The phenomenon of generative artificial intelligence (AI) has caught the attention of not only technology and business leaders but also the average individual. In fact, according to ISG Research, 85% of enterprises believe that investment in generative AI technology in the next 24 months is important or critical (from the ISG study: 2023 Future Workplace). This technology did not appear out of a vacuum; it leverages decades of work and is distinctly different from predecessor methods.
Historically, one of the first milestones of functional AI was its ability to recognize patterns via assimilating data into static algorithms. However, the use cases for this learn-only capability are narrow, largely because it requires intensive human management and training. Further advancements in machine learning and neural networks facilitated AI to learn and repeat tasks, enabling it to actively engage, interpret and replicate data inputs. The human role was less burdensome, enabling new business improvements and efficiencies.
Now, AI has a new, powerful capability: the ability to generate content.
While generative AI is likely to have a deep and lasting impact, there are many challenges to overcome – from navigating security, copyright data challenges, ethics and legal concerns to fine-tuning enterprise-grade use cases.
Our aim with this report is to look past the hype and conduct a comprehensive analysis of the current landscape for enterprise adoption of generative AI. It will be followed by further reports and snapshots of the landscape as it evolves. This study has two goals:
- To report our findings of top trends, use cases, solutions, industries and challenges related to adopting generative AI, and
- To share data-driven insights and actionable recommendations to assist businesses in achieving impactful transformations across diverse sectors.