Data, Risk Remain Key Challenges To Scaling Generative AI: Survey

Generative artificial intelligence adoption has reached a critical phase, with two-thirds of senior-level executives saying their organisation is increasing its investment in gen AI due to its strong value to date, according to a report by the Deloitte AI Institute. However, despite increasing expectations for gen AI impact, data, scaling, and risk challenges are limiting options and tempering leadership enthusiasm.

Deloitte's State of Generative AI in the Enterprise report was based on a survey of 2,770 director-to-C-suite-level respondents across 14 countries.

Respondents said that while their senior executives and board members are still intrigued by gen AI, enthusiasm is beginning to wane. Interest remains “high” or “very high” among most senior executives (63%) and boards (53%); however, those numbers have declined since Q1 2024, dropping 11% and 8%, respectively.

Around 75% of surveyed organisations are increasing their technology investments around data management due to gen AI. However, as enterprises look to scale, data-related issues are causing 55% of organisations to avoid certain gen AI use cases. To modernise their data-related capabilities, organisations are enhancing data security (54%), improving data quality practices (48%), updating data governance frameworks, and/or developing new data policies (45%).

Although respondents recognised that managing gen AI risk is critical, three of the top four reported barriers to successful gen AI deployment are risk-related, including worries about regulatory compliance (36%), difficulty managing risks (30%), and lack of a governance model (29%).

These concerns are likely driven by risks specific to gen AI, like model bias, hallucinations, novel privacy concerns, trust, and protecting new attack surfaces. To help build trust and ensure responsible use, organisations are working to build new guardrails and oversight capabilities. The top actions organisations 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%). 

While selecting and quickly scaling the gen AI projects with the most potential to create value is the enterprise goal, many gen AI efforts are still at the pilot or proof-of-concept stage, with 68% of respondents saying their organisation has moved 30% or fewer of their gen AI experiments fully into production.

Around 41% of organisations have struggled to define and measure the exact impacts of their gen AI efforts. Only 16% have produced regular reports for the chief financial officer about the value created with gen AI.

According to the report, as applications and use cases mature, leaders will be less inclined to invest based solely on lofty visions and the fear of missing out—making measurement a critical factor in maintaining interest and support from the C-suite and boardroom.

To demonstrate value, organisations are using specific key performance indicators for evaluating gen AI (48%), building a framework for evaluating gen AI investments (38%), and tracking changes in employee productivity (38%).