Relocating Chaos: Why Automation Pushes Uncertainty to the Edges
How automation shifts disorder from the center to the edges—and why governance must follow
On paper, automation looks like a force for order. Workflows are digitized, sensors are installed, algorithms optimize routes and schedules, and dashboards promise “real‑time” control. In reality, the moment automation meets the physical world, uncertainty reasserts itself.
The routes cross roads with traffic and weather, sensors drift out of calibration, and human supervisors quietly improvise around rigid logic. As I wrote in my last post about field operations, chaos is not a bug – it is the nature of systems that involve people, weather, traffic, customers and regulators. Most attempts to digitize or “add AI” to field operations fail not because the technology is weak but because the underlying governance is weak.
Automation does not reduce chaos; it relocates it.
The myth of clean automation
Efficiency narratives around AI and automation imply that more software means less chaos. Vendors point to demand forecasting, route optimization and “human‑in‑the‑loop” interfaces. There is real value here. For example, logistics analysts note that companies improved demand forecasts in 2025 by combining external signals (weather, sports schedules, local events, social sentiment) with store‑level inventory data. AI‑assisted routing engines generated alternate transport scenarios faster than human planners, especially during port congestion or road closures. Visibility platforms using predictive ETA models and anomaly detection filtered false alarms, clustered related delays and highlighted late‑stage risks. In short, AI helped surface uncertainty sooner and compress decision cycles.
AI did not eliminated surprises. Exception volumes dropped because thresholds were better aligned with operational reality, but AI did not prevent the underlying variability in demand, traffic, weather or human behavior. Multi‑agent pilots suggested targeted inventory moves across distribution centers, yet planners still made final decisions. The most reliable gains came from small, well‑defined bottlenecks. Automation changed who handled the chaos and when, but not whether the chaos existed.
Pushing chaos to the edges
When an automated system runs, the messy parts don’t vanish — they migrate to the edges of the system. These edges are where sensors meet reality, where humans improvise around rigid models, and where policies are tested against infinite edge cases. Consider the boom in robotaxis. Analyst Phil Fersht notes that robotaxis require us to surrender control over life‑and‑death decisions at scale. Waymo and Baidu vehicles have logged millions of miles, yet they still miss school buses or cats and collide with pedestrians. These failures aren’t because the AI can’t drive in controlled environments; they occur at the edges, where sensors encounter unmodeled situations and society hasn’t agreed who is liable when algorithms get it wrong.
Even the designers of autonomy acknowledge this. At the 2025 AI & Autonomy Summit, DARPA program manager Phillip Smith remarked that “machines are supposed to be serving humans, and humans don’t even know what they want — that’s a really hard thing”. The problem is not that software lacks rules; it’s that human intent is vague, context‑dependent, and often contradictory. Automation pushes complexity to the points where intent meets execution. When sensors detect only part of a situation, when policies assume edge cases away, or when humans override AI recommendations, the chaos reappears — just outside the scope of the algorithm.
The trust and accountability paradox
Robotaxis highlight another dimension of relocated chaos: societal trust. Despite millions of autonomous miles driven, consumers remain hesitant to entrust their lives to algorithms. The technology is improving, but accountability is unresolved. Robotaxi providers operate in a regulatory patchwork where state and federal rules conflict. When accidents occur — a cat killed, a school bus passed illegally, a pedestrian struck — no one knows whether the liability lies with the manufacturer, the city that approved the route, or the passenger who chose to ride. As Fersht observes, the technology is moving faster than society’s ability to adapt, regulate or trust it. Both the U.S. and China wrestle with the trade‑off between scale and trust. Scaling quickly without trust is dangerous; building trust without scale is pointless. The paradox is that society tolerates human drivers’ mistakes because we understand them and feel we can intervene. With robots, there is no negotiation or eye contact — just silent execution of code. Trust, like chaos, has been pushed to the edge.
When AI failures are really governance failures
Research on enterprise AI deployments in 2025 was damning.
