
Inside The AI Hype Cycle: What’s Next For Enterprise AI?
The world is witnessing the unprecedented adoption of Artificial Intelligence (AI) technology at a lightning-fast pace. But for a full-scale acceptance by the business community, there are still significant hurdles to overcome.
According to Google’s Gemini AI, aggregate AI revenue is expected to reach an astonishing $85 billion by 2029, marking a substantial increase from the estimated $16 billion achieved in 2024. OpenAI alone reports that its annual recurring revenue is projected to hit a remarkable $12.5 billion this year. These numbers demonstrate just how quickly AI is being adopted.
However, it’s essential to put things into perspective: The full impact and return on investment for AI remains unknown. To gauge the effectiveness of AI adoption accurately, we must track how it’s being implemented across industries and pinpoint where significant growth is occurring.
Upon closer examination, we find that AI adoption in enterprise settings is nuanced. Many consumers are still discovering the benefits of AI, while others are worried about job losses. On the other side, many enterprises struggle with implementing AI safely due to concerns around data sovereignty, safety, and cost.
The reality is that some businesses have seen massive success from AI service deployment, whereas others have experienced disappointment or failure. The companies we’ve studied have reported difficulties in adopting AI owing to these specific challenges. Many are cautious about deploying AI because it could lead to mission-critical errors, result in lawsuits, or expose critical data.
As I explore more than 100 enterprise deployments, I’m finding that success is concentrated within specific sectors and use cases. These include the financial services/insurance, healthcare, and retail industries—the top three most prominent verticals we’ve identified in our research sample.
Furthermore, I have discovered that AI applications are predominantly focused on productivity enhancements, such as documentation automation and customer service that replaces human workers. According to my analysis of over 100 deployments, the primary benefits reported include:
* Operational efficiency (29 use cases)
* Customer service automation (22 use cases)
* Personalization (15 use cases)
* Employee copilots (15 use cases)
One of the most striking findings from our early data analysis is that customized, proprietary AI platforms have garnered a significant amount of interest. This highlights the complexity required to deploy AI in enterprises—with data sovereignty, safety, and security at the forefront.
Most businesses are more comfortable creating their own bespoke AI solutions and “owning” the platform, according to our research findings. Among enterprise use cases and sectors surveyed, Google Vertex, Microsoft Azure AI, and Amazon Bedrock emerged as the most popular platforms used by enterprises, with OpenAI’s ChatGPT being the most widely used AI model.
As I delve into the top three industries analyzed—financial services/insurance, healthcare, and retail—it becomes clear that proprietary AI platforms were the most popular choice.
Source: www.forbes.com