Since 2015, Australia’s productivity has been flatlining. In fact, we’re experiencing the worst productivity growth in 60 years.
But not to fear – the supposed answer to our productivity woes is here. These days, the words ‘artificial intelligence’ and ‘productivity’ seem intrinsically linked, with many expecting the technology to boost performance across all industries.
In customer relations roles, this may well be true. Some of Australia’s biggest organisations, such as the Commonwealth Bank and Coles, are implementing AI for personalised customer experiences. Meanwhile, Westpac reportedly saw productivity gains of up to 46% among software engineers using AI tools.
Collectively, Australia is projected to be one of the top AI spenders in the Asia-Pacific region by 2027.
Many business leaders are optimistic about the potential of AI to transform how their companies create, deliver, and capture value.
The productivity paradox
But there seems to be a chasm between how CEOs view productivity growth and how employees are using AI. According to a study by Upwork, 47% of workers who use AI tools say they have no idea how to deliver the expected productivity gains. And 77% say AI tools have made them less productive while increasing their workload.
Even more worrying is that AI’s promise to boost productivity is often undercut by the complexity it introduces, particularly as enterprises are often underequipped to maintain visibility across their expanding operations and technology stacks.
Many enterprises now operate across multiple cloud providers and manage large portfolios of applications. However, without streamlining these services prior to adopting AI tools, which often need to integrate and exchange data across systems, it becomes difficult to realise the intended benefits.
With pressure to improve productivity and growth, many organisations have failed to create suitable foundations to maintain observability over so many moving parts. They may understand their business drivers and ambitions, but these often aren’t supported by a strong data strategy, training programs, or monitoring capabilities to ensure projects succeed and deliver real productivity gains.
The scale of spending without clear structures in place has led to two major inefficiencies: overwhelming the people AI is meant to support, and straining already-limited resources and talent pools that could have been used for other causes – like product innovation.
Cracks in the foundation
It’s a renewed form of technology debt. A McKinsey report, “Tech Debt: Reclaiming Tech Equity,” found that companies with significant tech debt divert up to 20% of budgets earmarked for new product investment into addressing challenges related to that debt.
In many cases, it is ironically reducing productivity. That strain is especially evident among the technology teams responsible for managing it.
New cloud and AI infrastructure adds complexity. Suddenly, a manageable set of platforms has grown into a sprawling, hard-to-track environment.
Teams are left ‘doing more with less’ – tasked with ensuring hundreds of applications, including new AI tools, are performing as they should, rapidly identifying and resolving malfunctions, and simultaneously introducing new capabilities and services. The economic climate makes new headcount difficult to come by, and where the budget is available, finding the right talent is near impossible amid the seemingly never-ending skills shortage.
This is especially difficult for security operations teams, many of whom say their workloads are growing due to AI, making day-to-day work more difficult and demanding. When we expand our cloud environments, we are also expanding the threat landscape and creating more potential entry points for cyberattacks.
Although each platform has an alert system to flag unusual activity, these systems often overlap, overloading IT teams. Whether an alert is actionable or a false positive, if there is no cohesive observability strategy across platforms, teams spend precious time sifting through a flood of alerts to assess their impact, slowing response times for critical incidents and risking business outages.
A clearer path forward
This is why organisations investing in AI need complete visibility over their operations prior to introducing new tools that only multiply complexity. This generates a streamlined environment, ensuring business continuity.
As businesses look to AI to offset the productivity slump, it is crucial that integration into existing operations is guided by transparency and supported by the ability to observe effectiveness in real time. That way, we can harness AI to enhance innovation, rather than diverting resources from it.
