Artificial intelligence deployments are no longer a risky bet for enterprises, but a "must-have" investment. Gartner has reported that 59% of organizations have deployed AI, with the average company expected to have 10 AI projects by the end of this year. It's obvious when a company is leading its industry or severely lagging, and both scenarios have clearly defined paths. But what about organizations whose AI is somewhere in the middle? What's holding them back, and how can they become a leader?

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Our first article in this two-part series addressed three areas of software development – Requirement Analysis, Design, and Engineering – that have already been influenced by AI to "automate automation" or accelerate maturity. As we continue to explore how the integration of engineering processes and AI will help shape future systems, this article will focus on three additional development aspects: Review/Testing, Operations, and Collaboration. We have come a long way since the days of traditional quality assurance, with new tools at our disposal including automated test environments, automated testing and "automated automation." As hypothesis-driven and test-driven development has enabled experimentation, it is imperative to left-shift quality control. In a high-performing enterprise, the onus lies on the developer to ensure all developed code causes no unexpected disruptions.

Artificial intelligence and thinking computers are a prominent plot device for Hollywood. Movies such as 2001: A Space Odyssey, Alien, and Terminator come to mind, as well as current day examples such as I, Robot, Ex Machina, and Transcendence. These pop culture examples underscore the fears and challenges we face as we attempt to program natural behavior and interact with intelligent technology. I think about this whenever I read the growing list of examples of how bots are becoming more pervasive, especially after seeing an article that proclaimed "the bots are taking over." In reality, bots do not carry such hyperbolic doomsday results.