Key Takeaways on Balancing Opportunity and Risk in Generative AI

This was the central question explored at RMIT Online’s Skills Fest session, "Navigating Data, AI, and Ethics in Storytelling" - a panel discussion featuring Alex Papli (Hypergen), Dr Claire Mason (CSIRO), and David McAmis (AWS). Together, they tackled one of today’s biggest challenges: balancing the dazzling opportunities of AI with the very real risks.

From healthcare to marketing, AI is already quietly shaping the world - deciding what we read, watch, and even buy. But the panel made it clear: adopting AI isn’t the endgame. Strategically applying it is.

Some jobs are perfect candidates for automation; think repetitive, data-heavy tasks or scalable content generation. As Alex Papli put it: 

“One of the things we see is that people aren’t sure what’s magic and what’s not. So one of the key things we like to do is demystify what it (AI) can do and what it can’t, because in the absence of understanding we think it’s magic and it can do everything.”

But there are areas where humans still win every time - empathy, deep ethical judgment, and true creative leaps. As the panel agreed, the future of work isn’t about knowing how to code, it’s about knowing how to prompt, test, and ethically guide AI systems. 

One of the liveliest parts of the discussion was about risk, and how ignoring it can come back to bite us.

Generative AI is powerful, yes, but it also brings: 

• Biases that can amplify inequities

• Hallucinations (AI confidently making things up) 

• Privacy risks when training data includes sensitive information

David McAmis put it simply: 

“AI is built on a foundation of data, and you have to have good data. For instance, I live in Melbourne, and so if I were to take a data set for household income or household attributes from Toorak … it doesn’t necessarily represent everyone that lives in Melbourne.”

For businesses just starting their AI journey, the advice was clear: get your use cases defined, clean your datasets, assess risk up front, and build ethical guardrails before launching. 

Beyond data quality, the panel raised an important but less-discussed risk: the homogenisation of language, creativity, and storytelling. If everyone uses the same tools the same way, we risk producing a flood of bland, indistinguishable content.

Dr Claire Mason warned that even the way we approach AI can unintentionally anchor our thinking: 

“We have to choose the right ways of working with Generative AI. For example we know that as humans, our responses tend to be anchored by the first piece of information we receive, or the first solution we’re given. So don’t go to Generative AI first, because then you’re going to be stuck on its thinking, rather than adding value as a human worker.”

Instead, she suggested reframing how we collaborate with AI: start with human ideas first, then use AI to pressure-test, expand, and refine them. This approach keeps human originality at the centre while still benefiting from AI’s speed and scale. 

The anxiety over AI-driven job loss is understandable, but the panelists pointed to a different reality: a shift from job replacement to productivity gains. Rather than eliminating roles, AI is enhancing them, freeing workers from repetitive tasks to focus on more strategic, creative work.

Claire Mason likened it to another technological leap: 

“Just as the internet opened up information that was previously just held in an expert’s mind or in a library, Generative AI is definitely opening up access to expertise and intelligence … Of course that’s going to create some fear that we’ve created an environment where expertise and intelligence are no longer in short supply. … But the thing is, the real world is messy and problems aren’t well defined, people seek novelty and the experience that we build up as a human in the world is not replicable by artificial neural networks.”

The panel’s message was clear: the future of AI-powered work isn’t about competing with the machine, it’s about collaborating with it. The organisations that will thrive are those that:

• Use AI strategically, not indiscriminately 

• Build diverse, representative datasets

• Put ethical guardrails in place early 

• Keep human creativity and originality front and centre

In short, AI can be a powerful amplifier, but it works best when we keep hold of the microphone. 

To check out the full discussion, watch it below.