TL;DR: The geospatial industry is still building “faster horses” — better dashboards and analysts — when the game has fundamentally changed. AI can now consume and act on spatial data at machine speed, making the human-in-the-loop the bottleneck. The winners will be companies that reformat their data for machines to query and reason over directly, not for humans to look at. The losers will be those who respond to this shift by building a better dashboard.
There was a famous discussion at the time of the arrival of the automobile – early 1900’s – that if you asked people what they most wanted they would have said a faster horse. We are in a similar conversation today, but instead of a horse the traditional geospatial industry has focus on a faster analyst.
I discussed this in part in my last article: You Know AI is Changing Everything: So Why Aren’t Your Projects Scaling? In this article I covered the first and second horizon’s respectively. The first horizon is today’s paradigm; that is data processes and delivery to human’s via maps and dashboards. This is the promise of ‘actionable insight’, as industry players are keen to market. Today that has become a critical bottleneck to decision making.
The combination of AI and the tsunami of new data has opened the second horizon, or a paradigm shift. This will dramatically impact decision velocity.
And it is this which will turn the geospatial value chain on its head.
Today’s Geospatial Value Chain
Geospatial has attached its value to maps and dashboards; the human interface to insight. Data is that necessary foundation, but it has always been treated as a means to an end: raw material to be processed, interpreted, and ultimately translated for a human decision-maker.
The traditional chain looks something like this:
Data Collection → Processing → Analysis → Visualization → Human Insight → Decision
Every link in that chain has been optimized for the final consumer: a person. Projections chosen for legibility. Symbology tuned for interpretation. Dashboards built for executive comprehension. The entire architecture of the industry – its tools, its talent, its business models – has been engineered around the moment a human looks at a screen and understands something.
This made sense when humans were the only things capable of consuming geospatial information. And for decades, getting that chain faster, cheaper, and clearer was the competitive frontier. A better analyst. A slicker dashboard. A faster render.
Faster horses.
But here is the problem. That final link – human interpretation – is now the slowest thing in the chain by several orders of magnitude. Data volumes have exploded. Satellite revisit rates have collapsed from days to hours to minutes. Sensor networks generate continuous streams. AI models can now process, correlate, and act on spatial information at machine speed.
And yet the industry’s answer has largely been to hire more analysts, build better dashboards, and add more layers to the map.
That is not a technology problem. It is an architectural one. The value chain was built for a world where humans were the only available processor.
And that world is ending.
Inverting The Geospatial Value Chain
So when I say the geospatial chain is inverting, am I simply revisiting the old – “data is the new oil” trope?
Not at all, I simply mean the definition of usable data has changed.
That might not seem like a big deal, but it is huge. The second horizon is not about better human workflows; it is about new machine readable data forming the connective tissue of a machine-readable Earth.
In other words the consumption layer is changing entirely. Format, latency, schema, ontology; these matter more than visualization, cartography, or even interpretability. GeoParquet and STAC aren’t faster cart wheels – they are the pipelines and refineries that make machine-speed decisions possible. Opus 4.7 is the engine that knows what to do with the fuel; more on this in a moment.
So what does this new value chain look like?
The Second Horizon Geospatial Value Chain
That word inverting suggests we are simply reversing the arrows in the old value chain. Incorrect. We are talking here about a more fundamental restructuring.
Decision Requirement → Agent/Model → Data Query → Real-time Processing → Action
There are some major key structural differences here:
First, it starts from the decision, not the data. Rather than collecting data and seeing what insight emerges, second horizon systems begin with a defined decision requirement and pull only what is needed. The question drives the data, not the other way around.
Second, visualization disappears from the critical path. Now it does not vanish entirely – humans still need oversight and suggested decision-making interfaces – but it’s no longer in the critical path. The machine does not need a choropleth map. It needs a clean, queryable, machine-addressable dataset.
Third, analysis becomes continuous, not episodic. Traditional geospatial delivers insight in reports and dashboards, these are reactive, discrete moments. The inverted chain runs continuously, updating decisions as conditions change.
