As artificial intelligence (AI) makes its way into the business world, it helps to view this process as onboarding a new and highly qualified team member. There is typically an initial training period where the individual learns their new role as well as how the business works and its values. Organizations have processes and people in place to impart this kind of knowledge on new colleagues and help them learn the ropes, so they can communicate and collaborate effectively with others.\nTo a certain extent, you can apply the same principles and steps to embedding a new AI system.\nStructuring learning\nMost new, highly skilled employees are anxious to learn what they need to know to hit the ground running. But to do so effectively, they first need to pick up the \u2018company lingo\u2019 \u2013 the unique language every organization has developed internally over time.\nSimilarly, a company needs to ensure that AI systems start with basic principles, then progressively build skills from set taxonomical structures. In this phase, the organizations that have the best data available to \u2018teach\u2019 their AI will end up having the most capable AI systems.\nTake Google \u2013 it released a dataset that helps companies teach their AI systems to understand how people speak. To create the best dataset, Google recorded 65,000 clips of thousands of different people speaking. This scale of training data has enabled Google\u2019s voice recognition to reach 95 percent accuracy.\nEnabling collaboration\nA key element of the learning process is explainable decision making \u2013 both on the part of established employees and new ones. As new coworkers onboard, having team members explain the decision-making process around certain aspects of business operations is essential. Likewise, new workers will have to explain their decisions as they bring about new ideas that challenge current thinking.\nPeople expect to be able to understand why someone (or something) else acts and decides the way they do, especially if those actions and decisions affect us directly. This transparency is key to successful collaboration. As AI promises to empower people and be an effective co-worker, advisor and helper, organizations will need to ensure that their AI systems are able to explain their actions and decision-making process.\nThis drive to understand AI decisions has led to several new regulations and advancements in technology. For instance, the new European Union\u2019s General Data Protection Regulations give individuals a \u201cright to explanation\u201d for decisions made by AI and other algorithms.\nIn the tech realm, NVIDIA, which has an AI-infused self-driving car platform called Drive PX that can \u201cteach\u201d itself to drive, recently added a capability to the platform that allows it to visually explain its driving style by displaying a video of a recently driven streetscape, highlighting areas that it gives the most weight to during navigation. This creates transparency, which enables NVIDIA to build trust between its AI systems and customers.\nImparting values\nEach organization has values that ground it. Having them, adhering to them, and defending them, has never been more relevant to business success than today. My colleagues at Fjord recently proclaimed that we\u2019re witnessing the rise of an Ethics Economy. Values live primarily in the actions and decisions of employees. Now more and more of an organization\u2019s decisions are being made by AI systems, so these systems need to \u2018live\u2019 these values too.\nThis is especially important as advances in technology create opportunities, but also fear and resentment. Imagine what would happen if an AI-powered mortgage lender denies a loan to a qualified prospective home buyer or if an AI-guided shelf-stocking robot collides with a worker in a warehouse.\nUltimately, AI represents its owner in every action it takes. It is their responsibility that AI algorithms act in a responsible way, as it\u2019s the organization that will be made liable for every misstep. The importance of \u2018Responsible AI\u2019 cannot be stressed enough and I will address this in more detail in one of my next posts.\nLooking at the similarities of onboarding a skilled new employee and integrating an AI system will help us understand the crucial steps we need to take to embed a new level of intelligence at the core of business.