![]() As the Times notes, “Medicine proved far more difficult than (IBM executives) anticipated. The New York Timesreports that after its Watson supercomputer won the quiz show “Jeopardy!” in 2011, expectations were high for new conquests such as curing cancer. It’s hard. Beyond the simplest uses of AI, as in chatbots and certain features of Google’s search engine, the deeper and more valuable applications of AI are proving to be difficult and slow.Įven a company with the AI prowess and deep pockets of IBM admits to struggling. Smaller firms will likely have an even more difficult time than their Fortune 2000 counterparts in identifying people with the right skills-or being able to afford that talent if they do find it. Kearney, that individuals with AI experience and skills are hard to find: “Two-thirds of companies answered that they can’t hire enough people who can generate insights from corporate data.” Business2Community reports, based on research from A.T. Talent shortage. Beyond its simplest applications, exploiting the capabilities of AI requires specialized talent. It’s just that the term ‘machine learning’ is not as sexy to put on your web page.”Ĭonfusing complexity. Related to the point above, Information Age recently discovered that confusing vendor messages and a lack of understanding were core problems midmarket AI use, stating “AI adoption in marketing is being hindered by marketer’s understanding of AI, with 40 percent thinking they are already using the technology.”įurthermore, factors such as over-reliance on outside agencies for marketing strategy, and difficulty integrating different tools and data, add complexity to any potential AI implementation. Today’s vendors are in large part doing the easy stuff.”Īn article from Martech Advisor adds, “AI is now nothing more than a marketing term used for any software application displaying even the most rudimentary intelligence…AI (has been) transformed from an almost unreachable goal into to a cute acronym designed to sell to the Fortune 500 and anyone with deep enough pockets.”Īnd per Philippe Botteri, a partner at global venture firm Accel, “A lot of…companies have been doing machine learning and (that work) has been rebranded as AI. It seems that every vendor, from IT operations management to business intelligence to digital marketing, is now using AI under the covers…The problem is that there are many different types of AI, from simple machine learning to much more complex deep learning and various types of cognitive computing. ![]() Yet, even proponents of chatbots acknowledge such efforts are not “plug and play.”įurther, VentureBeat notes fostering engagement with chatbots requires human efforts to “Ask (for) feedback from your users, make changes, ask (for) more feedback, and improve.” And even then, properly assessing engagement is difficult.ĪI-washing. Similar to greenwashing (in which companies exaggerate the environmental-friendliness of their operational practices for business benefit), marketing technology vendors may now be applying the “AI label” a bit too promiscuously.Īs a recent post from Intellyx explains: “There’s no question we’re in the AI-washing phase of the AI revolution now. Chatbots have been called the “gateway drug” for AI the simplest way to get started with this technology, and where most small and midsized companies are likely to go first. Though marketers in small to midsize firms are intrigued by the possible applications of AI, many remain wary, for a number of reasons including: For example, according to Gartner, “By 2020, 30 percent of all companies will employ AI to augment at least one of their primary sales processes.” That means 70 percent still won’t be applying AI to sales even three years from now.Īdditionally, recent research by Demandbase found that while “80 percent of all marketing executives believe AI will revolutionize marketing over the next 5 years… only 26 percent are very confident they understand how AI is used in marketing and only 10 percent of marketers are currently using AI today.”Īnd AI doesn’t make the list of the top marketing or technology challenges faced by small to midsized businesses today. Another study predicts the size of the AI market, $8 billion in 2016, will grow to $31.5 billion by 2025.īut the story is different in smaller firms. According to Forbes, 38 percent of enterprises are already using AI in some form, and that figure is projected to reach 62 percent by the end of 2018. Nevertheless, there are practical applications of AI that marketers in small to midsized companies can adopt today, without significant risk to or disruption of their current operations.ĪI use is picking up rapidly in large firms. But a combination of skepticism, fear, and confusion appears to be holding back adoption in the midmarket. Artificial intelligence (AI) is being embraced by enterprise marketers.
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