Research from Dynatrace sheds light on the challenges and risks associated with AI implementation.
The report underscores the need for a composite AI approach. This involves combining various AI types – such as generative, predictive, and causal – along with diverse data sources like observability, security, and business events. This holistic strategy aims to provide precision, context, and meaning to AI outputs, ensuring reliable results.
- 83% of tech leaders emphasise the mandatory role of AI in navigating the dynamic nature of cloud environments.
- 82% anticipate AI’s critical role in security threat detection, investigation, and response.
- 88% foresee AI extending access to data analytics for non-technical employees through natural language queries.
- 88% believe AI will enhance cloud cost efficiencies through support for Financial Operations (FinOps) practices.
“AI has become central to how organisations 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.”
While organisations express optimism about AI’s transformative potential, concerns linger:
- 93% of tech leaders worry about potential non-approved uses of AI as employees become more accustomed to tools like ChatGPT.
- 95% express concerns about using generative AI for code generation, fearing leakage and improper use of intellectual property.
- 98% are apprehensive about unintentional bias, errors, and misinformation in generative AI.
“Especially for use cases that involve automation and depend on data context, taking a composite approach to AI is critical. For instance, automating software services, resolving security vulnerabilities, predicting maintenance needs, and analysing business data all need a composite AI approach,” added Greifeneder.
“This approach should deliver the precision of causal AI, which determines the underlying causes and effects of systems’ behaviours, and predictive AI, which forecasts future events based on historical data.”
As organisations forge ahead with AI adoption, balancing enthusiasm with a mindful approach to challenges becomes paramount. The survey underscores the transformative potential of AI, but its effective integration requires careful consideration and a diversified AI strategy.
“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.
“If organisations 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.”
A full copy of the report can be found here (registration required)
(Photo by Matt Sclarandis on Unsplash)
See also: AI & Big Data Expo: Demystifying AI and seeing past the hype
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