AI Implementation Challenges and Risks: A Composite Approach Unveiled
A recent study sheds light on the challenges and risks associated with AI implementation, emphasizing the need for a composite approach to address them. Combining various types of AI and diverse data sources helps ensure reliable results and addresses concerns over automation use cases that depend on data context.
Key Findings
- “AI has become central to how organizations drive efficiency, improve productivity, and accelerate innovation,” said Bernd Greifeneder, Chief Technology Officer at Dynatrace.
- “The release of ChatGPT late last year triggered a significant generative AI hype cycle. Business, development, operations, and security leaders have set high expectations for generative AIs to help them deliver new services with less effort and at record speeds.”
Composite AI Approach
Taking a composite approach to AI is critical for use cases involving automation and data context. This strategy combines predictive and causal AI, as well as diverse data sources like observability, security, and business events to provide precision and meaning to AI outputs.
Balancing Enthusiasm with Mindful Approaches
“Predictive AI and causal AI not only provide essential context for responses produced by generative AI but can also prompt generative AI to ensure precise, non-probabilistic answers are embedded into its response,” says Greifeneder.
Boosting Productivity and Business Value
“If organizations get their strategy right, combining these different types of AI with high-quality observability, security, and business events data can significantly boost the productivity of their development, operations, and security teams and deliver lasting business value,” Greifeneder adds.
Full Report Availability
A full copy of the report can be found here (registration required)
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