'Composite AI' could be key to successful artificial intelligence in the enterprise
New research shows that businesses are increasing their investments in AI across many areas, but there are challenges and risks that they need to manage.
The study of 1,300 tech leaders from Dynatrace shows 98 percent are concerned that generative AI could be susceptible to unintentional bias, error, and misinformation. In addition 95 percent are concerned that using generative AI to create code could result in leakage and improper or illegal use of intellectual property.
They're still keen to reap the benefits, however. 83 percent of technology leaders surveyed say AI has become mandatory to keep up with the dynamic nature of cloud environments. 82 percent say AI will be critical to security threat detection, investigation and response, and 88 percent expect AI to extend access to data analytics to non-technical employees through natural language queries.
"AI has become central to how organizations drive efficiency, improve productivity, and accelerate innovation," says 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. However, as organizations endeavor to realize the expected value, it becomes evident that generative AI requires domain-specific tuning and integration with other technologies, including other types of AI. In addition, organizations must use AI securely and responsibly and monitor it closely to manage cost and user experience. This will help them provide accurate results, reduce expenses, and prevent employees from exposing sensitive data or creating vulnerabilities in their environments."
The key to adopting generative AI successfully could be a 'composite' approach. 95 percent say generative AI would be more beneficial if enriched and prompted by other types of AI that can provide precise facts about current states and accurate predictions about the future.
"One of the most significant challenges organizations face with generative AI is achieving meaningful responses that users can trust to solve specific use cases and problems," adds Greifeneder. "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 analyzing business data all need a composite AI approach. This approach should deliver the precision of causal AI, which determines the underlying causes and effects of systems' behaviors, and predictive AI, which forecasts future events based on historical data. 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. 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."
The full report is available from the Dynatrace site.
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