Alexandra Levits Water Cooler Wisdom Why AIs Not for Everyone

Alexandra Levit's Water Cooler Wisdom Why AI's Not for Everyone A recent MIT/Sloansurveyof senior corporate executives showed they see artificial intelligence (AI) as the single most disruptive new technology, and nearly 70 percent said they already have AI investments underway. Looking at these results, you might feel insecure. You might worry that you’re falling behind, and that your business will lose its competitive edge if you don’t get with the program (literally). And it’s tempting to march into your CEO’s office and demand approval for an AI strategy now. You might want to sleep on that, though, because in the project management domain, the timing might not be quite right. We Don’t Have it All Figured Out Just Yet Aswe talked about herelast year, AI has made interesting strides in the last few years, particularly with respect to deep learning. To refresh your memory, deep learning involves training computers to recognize patterns in data and then classify and categorize them as a human brain could do instantaneously. This is cool to be sure, but in the enterprise, a lot of deep learning applications are still in their infancy. They have yet to solve specific business problems or notably increase bottom-line profitability. One common problem is that most organizations still haven’t masteredbig dataand basic data analytics. While they’ve started collecting data, in many cases that data is just sitting around. And this data can’t necessarily be used for AI if it’s not in the right format and cleaned appropriately. So, before being able to realize the true potential of AI, you’ll need to first plan exactly how you intend to analyze the data you have and put it to use in the service of insightful business decisions. AI is Tougher Than It Sounds According to Brandon Allgood atForbes, the complexity of AI is another reason to put off an implementation. “As the CTO of a company with a foundation in AI, trust me when I tell youthat it’s harder to implement than you might think,” he wrote. “AI and machine learning are not commonplace today because we still lack a few essential building blocks, like a robust software infrastructure around core algorithms, or the interfaces to easily make use of those algorithms.” In an article forFortune.combased on the recentBrainstorm Techevent, panelists discussed the use of AI in small-to-medium sized businesses. “It probably makes no sense to dedicate limited resources to hire an AI expert, even if there were one available,” they concluded. “Maybe every industry needs an AI strategy, but not every business,” said George Kurtz, CEO of CrowdStrike, a cybersecurity specialist. “AI is just beginning, so having a strategy around it is a problem because you have to define what you’re talking about,” added Norman Winarski, founder of Winarski Ventures. “You have to be incredibly careful in how you deploy an AI solution, you need to think about how people will react, and it takes a lot of resources.” For more, check out the full post on the QuickBase Fast Track blog.