Why Australian Businesses is Not Prepared for the Ai Future

When it comes to artificial intelligence (AI), most large Australian companies show confidence and significant commitment. Riverbed’s latest AI & Digital Experience Survey 2024 reveals that 83% of business leaders believe their organisation is currently ahead of its peers in Ai adoption. This conviction contrasts with the fact that only 35% of these businesses feel fully prepared to successfully implement AI projects immediately.

The data also shows that, on average, businesses with a revenue of AU$250 million or more are already investing AU$22.9 million a year in Ai.

Further, 38% of respondents cited a lack of data quality as a significant barrier to Ai project’s success and recognised that a shortage of skilled professionals and security concerns can hinder Ai implementation.

This contradiction between perceived leadership and optimism versus the capacity to currently solve Ai challenges suggests some Australian leaders may have fallen victim to Ai readiness mirage.

Ai is a transformative technology that can potentially revolutionise industries by automating tasks, enhancing customer experiences, driving operational efficiencies, and increasing revenue.

However, enthusiasm and optimism won’t be enough to translate investment into results. Our data shows that nearly one in four Ai projects in implementation (23%) are underperforming, while 18% exceed expectations; the other 59% meet expectations.

These numbers aren’t terrible for implementing a new technology, but many of the projects haven’t matured yet, and, so far, they don’t justify the excitement expressed in our survey.

The responses suggest that many companies underestimate how long it may take to solve these challenges. They seem to be betting on rapid improvement without having fully confronted the challenges.

Ai challenges are complex

Take data as an example. High-quality, reliable data is the backbone of any successful Ai initiative. While most companies rate their data as excellent or good in attributes like accuracy, completeness, accessibility, usability, and quantity, only 44% say they are absolutely confident that their data preparedness will allow Ai projects to deliver the expected results.

Our experience shows that even when companies have excellent data for current needs, they are often unaware of the lack of data visibility or how their existing systems and infrastructure can be a barrier to developing significant Ai projects.

Another critical issue is the workforce. Ai is new territory for most organisations, and they need to train their workforce adequately to handle it. Yet only 49% of Australian companies offer extensive Ai training. This lack of training leaves employees underprepared to leverage Ai effectively, hampering the success of Ai projects.

Security also remains a critical concern. As Ai becomes more embedded in business operations, 79% of respondents expressed worries about Ai accessing proprietary data.

It’s not a sprint but a marathon

Currently, 55% of companies primarily use Ai to streamline processes and improve IT services. This focus on efficiency has delivered some tangible benefits, particularly in IT automation, where Ai is helping organisations reduce costs and increase productivity.

According to our research, by 2027, 58% of businesses expect Ai to drive growth, not just efficiency. To achieve this shift, organisations must rethink how they use Ai, moving beyond simple automation to more innovative, growth-oriented applications.

To bridge the gaps identified by our survey and avoid the current optimism becoming disappointing, companies must focus on building strong data foundations, improving workforce training, and implementing comprehensive governance frameworks.

Data frameworks that support Ai should increase accuracy, completeness, and consistency, which are critical for Ai to work. Data integration tools can unify data sources for Ai models, while observability solutions provide real-time insights into data, allowing issues to be identified and resolved early. Prioritising the use of real data over synthetic data is crucial, as it better captures the complexities needed for AI training.

Robust governance that prioritises data privacy and security is also essential. Without these frameworks, the risks associated with Ai, particularly around data breaches and regulatory compliance, could outweigh the benefits.

Finally, companies prioritising training—particularly in the responsible use of Ai — will be better positioned to avoid costly mistakes and fully realise the technology’s potential.

Only by addressing these core issues can they ensure that their Ai investments deliver long-term value, driving not just operational efficiencies but sustained growth.

Companies that confront their readiness gap head-on will be the most successful. The optimism surrounding Ai is justified, but it must be tempered with a realistic understanding of what’s required to implement it. The path forward isn’t about racing to adopt Ai — it’s about taking the time to build the infrastructure, skills, and strategies that will ensure Ai delivers on its promise. And here’s the silver lining – 89% of leaders expect to be fully ready to implement Ai projects within three years’ time.

Chris Parker is Vice President, Asia Pacific & Japan at Riverbed.

Last Updated on October 9, 2024 by Chris Parker