The never-ending effort to bake common business sense into artificial intelligence | ZDNet

Can common business sense be programmed into AI? Many are certainly trying to do just that. But there are decisions that often require a level of empathy — let alone common sense — that may be too difficult to embed into algorithms. In addition, while AI and machine learning are the hot tickets of the moment, technologists and decision makers need to think about whether it offers a practical solution to every problem or opportunity. 

These points came up at a panel at the recent AI Summit, in which participants agreed that AI shouldn’t be considered the default solution to every business situation that arises. (I co-chaired the conference and moderated the panel.) For starters, AI is still a relatively immature technology, said Drew Scarano, a panelist at the session and vice president of global financial services at AntWorks. “We might be too reliant on this technology, forgetting about the humans in the loop and how they play an integral part in complementing artificial intelligence in order to get desired results.”  

AI is being used for many purposes across all industries, but the risk is in de-humanizing the interpersonal qualities that help build and sustain companies. “Today we can use AI for anything from approving a credit card to approving a mortgage to approving any kind of lending vehicle,” said Scarano. “But without human intervention to be able to understand there’s more to a human than a credit score, there’s more to a person than getting approved or denied for a mortgage.”  

Scarano poo-poos the notion that AI systems comprise anything close to a “digital workforce,” noting that “it’s just a way to sell more stuff. I can sell 50 digital workers rather than one system. But digital workforce is just a bunch of code that does a specific task, and that task can be repeatable, or be customized.” Another panelist, Rod Butters, chief technology officer for Aible, agrees, noting that “at the end of the day, it’s a machine. In the end, it’s all 1s and 0s.” The way to make AI more in tune with the business “is to get better tooling, craft, and experience with applying these machines in ways that first and foremost is transparent, and secondly understandable in some way, and ultimately something that is achieving an outcome that is business oriented or community oriented.”

AI may be able to deliver fine-grained results based on logic beyond the capacity of human brains, but this may actually “run counter to what the business needs to be doing strategically,” says Butters. “Because you can’t have the visibility, you get unintended consequences, which can lead to complete disparities and equity in the application of processes to your customer base.” Importantly, “there needs to be a feedback loop to ensure solutions you’re implementing are resonating with your customers, and they’re enjoying the experience as much as you’re enjoying creating the experience,” according to panelist Robert Magno, solutions architect with Run:AI.

Other experts across the industry also voice concern that AI is being pushed too hard in ways in may not be needed. “AI is not the solution to every business problem,” says Pieter Buteneers, director of engineering in machine learning and AI at Sinch. “It sounds sexy, but there are going to be times when it’s better to lean into how to best address customer needs rather than blindly investing in new technology.”

While AI has the potential to make business processes more efficient and affordable, “at the end of the day, it is still a machine,” Buteneers says. “AI lacks human emotion and common sense, so it can make certain mistakes that humans, instinctively, would not. AI can be easily fooled in certain ways that humans would spot from a mile away. For those who worry that AI will replace human jobs, we invariably need people working alongside AI bots to keep them in check and maintain a human touch in business.”  

AI initiatives “must be aligned with the company’s operational needs and workflows to ensure a high level of adoption,” agrees Sameer Khanna, senior vice president of engineering at Pager. “Identifying real world problems with user feedback is essential. Once the product is rolled out, there must be a continuous effort to engage users, monitor performance and improve solutions over time.”

There are areas worth exploring with AI, however. For instance, “AI can reach and even surpass human performance in strictly defined tasks such as image recognition and language understanding,” Buteneers says. “Harnessing the power of natural language processing enables AI systems to understand, write and speak languages like humans do. This offers tremendous benefits for businesses — deploying an NLP-equipped chatbot or voicebot to complement the work of live service agents, for example, frees up those live agents to respond to complicated inquiries that require a more human approach.”   

Buteneers notes that “breakthroughs in NLP are making an enormous difference in how AI understands input from humans. I’ve helped design chatbots that can now understand 100+ languages at once, with AI assistants that can search for answers within any given body of text. AI can even make live customer service agents more effective by reading along during a conversation and offering them suggested responses based on previous conversations, customer context or from a larger knowledge base. Different algorithms in the NLP field can identify and analyze a message that may be fraudulent, which can allow organizations to weed out any spam messages before they get sent to consumers. The applications of NLP can provide countless benefits to any business: it can help save time and money, enhance the customer experience, and automate processes.”  

Still, human oversight is essential to ensuring these solutions serve customers. “Reviewing AI results should be the standard design process of algorithms — it’s ignorant to believe that once you’ve set up your model, your job is done,” Buteneers says. 

Khanna relates how his own company’s ideas for AI projects “come primarily from collaboration between our data scientists and internal and client business stakeholders.” This partnership “generates well-defined and feasible AI projects that are grounded in business realities,” he adds. “Our data engineers, data scientists, and machine learning engineers then implement these projects using open-source technologies and proprietary products from cloud providers.”