The late German philosopher Theodor W. Adorno once stated, “Words of the jargon sound as if they said something higher than what they mean.” That’s exactly why marketers and vendors love buzzwords — they want potential clients to think what they offer is something special or cutting edge. Artificial Intelligence (AI) falls into the buzzword or jargon category. Eric Siegel (@predictanalytic), a former computer science professor at Columbia University, writes, “A.I. is a big fat lie. Artificial intelligence is a fraudulent hoax — or in the best cases it’s a hyped-up buzzword that confuses and deceives.” Most often, when people talk about AI, they are talking about some form of machine learning. Siegel explains, “The much better, precise term would instead usually be machine learning — which is genuinely powerful and everyone oughta be excited about it.” Author and AI expert Tom Taulli (@ttaulli) believes even the term machine learning can be confusing. He writes, “Machine learning is definitely a confusing term. Is it AI or something different?” The easy answer, as noted above, is: Machine learning is different than AI. It’s a technology that falls under the broader AI category.
Why businesses should get excited about machine learning
Taulli rightly asserts businesses shouldn’t get excited about machine learning simply because it’s fashionable. He writes, “There needs to be a clear-cut business case for machine learning. It should not be used just because it is trendy.” Tech writer Grace Frenson (@GraceFrenson) suggests eight ways machine learning can help businesses. They are:
1. Accurate sales forecasts. Frenson writes, “Machine learning can analyze past customer behavior and make sales predictions based on it. As a business owner, no money goes wasted purchasing unnecessary inventory. They simply fill orders based on the amount forecasted by the machine.” To be accurate, machine learning can only forecast the future if you believe it will look like the past. Alan J. Porter (@alanjporter), Head of Strategic Services for [A], explains, “ML is great at recognizing patterns but not much else” and “ML assumes tomorrow is going to be the same as today.” To obtain more advanced analytics, companies need to deploy cognitive computing systems, like the Enterra Cognitive Core™, which all leverage machine learning.
2. Better sales offers. “Studying previous sales data,” Frenson writes, “can help machine learning technology to provide better recommendations to business owners. As a result, customers get the right offers at the right time. This means more sales without having to plan or bet on ads.”
3. Simplified product marketing. According to Frenson, “Machine learning takes the guesswork out of marketing. By processing huge amounts of data, it can identify highly relevant variables that businesses may have overlooked. This allows you to create more targeted marketing campaigns that customers are more likely to engage with.”
4. Automate time-intensive tasks. Frenson writes, “Data entry is one of the easier tasks for a business but because it’s so repetitive, it’s more vulnerable to errors. This can be avoided with the help of machine learning which not only processes data fast but also does it accurately. This allows skilled human employees to focus more on meaningful tasks and provide extra value to your organization.” What Frenson describes is most often referred to as robotic process automation (RPA) rather than machine learning. RPA is generally considered a rules-based technology and a threshold technology for other forms of AI.
5. Spam detection. Frenson notes the drawback of simple rules-based technology. She writes, “Email providers used to fight spam using rule-based programming. It remained problematic for a while since it did not properly catch all spam emails coming into inboxes. Machine learning today can detect spam more accurately using neural networks to get rid of junk and phishing emails. It does so by constantly identifying new threats and trends across the network.”
6. Smarter workplaces. When you start talking about “smart” or “intelligent” systems, you are generally discussing cognitive technologies that leverage machine learning. Frenson writes, “Machine learning can produce smart assistants which can improve productivity in the workplace. For example, we now have intelligent virtual assistants who can transcribe and schedule meetings.”
7. Maintenance predictions. Predicting timely preventive maintenance, Frenson notes, “is especially important for manufacturing firms where maintenance is completed regularly. Failing to maintain equipment in a timely and accurate way can be very costly. With machine learning, factories can gain insights and patterns which might have been overlooked before. This reduces the chances of failure and increases productivity in manufacturing.”
8. Real-time decision making. Frenson asserts, “Your business can make more informed decisions with machine learning since it can process massive amounts of data in a short amount of time.” I agree that machine learning, when it is part of cognitive computing system, can help decision-makers make better decisions. And better decisions matter. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), assert if you can improve a company’s decision making you can dramatically improve its bottom line. They explain, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”
As Taulli noted, machine learning is only valuable if it can help address a real business challenge. JP Baritugo, director at management and IT consultancy Pace Harmon, agrees. He states, “The main culprit of unsuccessful or subpar digital transformation initiatives such as AI or ML tends to be using a technology-first approach. Instead, organizations need to determine what they are transforming to, how, and with whom. Articulating the organizational aspirations to enhance services, delivery, and/or the customer engagement model with AI or ML will help define the digital strategy.” Sarah Burnett, executive vice president at Everest Group, adds, “You must be clear about what you want AI to do for you, what questions you want it to answer, what business problem you want it to solve. Collaboration with business people is critical to make sure that you fully understand the business problem that you’re trying to address with AI.” Once you understand how machine learning or cognitive computing can help solve business problems, like Siegel, you’ll understand machine learning is “genuinely powerful and everyone oughta be excited about it.” In Part Two of this article, I will discuss some of the groundwork that must be completed and some of the challenges that must be overcome to make machine learning truly useful for businesses.
 Eric Siegel, “Why A.I. is a big fat lie,” Big Think, 23 January 2019.
 Tom Taulli, “Machine Learning: What Is It Really Good For?” Forbes, 23 May 2020.
 Grace Frenson, “8 Ways Your Business Can Benefit From Machine Learning,” Market Scale, 8 June 2020.
 Alan J. Porter, “Machine Learning Isn’t Rocket Science,” CMS Wire, 9 September 2019.
 Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
 Stephanie Overby, “Artificial intelligence (AI) vs. machine learning (ML): 8 common misunderstandings,” The Enterprisers Project, 19 May 2020.