- August 20, 2024
- 4 min read
Deloitte Survey Reveals Enterprise Generative AI Production Deployment Challenges
Enterprises Embrace Generative AI, but Challenges Persist
The UBOS homepage is buzzing with discussions around a recent Deloitte survey that delves into the intricate landscape of generative AI agents for businesses. The survey, titled “The State of Generative AI in the Enterprise: Now decides next,” gathered insights from 2,770 business and technology leaders across 14 countries and six industries, providing a comprehensive look at the current state of generative AI adoption in enterprises.
Key Findings: Progress and Persisting Obstacles
The survey’s key findings paint a picture of organizations striving to capitalize on the potential of generative AI while grappling with issues of scalability, data management, risk mitigation, and value measurement. Here are some notable highlights:
- 67% of organizations are increasing investments in generative AI due to strong early value.
- 68% have moved 30% or fewer of their generative AI experiments into production.
- 75% have increased investments in data lifecycle management for generative AI.
- Only 23% feel highly prepared for generative AI-related risk management and governance challenges.
- 41% struggle to define and measure the exact impacts of their generative AI efforts.
- 55% have avoided certain generative AI use cases due to data-related issues.
“I see a lot of our clients are prototyping and piloting, but not yet getting to production,” said Kieran Norton, principal at Deloitte. “A lot of that relates to concerns around both data quality and implications thereof, including bias getting into a model.”
Risk Concerns Impacting Enterprise AI Deployments
The Deloitte survey aligns with recent reports, such as the one from PwC, highlighting the gap between interest in generative AI and assessing associated risks. However, the Deloitte report goes a step further, suggesting that AI risks might be impacting enterprise deployments.
According to Norton, executives have a significant level of concern, and they’re not willing to move forward until they feel those concerns can be addressed. The report highlights key risks, including data quality, bias, security, trust, privacy, and regulatory compliance.
While these risks are not entirely new, Norton emphasized that there are nuances to generative AI. He believes organizations can leverage their existing risk management programs to address these challenges but acknowledges the need to enhance certain practices, such as data quality management, to mitigate the specific risks posed by generative AI.
“There are some nuances that have to be addressed, but it’s still core governance at the end of the day,” Norton said. “Data quality has been a concern for a long time, and so maybe you need to dial up what you’re doing around data quality in order to mitigate the risk.”
Demonstrating the Value of Generative AI Initiatives
One of the significant findings in the report was that 41% of organizations struggled to effectively measure their generative AI efforts. Even more concerning is the finding that only 16% have produced regular reports for their company’s CFO detailing the value created by generative AI.
Norton explained that this difficulty stems from the diverse range of use cases and the need for a more granular, use-case-specific approach. “If you have 20 different use cases you’re exploring across different parts of the organization, you know, you probably have apples, oranges, bananas, and pineapples, so you’re not going to be able to measure all those in a similar fashion,” he said.
Instead, Norton recommends that organizations define key performance indicators (KPIs) for each specific use case, targeting the business problems they are trying to solve. This could include metrics like productivity, efficiency, or user experience improvements, depending on the particular use case.
“I think it’s really breaking it down to the use case level, more than it is approaching it as an overall portfolio,” he said.
Conclusion: Navigating the Complex Generative AI Landscape
The Deloitte survey highlights the complex landscape enterprises are navigating as they embrace generative AI agents and reinforcement learning. While early successes are driving increased investments, the path to widespread implementation remains fraught with obstacles. Addressing data quality, risk management, and value measurement will be crucial for organizations to unlock the full potential of generative AI.
As enterprises continue to explore and experiment with generative AI, solutions like the UBOS platform can provide a low-code/no-code environment for rapid development and deployment of AI applications. By leveraging UBOS templates and integrations with cutting-edge technologies like Chroma DB and OpenAI ChatGPT, enterprises can accelerate their generative AI journey while mitigating risks and maximizing value.