IBM reveals that just about half of the challenges related to AI adoption concentrate on data complexity (24%) and difficulty integrating and scaling projects (24%). While it could be expedient for marketers to “slap a GPT suffix on it and call it AI,” businesses striving to really and AI and ML face a two-headed challenge: first, it’s difficult and expensive, and second, since it’s difficult and expensive, it’s hard to come back by the “sandboxes” which are essential to enable experimentation and prove “green shoots” of value that may warrant further investment. Briefly, AI and ML are inaccessible.

Data, data, in all places

History shows that almost all business shifts at first seem difficult and expensive. Nevertheless, spending time and resources on these efforts has paid off for the innovators. Businesses discover recent assets, and use recent processes to attain recent goals—sometimes lofty, unexpected ones. The asset at the main focus of the AI craze is data.

The world is exploding with data. In accordance with a 2020 report by Seagate and IDC, through the next two years, enterprise data is projected to extend at a 42.2% annual growth rate. And yet, only 32% of that data is currently being put to work.

Effective data management—storing, labeling, cataloging, securing, connecting, and making queryable—has no shortage of challenges. Once those challenges are overcome, businesses might want to discover users not only technically proficient enough to access and leverage that data, but in addition capable of accomplish that in a comprehensive manner.

Businesses today find themselves tasking garden-variety analysts with targeted, hypothesis-driven work. The shorthand is encapsulated in a standard refrain: “I often have analysts pull down a subset of the information and run pivot tables on it.”

To avoid tunnel vision and use data more comprehensively, this hypothesis-driven evaluation is supplemented with business intelligence (BI), where data at scale is finessed into reports, dashboards, and visualizations. But even then, the dizzying scale of charts and graphs requires the person reviewing them to have a robust sense of what matters and what to search for—again, to be hypothesis-driven—with a view to make sense of the world. Human beings simply cannot otherwise handle the cognitive overload.

The moment is opportune for AI and ML. Ideally, that may mean plentiful teams of knowledge scientists, data engineers, and ML engineers that may deliver such solutions, at a price that folds neatly into IT budgets. Also ideally, businesses are ready with the fitting amount of technology; GPUs, compute, and orchestration infrastructure to construct and deploy AI and ML solutions at scale. But very similar to the business revolutions of days past, this isn’t the case.

Inaccessible solutions

The marketplace is offering a proliferation of solutions based on two approaches: adding much more intelligence and insights to existing BI tools; and making it increasingly easier to develop and deploy ML solutions, within the growing field of ML operations, or MLOps.