While some businesses still struggle to gain business value from their AI projects, there’s no denying the huge increase in AI adoption that shows no signs of slowing down. In fact, the headline of a recent HBR article proclaims, “AI Adoption Skyrocketed Over the Last 18 Months.”
If recent analyst predictions are to be believed, we can expect additional AI maturation and adoption for 2022. Below, we’ve outlined some key AI trends that are expected to accelerate in the year ahead.
A Shift in Focus from Enterprise AI to Everyday AI
Successfully scaling, systemizing, and employing robust data projects and processes at every level of the company—or “everyday AI”—will become increasingly essential for businesses to thrive in the digital future. To do this right, organizations need to shift focus from viewing AI as a technology for a particular project or set of projects and instead scale it out at a level that will sustain the business in the years ahead.
Code-Free Tooling Empowering Business Users
As code-free tooling evolves, more people will have ongoing access to data for decision-making at every level of the business. Many platforms provide code-free support for ingesting data, processing dates and times, clearing complex text fields, combining datasets, and creating new machine learning models. This means that non-data scientists are already significantly contributing to data science projects, and this work will only become more valuable as code-free tooling becomes more prevalent.
Companies Will Become Better at Deploying Models
Emboldened by the success of existing data science and AI initiatives, many organizations are experimenting with implementations in other parts of the business. Because the next set of processes to be automated by data science are less understood, new risks will inevitably emerge. However, it’s imperative that companies proceed, even with potential risks, as this experimentation is key to ushering in the future of new data science and AI implementations.
Machine Learning Will Be Applied to Enterprise IT Operations and IT Service Issues
As data science becomes more operationalized and democratized, IT will begin deploying machine learning models to solve enterprise IT operations and improve IT service. This will make IT a key stakeholder in the business value of machine learning and also support additional applications of the technology.
Evolving Regulatory Landscape
AI’s evolution and maturation invariably mean changes to how the technology is regulated and governed—check out this previous APEX of Innovation post for more on how this might shake out.
You can read more about the above AI trends in this Information Age article.