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2018 was a breakout year for AI. Virtually every vendor has or is working on an AI story, and businesses are looking for the easiest ways to embrace and benefit from the technology as soon as possible to maintain their competitive edge. But many would argue that 2018 consisted more of hype than reality.

In 2019, expect that line between hype and reality to blur as organizations shift to real-world usage and applicability. So, what else is to come in 2019? First and foremost, with AI having the potential to be the most pervasive technology in our lifetime, organizations will prioritize solutions that incorporate AI, whether as an embedded feature of a product or solution, or by leveraging a specialized infrastructure to support a custom use case that incorporates model creation, training, tuning, inference, etc.

Now, one of the key areas to AI being successful is leveraging mountains of high-quality data. But many organizations simply don't have a grip on their own data, never mind enough of it to support their AI goals. This brings me to my second prediction. The availability of massive data sets to the public will likely explode. Now, don't get me wrong, there are already entities that provide free data sets to the public for training.

Think Kaggle, for example. But I believe companies will begin forming based solely on the promise of offering clean and high-quality data sets to support AI initiatives. Now, whether you're already leveraging AI or planning to over the next year, a big issue emerging now is around AI ethics.

Where should AI be leveraged? Should there be limits imposed on AI systems? How can an organization or a person trust a result or insight from AI? Who's policing the use of AI? To that end, I predict that trust and transparency will be a growing focal point throughout the year. But to get there, the focus should first be on explainability, i.e. how did AI come up with its answer, conclusion, or prediction?

Now, this is especially challenging when leveraging deep neural networks with multiple layers that rely on one another. In 2019, organizations must be accountable for their actions based on the guidance from AI and, therefore, the emergence of tools and technologies to help with transparency into how AI decisions are made will be essential.

Now, going hand-in-hand with explainability is AI bias. AI bias will start being viewed as less of a negative term and more of a neutral term since I'd argue the point of AI is to detect bias. Organizations will look to uncover flaws in their own organizations to become better both internally, so think business practices, data workflows, processes, and data sets, and externally in creating the right high-quality products for improved customer satisfaction.

My final prediction is around the rise and, to some extent, the fall of the data scientist. Throughout 2018 we've heard a lot about skills gaps when it comes to AI, especially around the shortage of data scientists. In 2019, the importance of having a data scientist will be diminished as organizations will turn to AI with or without a data scientist.

This will really put an emphasis on AI technology providers to create simplified tools and solutions, as well as professional services to empower generalists, reduce time to value, and truly democratize AI. So, everyone buckle up. The AI market is just getting started, and in 2019 should be filled with exciting technology announcements and amazing new use cases.