Do Business Leaders Risk Falling Behind If They Wait to Adopt AI?

February, 22 2019

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Written By
Alyssa Verzino
AI at Work


Something we talk about on AI at Work is a debate around whether AI is a fundamentally different type of technology from the viewpoint of competitive dynamics. Previously, when a new software was launched, it might have made sense to wait until a few rounds of updates had passed and all of the bugs had been worked out before adopting the new technology. Waiting to implement the newer advances wouldn’t mean you would be left behind or at a disadvantage compared to those who had embraced them early on.

Some people argue that AI is fundamentally different. Someone who got started, say, 20 years ago will be progressing at a rate that someone who started one year ago simply won’t be able to keep up with. Others might push back, saying that eventually the compute will be fast enough that catching up to others will be fairly quick, even for late adopters.

Here’s a roundup of the insights guests on our podcast have shared with us in answering the question “is AI a fast-follower technology?”

Professor Tom Davenport of Babson College says “I think, for the most part, it's different. I think there are some things that it can be helpful to wait on. I mean, if you're the first in your industry to try to develop a taxonomy for a particular key process, you're probably better off having somebody else pay for that. Then you can get it later. In general, I think the learning that is required on the part of the systems, on the part of the people, the data that you have to accumulate - I just don't see it as a fast follower kind of technology, for the most part.”

Erin Winick is the associate editor for The Future of Work, MIT Technology Review. She shares, “I think with where AI is at right now, at least where we see the technology, yes, it is an advantage to get in early. I think it's largely an institutional thing of learning where it fits into your functionality and what to do with the data you get out of it and knowing what data to put into it. So much of AI is, often, training these models. The longer you can get them running, they really do get more effective.

Steve Peltzman, Chief Business Technology Officer of Forrester Research, agrees. In the age of the customer especially, there’s an immediacy to solving problems in real time - switching costs are low and a company’s reputation is hinged upon the reviews. Steve says, “you’ve got to fix it right then, right there. That's the expectation. With the expectations going up, you have to be able to react. The only way I think you can react that fast is with AI and help like this. With as much change as we talk about that has to happen here with the training, and the infusion into the process, and all that stuff, and the culture, if you don't start now, you're way behind. In fact, you're already behind. I feel very behind even though we're ahead.”

Others highlight the importance of making sure it is the right time for your business. Brendan Kohler, co-founder and CTO of Sentanai and co-founder of Hyperplane Venture Capital talks about a different way of looking at adopting new technologies. He says “there are certain inflection points within an organization that’s focused on executing, where they have opportunities to adopt a different process, a different model, different technologies."

These inflection points happen every few years when some part of the business process breaks down or IT technologies start failing to support the execution of a business as it grows. Brendan says “it's important that companies at those inflection points evaluate all of their options, because they really only get those chances every few years within an organization, especially if they're a large organization.” Timing remains critical, but in a different sense - perhaps strategically evaluating when it is appropriate to adopt new technologies at said inflection points is more critical than rushing forward to adopt them for fear of missing out.

James Cham of Bloomberg Beta takes a similar stance - it is more important to be strategic about when to adopt AI and when not to rather than adopting the latest technologies for the sake of adopting the latest technologies. At the same time, he says “there’s a realization that the people who figure out how to apply machine learning in the right ways, and sooner, are going to be the ones that win.” Both timing and strategy are of the essence, it seems.

Many factors play into how the timing of when a company adopts AI will impact its success. Some say that being late to adopt AI will mean getting left behind while others suggest each company might have its own unique “sweet spot” in terms of bringing on this new technology.

There is consensus around the fact that AI is a fundamentally different type of technology. But, as Erin Winick points out, predicting the future is...well, unpredictable. There are many variables executives need to consider when deciding if it is time for their company to start leveraging AI. At the same time, one thing is important to remember - given AI’s inherent nature of learning and improving over time, there’s no reason not to get a head start.

If nothing else, it will help you learn from experience. As host Rob May points out, “One of the interesting things about companies that are deploying AI is that the last wave of technology, which was the social-mobile cloud wave, it was not difficult to understand if you were a non-technical person. It was relatively straightforward. Now, you have this wave of IoT, blockchain, and artificial intelligence. These are technologies that are hard to understand, sometimes, even if you are technical, depending on how technical you are.” Given this, one of the best ways to learn is to apply AI at your organization. And, as Tom Davenport advises, it's important not to get stuck in pilot and proof of concept mode. He advises that “Having a clear set of criteria for when you go into production deployment is very critical for AI-first executives.”

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