AI technology is crucial as it enables human capabilities – understanding, reasoning, planning, communication, and perception – to be undertaken by software increasingly effectively, efficiently and at low cost. The past few years have been an exciting period for artificial intelligence. We used to think of it as science fiction, yet today, AI has become an everyday component of life. Here we have decided to focus on a trend that matters most urgently to IT leaders—concrete AI insights for the team and business. The potential impacts of AI are wide-ranging—as are the related forecasts, on everything from sentient to generative and responsible AI, to collaboration and automation. Here is a list of the top five AI predictions for 2023 from IT leaders.
Automated content and commerce
With Generative AI producing, in addition to software code, a variety of novel content such as images, video, music, speech and text, Gartner predicts that by 2025, 30% of outbound messages from large organizations will be synthetically generated. IDC predicts that by 2026, the massive (>trillion parameter) foundation models behind this automated content development “will become standard industry utilities provided only by the largest vendors.”
Low Code AI is making strides in the industry
The AI model development process is complex, laborious, and iterative. And building a good set of models requires days and thousands of experiments. Low code AI/data science platforms have changed all that. The drag ‘n’ drop interface provided by low code data science platforms helps create experiments faster.
Proper model package definition will improve the operational benefits of AI
Productionalizing AI includes directly codifying, during the model creation process, how and what to monitor in the model once it’s deployed. Setting an expectation that no model is properly built until the complete monitoring process is specified will produce many benefits, not the least of which is smoother artificial intelligence operations.
Distributed Model Training is at the Core of AI modeling
Data science teams need to experiment with thousands of models. And AI models can get pretty complex, with millions of parameters. With low code at the helm, the ability to work on multiple experiments simultaneously increases multifold. But to realize those thousands of experiments, data science teams need a cost-effective computing system that scales up per the requirements. Training these complex, memory-intensive experiments using conventional methods is a big challenge.
There will be a handful of enterprise-class AI cloud services
Clearly, not every company that wants to safely deploy AI has the resources to do so. The software and tools required can simply be too complex or too costly to pull together in piece-parts. As a result, only about a quarter of companies have AI systems in widespread production. To solve this challenge and address a gigantic market opportunity, I predict that 2023 will see the emergence of a handful of enterprise-class AI cloud services.
