Redefining Enterprise Value Through AI is essential for modern businesses.
As businesses move beyond experimental “efficiency gains,” we’re seeing a significant shift toward AI-native architectures, redefining enterprise value through AI, that go beyond saving time to unlock entirely new business models.
The Australian Financial Review recently featured Mantel in a deep dive into the evolving landscape of artificial intelligence.
In this context, redefining enterprise value through AI is a critical consideration for companies looking to adapt and thrive.
At Mantel, we’re seeing our clients move past the initial hype. While early adopters like nib are already reporting substantial wins, including reaching a record 1.95 million customers while lowering their operating expense ratio, the real prize lies in the next 12 to 24 months. Our AI leader, Emma Bromet, highlights that while traditional ROI language like “10x uplift” is still maturing in this space, the strategic value is being proven through enhanced velocity, intelligence, and customer experience.
Key impact points
- 86% of claims at nib are now processed by AI without human intervention, a strategy we’re watching closely as a benchmark for operational excellence.
- 10% reduction in call centre volumes achieved through intelligent automation.
- 70% reduction in “cycle time” for home loan applications at Lendi Group, proving that AI-native approaches slash time to value.
- 1% reduction in operating expense ratios (from 17.5% to 16.5%) at nib, demonstrating that AI investments preserve margins even during growth.
The conversation for the C-suite is changing. It’s no longer just about “doing the thing you did before faster”; it’s about addressing the existential risk of being left behind. We believe 2026 will be the defining year for AI-native organisations. As these transformational experiences go live, the businesses that have built AI into their core architecture, rather than just bolting it on, will command a clear valuation premium in the market.
“The benefits are being quantified, but the benchmarks or language traditionally associated with return on investment metrics... are not being commonly used by executives to articulate the success [of AI investments].”
Emma BrometPartner - Data | Mantel
