CMSWire's Top 10 AI and Machine Learning Articles of 2019 – CMSWire
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Would you believe me if I told you artificial intelligence (AI) wrote this article?
With 2020 on the horizon, and with all the progress made in AI and machine learning (ML) already, it probably wouldn’t surprise you if that were indeed the case — which is bad news for writers like me (or not).
As we transition into a new year, it’s worth noting that 73% of global consumers say they are open to businesses using AI if it makes life easier, and 83% of businesses say that AI is a strategic priority for their businesses already. If that’s not a recipe for even more progress in 2020 and beyond, then my name isn’t CMSWire-Bot-927.
Today, we’re looking back at the AI and ML articles which resonated with CMSWire’s audience in 2019. Strap yourself in, because this list is about to blast you into the future.
ML and, more broadly, AI have become the tech industry’s most important trends over the past 18 months. And despite the hype and, to some extent, fear surrounding the technology, many businesses are now embracing AI at an impressive speed.
Despite this progress, many of the pilot schemes are still highly experimental, and some organizations are struggling to understand how they can really embrace the technology.
As the business world grapples with the potential of AI and machine learning, new ethical challenges arise on a regular basis related to its use.
One area where tensions are being played out is in talent management: a struggle between relying on human expertise or in deferring decisions to machines so as to better understand employee needs, skills and career potential.
Marketing technology has evolved rapidly over the past decade, with one of the most exciting developments being the creation of publicly-available, cost-effective cognitive APIs by companies like Microsoft, IBM, Alphabet, Amazon and others. These APIs make it possible for businesses and organizations to tap into AI and ML technology for both customer-facing solutions as well as internal operations.
The workplace chatbots are coming! The workplace chatbots are coming!
OK, well, they’re already here. And in a few years, there will be even more. According to Gartner, by 2021 the daily use of virtual assistants in the workplace will climb to 25%. That will be up from less than 2% this year. Gartner also identified a workplace chatbot landscape of more than 1,000 vendors, so choosing a workplace chatbot won’t be easy. IT leaders need to determine the capabilities they need from such a platform in the short term and select a vendor on that basis, according to Gartner.
High-quality metadata plays an outsized role in improving enterprise search results. But convincing people to consistently apply quality metadata has been an uphill battle for most companies. One solution that has been around for a long time now is to automate metadata’s creation, using rules-based content auto-classification products.
Although enterprise interest in bots seems to be at an all-time high, Gartner reports that 68% of customer service leaders believe bots and virtual assistants will become even more important in the next two years. As bots are called upon to perform a greater range of tasks, chatbots will increasingly rely on back-office bots to find information and complete transactions on behalf of customers.
If digital workplaces are being disrupted by the ongoing development of AI driven apps, by 2021 those disruptors could end up in their turn being disrupted. The emergence of a new form of AI, or a second wave of AI, known as augmented AI is so significant Gartner predicts that by 2021 it will be creating up to $2.9 trillion of business value and 6.2 billion hours of worker productivity globally.
AI and ML took center stage at IBM Think this year, the show’s major AI announcements served as a reminder that the company has some of the most differentiated and competitive services for implementing AI in enterprise operational processes in the market. But if Big Blue is to win the AI race against AWS, Microsoft and Google Cloud in 2019 and beyond, it must improve its developer strategy and strengthen its communications, especially in areas such as trusted AI and governance
Sentiment analysis is the kind of tool a marketer dreams about. By gauging the public’s opinion of an event or product through analysis of data on a scale no human could achieve, it gives your team the ability to figure out what people really think. Backed by a growing body of innovative research, sentiment-analysis tools have the ability to dramatically improve your ROI — yet many companies are overlooking it.
Pop quiz: Can you define the differences between AI and automation?
I won’t judge you if the answer is “no.” There’s a blurry line between AI and automation, with the terms often used interchangeably, even in tech-forward professions. But there’s a very real difference between the two — and it’s one that’s becoming evermore critical for organizations to understand.