Industrialization of AI in the Enterprise

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We recently published for the wide audience that 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020.  Two megatrends – industrialization of AI platforms and democratization of AI – indicate that production workloads and high-scale AI applications are looming in the near future.  This means that AI will be reaching significantly more people via democratization of AI, and it requires industrialized platforms that accelerate and automate the AI development and implementations process to make AI accessible to the masses. Let’s take a deeper look at industrialization of AI on the Hype Cycle for Artificial Intelligence, 2020. The industrialization of AI platforms enables reusability, scalability and safety, which accelerate AI adoption and growth. If early AI adopters were mostly a grassroots and bottom up movement, the current AI wave is top-down. The C-suite are leading the charge in initiating AI projects now, with nearly 30% of the projects directed by CEOs. These projects aim to swiftly deliver value to the enterprise and catch up with the early adopters. That’s why the Machine Learning profile has already crossed into the Trough of Disillusionment: Simply mastering ML is not enough. The current wave expects AI tools to be on par with the enterprise production requirements and known processes, such as convenient AI development environments, automation of routine tasks, production stability and reliability. If a starting point for early adopters was their expertise with ML, industrialization of AI brings ML-based solutions in the form that does not require developing AI from scratch: Decision Intelligence, Intelligent Applications and AI Cloud Services are at the Peak of Inflated Expectations, followed by AI Marketplaces. Decision Intelligence, for example, indicates that companies want to use AI to make better decisions faster. Moreover, new trends – Generative AI, Small Data and Composite AI – signify that in addition to ML, enterprises put together multiple means of making a decision into an AI solution. Responsible AI and AI Governance are increasing in priority when AI is at an industrial scale. They signify the move from declarations and principles to operationalization of AI accountability at the individual, organizational and societal levels. They address trustworthiness of AI, which is the top AI challenge right now. Meeting AI is like meeting a new person: You don’t know whether you can rely on this stranger’s answers. It’ll take time to figure out what questions you can ask this newcomer and how much you can rely on those insights. With AI maturity, organizations will learn a lot and will make fewer mistakes, but they should remain humble and keep learning, as new challenges, like deepfakes and AI security, arise along with the AI progresses.   Follow Svetlana on Twitter @Sve_Sic

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