‘Composite AI’ could drive success as investments increase
Dynatrace has highlighted that, whilst 83% of technology leaders state that AI has become mandatory, 95% say that generative AI (GenAI) would be more beneficial if prompted by other types of AI.
The company states that there is a need for a composite approach to AI, whereby organisations combine multiple types of AI. These could include GenAI, predictive and/or causal AI and different data sources such as observability, security and business events. This approach could enable more advanced reasoning and offer better precision, context and meaning to AI outputs.
These findings were released today (12th December 2023) as part of an independent global survey of CTOs, CIOs and other technology leaders in large organisations.
AI investments continue to increase across organisations
In its report, Dynatrace highlights that organisations are increasing AI investments across all business areas. According to the survey, reasons for this include improving productivity, automating tasks, reducing costs and keeping pace with competition.
Alongside the clear advantages, however, there are challenges and risks that organisations need to manage, from ensuring the outputs of generative AI are trustworthy to support business-critical use cases, to maintaining compliance with internal policies and global regulations related to data security and privacy.
Key findings from the research include that 83% of technology leaders say that AI has become mandatory in order to keep up with the dynamic nature of cloud environments. Additionally, 82% state that AI will be critical to security threat detection, investigation and response.
Indeed, AI leaders have continually advocated for integrating AI into enterprise security strategies moving forward. In addition, as businesses become more cyber-aware, AI technology will only continue to hold value moving forward. Dynatrace reveals that 88% of those surveyed expect AI to extend access to data analytics to nontechnical employees through natural language queries, in addition to thinking AI will enable cloud cost efficiencies.
“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.
“Development, operations, and security teams have seen significant potential in generative AI’s ability to help them deliver new, more secure apps and services at record speeds. However, to realise the benefits of this technology, these teams need to use generative AI in a way that delivers predictable, trustworthy, and precise answers in real-time.
“Organisations must also integrate the technology securely and responsibly, to minimise the risk of employees exposing sensitive data or introducing vulnerabilities to their environments.”
Boosting productivity with AI
However, 93% surveyed by Dynatrace are concerned that AI could be used for non-approved uses as employees become more accustomed to using tools such as ChatGPT. In fact, it has recently been found that one in ten businesses fear that they will not survive the next 12 months due to ‘AI cyberattack’ fears.
Other anxieties facing industry leaders include economic uncertainty and increased data security threats compared to the last 12 months. The Dynatrace survey found that 98% of technology leaders are concerned that GenAI in particular could be susceptible to unintentional bias or misinformation.
The survey also found that 95% of technology leaders are concerned that using GenAI to create code could result in illegal use of intellectual property. As a result, those surveyed believe that GenAI 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 organisations face with generative AI is achieving meaningful responses that users can trust to solve specific use cases and problems,” continued Greifeneder. “Teams need to engineer prompts with detailed context and precision quickly, easily, and cost-effectively, and while validating the accuracy of a generative AI’s outputs.”
He continues: “To achieve this, it’s important to recognize that not all AI is created equal. Most use cases for generative AI, and especially more complex ones, such as optimising software code and resolving security vulnerabilities, require a composite approach. 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, provide essential context for prompting generative AI.
“If organisations get their strategy right, combining these different types of AI with high-quality observability, security, and business events data, they can significantly boost the productivity of development, operations, and security teams and deliver lasting business value.”
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