The term “AI” – or artificial intelligence – is a huge buzzword in today’s marketplace and is being thrown around quite freely. The hype escalates continuously with the emergence of staggering new AI competencies. But there is also a tendency to include machine learning – or ML – under the AI umbrella, which can lead to grossly overselling ML initiatives and their abilities. Remembering that AI refers to human-level capability, it’s critically important to differentiate the ML model. Failing to do so will overinflate expectations and negatively impact how true ML can support business operations.
Properly applied and monitored, ML can boost the efficiency of existing business processes, issue actionable predictions, and create tangible value. Examples of this use might be predicting which customers are likely to cancel, providing an organization with the opportunity to deliver incentives; or identifying potential fraudulent credit card transactions during the transaction. These practical usage cases can significantly impact business operations but make no mistake – these activities have nothing to do with AI. This is machine learning technology all the way.
That said, most people still confuse the two. AI has become a catch-all term that vaguely conjures up a type of technology capable of any intellectual task that humans can handle. By linking machine learning processes to AI, we are ensuring that ML projects will fail to deliver on their promise. It’s important to keep ML’s capabilities and limitations front and center when setting up ML initiatives.
And while we’re on the subject, let’s take a closer look at the exploding world of “artificial intelligence – increasingly poised as a powerful technology with human-level capabilities. A bit overblown? We think so – in fact, applying the word intelligence to describe a machine can be imprecise at best. And even though these machines utilize advanced methodologies like ML, natural language processing, rule-based systems, speech recognition, computer vision and more, there is always room for error and misinterpretation. At the end of the day, an AI system still hasn’t been able to pass the Turing Test – which is a three-person exercise in which a computer uses written communication to mislead a human into thinking that it’s another person. But despite staggering advances in artificial intelligence, a computer has yet to pass the test.
But let’s not forget that by toning down the AI rhetoric, both AI and ML can be true value propositions if framed and applied correctly, with realistic expectations of their role in business processes. Ultimately, AI and ML initiatives remain a moving target as we navigate the ongoing technological revolution. Grappling with the enormous challenges will necessitate that companies remain agile, flexible, and intuitive.
Partnering with an established BPO outsourcing company like Anexa can help you identify the activities you can successfully outsource, leaving you to the business of running your business and making the right decisions. With 20 years of experience in the outsourcing field, we manage a pool of highly trained, bilingual (Spanish and English) agents. These talented professionals bring experience and expertise to a wide range of business areas like marketing, sales, promotion, technical support, and customer service.