As AI Accelerates, Trust Matters More

AI is making software faster and cheaper to build. New apps, workflows and automations can now be created very quickly. But every one of those systems still depends on data it can rely on.

As the cost of building falls, more value shifts from the software layer to the data underneath it, especially in systems used for analysis, automation and decision-making.

At Geoscape, we are seeing this directly. Demand for location data is growing across proptech and agtech startups building on our APIs, as well as enterprise organisations in financial services, insurance and government. Across these sectors, the common requirement is trusted data.

In many markets, raw data capture is no longer the primary bottleneck. Open data, scraped content, sensor feeds, imagery and AI-assisted extraction have made it easier to collect large volumes of information from many sources.

The harder problem is turning those inputs into decision-grade data.

That means resolving source conflicts, maintaining stable identifiers, linking metadata and enrichment layers, tracking provenance, aligning schemas and keeping data current as the physical world changes. AI can help extract, classify and analyse information, but it does not remove the need for verification. If the underlying data is wrong, AI will not fix it. It will amplify it.

That matters most in high-stakes settings, where the cost of acting on bad information is real.

Since the conflict in Iran began, social media has been flooded with AI-generated images and videos falsely presented as real war footage. In many cases, this content spreads widely before journalists or fact-checkers are able to debunk it. (Source: BBC – https://www.bbc.com/news/articles/ckg8wvz427vo)

It also reinforced a basic question: how do decision-makers verify the quality and reliability of the data feeding those systems when errors can cost lives?

In Australia, a major bank disclosed a suspected A$1 billion mortgage fraud issue, with reports pointing to falsified loan documents, including payslips and financial statements suspected to have been generated or altered using AI tools.

The fraud exposed a verification weakness. When AI makes convincing forgeries easier to produce, the trust and validation layer becomes a critical line of defence. (Source: Australian Financial Review – https://www.afr.com/companies/financial-services/cba-probes-1b-in-suspected-fraudulent-home-loans-calls-in-police-20260223-p5o4mc)

Both examples point to the same underlying issue. When the stakes are high, organisations need data that is transparent, traceable and defensible, with lineage and outputs that can be tested and explained.

Buildings, addresses and land parcels change over time, and data sources can conflict. Trusted location data cannot be treated as static. It depends on ongoing validation, conflation of competing sources, stable identifiers, clear lineage, provenance and repeatable refresh pipelines.

AI will continue to lower the cost of building software. As that happens, the organisations that stand out will be those that provide reliable, explainable and continuously maintained foundational data others can build on with confidence.