The Glitch Report: What Went Wrong in Tech in 2025 (and How We Fixed It)
The tech world loves to celebrate breakthroughs, but 2025 reminded everyone that even the sharpest minds can stumble—hard.
On Techronicler, we’ve gathered raw, unfiltered reflections from business leaders, thought leaders, and tech professionals who faced real setbacks: algorithm whiplash, AI misfires, scaling traps, and strategy misreads.
What fascinates most is not the failures themselves, but the ruthless honesty in how they dissected the damage and rebuilt stronger.
These stories expose the hidden costs of speed, hype, and assumption in a year that punished complacency.
Curious how a single misstep in trust, timing, or tech choice can wipe millions, yet spark the most disciplined pivots?
Dive into Techronicler’s collection of hard-earned lessons—where vulnerability meets strategy, and 2026 is already being engineered differently.
Read on!
Overengineered Sensors Triggered False Alerts
Our biggest tech challenge in 2025 was overengineering our sensor calibration system for the GermPass units.
We added predictive algorithms to anticipate wear patterns, but it actually slowed down our real-time response and caused false maintenance alerts.
Facilities were getting notifications when nothing was wrong, which killed trust in the system.
I pulled the plug after two hospitals complained about unnecessary service calls.
We went back to our original direct-feedback model—simple sensors that report actual performance, not predicted problems.
Our false alert rate dropped 87%, and customer satisfaction scores jumped immediately.
For 2026, I’m keeping our tech philosophy brutally simple: if it doesn’t directly kill germs faster or make the system more reliable, we don’t add it.
When your friend dies from touching a door handle like mine did, you realize the only metric that matters is whether the surface is actually clean after someone touches it—not how smart your dashboard looks.
Debra Vanderhoff
Founder & COO, MicroLumix Bioscience Technologies
AI Explainability Gap Slowed Enterprise Deals
Our biggest slip in 2025 came from underestimating how quickly enterprise buyers would shift their expectations around AI explainability.
We focused heavily on predictive power and automation inside Upside, but we did not invest early enough in transparency features that help revenue leaders understand why the system reaches certain conclusions.
This slowed some enterprise sales cycles and forced us to adapt mid year.
For 2026, we rebuilt our product roadmap around clarity and trust, adding interpretable models, auditability, and scenario based insights.
We also expanded customer feedback loops inside our design reviews.
The goal is to ensure the technology drives confidence, not just efficiency, and that the platform evolves in lockstep with the real needs of operators.
Mădă Seghete
Co Founder & CEO, Upside
Untested Rollout Tanked Inventory Accuracy
Our biggest tech misstep in 2025 was rolling out a new VMI inventory tracking system across 15 customer locations without proper pilot testing.
We pushed it live to hit Q1 goals, and within three weeks our restocking accuracy dropped from 94% to 71%.
Contractors were running out of critical parts mid-job because the algorithm couldn’t account for seasonal demand patterns we’d learned over decades.
The worst part? We had a 19-year warehouse manager in Salt Lake whose manual system was actually outperforming our new software.
We’d spent six figures on tech that made us worse at the exact thing that differentiated us—knowing what our customers need before they do.
For 2026, we completely changed our approach.
Now we pilot any system at just 2-3 locations for a full quarter, and our veteran team members have veto power if the tech doesn’t match real-world job site conditions.
We’re also building a hybrid model where the software handles routine restocking but our people make the judgment calls on project-based inventory.
The lesson: in the trades, relationships and experience trump algorithms.
Our customers don’t care how sophisticated our backend is—they care that we show up with the right copper fitting when their crew needs it at 6 AM.
Jacob Reese
Vice President, Standard Plumbing
Overbuilt Dashboard Killed Partner Adoption
In 2025, we rolled out a new analytics dashboard meant to help university partners track enrollment metrics and ROI in real-time.
The problem? We built it based on what we thought provosts and program directors needed, not what they actually asked for.
Usage was dismal—about 15% adoption—because it was too complex and didn’t answer their most urgent question: “Are we on track for breakeven?”
The real damage wasn’t the development cost. It was the credibility hit when partners asked basic questions our fancy dashboard couldn’t answer simply.
We had designed for comprehensiveness when they needed clarity.
Faculty coaches started creating their own Excel sheets because our tool slowed them down.
For 2026, we scrapped the feature bloat and rebuilt around three core metrics our partners consistently ask about: enrollment pipeline health, faculty utilization rates, and projected breakeven timeline.
We involved actual program directors in weekly build reviews—not just at the end.
Now we’re seeing 78% regular usage because it saves them time instead of creating more work.
The lesson: technology should reduce friction, not create homework.
When your users build workarounds, your solution is the problem.
Cheryl Cassaly
VP of Marketing, Rehab Essentials
AI Inventory Ignored Driver Expertise
Our biggest tech slip in 2025 was trusting a “smart” inventory management system that promised to predict micro market restocking needs using AI.
It kept suggesting we stock energy drinks during a client’s wellness month and flagged fresh salads as “low performers” when they were actually selling out daily—the sensors just couldn’t detect the lighter weight items properly.
