Amid continued experimentation, increased investment, and early enthusiasm, there is a growing need to demonstrate the value of generative artificial intelligence initiatives, according to Deloitte.
The third quarterly edition of the Deloitte AI Institute's "State of Generative AI in the Enterprise" report revealed that data and risk remain key challenges to scaling Generative AI.
While surveyed organisations are beginning to scale past proof-of-concept, Deloitte found that 41% have struggled to define and measure the exact impacts of their GenAI efforts and only 16% have produced regular reports for the chief financial officer about the value being created with GenAI.
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.
According to Deloitte, to demonstrate value, organisations are using specific KPIs for evaluating GenAI performance (48%); building a framework for evaluating GenAI investments (38%); and tracking changes in employee productivity (38%).
"As promising experiments and use cases begin to pay off, it’s clear that we have arrived at a pivotal moment for Generative AI, balancing leaders’ high expectations with challenges such as data quality, investment costs, effective measurement and an evolving regulatory landscape," says Jim Rowan, applied AI leader and principal at Deloitte Consulting LLP.
"Our Q3 survey has revealed that now more than ever, change management and deep organisational integration are critical to overcoming barriers, unlocking value and building for the future of GenAI."
The survey also found that executives are zeroed in on data lifecycle management as a foundation for GenAI deployments, as data is taking center stage for AI-savvy leaders, with 75% of organisations increasing their technology investments around data management due to GenAI.
However, as enterprises look to scale, unforeseen roadblocks were exposed— with data-related issues causing 55% of surveyed organisations to avoid certain GenAI use cases. Solving for data deficiencies has emerged as a crucial step in addressing the GenAI-specific demands of data architectures.
To modernise their data-related capabilities, organisations are enhancing data security (54%); improving data quality practices (48%); and updating data governance frameworks and/or developing new data policies (45%).
