The Mainstream Adoption of Generative AI
In 2023, generative AI became widely recognized as companies sought inspiration from the success of ChatGPT. This momentum has carried into 2024, as companies now aim to fully harness the potential of generative AI in their workflows.
Concerns and Barriers to Adoption
However, despite the growing interest in generative AI, a recent survey conducted by Forrester Consulting reveals that many organizations still harbor concerns and face barriers to its adoption. Some of these roadblocks include the risk of hallucinations, which have been well-documented, and the challenges of operationalizing foundation models for planned use cases. These issues often leave organizations stuck in the exploration or experimentation stage, hindering their progress in embracing generative AI.
Despite these challenges, the survey also highlights the widespread recognition of the transformative potential of generative AI across industries. In fact, 83% of the respondents confirmed that they are either exploring or experimenting with generative AI. Moreover, over 60% consider it critically or highly important for their business strategy, leading them to increase investment in data/AI initiatives by up to 10% in the next 12 months.
“This echoes a sentiment of exploration and curiosity, where organizations are captivated by the breadth of potential applications, anticipating an inclusive embrace of the diversity of its transformative capabilities over the next two years,” the survey noted.
The survey further reveals that organizations have already identified multiple potential applications for generative AI. These include enhancing customer experiences, product development, self-service data analytics, and knowledge management.
However, alongside these promising findings, the survey also uncovers various roadblocks faced by organizations in implementing generative AI. These include concerns about violating data protection and privacy laws, as well as the challenge of developing the necessary skills and governance to effectively manage generative AI. Additionally, biases and hallucinations impacting the quality of generative AI outputs are also a significant concern raised by 50% of the respondents.
