Confidence Engineering - Part 3: Adoption Déjà Vu
Confidence Without Authority Is Just a Dashboard
The Collision You Can See Coming
Part 2 ended with a confession: this framework is a work in progress. We’re learning in real time. The technology is new and the patterns haven’t fully emerged.
But here’s the thing. The technology is new. The organizational failure mode isn’t.
I’ve spent twenty years watching companies adopt new operational paradigms. Monitoring. DevOps. Cloud governance. SRE. The technology changed every cycle. The way organizations failed to adopt it didn’t.
I can tell you exactly what’s going to happen with AI governance. Not because I’ve solved it. Because I’ve watched this movie before.
You’re going to build the instrumentation. Accuracy metrics, rollback triggers, staged authority gates. Leadership will nod approvingly at the dashboard.
Then nothing will happen.
The AI will sit in “suggest” mode for eighteen months. Not because the metrics are bad. Because advancing to “approve” requires a signature nobody knows how to get.
Legal will wait for security to bless it. Security will wait for compliance to scope it. Compliance will wait for the business to accept residual risk. The business will wait for “IT” to tell them it’s safe. Platform teams will hold a dashboard nobody asked for and nobody acts on.
This is the same pattern that killed SRE adoption at most organizations. Separate function, good metrics, no authority to act. The dashboards were green. The incidents kept happening. The feedback loop never closed because nobody owned the decision to change anything.
Confidence engineering without decision rights is SRE all over again. Different technology. Same organizational failure.
What You’re Going to Hit
Let me tell you the walls you’re about to run into.
The ownership vacuum. Your staged authority model has gates. Who can open them? In most organizations, nobody will want that job. Approving advancement means owning the outcome if it fails. In cultures where failure is punished, nobody volunteers for the firing squad. So the gates stay closed. Forever.
The accountability dodge. Leadership will ask for “shared ownership” across legal, security, compliance, and the business. Shared ownership means no ownership. When the first serious failure happens, it won’t be an operational incident. It will be a political crisis. Everyone will point at everyone else. The postmortem will produce nothing actionable because the root cause is “nobody was empowered to decide.”
The sentiment disconnect. Your metrics will be green. Leadership will still be nervous. You’ll think they don’t understand the data. They’ll think you don’t understand the risk. You’re both right. The metrics measure capability confidence. Their concern is liability, reputation, career exposure. You’re answering a question they didn’t ask.
The shadow adoption. While governance stalls, practitioners will route around controls. They’ll use AI tools without approval because the approval process is too slow, too unclear, or too risky to engage with. This is exactly what happened with cloud adoption. And SaaS adoption. And every other technology wave where governance couldn’t keep pace with capability.
I’ve watched all four of these patterns play out across monitoring, DevOps, cloud, and SRE adoption. Same song, different verse.
The Preconditions You Probably Don’t Have
I wrote in the Decide or Drown series about preconditions. Servant leadership. Psychological safety. Customer obsession. Willingness to measure honestly. Without these, no framework succeeds.
The same preconditions that killed every other adoption will kill your AI governance.
If people can’t speak honestly about what’s not working, you won’t learn from failures. The feedback loop breaks before it starts.
If leadership won’t act on evidence when the answer is uncomfortable, your metrics are decoration. Green dashboards, unchanged behavior.
If accountability means blame, nobody will own the gates. Diffuse ownership feels safer. It isn’t. It just means failures become political instead of operational.
If decisions get revisited every quarter based on who’s in the room, staged authority becomes staged chaos. You’ll advance and retreat based on sentiment, not evidence.
Because you don’t “trust” it.
Part 2 gave you the engineering. But engineering assumes someone will act on what it produces. That assumption may not hold.
What Accountability Actually Means
Here’s the fork in the road.
Most organizations are going to treat AI governance the way they treat every other operational risk: diffuse ownership, endless committee review, and nobody empowered to say yes.
The organizations that actually move will figure out something different. Accountability that enables action instead of preventing it.
I wrote about blameless postmortems in the Platform Resiliency series. The principle applies here. When incidents become blame exercises, people hide information. When people hide information, the feedback loop breaks. When the feedback loop breaks, the same failures recur.
Named accountability isn’t about having someone to punish. It’s about having someone empowered to act on evidence, adjust thresholds, and feed learnings back into the model.
The question most organizations won’t ask: who has the authority to advance through staged gates, and will they actually use it?
If failure is career-ending in your culture, the answer is nobody. The gates stay closed. The AI stays in limbo. And everyone else who figured out accountability will move faster.
Sentiment as Warning Signal
Part 1 argued against measuring stakeholder feelings as your primary confidence signal. That’s still true.
But watch sentiment anyway. Not to judge the AI. To judge your governance model.
When practitioners route around controls, that’s a signal your staged authority is too restrictive or your approval process is too slow. The shadow usage isn’t defiance. It’s feedback you’re not collecting.
When leadership stays nervous despite green metrics, that’s a signal your evidence isn’t addressing their actual concern. They’re worried about liability, not accuracy. The dashboard is answering a question they didn’t ask.
When adoption stalls despite clear metrics and defined gates, that’s a signal the decision rights are unclear or the accountability is too diffuse. People aren’t resisting the AI. They’re resisting the ambiguity about who can say yes.
These are the early warning signs. By the time they’re obvious, the pattern is entrenched.
The Question Underneath the Question
You came to this series asking whether to trust AI.
Part 1 told you that’s the wrong question. Replace it with: what evidence and controls would justify relying on this system for this scope?
Part 2 gave you the engineering. Metrics, instrumentation, staged authority.
Part 3 is telling you what’s going to happen next.
You’re going to build the instrumentation. You’re going to hit the governance wall. You’re going to discover that the blocker was never AI confidence. It was whether your organization trusts itself to handle failure.
Can people speak honestly about what’s not working without fear? Will leadership act on evidence when the answer is uncomfortable? Does accountability mean ownership of learning, or ownership of blame?
These aren’t AI questions. They’re the same questions that determined whether your organization successfully adopted DevOps, or cloud governance, or SRE, or any other operational paradigm that required organizational change alongside technical change.
The technology is new. The failure mode is ancient.
Trust your governance model. Have confidence in your systems.
The Fork
If your organization has the preconditions, this series gives you a path: reframe the question, build the instrumentation, figure out decision rights before you need them, and advance through staged authority as evidence accumulates.
If your organization doesn’t have the preconditions, you have a different problem. One I wrote about in Decide or Drown Part 4. Recognizing improvable environments versus unsalvageable ones. The same diagnostic applies.
Some organizations will use AI governance as the forcing function to finally build the accountability structures they should have built years ago. The technology pressure creates the organizational urgency.
Some organizations will fail at AI governance the same way they failed at everything else. The technology will be blamed. The organizational dysfunction will continue.
I can’t tell you which one you’re in. But I can tell you the collision is coming. What you do before you hit it determines whether it’s a course correction or the Titanic.
Photo by the blowup on Unsplash