Australian firms boost AI investments for business operations, survey finds
New data from a survey conducted by Dynatrace highlights that Australian organisations are escalating their investments in AI solutions across entire business operations. The aim is to enhance productivity, automate tasks, reduce expenditures, and outpace competitors.
The survey, which engaged 1,300 global technology leaders, including CTOs and CIOs, from significant organisations, disclosed significant reliance on artificial intelligence in Australia. However, while realising its imperative, 96% of participants also endorsed the idea that generative AI could realise greater benefits if enriched and prompted by other AI types.
Although the extensive adoption of AI presents clear benefits, the research also highlighted challenges and risks, such as maintaining compliance with data security and privacy regulations and assuring the trustworthiness of the outputs of generative AI to support business-critical use cases.
These findings emphasise the necessity of a multi-faceted AI approach wherein organisations merge different types of AI like generative, predictive and causal with disparate data sources like observability, security, and business events. This strategy aids more advanced reasoning and brings precision, context and meaning to AI outputs.
From the Australian data collated, it is evident that most technology leaders now consider AI as mandatory to deal with the dynamic nature of cloud environments, with 69% holding this view.
Furthermore, 82% of tech leaders asserted that AI's importance in security threat detection and response couldn't be overstated. 86% expect AI to extend data analytics to non-technical employees, while 41% have already had to alter job roles and recruitment due to AI's influence.
Bernd Greifeneder, Chief Technology Officer at Dynatrace, stated, "AI has become central to how organisations drive efficiency, improve productivity, and accelerate innovation." He expounded that as companies seek to realise the predicted value of generative AI, it becomes apparent that this AI type necessitates domain-specific tuning and integration with other technologies.
Besides the benefits, tech leaders voiced concerns about AI's potential for non-approved uses and the possibility of intellectual property misuse. Furthermore, 99% expressed worry that generative AI could be susceptible to unintentional bias, error, and misinformation.
The consensus was that generative AI would be more beneficial if it were enriched and prompted by other types of AI algorithms that can supply precise facts about current situations and accurate future predictions.
According to Greifeneder, one of the major challenges with generative AI is achieving meaningful responses that users can trust to solve specific use cases and problems. He stated, "For instance, automating software services, resolving security vulnerabilities, predicting maintenance needs, and analysing business data all need a composite AI approach."
If strategised correctly, a strong and comprehensive vision integrating predictive, causal and generative AI, supplemented with high-quality observability, security, and business event data can drive productivity within development, operations, and security teams and deliver consistent business value.
