AI Didn’t Break Delivery—Change Management Did | JAM

By Jordan Hauge — Published December 20, 2025 — Category: Execution Risk, Change Management

Recent high-profile outages weren’t “random tech failures.” They were reminders that the biggest constraint on AI-era progress is execution control: release discipline, change management, and decision ownership.During the last-minute holiday rush, Target reported significant app/website issues.In Australia, an inquiry into an Optus emergency-call outage tied failures to a firewall upgrade and called out change-management and process gaps.Cloudflare published a postmortem for a Nov 18, 2025 outage triggered by a bug in a configuration-generation path.If your organization is pushing AI initiatives while change risk is rising, the move isn’t “more velocity.”It’s stabilization: fewer active bets, clearer decision rights, and stability metrics that matter.

The pattern executives are reacting to right nowIf you lead product, engineering, or technology, you’ve felt the shift: the board wants AI outcomes, the business wants faster delivery, and your systems are changing more often than ever.Meanwhile, public reminders keep landing:Target faced widespread app/website issues during the final holiday shopping rush—when reliability is business-critical.An inquiry into an Optus emergency-call outage found failures during a firewall upgrade and issued recommendations focused on change-management and incident response gaps.Cloudflare documented a Nov 18, 2025 outage caused by a bug tied to Bot Management configuration generation—classic “small change, big blast radius.”Here’s the assertion (based on what these incidents have in common):In 2026, the bottleneck isn’t imagination. It’s controlled execution.Most organizations can identify AI use cases. Fewer can ship changes safely, repeatedly, and at scale.That’s also where current CIO guidance is pointing: align AI to outcomes, upgrade governance and data readiness, and pilot agentic systems in high-impact workflows.Why this is intensifying in the AI eraAI doesn’t just add features. It changes how systems behave and how quickly priorities shift.What we’re seeing across industries is a collision between:Pressure to operationalize AI (not just experiment)A change surface area that expands faster than release discipline matures.Responsible AI and governance are becoming “real work,” not slideware.PwC’s 2025 Responsible AI survey is a good example of how leaders are treating governance as operational practice (monitoring, inventorying use cases, ownership models).The takeaway:AI raises the cost of sloppy execution, because you are introducing more moving parts - models, data flows, orchestration and policy constraints into already-complex environments.Five early warning signals your initiative is at riskThe following signs aren’t theoretical. They’re the most consistent “pre-failure” signals we have seen across complex initiatives.Velocity is high, confidence is lowTeams are shipping, but leadership doesn’t trust forecasts. If no one will bet on the roadmap, you don’t have a plan... you have activity.Alignment exists in meetings, not in decisionsEveryone agrees until a real tradeoff appears. Then decisions stall, escalate, or get revisited repeatedly. That’s a decision-rights problem.Stability is degrading while output stays steadyIncidents, rollbacks, and operational load rise while the roadmap expands. This is how initiatives become fragile without looking “blocked.”If you’re looking for a practical measurement model, DORA’s “Four Keys” (deployment frequency, lead time, change failure rate, time to restore) exists specifically to track speed & stability together.Busy teams, unclear outcomesCalendars are full. Status is frequent. Yet “what success looks like in 90 days” varies by leader. This is how initiatives drift.Technical debt is being negotiated, not reducedDebt exists everywhere. The risk signal is when it becomes normalized: a permanent tax on velocity and reliability. Even mainstream research and industry reporting repeatedly highlight the productivity cost of “bad code” / tech debt as a persistent drag.What “stabilization” actually means (and what it isn’t)Stabilization is not “starting over.”It’s not a reorg. It’s not a new process rollout.Stabilization is regaining control without disrupting momentum.In practice, that usually means four things:Reduce the number of active betsMost stalled initiatives are running too many workstreams in parallel. Narrowing scope is not retreat. It’s risk management.Clarify decision ownershipWho decides when priorities conflict? If the answer is “the steering committee,” you’ve outsourced decisions to delay.Sequence change to reduce blast radiusBig-bang releases feel decisive. They’re often where initiatives go to die.Treat stability as a first-class metricNot vanity dashboards or metrics tied to delivery integrity (change failure rate, time to restore, production incident trends).A 15-minute executive triage (use this today)If you’re an exec sponsor or a functional leader, run this with your leadership team. No prep.What is the single most important outcome in the next 90 days?Who has final decision authority when priorities conflict?What are the top 3 risks that could make this miss?If we cut scope by 25%, what stops? (Be specific.)What decision are we delaying that will be harder in 30 days?Are stability and delivery trending in the same direction? (If you don’t know, that’s an answer.)Which dependency is most likely to surprise us?What are we pretending is true?If you can’t answer #1 or #2 quickly, your issue is control. Everything else is downstream.Why this matters for budget holdersThis isn’t a “best practices” conversation.It’s an economic one.Large-scale IT initiatives frequently run over budget and under-deliver on value. McKinsey’s analysis of 5,400+ IT projects with Oxford’s BT Centre is widely cited for a reason.So when reliability incidents hit at the worst time (holiday retail) or change control fails in critical infrastructure (emergency calls) the lesson executives absorb is simple:The cost of uncontrolled change is now too visible to ignore.Where JAM fitsJAM supports organizations when initiatives are already in motion and execution risk is real; stabilization, transformation, and senior product/engineering leadership.If this article describes what you’re seeing, the highest-value next step isn’t a giant discovery process.It’s a short conversation to identify which signal matters most right now and what sequence will reduce risk fastest.FAQIs this article saying “don’t move fast with AI”?No. It’s saying you can’t scale AI-driven change without execution control. CIO guidance is increasingly emphasizing governance and readiness alongside AI outcomes.What if we already have strong DevOps?Great.Stabilization is usually about decision rights, scope discipline, and sequencing. DORA metrics help confirm whether speed and stability are moving together.What’s the fastest thing we can do next week?Run the triage questions above and commit to reducing active bets. Then align decision ownership in writing.