The role
Yotta was establishing itself in the region, and Horizons was effectively the proposition I was taking to market in Australia and New Zealand. My role covered direct sales, customer engagement, presales, high-level solution design and account development.
Horizons was much more than a long-term road-modelling tool. It combined asset condition and deterioration modelling with analytics, monitoring and external context to help infrastructure owners understand not only what would happen to an asset over time, but why one section of the network could matter more than another.
The problem space
Road and infrastructure owners do not simply need to know what is broken today. They need to understand how condition changes over time, what different intervention strategies do to cost and risk, and where limited capital should be applied across an entire portfolio.
Horizons added another layer: the importance of an asset is not defined by pavement condition alone. The platform could incorporate geographic, environmental, demographic and other contextual factors into the decision model.
At the other end of the time horizon, the same platform could model intervention and funding strategies decades into the future, including very long-range network plans for councils and state road authorities.
That made Horizons an early form of asset decision intelligence: physical condition + external context + predictive modelling + investment choice.
Why it matters
Modern Industrial AI is increasingly trying to close the same loop at much greater speed: condition → context → risk → intervention → investment → execution.
Yotta is particularly relevant to that story because Horizons was already demonstrating that useful asset intelligence cannot come from the asset record alone. Decisions improve when physical condition is enriched with environmental, geographic, demographic and social context.