There are many tools coming up in the market that are branded as artificial intelligence (AI) or expert tools. The customer support market is especially proliferated with robotic process automation (RPA), chatbots and automation technology. Many companies are at a loss on how to evaluate them and know when to use what. Their focus is on return on investment (ROI), and these tools don't specifically cater to the customer's needs. Below, I discuss how to think beyond buzzwords and explore the differences in technology to help business leaders pick the right solution for their customer support needs.

As artificial intelligence (AI) matures and new applications boom amid a transition to Industry 4.0, we are beginning to accept that machines can help us make decisions more effectively and efficiently. But, at present, we don't always have a clear insight into how or why a model made those decisions – this is'blackbox AI'. In light of alleged bias in AI models in applications across recruitment, loan decisions, and healthcare applications, the ability to effectively explain the workings of decisions made by AI model has become imperative for the technology's further development and adoption. In December last year, the UK's Information Commissioner's Office (ICO) began moving to ensure businesses and other organizations are required to explain decisions made by AI by law, or face multimillion-dollar fines if unable. Explainable AI is the concept of being able to describe the procedures, services, and outcomes delivered or assisted by AI when that information is required, such as in the case of accusations of bias.

Let's say that I find it curious how Spotify recommended a Justin Bieber song to me, a 40-something non-Belieber. That doesn't necessarily mean that Spotify's engineers must ensure that their algorithms are transparent and comprehensible to me; I might find the recommendation a tad off-target, but the consequences are decidedly minimal. This is a fundamental litmus test for explainable AI – that is, machine learning algorithms and other artificial intelligence systems that produce outcomes that humans can readily understand and track backwards to the origins. Conversely, relatively low-stakes AI systems might be just fine with the black box model, where we don't understand (and can't readily figure out) the results. "If algorithm results are low-impact enough, like the songs recommended by a music service, society probably doesn't need regulators plumbing the depths of how those recommendations are made," says Dave Costenaro, head of artificial intelligence R&D at Jane.ai. I can live with an app's misunderstanding of my musical tastes.

If you are a CIO, VP of IT operations or some other type of IT leader, you are under constant pressure to ensure that IT systems operate at maximum efficiency. Systems must meet increasing service-level expectations in terms of performance, availability and security. In fact, you're probably already anticipating that this challenge is only going to get bigger. After all, you must deal with skills shortages and are tasked with supporting a growing number of IT initiatives such as cloud migrations, digital transformation, M&A integrations and other strategic projects. To address these challenges, you need to think about leveraging "next practices," not best practices.

Everyone in, and serving, manufacturing businesses has encountered terms such as Industry 4.0, Digital Transformation, and Smart Factory. Small compute footprints, low-cost, high-availability communication systems, high-capacity–low-cost memory, rapidly evolving sensor technology, and new time-series data structures supporting real-time analytics are all conspiring to empower industrial operators to transform their businesses from "tribal knowledge systems" to data-driven operations. Twenty years ago -- in the dawn of the Internet age, at a converting operation then part of International Paper, we relied on Jake. Jake was a medium age, good natured man who had worked the plant for seventeen years -- since high school. Jake, unusual in that he was able to stand the middle ground between Union and Management, epitomized "tribal knowledge."

This was the most often asked question at this year's Blackhat Conference 2016, especially for anyone with even a scent of Machine Learning algorithms in their product. With the biggest issue facing the SOC being the inability to sift through 1,000's of alerts per day due to a shortage in employees. It doesn't take a genius to get to the question of what it's going to cost me in man hours to sift through a new mouse-traps false positives. How many more Junior Analyst do I need to add to my team to look over my box? In the last five years I've watched more and more SOCs being built on the backs of Junior Security Analyst.