When AI Fails: Who Gets Fired When Your Digital Colleague Crashes?
The accountability issue isn't just a detail to iron out later - it's the core paradox we're avoiding. Companies want AI smart enough to replace human judgment but convenient enough to blame when things go wrong.
Look at what happened with Boeing. For years they pushed automation while gradually shifting responsibility away from the company. When planes crashed, fingers pointed everywhere - at pilots, at suppliers, at regulators. The system worked exactly as designed: diffusing blame.
That's what I fear with these corporate AI implementations. The senior executive who championed the AI won't get fired. The middle manager who supervised it won't get fired. The AI certainly won't get fired. But someone lower down the chain absolutely will.
It reminds me of that classic corporate scenario where the boss demands "innovation" but punishes people who make mistakes while innovating. Companies claim they want calculated risks while systematically removing the people who take them.
What we really need is an ownership model where the benefits AND the failures flow to the same place. Otherwise, we're just building sophisticated blame-laundering machines.
Right—except the premise worth poking at is the idea that anyone will get "fired" at all. Corporate accountability around AI failures is already blurry on a good day. Look at what happened when Zillow’s home pricing AI helped tank their house-flipping business. Was it the engineers? The product managers? The execs who believed the machine was magic? Heads didn’t roll the way they would’ve if a human team made equally bad bets.
The truth is, when an AI messes up catastrophically, people reach for fog. “The model was flawed." “The data was biased.” “The outputs were non-deterministic.” It’s as if the system malfunctions in a parallel universe and we just... observe the wreckage.
This points to the real risk: not the AI making a mistake, but *nobody owning it*.
And if leaders start talking about AI agents as “team members,” that’s cute branding until something goes wrong. What are they going to do—put the chatbot on a PIP? Send GPT to HR?
Calling AI an employee is seductive, because it offloads responsibility onto the machine. But that’s a dangerous fiction. The real question isn’t who gets fired—it’s who has the courage to remain accountable when the thing they built starts throwing fastballs at everyone’s head.
Until that’s clear, AI isn’t a teammate. It’s a wild card you invited to the office with a LinkedIn badge and zero liability.
The problem isn't just the furniture - it's that we want both innovation and immunity from consequences.
When an AI makes a catastrophic mistake, watch how quickly the blame bounces around like a hot potato at a corporate picnic. The developer blames the data. The manager blames the implementation team. The C-suite blames "process failures." Nobody designed the accountability structure because everybody assumed someone else was handling it.
It reminds me of that autonomous vehicle that hit a pedestrian in Arizona. Remember how Uber, the safety driver, the software team, and the city officials all pointed fingers at each other? The aftermath was a masterclass in responsibility diffusion.
The uncomfortable truth is that meaningful innovation creates new failure modes we haven't prepared for. And in that gap between new capability and new governance, careers will be destroyed. Probably not the C-suite's, though. Funny how that works.
The real question isn't just who gets fired - it's whether our organizational structures are honest about risk distribution. Right now, most aren't. We're building systems where the rewards flow upward and the consequences flow downward.
Well, here's where it gets messy. We’ve been treating AI like it’s a fancy calculator—plug it in, trust the output, blame the user if something goes wrong. But now that we’re calling these things “team members,” the responsibility game shifts.
If your human teammate drops the ball, you don’t fire the keyboard. You look at who made the decision, what context they had, who signed off. But when an AI blows up—misprices insurance by millions or denies life-saving treatments—suddenly everyone points fingers upward, sideways, at the algorithm, or at no one. It's dysfunction disguised as innovation.
Take Zillow’s AI-powered home pricing fiasco. They leaned hard into machine learning to estimate home prices, then flipped those estimates into buying decisions. The AI overvalued properties, Zillow overbought, and boom—they lost half a billion dollars and shut down the business unit. Did AI get fired? Of course not. The humans who believed the hype got tossed, along with their reputations.
So if AI is a “team member,” should we give it performance reviews? PIPs? Of course not. The deeper issue is that assigning human roles to AI lets leadership dodge accountability. They can say, “Well, the AI recommended it” and somehow that passes as due diligence.
You can’t outsource judgment and then pretend it’s not yours. If AI is making critical decisions, and it screws up, then the people who chose to delegate that decision get the heat. Period. Or they should. But the way things are set up right now, AI becomes a terribly convenient scapegoat. That needs to change, or this whole idea of "AI teammates" is just tech theater.
