A listening tour of the Australian boardroom revealed that executives are keen on the possibility of artificial intelligence (AI) unleashing economic jackpots, but they are working to translate their aspirations into beneficial and tangible results.
Returning from a recent five-state tour, Brennan's head of digital solutions Steve Anderton revealed to ITNews a landscape of “limited adoption, but a lot of interest.”
Stolen by unclear goals, ambiguous or nonexistent indicators of success and enthusiastic hypersensitivity opened up widening cracks between AI champions and sponsors. Most notably, the CFO who held the strings of his wallet. The board's directors, owners and investors were exacerbated by the sense that they were rushing to produce AI before an essential foundation was laid, Anderton found.
“The hype was driving a sense of urgency to look into these technology sets. [but] We weren't ready…and the business cases weren't piled up,” Anderton said. [which made] It's really difficult to release funds. ”
This gap between suction and execution means that only 5% of AI pilots have graduated to production, Anderton said he cited Brennan's research partner Adapt.
Anderton attributed the low conversion rate to not being in the right state and not being able to enhance the dysfunctional AI working groups and platforms and infrastructure to support scale. Pilots were also often carried out in isolated environments where investments were not able to justify or were not prepared for the reality of production.
In particular, 60% of CFOs surveyed say they lack confidence in writing effective AI use cases in which their business guarantees investment.
To resolve these tensions, Anderton proposed “micro-innovation” to fill this perception gap and kickstart innovation. He said it started with a sensual team of decision makers and technicians. Prototyping quickly and repeatedly prove agreed success metrics for overinvestment in dangerous initiatives that have limited or unclear benefits.
“So, when you reach the end of that prototyping phase in weeks rather than months, you can say that business cases stack up…but it takes the cultural change of the organization,” he said.
Anderton advised the AI champions to “not eat elephants at once.” Instead, choose a highly visible use case with low investment and risk, adding a significant impact.
Is it the key to scaling AI into the real world? Fixed infrastructure
Anderton said that we need to move AI pilots or prototypes into the real world and support them, and we need a foundation of governance.
Businesses need to have a powerful data platform, comprehensive data governance, and a modern, cloud-based platform to innovate and scale quickly.
The surge in Shadow AI, which uses easily available tools such as ChatGPT and Microsoft Copilot without monitoring, has raised additional risks such as data leaks.
“These tools are so easy to get, users are signing up and starting to expand into business,” he says, guiding risk aversion management and shuttering AI projects until they understand the risks.
Conversely, a robust, flexible modern infrastructure with strong governance wrapped in data platforms, processes and policies has strengthened the adoption of AI in businesses. This could include the deployment of governance, risk and compliance platforms such as Microsoft Purview to define safety “guardrails.”
“We can quickly bring new data sources, ingest them on our data platforms, and model that data and present it to our stakeholders. [and] The driving user is back to business.
“We don't want to wait to provide infrastructure or services to support these. Cloud-native solutions. Touring is absolutely essential to supporting that innovation.”
People still missed links to fake the future of AI
It was also important to address cultural challenges such as the rational fear of losing workers' jobs, but not necessarily for reasons cited elsewhere.
“It's not that AI will take your job, but potentially people who can use it are likely to get your job,” he said, highlighting the need for effective change management and training in AI deployments.
Lessons learned from his nationwide listening tour were the urgent need for Australian companies to adopt a “bimodal approach,” building innovative AI on the corporate technology and governance base.
“Don't get stuck with technology,” Anderton warned.
“Securing a foundation, but setting up a framework that comes with results and values,” you try to achieve. He said that investments should naturally follow measurable and expected benefits, and that AI adoption should be accelerated from aspiration to reality.
Listen to the full podcast below or visit the ITNews page for your favorite podcast platform.
To learn more about how kThe Enterprise AI Innovation program is now high gear and visit Brennan's website Brennanit.com.au.
