Mission-Ready Data: What Space Systems Teach Us About Scaling Agentic AI
Agentic AI – and scaling it for tangible value – requires data that is safe enough to act on, not just clean enough to report.
At Zero100, people know me as something of a space geek. It’s personal, but it’s also professional. Before joining Zero100, I led technology and operations teams at Amazon and Microsoft, then worked on Amazon’s Project Kuiper, now Project Leo, helping build the first low-earth orbit satellite. What stayed with me from that work was not just the ambition to “work hard, have fun, make history.” It was the deep operating discipline required to ship product to space.
On Amazon Kuiper, we were building a digitally connected manufacturing environment in real time. We stood up the broader SAP-MES-E2Open process backbone while engineers iterated designs at an incredible pace. Looking back, we succeeded in part because of a team that loosely resembled a Zero100 fusion team. Wizards were building software and processes in real time, Citizens contributed critical components from inside the workflow, and Translators kept the system coherent and the work moving. It was fast, cross-functional, and grounded in the reality that when systems start driving action, trust is not optional.
Recently, I was watching the launch of NASA’s Artemis II with my kids. Like most kids, they were focused on the rocket. I was thinking about everything behind it. In space systems, the visible moment is the launch. The real work happens beforehand: validating interfaces, testing tolerances, sequencing dependencies, simulating failure modes, and defining exactly what the system should do when reality does not match the plan.
Enterprise AI is entering a similar moment – and the systems here are dependent on data. The leaders in our community know it too, with a quarter of our recent conversations over the last six weeks centering on whether underlying data, business context, permissions, and exception logic are strong enough to support safe agentic action.
From Report-Ready to Flight-Ready
For the last twenty years, most enterprise data has been built for human interpretation. Dashboards, reports, planning systems, control towers, and exception alerts all assumed the same operating model: the system surfaces information, and a person decides what to do next.
Agentic AI changes that model. Once systems can reason across workflows, recommend interventions, trigger processes, and eventually execute bounded decisions, data stops being just a reporting asset. It becomes part of the operating surface of the company, which requires a higher standard.
Our latest data readiness research posits that the ultimate standard is whether your data is safe enough to delegate decisions to machines that can take autonomous action. Dashboards could survive dirty data because humans were still in the loop, but agents cannot. In the dashboard era, bad data created bad meetings. In the agentic era, bad data can trigger the wrong actions at machine speed, compounding risk across workflows, customers, and financial performance. Despite this, just 11% of the supply chain and operations leaders we surveyed as part of our AI Blueprint™ process say their data is ready for initiatives.
From Observation to Action
Many companies are circling concepts like semantic layers, golden inputs (the smallest set of fields that directly drive a decision’s action), shadow mode (running an AI model behind a human proxy), knowledge graphs, approval boundaries, and decision rights. The aim is to solve the deeper problem: creating enough trust for a system to move from observation to action.
This is also where the connection to Zero100’s PowerThreads becomes important. If PowerThreads represent the end-to-end workflows where companies are concentrating the vast majority of AI investment, then flight-safe data determines whether those investments remain analytical pilots or become operational capabilities. In other words, PowerThreads help identify where to place the bet, while action-safe data determines whether that bet can scale safely.
That challenge is especially visible in supply chain and operations, where the cost of acting on a weak signal compounds quickly. For instance, a demand planning agent should not revise a forecast simply because it detects a spike. It needs to know whether the spike reflects real demand, a promotion, channel stuffing, customer pull-forward, weather effects, or a bad signal.
The question is not whether the data exists somewhere in the enterprise, but whether the system can trust it, interpret it correctly, and use it safely enough to act. This means asking which decisions are even ready for bounded autonomy and, then, what evidence is required to delegate them. The latter should include considerations like the level of data quality and control.
The Building Blocks Are in Place
For executives, the implication is straightforward. In space or otherwise, systems must be grounded in trust. For agentic AI, competitive advantage will not come from a flashy agent demo. It will come from building decision systems that agents can safely participate in.
Three next steps are more useful than a broad transformation plan:
- Start with one or two high-value workflows. Prioritize the workflows inside the PowerThreads where AI investment and enterprise value are already concentrating.
- Define the golden inputs and ownership model. Identify the small set of signals that actually drive the decision, then assign clear decision owners, stewards, and custodians.
- Run in shadow mode before scaling autonomy. Test recommendations, monitor overrides, tighten thresholds, and prove the system is safe before expanding its authority.
In the agentic era, the cost of getting this wrong is not a bad insight, it is a bad action. Before agents can help run the business, data has to clear mission control.