An ISACA analysis found that the biggest AI failures of 2025 were not technical but organizational: weak controls, unclear ownership and misplaced trust. The authors argued that success in 2026 requires strengthening how we plan, govern and deploy these systems.
A meta‑analysis of AI initiatives noted that more than 80 % of AI projects failed, not because models were inadequate but because leaders treated AI as a “model problem” rather than a foundation problem.
Forrester reported that only 15 % of AI decision‑makers saw an EBITDA lift from AI. The pattern that separated winners from losers was simple: those that succeeded invested heavily in data readiness, governance, metadata quality and semantic clarity. Many allocated 50–70 % of their AI budgets to these foundations.
In other words, what we call “AI failures” are often governance failures that technology exposes. The technology works as designed; it simply reveals ambiguities, conflicting incentives and missing context. The human‑machine field operations matrix in my last post showed that as we move from human to hybrid to machine field agents, the tolerance for informal rules collapses. Humans can improvise around ambiguity; machines cannot. When rules are implicit or contradictory, machines execute them literally, stall on ambiguities and force organizations to confront uncomfortable accountability questions.
Autonomy in logistics: micro‑wins and macro limits
The logistics sector illustrates how automation relocates chaos rather than removing it. AI pilots have delivered measurable improvements: better demand forecasting through signal expansion, AI‑assisted routing that reduces planner workload during disruptions, and document‑intelligence systems that accelerate customs and compliance workflows. Exception identification systems reduce noise by filtering false alarms and clustering related delays. These are real, valuable wins.
Yet none of these systems operate in isolation. They rely on clean data, clear policies and human judgment. Forecasts improved because teams included weather, sports schedules and social sentiment — context that the algorithms alone could not infer. Routing engines still require humans to choose among AI‑generated options, and their performance depends on up‑to‑date traffic and weather feeds. Visibility platforms reduce false alarms by aligning alerts with operational thresholds, which is itself a governance decision about what constitutes an exception. In 2026, analysts expect AI capabilities to be embedded directly into transportation management systems and warehouse management systems, with tools dynamically weighting service, cost and emissions. They also expect context‑retention protocols (like the Model Context Protocol) and graph RAG techniques to maintain continuity and understand relationship‑rich data. These trends reinforce the point: as automation scales, the importance of context, boundaries and governance increases, not decreases.
From chaos to governed chaos
If automation relocates chaos to the edges, then governance must relocate order to those edges. The lessons from 2025 point to a few principles:
Start with outcomes, not experiments. Define the business result you need and assign a named owner. Don’t deploy AI for its own sake.
Invest in foundations. Data readiness, metadata quality, and semantic clarity determine whether AI systems can interpret context. Allocate budgets accordingly.
Make governance explicit. Keep an inventory of every model, agent and automation, and govern them with standards and approvals. Review what each system can do, where it can act and who might be affected.
Design for edge cases. Assume sensors will fail and human improvisation will occur. Model handoffs between machine and human agents. Ask yourself: if a machine followed our current rules perfectly, would we be comfortable with the outcome? If the answer is “it depends,” governance is not ready.
Share responsibility. AI requires cross‑functional stewardship — business, technology, risk and communications all need roles. Include third‑party risk reviews in every AI purchase.
Build resilience. Detect problems early, communicate what happened and fix issues quickly. Capture near misses and update processes to prevent repeat failures.
The future of automation and autonomous systems is not about eliminating chaos. It is about governing how chaos expresses itself. Machines will continue to execute rules literally, expose hidden gaps and demand explicit boundaries. Autonomous vehicles will drive, drones will inspect, and AI agents will propose procurement strategies. But unless organizations invest in governance and context, automation will simply shift uncertainty to the edges, where the consequences are more visible and more dangerous.
Chaos can’t be eradicated. It can be channeled. Automation relocates it; governance must accompany it. Organizations that learn this lesson will harness AI’s power without being blindsided by its edges. Those that don’t will find that the hardest part of automation was never the technology — it was the leadership debt they ignored.