Lastly, the human moves from processor to governor. The nature of this shift depends on the decision at hand. For lower-order, high-frequency decisions – monitoring, alerting, routine classification – the agent acts autonomously, at machine speed, without waiting for human input.
For higher-order decisions, the dynamic is different. Here the agent becomes what I think of as another brain in the room: synthesising data, surfacing options, and reasoning over context at a speed and scale no analyst can match. But the human remains in the loop, augmented rather than replaced, making the final call with better information than they have ever had before.
In both cases, the human moves upstream. Defining parameters, setting thresholds, and intervening at exceptions rather than sitting inside every decision loop. The value of human judgment does not diminish in the second horizon.
So, when we consider the flow, visually the contrast is:
Old: Data → [human makes sense of it] → Decision
New: Decision requirement → [machine pulls, processes, acts] → Human as governor, not processor
This is the second horizon in a nutshell.
Revisiting the Faster Horse
Let’s go back and consider the horse replaced by automobile. Consider the fuel source of each of these transportation modes: oats versus oil. Oats had to be consumed by something with a digestive system. Oil can be processed industrially, at scale, without biological limitation. Data today is still largely being ‘grown’ as oats: formatted, delivered, and visualized for human digestion. The second horizon requires it to be refined as fuel.
Think about that.
Now this is not meant to be a technical article, but we are moving towards a new processing layer. For those techies in the room, the architecture looks like this:
GeoParquet → machine-readable, cloud-native, queryable spatial data
Opus 4.7 is Anthropic’s latest model, notable for its ability to reason over meaning and relationships in data, not just pattern-match on it. It fits into this flow as follows:
Opus 4.7 → semantic reasoning layer that understands what the data means and how to use it in service of a decision requirement
That flow looks like this;
Decision Requirement → Agent → [queries GeoParquet] → [Opus 4.7 reasons over results] → Action or Human Decision
In other words an agent can receive a decision requirement, find and query the right data at machine speed, reason over what it means, and act or become that second brain in the room for a human. This closes the loop at a speed no human analyst can match.
This is decision velocity.
Winners and Losers
In my last article I mentioned the need for geospatial incumbents to maintain their horizon one focus, but to explicitly fund and resource the second horizon separately. That is part of tomorrows winning formula, but so too is where the incumbents sit today.
Let’s close this article out by considering that.
The incumbents with the most to lose today are those whose entire value proposition sits in the visualization and delivery layer or the final mile of the old chain. If the machine no longer needs the map, the map business faces an existential question.
But that is not the whole story.
The large global mapping and location data platforms have something the pure-play AI world desperately needs and consistently underestimates: deep, clean, structured geospatial data at scale. That is not a legacy liability. That is a foundation …
If they choose to build on it rather than defend around it.
Their opportunity is in becoming the semantic data infrastructure layer for autonomous and agentic systems. They already have the data. The question is whether they restructure it for machine consumption rather than human delivery.
The opportunity is different but equally real for those sitting on dense, structured 3D spatial data: point clouds, digital twins, high-resolution built environment data. This is exactly the kind of spatial data that world models need. The question for these companies is whether they position as visualization tools or as machine-readable spatial data layers for AI systems.
The losers will be those who answer the second horizon with a better dashboard. The winners will be those who recognize that the most valuable thing they own is not their platform, it is their data, restructured as fuel.
Closing Thoughts
The horse didn’t lose because it got slower. It lost because the world it was built for stopped being the world that mattered.
The geospatial industry has spent decades building extraordinary horses. The question now is not how to make them faster. It is whether the industry has the courage to build the automobile, and the wisdom to recognise that the data it already holds is the most valuable fuel in the tank.
The second horizon is not coming. It is here. The only question is who builds it.
Matt Sheehan
Matt Sheehan is a senior executive and AI strategist with over 25 years of experience leading complex organizations through technology-driven transformation. He specializes in moving AI from experimentation to enterprise-scale value — closing the gap between technology capability and measurable business outcomes. Matt has built the frameworks, the teams, and the delivery systems that turn AI pilots into scalable operating models, with a particular focus on decision velocity: compressing the distance between insight and action in environments where the cost of being slow is real.