Australian businesses increase AI investment for improved productivity

New survey findings from Dynatrace indicate that Australian businesses are ramping up their investments in artificial intelligence (AI) to enhance productivity, automate tasks, reduce costs and maintain a competitive edge. 

The survey, involving 1,300 CTOs, CIOs, and other tech leaders, highlights the indispensability of AI in the changing landscape of cloud environments, with 69% of technology leaders in agreement. Additionally, 82% believe AI will be critical in detecting, investigating, and responding to security threats.

Moreover, 86% anticipate AI will make data analytics accessible to non-technical employees via natural language queries, revolutionising how businesses interact with data. Interestingly, 41% of organisations have already shifted their recruitment focus due to AI's increasing prevalence. Meanwhile, 75% of tech leaders deem AI crucial in aiding FinOps practices to ensure cloud cost efficiencies.

Over the upcoming 12 months, 74% of tech leaders express intentions to increase their AI investment to expedite development by auto-generating code.

According to Bernd Greifeneder, Chief Technology Officer at Dynatrace, AI's role has become pivotal in organisations looking to streamline their operations: "AI has become central to how organisations drive efficiency, improve productivity, and accelerate innovation." 

Greifeneder notes how the launch of ChatGPT in the previous year sparked a notable surge in excitement surrounding generative AI. Various leaders across business, development, operations, and security domains have raised expectations, anticipating generative AIs to facilitate the efficient delivery of new services at unprecedented speeds.

However, despite recognising the potential of AI, 86% of technology leaders express concerns about the unauthorised use of AI tools like ChatGPT. Meanwhile, 97% voice concerns about potential IP leakage and misuse.

A staggering 99% of tech leaders worry about generative AI's susceptibility to unintentional bias, errors, and misinformation. Meanwhile, 96% agree that generative AI could present more significant benefits if prompted by other types of AI, delivering accurate present-state facts and reliable future predictions.

Greifeneder iterates the importance of a composite AI method by addressing the challenges of trusting generative AI outputs. He explains that automating scenarios that depend on data context and resolving specific use cases require a balanced approach to AI.

Greifeneder explains, "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."

"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."

Greifeneder underscores that the precision of causal AI and predictive AI is key to providing essential context for generative AI responses. If strategised correctly, amalgamating these different types of AI with data sourced from high-quality observability, security, and business events can boost the productivity of development, operations, and security teams while delivering lasting business value.