Humans absorb ambiguity.
Machines surface it.
Autonomous systems punish it.
Appendix 1: Where does chaos live in your system?
Before adding more automation or AI, ask yourself where chaos currently lives in your operations.
Ask these questions honestly.
1. Decision edges
Where do people routinely override plans, routes, schedules, or recommendations?
Are those overrides logged—or do they disappear into “experience”?
Signal of risk:
If overrides are common and undocumented, chaos already lives at the edge.
2. Sensor edges
Which inputs do you trust by default?
What happens when a sensor is wrong, late, or missing?
Signal of risk:
If “bad data” is handled informally, automation will amplify it.
3. Exception edges
What percentage of your operations are treated as “exceptions”?
Who decides what qualifies as an exception—and when?
Signal of risk:
If exceptions are resolved through chat, calls, or heroics, AI will fail loudly here.
4. Accountability edges
When something goes wrong, can you answer who decided what?
Or do you only see outcomes, not decisions?
Signal of risk:
If accountability is narrative-based, autonomy will force uncomfortable questions.
5. Incentive edges
Do KPIs reward local optimization over system health?
Do people get punished for following rules that lead to bad outcomes?
Signal of risk:
If incentives and intent diverge, automation will accelerate bad behavior.
Automation will not fix the areas where your answers felt uncomfortable.
It will move them into production.
Appendix 2: Common failure patterns (and what to do instead)
Pattern 1: “Human-in-the-loop” as a patch, not a design
What organizations do
Add AI
Let humans override it
Call it “safe”
What actually happens
Humans silently compensate for bad logic
No one fixes the root problem
Trust erodes
What to do instead
Treat overrides as governance signals
Log, classify, and design them explicitly
If humans must intervene, define when, why, and with what authority
Human-in-the-loop is not a safety feature if you don’t govern the loop.
Pattern 2: Automating outcomes instead of decisions
What organizations do
Track KPIs
Optimize results
Ignore how decisions are made
What actually happens
Systems “work” until context changes
Failures are inexplicable after the fact
What to do instead
Capture decision context, not just outcomes
Make decision rules auditable
Design for post-mortems before incidents
Pattern 3: Treating autonomy as a maturity upgrade
What organizations believe
“We’ll automate once we’re ready”
“AI is the next level”
Reality
Autonomy raises the bar
It doesn’t forgive immaturity—it exposes it
What to do instead
Introduce autonomy where governance is strongest, not weakest
Start with bounded, observable domains
Expand only when exceptions are understood
Before automating a process, ask one question:
If a machine followed our current rules perfectly, every time, would we accept the result?
If the answer is “it depends,”
the problem is not AI.
It’s governance.
Sources & Further Reading
ISACA (2025) – Avoiding AI Pitfalls in 2026
Why most AI failures are organizational and governance-related, not technical.
https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2025/avoiding-ai-pitfalls-in-2026-lessons-learned-from-top-2025-incidentsMetadata Weekly (Dec 2025) – The 2026 AI Reality Check
Data on why AI pilots fail without strong governance, data, and context foundations.
https://metadataweekly.substack.com/p/the-2026-ai-reality-check-its-the
Logistics Viewpoints (Dec 2025) – What Actually Worked in AI for Logistics
Practical analysis of where AI delivered value—and where it didn’t.
https://logisticsviewpoints.com/2025/12/22/ai-in-logistics-what-actually-worked-in-2025-and-what-will-scale-in-2026/HFS Research (Dec 2025) – Robotaxi Chaos and Accountability
Autonomy as a governance and trust problem, not just a technology problem.
https://www.horsesforsources.com/robotaxis_122025/University of North Dakota – AI & Autonomy Summit (2025)
Industry and DARPA perspectives on the difficulty of encoding human intent into machines.
https://blogs.und.edu/und-today/2025/10/ai-autonomy-summit-showcases-grand-forks-as-national-hub/