start integrating AI. Making improvements to business isn’t a fashion-statement or hype cycle. It’s something business leaders do.

AI, when developed and deployed right, improves business outcomes.

The big problem with companies pursuing AI for the hype is that they so rarely consider the business outcome (or maybe the business outcome is simply the short-term PR boost, who knows?). If you don’t have a business outcome then deploying AI will only chew up your resources.

If you do have some business outcomes to improve, the next challenge is figuring out if they’re the right fit for AI as it exists today, within your company’s resources. This is important because, as we all have seen, you can spend more time and money on AI than the outcome will be worth. The faulty logic that grows from this is that we need to pursue bigger, more “disruptive” projects to justify the ROI on our AI spend.

However, the inverse is true especially for those who are getting started with AI. The AI of today that is within reach of those outside large universities or government institutions, is fantastic at incremental improvements that add up to a big win. In the same way that Olympic athletes will train and tune their systems to make gains of less than a second, a powerful contemporary AI system will help you optimize your business decisions for continuous improvement.

Instead of throwing a ton of money into a giant, multiyear project, the smart companies are delivering many smaller projects and the teams are themselves increasing their own abilities to develop and deploy AI solutions.