Whereas these prognostications could show true, at this time’s companies are discovering main hurdles after they search to graduate from pilots and experiments to enterprise-wide AI deployment. Simply 5.4% of US companies, for instance, had been utilizing AI to provide a services or products in 2024.
Transferring from preliminary forays into AI use, comparable to code era and customer support, to firm-wide integration is determined by strategic and organizational transitions in infrastructure, information governance, and provider ecosystems. As properly, organizations should weigh uncertainties about developments in AI efficiency and find out how to measure return on funding.
If organizations search to scale AI throughout the enterprise in coming years, nonetheless, now could be the time to behave. This report explores the present state of enterprise AI adoption and presents a playbook for crafting an AI technique, serving to enterprise leaders bridge the chasm between ambition and execution. Key findings embrace the next:
AI ambitions are substantial, however few have scaled past pilots. Absolutely 95% of corporations surveyed are already utilizing AI and 99% count on to sooner or later. However few organizations have graduated past pilot initiatives: 76% have deployed AI in only one to a few use circumstances. However as a result of half of corporations count on to totally deploy AI throughout all enterprise capabilities inside two years, this yr is essential to establishing foundations for enterprise-wide AI.
AI readiness spending is slated to rise considerably. Total, AI spending in 2022 and 2023 was modest or flat for many corporations, with just one in 4 rising their spending by greater than 1 / 4. That’s set to alter in 2024, with 9 in ten respondents anticipating to extend AI spending on information readiness (together with platform modernization, cloud migration, and information high quality) and in adjoining areas like technique, cultural change, and enterprise fashions. 4 in ten count on to extend spending by 10 to 24%, and one-third count on to extend spending by 25 to 49%.
Knowledge liquidity is among the most necessary attributes for AI deployment. The flexibility to seamlessly entry, mix, and analyze information from numerous sources permits corporations to extract related info and apply it successfully to particular enterprise situations. It additionally eliminates the necessity to sift by way of huge information repositories, as the information is already curated and tailor-made to the duty at hand.
Knowledge high quality is a serious limitation for AI deployment. Half of respondents cite information high quality as essentially the most limiting information problem in deployment. That is very true for bigger corporations with extra information and substantial investments in legacy IT infrastructure. Firms with revenues of over US $10 billion are the almost certainly to quote each information high quality and information infrastructure as limiters, suggesting that organizations presiding over bigger information repositories discover the issue considerably tougher.
Firms should not dashing into AI. Almost all organizations (98%) say they’re prepared to forgo being the primary to make use of AI if that ensures they ship it safely and securely. Governance, safety, and privateness are the most important brake on the pace of AI deployment, cited by 45% of respondents (and a full 65% of respondents from the most important corporations).
Obtain the complete report.
This content material was produced by Insights, the customized content material arm of MIT Know-how Evaluation. It was not written by MIT Know-how Evaluation’s editorial employees.