Generative AI, like predictive AI earlier than it, has rightly seized the eye of enterprise executives. The know-how has the potential so as to add trillions of {dollars} to annual world financial exercise, and its adoption for enterprise purposes is anticipated to enhance the highest or backside strains—or each—at many organizations.
Whereas generative AI gives a powerful and highly effective new set of capabilities, its enterprise worth just isn’t a given. Whereas some highly effective foundational fashions are open to public use, these don’t function a differentiator for these trying to get forward of the competitors and unlock AI’s full potential. To realize these benefits, organizations should look to reinforce AI fashions with their very own knowledge to create distinctive enterprise insights and alternatives.
Making ready a corporation’s knowledge for AI, nonetheless, unlocks a brand new set of challenges and alternatives. This MIT Know-how Assessment Insights survey report investigates whether or not firms’ knowledge foundations are able to garner advantages from generative AI, in addition to the challenges of constructing the required knowledge infrastructure for this know-how. In doing so, it attracts on insights from a survey of 300 C-suite executives and senior know-how leaders, as effectively on in-depth interviews with 4 main consultants.
Its key findings embrace the next:
Knowledge integration is the main precedence for AI readiness. In our survey, 82% of C-suite and different senior executives agree that “scaling AI or generative AI use instances to create enterprise worth is a prime precedence for our group.” The number-one problem in attaining that AI readiness, survey respondents say, is knowledge integration and pipelines (45%). Requested about difficult facets of information integration, respondents named 4: managing knowledge quantity, shifting knowledge from on-premises to the cloud, enabling real-time entry, and managing adjustments to knowledge.
Executives are laser-focused on knowledge administration challenges—and lasting options. Amongst survey respondents, 83% say that their “group has recognized quite a few sources of information that we should deliver collectively so as to allow our AI initiatives.” Although data-dependent applied sciences of current many years drove knowledge integration and aggregation applications, these had been usually tailor-made to particular use instances. Now, nonetheless, firms are on the lookout for one thing extra scalable and use-case agnostic: 82% of respondents are prioritizing options “that can proceed to work sooner or later, no matter different adjustments to our knowledge technique and companions.”
Knowledge governance and safety is a prime concern for regulated sectors. Knowledge governance and safety issues are the second most typical knowledge readiness problem (cited by 44% of respondents). Respondents from extremely regulated sectors had been two to a few instances extra prone to cite knowledge governance and safety as a priority, and chief knowledge officers (CDOs) say it is a problem at twice the speed of their C-suite friends. And our consultants agree: Knowledge governance and safety must be addressed from the start of any AI technique to make sure knowledge is used and accessed correctly.
This content material was produced by Insights, the customized content material arm of MIT Know-how Assessment. It was not written by MIT Know-how Assessment’s editorial workers.
Obtain the complete report.
