AI is Ready for Business, but are Businesses Ready for AI?

Artificial intelligence is seen as a compelling solution that can vastly expand business intelligence. But are businesses really ready for AI and the infrastructure it requires? Not quite, a study of 600 executives published by MIT Technology Review suggests.

“Addressing shortcomings in companies’ data management and infrastructure, as well as internal structural and process rigidities and talent deficits, loom large among challenges,” the survey’s authors state. Seventy-two percent of executives say data issues may hold back their AI progress. Improving processing speeds, governance, and quality of data, as well as its sufficiency for models, are the main data imperatives to ensure AI can be scaled, they report.

A relatively small number of organizations, 14%, can be considered on the way to becoming “AI-driven.” which the survey’s authors define as “AI and machine learning underpinning almost everything the enterprise does—by 2025.”

See also: State of AI Report Notes Continued Challenges

At the same time, companies view wider AI adoption as “mission-critical for their future.” Executives report they plan a major expansion of use cases in all core functions in the next three years. Well over half expect AI use to be widespread or critical in their IT, finance, product development, marketing, sales, and other functions within the next three years.

The surveyed companies’ data and AI strategies are closely interlinked, the survey also finds. Over three-quarters (78%) say that “scaling AI and machine learning use cases to create business value” is their top priority for enterprise data strategy over the next three years.

Companies also plan sizeable increases in AI investments over the next three years. Spending on data security over the next three years will rise by 59%, on data governance by 52%, on new data and AI platforms by 40%, and on existing platforms by 42%. Investment growth intentions are strongest in the financial services industry.

Democratization is a vital element of AI development. “The greater the number of employees
in an organization who can configure and improve AI algorithms, the more AI-based innovations are likely to materialize,” the survey’s authors state. They also urge greater openness with AI development, that “future success in innovating with AI will rely at least in part on the data, insights, and tools they are able to source externally. Data technology that favors open standards and open data formats is well placed to facilitate such collaboration.”