Two-thirds of respondents (67%) to a newly released Deloitte survey said their organizations are increasing investment in generative AI due to its “strong value to date"—though challenges such as data quality, effective measurement of Gen-AI’s benefits, investment costs, and an evolving regulatory landscape are creating barriers in a period that one “digital evangelist,” Vala Afshar of Salesforce, called “the year of AI implementation.”
“Our Q3 survey has revealed that now more than ever, change management and deep organizational integration are critical to overcoming barriers, unlocking value, and building for the future of Gen-AI,” Jim Rowan, Deloitte Consulting's applied AI leader and principal, said of the group’s “State of Generative AI in the Enterprise: Now Decides Next,” its survey of 2,770 director- to C-suite-level respondents across 14 countries.
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Survey respondents said that while their senior executives and board members are still intrigued by Gen-AI, there are signs of enthusiasm beginning to wane as the “new technology” shine wears off.
Interest remains “high” or “very high” among most senior executives (63%) and boards (53%), according to the Deloitte survey, the results from which were released on Aug. 20.
However, those numbers have declined since the group’s first-quarter 2024 survey, dropping 11 percentage points and eight percentage points, respectively. While selecting and quickly scaling the Gen-AI projects with the most potential to create value is the goal, many of those efforts are still at the pilot or proof-of-concept stage, with a large majority of respondents (68%) saying their organization has moved 30% or fewer of their Gen-AI experiments fully into production.
It's (mostly) about data
Three-quarters (75%) of organizations are increasing their technology investments around data management due to Gen-AI, according to the Deloitte report. However, unforeseen roadblocks have been exposed—with data-related issues causing 55% of the surveyed organizations to avoid certain Gen-AI use cases.
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Solving data deficiencies has emerged as a crucial step in addressing the Gen-AI-specific demands of data architectures. To modernize their data-related capabilities, organizations are enhancing data security (54%); improving data quality practices (48%); and updating data governance frameworks and/or developing new data policies (45%).
Struggles with trust, shifting regulatory landscape
Although respondents recognized that managing Gen-AI risk is critical, three of the top four reported barriers to successful deployment are risk-related, including worries about regulatory compliance (36%); difficulty managing risks (30%); and lack of a governance model (29%). Likely driving these concerns are risks specific to Gen-AI, like model bias, hallucinations, novel privacy concerns, trust, and protecting new attack surfaces, according to the survey.
To help build trust and ensure responsible use, organizations are working to build new guardrails and oversight capabilities, according to the Deloitte report. The top actions organizations are taking include establishing a governance framework for using Gen-AI tools and applications (51%); monitoring regulatory requirements and ensuring compliance (49%); and conducting internal audits/testing on Gen-AI tools and applications (43%).
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Said Costi Perricos, Gen-AI leader of Deloitte Global: “We are seeing continued enthusiasm for Gen-AI across organizations, and leaders are deriving the most value from the technology by deeply embedding it into critical business functions and processes.”
He added, “Our research indicates that the top benefits of Gen-AI are extending beyond improved efficiency, productivity and cost reduction, with more than half pointing to increased innovation, improved products and services, enhanced customer relationships and other types of value.”