We wasted two months tweaking algorithms while our route drivers knew exactly what was needed just by looking.
One healthcare client almost switched vendors because the system kept leaving their night shift without the healthy options they specifically requested.
For 2026, we went back to basics—our drivers use simple mobile checklists combined with their actual eyeballs and customer conversations.
Restocking accuracy jumped from 73% to 94%, and we’re getting compliments again instead of complaints. Sometimes 30 years of industry experience beats fancy algorithms.
Louis Baresh
Full-Service Vending, Executive Refreshments
Fancy Routing Caused Service Chaos
Our biggest tech fail in 2025 was rolling out a scheduling software that was supposed to optimize our technicians’ routes across Northern Virginia.
It looked great on paper—promising to cut drive time by 30% and fit in more jobs per day. Instead, it created absolute chaos for three weeks straight.
The algorithm didn’t account for actual traffic patterns on the Beltway or the reality that a water heater replacement in McLean takes longer than a faucet repair in Arlington.
Our techs were showing up late, customers were frustrated, and our perfect Monday-Friday, 9-to-5 promise started falling apart.
We saw our response time metrics drop and had to personally call dozens of customers to apologize.
For 2026, I went back to my ITIL roots and built our own simple routing system using basic tools our team actually understands.
We now block service areas by day (Arlington Monday, Falls Church Tuesday, etc.) and let our experienced dispatcher make the final calls based on their knowledge of the neighborhoods.
Our on-time rate is back above 95%. The lesson from my government IT days still holds: the fanciest system means nothing if your people can’t use it effectively.
Sometimes a spreadsheet and human judgment beats an algorithm that doesn’t know your territory.
Amanda Casteel
Co-Owner, Cherry Blossom Plumbing
CRM Overreach Dropped Solar Close Rates
Our biggest tech slip in 2025 was rolling out a new CRM system across four states without proper training for our sales teams.
We thought the AI-powered lead scoring would instantly boost conversions—instead, our close rates dropped 12% in Q2 because reps were fighting the software instead of talking to homeowners about solar savings.
The platform was designed for enterprise SaaS companies, not residential solar.
It tried to automate relationship-building, which is exactly what you can’t automate when someone’s making a 25-year energy decision. We lost deals because follow-ups felt robotic.
For 2026, we went back to basics with a simpler system that improves our process instead of replacing it.
Now the tech handles scheduling and documentation while our team focuses on educating families about the 30% federal tax credit and utility incentives.
Our Q1 close rate jumped to 34%—highest we’ve ever seen.
The takeaway? Don’t let shiny tech distract you from what actually closes deals. In solar, that’s trust and education, not algorithms.
We use technology to remove friction, not create it.
Stanford Johnsen
Founder & Chief Sales Officer, Capital Energy
Scheduling Upgrade Led to No-Shows
My biggest tech fail in 2025 was finally upgrading our scheduling software after years of using a basic system.
We picked a platform that looked perfect—route optimization, automated appointment reminders, the works.
Within two weeks, we had three no-shows because customers never received the texts, and our technicians were being routed in circles that made zero sense for North Sacramento traffic patterns.
The real damage wasn’t just the missed appointments.
Our callback guarantee—which has always been free and immediate—got delayed because the system kept marking completed jobs as “pending.”
Customers who’d always gotten same-day responses were now waiting 48+ hours, and that’s not who we are.
For 2026, I stripped it down to what actually matters: reliable appointment confirmations and a simple map view our techs can override when needed.
The AI routing looked impressive in demos, but my guys know that taking Highway 99 at 3pm is suicide.
Sometimes boots-on-the-ground knowledge beats algorithms.
We’re back to 98% on-time arrival and zero delayed callbacks.
The Lego Dan contest entries are flowing again, which tells me customers are happy enough to have fun with us. That’s my metric that matters.
AI Overload Increased Callbacks
Our biggest tech fail in 2025 was overcomplicating our dispatch system with too many AI-driven features at once.
We added predictive scheduling, automated tech assignments, and smart routing all in one quarter—and it backfired.
Our call volume actually increased because customers were getting confused by the automated responses, and techs were missing the context they needed to prepare for jobs.
The data told the story: callback rates jumped 18% and our first-call resolution dropped.
We were so focused on automation that we forgot the “old-fashioned customer care” part of our model.
Turns out, AI works best when it improves human judgment, not replaces it.
For 2026, we’re rolling out AI in phases—starting with just tech assignment optimization based on historical performance data.
We’re using AI to identify that Johnny excels at commercial water heaters while Joey has the lowest callbacks, then letting our human dispatchers make the final call.
The tech suggests, the human decides.
The lesson from fifteen years in dispatch and eight years running CIC: layer technology slowly.
Your systems should make your team more effective, not more frustrated.
On behalf of the Techronicler community of readers, we thank these leaders and experts for taking the time to share valuable insights that stem from years of experience and in-depth expertise in their respective niches.
If you wish to showcase your experience and expertise, participate in industry-leading discussions, and add visibility and impact to your personal brand and business, get in touch with the Techronicler team to feature in our fast-growing publication.
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