1. Matt Sheehan | Spatial-Next: You Know AI is Changing Everything: So Why Aren’t Your Projects Scaling?
The Gist: Most AI projects fail not because the technology is wrong, but because they are technology-led rather than outcome-led. The organisations scaling AI successfully started with a business problem. Two frameworks — the Six-Stage AI Maturity Model and the Opportunity-to-Value Framework — give leaders both a map of where they are and an operating system to get there.
Why it’s Relevant: This is the execution problem at the heart of the Decision Layer. Knowing AI matters is not enough. The discipline to ask three hard questions before any project moves forward — does this solve a real problem, can it scale, and will people actually use it — is what separates pilots from platforms.
2. Matt Sheehan | Spatial-Next: The Geospatial Value Chain is About to Invert
The Gist: The geospatial industry has spent decades optimising for human consumption — maps, dashboards, analysts. But AI can now consume and act on spatial data at machine speed, making the human-in-the-loop the bottleneck. The winners will reformat their data for machines to query and reason over directly. The losers will respond to this shift by building a better dashboard.
Why it’s Relevant: This is a precise, industry-specific illustration of what a second horizon looks like in practice. The value chain doesn’t just speed up — it structurally inverts. Data stops being raw material processed for human insight and becomes the fuel for autonomous, machine-speed decision making. Every industry with a legacy data delivery model should be asking the same question.
3. Insurance Edge: Stand World Model Makes its Debut
The Gist: Stand has launched what it describes as the first physics-native frontier model for the built environment — simulating how fire, wind, water, and seismic forces interact with individual structures at sub-meter resolution. In a validation against the January 2025 California wildfires, it correctly predicted structural survival outcomes at nearly double the accuracy of traditional insurance models.
Why it’s Relevant: This is a real-world horizon two player in action. Stand isn’t building a better risk dashboard — it is restructuring the entire decision architecture around physical truth computed at machine speed. It is also a compelling example of starting with the problem: a $1.3 trillion coverage gap in California, solved not by pricing risk more accurately but by designing it out, structure by structure.
4. Forbes Technology Council: Hidden Supply Chain Factors That Can Derail Business Strategy
The Gist: Technology strategy looks strong in theory until supply chain realities intervene. From sub-tier supplier concentration and hardware availability to decision latency and AI agent autonomy, Forbes Technology Council members identify the hidden factors — often invisible in the boardroom — that determine whether a strategy actually executes.
Why it’s Relevant: A timely reminder that decision velocity has an upstream problem. Even the best AI architecture stalls if the data feeding it is disconnected, delayed, or locked inside fragmented systems. As one contributor puts it: value is not created by insight, it is created by how quickly you turn that insight into action. That is the Decision Layer problem in a single sentence.