Artificial intelligence will humanize recommendation engines, improve the accuracy of logistics engines, and represent a monumental change in the friendliness of chatbot engines. Learning new languages (Duolingo), finding new dinner plans (Replika) and making photography exciting again (Prisma) is how our business partners will be introduced to the potential of artificial intelligence.\nHow we plan for AI\nIf I asked you how to build a house, you\u2019d have a series of steps in mind. When asked how to validate a company\u2019s technology security perimeter, other action steps come immediately to the forefront. And when booking a vacation to Brazil, a clear approach to get you on the beach fast rushes to the mind.\nWe\u2019re of course not talking about building houses, creating security resilience, or booking vacations. We\u2019re talking about how to introduce business leaders, scientists and medical professionals to the power of artificial intelligence. So where do we start? What\u2019s our first step?\nThree steps toward AI enlightenment \nWe start with a framework for all intelligence agents. Artificial intelligence can be separated into two categories: (1) thought processes and reasoning and (2) behavior. Whether you lean more toward the mathematics and engineering side (rationalist) or closer to the human-centered approach (behavior), the heart of AI is trying to understand how we think.\nThe first step: Decide which of the four categories of artificial intelligence the enterprise will explore.\n\nThinking humanly: systems that think like humans\nActing humanly: systems that act like humans\nThinking rationally: systems that think rationally\nActing rationally: systems that act rationally\n\nThe second step: determine the intent of our artificial intelligence initiative.\nThinking humanly (cognitive modeling) blends artificial intelligence with models\u2014as in the case of neurophysiological experiments. Actual experiments in the cognitive sciences depend on human or animal observations and investigations. Acting humanly (Turning Test) attempts to establish a line between non-intelligence and satisfactory intelligence. Thinking rationally captures \u201cright thinking\u201d in computer language. Coding logic is fraught with challenges, since informal knowledge doesn\u2019t translate well to formal notation. Acting rationally is about acting. Agents perform acts, and \u201crational agents\u201d can autonomously maneuver, adapt to change and evolve (learned intelligence).\nThe third step: identify the capabilities required.\nThinking humanly capabilities:\n\nObservation\nMatching human behavior\nReasoning approach to solving problems\nSolve problems\nComputer models to simulate the human mind\n\nActing humanly capabilities:\n\nNatural language processing\nAutomated reasoning\nMachine learning\nKnowledge representation\nComputer vision and robotics\n\nThinking rationally capabilities:\n\nCodify thinking\nPattern argument structures\nCodify facts and logic (knowledge)\nSolve problems in practice (not principle)\nSolve problems with logical notation\n\nActing rationally:\n\nThought inferences\nAdapt to change (agents, chatbots)\nAnalyze multiple correct outcomes\nOperate autonomously\nCreate and pursue objectives\n\nStep beyond\nArtificial intelligence, since the mid-1940s, has moved across the plane of learning from philosophy to control theory. The philosophy of logic and reason established the foundations of learning, language and rationality. Mathematics formally represented computations and probabilities. Psychology illuminated the phenomena of motion and psychophysics (experimental techniques). Linguistics studied morphology, syntax, phonetics and semantics. Neuroscience poked at the function of the nervous system and brain. Control theory combines the complexities of dynamic systems and how behavior is modified by feedback.\nNavigation, neural networks, gene expression, climate modeling and production theory all stem from control systems engineering.\nIt\u2019s easy to become tangled up in the possibilities of artificial intelligence. First, we must decide which of the four categories of artificial intelligence we will explore. Second, we must determine the intent of our artificial intelligence initiative. Third, we must identify the capabilities required. In sum: Start with a plan and clarify your first three steps for your organization to realize the potential of artificial intelligence.