This decade, the simulated “intelligence” of computerized systems today will evolve into digital assistants that possess some autonomous intellect.
As we move into the next decade, enterprise artificial intelligence (AI) is slated to take a turn for the “intellectual”—with intelligent digital assistants, process and product innovation as well as operational systems leading the way.
1. Moving to “Intellectual Digital Assistants.”
To meet growing enterprise user expectations, AI Digital Assistants will evolve into “Intellectual Digital Assistants.” Users no longer are satisfied with just telling Digital Assistants what to do and having them automatically execute certain tasks or basic configurations. 2020 will be the year when these digital assistants, using AI and machine learning (ML), start to understand the context of what users are doing, recommend potential next steps (based on completed actions), identify mistakes and auto-correct inputs, and start to engage with users in dynamic, on-the-fly conversations.
2. AI helps define a “new normal”
In 2020, AI and machine learning platforms will start to challenge conventional thinking when it comes to enterprise business processes and expected outcomes. In other words, these systems will re-define our default assumptions about what is “normal.” This will make business process re-engineering and resource training more efficient. When examining supply chain processes, for example, AI platforms have observed that default values—related to expected delivery dates and payment dates— typically are used only 4% of the time. Users almost always plug in their own values. Therefore, AI and machine learning systems will start enabling us to disregard default values, as we understand them today, and act more quickly through trust in our data. We will no longer be beholden to predefined rules, defaults, or assumptions.
3. Operationalizing AI
Industry-specific templates will make AI easier to use and deploy in 2020. In manufacturing, AI and machine learning systems, will take advantage of templated processes to help enterprises better manage their parts inventories, improve demand forecasting and supply chain efficiency, and improve quality control and time-to-delivery. In healthcare, organizations will leverage AI and machine learning to better integrate data that is segregated in application silos, exchange information with partners across the care continuum, and better use that data to respond to regulatory and compliance requirements. And, in retail, companies will use AI and ML to better predict demand patterns and shipment dates, based on defined rules, and improve their short- and long-term planning processes.
