For more than a decade, I spent thousands and thousands of dollars buying products from a consumer technology company that did well over $1.5 billion in annual revenue. I didn’t only buy their products, I recommended them. To friends. To colleagues. Sometimes to strangers who would listen.
My experience used to be exceptional when using this app-driven equipment. Seamless and invisible in the best way. Then, over 18 months ago, something happened.
Something broke.
Not the hardware, but the software used to control it. These products I’d sworn by and used in my home for over ten years began to randomly disappear and reappear in the app. Tried and true actions became unreliable. What was once effortless and joyful turned into a seething source of angst.
Sometimes, when things go wrong, a fresh start is needed. Not just the stalwart move of “unplug it and plug back in,” but a good, old-fashioned factory reset. Then the extra effort of setting it up like new.
I tried that with high hopes after a major move to a new house. After hours of factory resetting eight pieces of equipment and installing the entire setup from scratch, eager to experience the great satisfaction of a working system, the problems persisted.
Ugh. (Not the actual word used, but that one is safe for work.)
Four weeks ago, a “Happy Holidays” email arrived from the company’s new CEO. After finishing the holiday niceties in the first paragraph, two sentences in the second one stood out:
“…we’ve recently fallen well short of the standard you expect from us, and that we expect of ourselves. There have been many moments this year that reminded us how much your trust matters.”
Wait. What?!?
Quick research revealed that my equipment problems weren’t an isolated event. The system issues I experience exposed the visible cracks of a much deeper, software-driven failure. One that triggered widespread customer backlash, intense community outrage, revenue impact, layoffs, and leadership changes.
Did I mention the holiday email was from a new CEO?
This revelation had my attention and makes this event worth studying. Not to shame a company. Not to pile on. But to learn how this happens.
And how to prevent it.
Because it will happen again. To other companies.
But let’s hope not yours.
Why This Kind of Failure is Becoming More Common
My firm, Zenergy Technologies, spends all our time helping organizations prevent these kinds of failures. Reading about this twisted my insides. Not because it was unique, but because it is painfully familiar.
Modern software systems are more complex than ever. Products that were once mostly hardware are now software platforms. These platforms include apps, cloud services, firmware, APIs, security layers, discovery protocols, and more.
And everything is interconnected.
Add AI-assisted development into the mix, and velocity can increase dramatically.
For the record, I’m excited and optimistic about AI. But speed without guardrails doesn’t reduce risk. It multiplies it.
Until every AI provider stops using the caveat to “Double-check all responses. AI can make mistakes,” we must do the same thing we’ve always done: Use humans to verify, validate, and test. Not only at the end, but throughout the entire lifecycle.
What Went Wrong (at a high level)
A single bad decision didn’t cause this. A chain reaction of bad ones did.
The list of bad decisions is long:
- A full app rewrite with no rollback path or plan
- Core functionality removed or broken
- Major architectural changes that introduced latency and fragility
- Device discovery failures that made products disappear
- A shift from local control to cloud-dependent workflows
- Performance penalties on older but still-supported hardware
- Strong warnings from engineers that were ignored
- Quality and research teams reduced at the worst possible time
- And a hard business deadline tied to a launch that overrode readiness.
Each decision could be defensible in isolation. But together, they brewed up a perfect storm.
Perhaps the most dangerous assumption was this one:
“We can fix it after launch.”
That mindset might work for greenfield products. It does not work when millions of paying customers already rely on your system daily.
Trust, once broken, is expensive to rebuild.
Seven Ways Companies Can Avoid This Fate
This is the part that matters most. These lessons apply whether you’re a startup or a global brand.
- Never rewrite everything without a safety net. Complete rewrites are among the riskiest moves in software. If you must do one, ensure compatibility layers, phased rollouts, and real rollback paths exist. “No rollback” is not a technical problem. It's a leadership failure. One that can lead to a crisis.
- Protect core workflows above all else. Innovation is meaningless if the basics don’t work. New features should rarely ship at the expense of foundational ones.
- Listen when experienced engineers say “It’s not ready.” When senior engineers raise concerns, that is a smoke signal, not resistance. Ignoring those warnings doesn’t make risks disappear. It packages them up and hands them to customers.
- Decouple software readiness from hardware marketing. If software isn’t ready, delay the launch or create a separate path. Forcing unfinished software onto an installed base to meet a marketing date is how goodwill goes “poof” overnight.
- Treat QA as a tool that makes everything else work better instead of a line item to trim. Testing is not a phase. It’s a discipline. Cutting QA and research during a major architectural change is like removing instruments mid-flight to reduce aircraft weight.
- Design for the real world, not ideal networks. Homes, offices, dorms, and small businesses can have messy networks. If your product only works under perfect conditions, does it truly work?
- Treat trust as a first-class metric. Revenue, growth, and innovation matter, but trust compounds. Once lost, customers don’t only leave. They warn others. Loudly.
Final Thoughts
Every company makes mistakes, even the good ones. The great ones learn before the market forces them to.
This case wasn’t about incompetence. It was about speed overtaking judgment, ambition trampling safeguards, and leadership forgetting how fragile customer trust is.
Software now runs our homes, our businesses, and increasingly our lives. As complexity rises and AI accelerates development, the need for human verification, validation, and thoughtful system design has never been greater.
The companies that understand this won’t only avoid disaster and become a cautionary tale, they’ll earn loyalty that survives their inevitable, but smaller mistakes.
For any leader who wants to preempt a potential software failure, here’s a gut-check(list) that will come in handy. Use this as a pre-launch, mid-program, or post-incident review. If you can’t confidently answer “yes” to most of these, the risk may already be higher than you think.
GUT-CHECK(LIST) FOR LEADERS
STRATEGY & LEADERSHIP
• Do we have an explicit definition of “ready” that cannot be overridden by marketing or revenue deadlines?
• Have we identified which customer workflows are non-negotiable and must never degrade?
• Are trust, reliability, and customer confidence tracked as first-class business metrics?
• Do senior leaders understand the technical tradeoffs being made, or only the delivery date?
ARCHITECTURE & CHANGE
• If this is a major rewrite, do we have a real rollback path, not a theoretical one?
• Can customers continue using existing functionality during migration?
• Have we tested backward compatibility with older but still-supported hardware?
• Are we introducing new dependencies (cloud, latency, discovery services) that materially increase fragility?
ENGINEERING & DECISION MAKING
• Have senior engineers formally reviewed and signed off on readiness?
• Were any critical warnings raised, and if so, how were they resolved?
• Are we rewarding speed alone, or speed with quality and sustainability?
• Do engineers feel safe escalating concerns without career risk?
QUALITY, TESTING & REAL-WORLD USE
• Do we test under real-world conditions, not just ideal lab environments?
• Have QA and user research capacity increased alongside system complexity?
• Are customer beta programs representative of actual usage, not just power users?
• Have we tested recovery scenarios as rigorously as success paths?
AI & ACCELERATED DEVELOPMENT
• Where AI is used, do we have mandatory human verification and review?
• Are AI-generated components treated with the same scrutiny as hand-written code?
• Do we understand where AI increases speed but also increases hidden risk?
CUSTOMER IMPACT & COMMUNICATION
• Do customers clearly understand what’s changing and why?
• Is there an opt-out, delay, or phased rollout for high-impact changes?
• If something breaks, do customers feel heard, respected, and informed?
• Are support teams empowered with real answers, not scripts?
FINAL GUT CHECK
• If you were a long-time customer, would you feel excited or anxious about this release?
• If this failed publicly, would you be confident explaining the decisions behind it?
• Are you shipping because you’re ready or because you’re out of time?
