When it comes to AI, sitting on the sidelines won鈥檛 keep you safe. At the AGSM 2025 Professional Forum, Dr Sue Keay, Director 草莓视频 AI Institute, shared why progress starts with the courage to lead before you feel ready.
For business leaders navigating the rapid rise of artificial intelligence, the instinct to wait until everything feels certain can be paralysing.
Many organisations are holding off on AI adoption, hoping for a moment when it will feel safe, predictable, or perfectly regulated. But that moment never comes.
It鈥檚 a mindset that has put Australia on the back foot. According to聽, Australians lead the world in apprehension about AI. The irony? Inaction is its own risk. Especially when competitors are building capabilities in the background.
At AGSM鈥檚 2025 Professional Forum, keynote speaker Dr Sue Keay, Director of 草莓视频鈥檚 AI Institute, challenged leaders to rethink their relationship with risk.
鈥淎I success,鈥 she said, 鈥渟tarts with experiments 鈥 and a tolerance for failure.鈥
According to Keay, the future won鈥檛 be claimed by the cautious. It will belong to those willing to try, test and learn their way forward 鈥 even if that means getting it wrong the first time.
鈥淚f you remain in a state of fear, you鈥檒l focus overly on risks and regulations and you won鈥檛 be considering the opportunities these technologies can bring to your business,鈥 she warned.
Keay encouraged leaders to focus on building momentum with practical steps.
Organisations that start small build what she calls their 鈥楢I muscle鈥, gaining the experience to scale more confidently as the technology evolves.
鈥淚f you鈥檙e not prepared to experiment and find safe ways to explore AI,鈥 she said, 鈥測ou鈥檙e not going to build that muscle. And it will be that much harder when these technologies hit you at scale.鈥
And the benefits of early adoption, she noted, taper over time. As competitors experiment and refine their own approaches, the advantage of moving first begins to fade.
For organisations serious about staying competitive, the time to make the leap is now.
Robotics offers some of the most vivid examples of small AI experiments growing into industry-wide transformation.
Keay described how gas companies, unable to fly inspectors to remote facilities during COVID, deployed robot dogs to capture imagery and assess safety.
鈥淲e actually have robot dogs being used in gas facilities,鈥 she said. 鈥淲hat began as a crisis workaround quickly became standard operating procedure.鈥
She also shared the example of COTSbot, an autonomous robot developed to help protect the Great Barrier Reef. This underwater vehicle identifies and removes coral-eating crown-of-thorns starfish, a major threat to reef ecosystems.
These stories reinforce a simple truth: starting small doesn鈥檛 mean staying small 鈥 and the effort you invest early can unlock benefits far beyond what you imagined.
So, how can leaders turn small experiments into lasting capability? It starts with mindset.
While AI may be powered by code, leading its adoption isn鈥檛 about being the most technical person in the room. It鈥檚 about setting the tone for curiosity, responsibility and a willingness to adapt.
鈥淵ou will have to make decisions about these technologies, whether you have a computer science degree or not,鈥 Keay said.
Leaders need to be brave enough to ask questions, experiment openly and guide their teams through uncertainty. Because if you鈥檙e hesitant and risk-averse, your people will be too.
And, like it or not, AI is already seeping into the workplace. Employees are bringing in tools on their own, using generative AI to draft documents or solve problems in ways you can鈥檛 always see or control.
Without clear policies, education and oversight, those same tools can create unintended risks 鈥 from sharing sensitive data with public models to relying on flawed outputs.
鈥淚f you haven鈥檛 invested time in teaching your teams what responsible AI use looks like,鈥 Keay said, 鈥測ou can鈥檛 expect them to know where the line is.鈥
Just as cybersecurity has become a shared responsibility, AI literacy now needs to be part of every organisation鈥檚 culture.
Training people to treat confidential data carefully, understand the limits of AI tools and report issues early can make all the difference.
With the right mindset in place, Keay was candid about where the real work begins: the unglamorous foundations.
To adopt AI responsibly, leaders must first understand the nuts and bolts of their own business. That means mapping out workflows in detail, cleaning up messy data sets and putting clear governance frameworks in place.
鈥淪uccessful AI doesn鈥檛 start with flashy pilots,鈥 she said. 鈥淚t starts with understanding exactly how your processes work today. And where smarter tools can genuinely add value.鈥
Done properly, this groundwork doesn鈥檛 just enable better AI adoption. It strengthens operations overall, exposing inefficiencies and clarifying where teams can improve.
Finally, Keay urged leaders to be transparent about what doesn鈥檛 work. Too often, she said, organisations hide AI missteps out of embarrassment or fear. But sharing failures can actually build trust with employees, customers and stakeholders.
鈥淲e should seriously consider how we鈥檙e sharing information about when AI doesn鈥檛 work the way we expect,鈥 she said.
The same way mandatory reporting has improved cybersecurity. Normalising honest conversations about AI鈥檚 limits can help teams learn faster 鈥 and make smarter decisions the next time.
In the end, Keay鈥檚 message was simple. The organisations that will thrive are those willing to get started, even if the first steps feel imperfect.
Because every experiment builds capability. Every lesson, even the hard ones, brings progress. And every leader who models curiosity over fear sends a signal: we鈥檙e ready to shape the future, not wait for it.