Microsoft's 2019 data and AI tech immersion workshop demonstrated the vendor's strategy to democratize AI by providing a small group of about 30 journalists, industry analysts and other tech industry experts with hands-on experience in programming AI bots using the cognitive services in the Microsoft Azure public cloud platform. It provided meaningful glimpses of the future of AI in enterprise applications, from prebuilt AI models in Azure and machine teaching efforts of today to a future quantum coprocessor that will one day function as Azure's sidekick in a hybrid computing model.

The immersion approach of the workshop, which I attended, mimics the real-world experience of AI users who aren't data scientists. Most attendees did not own or have access to the massive data sets needed to complete the exercises on a variety of real-life AI use cases. The software giant overcame that obstacle by providing an open remote desktop connection app on our individual workstations, giving us access to the immersion environment and preloaded data sets in Azure.

The vendor also provided the credentials we needed to access the data and complete the exercises, which means the sign-on process was different from that of a typical user, but we were required to jump through a few more hoops along the way. Even though some attendees grumbled about the extra steps, they helped to make clear that using AI was becoming considerably more user-friendly.

Why AI's budding user-friendliness matters to enterprises Data democratization has been the key to reconfiguring companies to become data-driven enterprises. Democratizing AI will likewise become essential to unlocking every byte of data in ever-expanding data sets fast enough for responsive action to take place in real time. Thus, AI "is in virtually every existing technology, and creating entirely new categories," according to the report, "Gartner Top 10 Strategic Technology Trends for 2019." Furthermore, the AI megatrend is far from peaking, despite a shortage of data scientists. According to a LinkedIn study, U.S.-based businesses have been hard pressed to fill more than 150,000 data scientist jobs. The study concluded that demand for data scientists will be off the charts for the foreseeable future. The software industry is banking on more AI as the answer to its growing skills gap. In its 2019 trends report, Gartner also said smart automation and AI-driven development will resolve many talent shortage issues with technologies and best practices for embedding AI into applications and using AI-powered tools in the development process. Gartner predicted that, "by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader use by citizen data scientists. Between citizen data scientists and augmented analytics, data insights will be more broadly available across the business, including analysts, decision-makers and operational workers." The immersion experience at the Microsoft workshop, which took place in early spring, served to underscore these predictions. I was able to build an AI-based bot with the Virtual Assistant accelerator in a matter of minutes. The real-world scenario of an auto manufacturer seeking to make a bot to respond to driver voice commands and visual feedbacks made the exercise more meaningful. It was only one of the day's four lab exercises to be completed in two hours. Considering I hadn't worked with the technology before, the fact that I could successfully complete the exercises in that short a time drove home how realistic the goal of AI democratization truly is. The day before, the workshops centered on data, the key component in training and using AI. I was far more familiar with Azure SQL Data Warehouse, Azure Databricks, Azure Data Factory, and Microsoft Power BI. It was obvious that these technologies, too, are getting progressively easier for users with diverse skill sets to master.