Overcoming the Hurdles of Showing AI ROI in Australian Businesses,

Overcoming the Hurdles of Showing Ai Roi in Australian Businesses,

Overcoming the Hurdles of Showing AI ROI in Australian Businesses,

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Australian organizations are at a crucial point in the rapidly evolving field of AI.

The potential financial benefits of AI adoption are significant, with some studies indicating that having an AI portfolio can result in over $100 million in additional EBITDA. However, realizing a return on investment (ROI) through AI projects is not without its difficulties.

Surprisingly, as many as 85% of AI projects in enterprises fail to deliver on their promised business outcomes, surpassing the challenges seen in previous digital transformation efforts.

Failure in AI deployments can have severe consequences, as seen in Australia with the “Robodebt” scandal that led to a Royal Commission investigation.

Expert Advice from Gartner Analyst

While there is excitement about the potential of AI, reports have shown that 80% of Australians have concerns about the risks associated with AI, considering them a global priority.

Despite these risks and public hesitancy, CIOs are investing heavily in AI projects. Research by KPMG revealed that more than half of Australian companies are allocating 10-20% of their budgets to AI.

This places additional pressure on CIOs and IT teams to ensure that AI projects deliver tangible value. Gartner research has identified estimating and demonstrating business value as the primary barrier to successful AI projects.

Nate Suda, a senior director analyst at Gartner, highlighted the challenges organizations face in showcasing the value of AI, which includes managing costs, productivity gains, and strategic planning to ensure AI investments pay off.

Cost Management Considerations

Controlling costs is a major challenge in AI deployments. Unlike traditional search engines that have low expenses, generative AI can be costly due to its interactive nature.

Users often engage in multiple interactions to refine responses, leading to increased costs with each exchange. These costs can escalate if user behavior deviates from initial expectations.

Organizations are adopting a slow scaling strategy to mitigate these risks, starting with a limited number of users before gradually expanding to accurately assess performance and costs.

Productivity Challenges

Although AI offers productivity enhancements, translating these gains into tangible financial benefits is complex. Simply saving time does not always equate to revenue generation or cost reduction.

It is essential to clarify how productivity improvements contribute to overall value, whether through revenue generation or cost savings.

Distinguishing between benefits and value is crucial, as enhancements like improved speed must translate into financial gains to deliver on AI’s promised value.

Risks of Cost Overruns

There is a significant risk of cost overruns due to unforeseen user behavior in AI systems. If a system becomes popular beyond expectations, costs can escalate rapidly, underscoring the need for careful planning and real-time monitoring.

Understanding and modeling user behavior are critical in managing costs effectively in AI deployments.

Strategic AI Deployment Framework

Gartner has developed a framework called “Defend, Extend, and Upend” to explain how AI can deliver value while managing risks. Each level of deployment offers different potential risks and benefits:

  • Defend: Involves incremental improvements using AI to enhance existing tools, leading to small wins but aggregating these wins for significant financial returns can be challenging.
  • Extend: Embeds AI in existing applications for targeted improvements, requiring careful planning for anticipated value delivery.
  • Upend: Involves developing new AI-driven models or applications, offering substantial rewards but requiring significant investment and carrying higher risk.

Effective Management of AI

Similar to digital transformation, overly ambitious AI projects can lead to cost overruns and slow ROI, causing frustration among executives and possible project abandonment.

CIOs should adopt a cautious, scalable approach to AI implementation to ensure early articulation of ROI and project sustainability.

author avatar
roosho Senior Engineer (Technical Services)
I am Rakib Raihan RooSho, Jack of all IT Trades. You got it right. Good for nothing. I try a lot of things and fail more than that. That's how I learn. Whenever I succeed, I note that in my cookbook. Eventually, that became my blog. 
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