AI Won't Run the Business World: Why We Still Need Human Intelligence
ClearSale at Business.com: Is there a time on the horizon when humans can turn over their businesses to advanced deep-learning neural networks and relax while artificial intelligence does all the work? The early 2000s saw a boom in machine learning, AI and automation — and despite the advances that have given us chatbots and personalized suggestions for everything from shoes to potential spouses, we humans are still necessary for effective business processes in virtually every type of industry.
That's because while artificial intelligence is efficient, it relies on humans to build its intelligence and take on tasks it can't yet handle.
Neural networks have to get the information they use to recognize patterns from humans. We have to decide which data to “feed” the neural networks to train them to handle specific tasks, such as facial recognition or similar products. This is called supervised learning and it's as far as the technology reliably goes for now. Deep-learning technology can do a lot of things it couldn't do a decade ago, according to Roger Parloff at Fortune, but it hasn't evolved enough to let networks sift through raw data on their own to consistently find relevant patterns.
AI is great at using massive amounts of human-selected data, but the knowledge and experience that allow us to select the data is still our domain. And sometimes, our human customers just want to interact with another person. What does this mean for business? For the foreseeable future, machine learning will continue to deliver efficiency that humans can't match, while humans provide the discernment and interpersonal skills that AI cannot. In other words, it's a partnership, not a competition, as demonstrated in a variety of industries.
What chatbots can (and can't) do for customer service operations
Like other AI tools, chatbots have to be trained by human interactions, and in most cases training allows chatbots to offer initial responses to basic customer questions. This frees up human customer service representatives to focus on more complicated customer inquiries. However, it's worth noting that customers may not use chatbots the way retailers and B2B merchants expect them to.
Terena Bell at CMS Wire reported that although many merchants train their chatbots to answer questions about products, US-based customers prefer to deal with humans before they make a purchasing decision. They're more likely to use chatbots after they buy to handle shipping inquiries and updates. This means chatbots need human instruction and need to be used to meet human customers' actual preferences.