What does it take to move from AI experimentation to enterprise-wide impact?
In this episode of AI Now, Gideon Ratner from Quantium joins Mallesons partners Bryony Evans and Peta Stevenson to share what Quantium has learned from its own AI transformation, and from working with organisations across sectors including retail, banking, telecommunications and the public sector.
The discussion offers practical lessons for business leaders looking to scale AI beyond pilots and proof-of-concepts. Gideon shares real examples of how organisations are using AI to redesign workflows, improve decision-making and turn experimentation into business value.
What it means for business
As AI tools become more capable and accessible, competitive advantage is increasingly being shaped by leadership, execution and organisational change.
“The single clearest and strongest predictor of successful AI transformation is the leader's own usage of AI. Where we see a CEO or chairperson personally using AI day-to-day, sharing their successes. I think executives and CEOs who are not using AI will very quickly be replaced by other leaders who are.” – Gideon Ratner, Quantium
Listen to these moments
AI strategy is about value, not tools
Adopting a large language model does not equate to an AI strategy. Organisations making the most progress are identifying where AI can create value, setting clear goals, and pursuing targeted use cases while continuing to strengthen governance, risk settings and data foundations.
Guardrails can accelerate innovation
Good governance does not slow AI adoption. Strong controls, clear risk settings and confidence in the underlying guardrails can enable organisations to move faster and experiment more confidently.
“I often talk about guardrails with our clients as with a racing car. The stronger your brakes are, the more powerful your brakes are, the less you'll end up using them because you have the confidence that they're there. So you'll drive fast and take the turns fast because you know if you need them, they're there and they're in place.” - Gideon Ratner, Quantium
The next challenge is moving beyond pilots
Building proofs of concept has become easier but scaling them remains difficult. Success depends on business ownership, change management, clear accountability and a willingness to redesign processes, not just introduce new technology.
Agents will reward operational discipline
Excitement around AI agents continues to grow, but effective deployment requires more than advanced models. Clear processes, well-defined responsibilities, quality data and consistent operating procedures will increasingly separate successful implementations from unsuccessful ones.
Leadership is still the biggest differentiator
The strongest predictor of successful AI transformation in Quantium's experience is leaders who actively use AI themselves, share their successes and failures, and model new ways of working for the rest of the organisation.
Measure value from day one
As organisations invest more heavily in AI, expectations around returns are increasing. The lesson from Quantium's client work is simple: define the value pool, establish the baseline, and build measurement into the process from the outset.
What's next?
If the first phase of AI adoption was about access to tools, the next phase will be about execution. Organisations that move fastest are likely to be those that combine leadership commitment, practical experimentation and clear accountability for outcomes.
The technology will continue to evolve. The bigger challenge may be helping people, processes and organisations to evolve with it.
AI Now is a podcast series from Mallesons exploring the evolving use of AI, the legal landscape and what it all means for business. Listen to the full conversation with Gideon Ratner and subscribe for upcoming episodes.
Listen to all the episodes of AI Now here.
Register your interest to be the first to receive our comprehensive Guide!
14 countries | Regional themes | Regulatory issues - power, water, land, telco, foreign investment, tax, critical infrastructure and security and data localisation.