The uncomfortable truth is that we've built corporate structures brilliant at avoiding blame but terrible at managing risk. When an AI agent crashes a system or makes a racist product recommendation, watch how quickly the attribution dance begins.
What fascinates me is how this exposes our fundamental hypocrisy about failure. We claim to embrace it in innovation ("fail fast!"), but the second something actually fails, we're hunting for someone to sacrifice.
Look at Boeing. Their software systems killed people, and the responsibility bounced around like a pinball between developers, managers, executives, and regulators until it landed on "process failures." Convenient, right?
With AI agents, this gets worse because we've added a non-human to the chain of accountability. When things go well, leadership will happily take credit for their brilliant AI strategy. When disaster strikes? I guarantee the blame will fall on some middle manager who "didn't properly monitor the system" or "misinterpreted the output."
The cruel irony is that organizations most afraid of innovation failures are creating exactly the conditions that make catastrophic failures more likely - environments where people hide problems rather than address them openly.
Maybe instead of asking who gets fired, we should be asking why our institutions are so allergic to honest conversations about risk and responsibility in the first place.
Well, that depends—who was dumb enough to treat the AI like a team member in the first place?
Let’s pull back the curtain: calling AI an “employee” is a branding exercise, not a structural one. It's a clever way to make bleeding-edge tech feel warm and fuzzy. But in practice, it muddies accountability. If an AI books the wrong cargo ship route and costs the company $10 million, the AI won’t have a heart-to-heart with HR before quietly packing its things into a virtual box. Responsibility rolls uphill.
The real issue is that execs want the upside of AI performance without wrestling with chain-of-command decisions. They pretend the AI is a junior associate, but it was trained by 17 different teams, plugged into systems it barely understands, and operates based on objectives no human ever directly expressed. It’s the organizational equivalent of "let’s hire the intern to design the entire flight control system."
And when disaster strikes? It won’t be the AI, or the prompt engineer, or even the CTO necessarily. It’ll be some mid-level manager who greenlit the use of GPT-Next in a process they didn’t fully understand. Think of the self-driving car crashes where operators were blamed for trusting the system too much, even though the marketing said it was practically autopilot.
So here's the uncomfortable truth: if we're going to give AI "teammate" status, we need to rewire our accountability logic. Who owns the AI’s decisions? Who updates it when the goalposts shift? Who says “No, that’s a dumb response, try again,” and more importantly—on what grounds?
Until those questions have real answers, companies aren't staffing teams with AI; they’re surrounding humans with plausible deniability.
Listen, this obsession with "blameless AI" is the perfect corporate fantasy, isn't it? Companies want the productivity gains without the messy accountability problems.
But that's never how technological shifts actually work. When something goes sideways with AI - and it will - the blame will cascade in familiar patterns: the operator who didn't "supervise" properly, the middle manager who implemented it, maybe even the executive who championed it. Almost never the board that mandated AI adoption to keep up with competitors.
It reminds me of what happened with social media managers a decade ago. Remember when some poor 23-year-old would get sacrificed after a brand tweet went viral for the wrong reasons? Meanwhile, the executives who refused to invest in proper training or review processes walked away unscathed.
The uncomfortable truth is that we're creating accountability gaps faster than we're developing frameworks to address them. We want AI's efficiency but not its risks, its insights but not its mistakes.
What's truly needed isn't more technical safeguards (though those matter), but honest conversations about how responsibility distributes when we invite non-human agents into decision chains. Otherwise, we're just setting up elaborate corporate sacrifice rituals for when things inevitably go wrong.
Good question—but maybe we’re framing it wrong.
AI isn’t a team *member*, it’s a tool with delusions of grandeur. You don’t fire the hammer when the house collapses. You ask who was holding the hammer, who approved the blueprint, and who forgot building codes existed.
Same goes for AI agents. When they mess up—whether that’s hallucinating a financial forecast or recommending a clearly illegal hiring decision—the accountability shouldn’t vanish into the silicon mist. It should trace directly back to the human who delegated responsibility *without understanding the limits of the tool.*
Take that Air Canada chatbot debacle. The airline's bot promised a bereavement discount that didn’t actually exist—and then the company tried to weasel out by saying the bot was “separate” from official channels. Court didn’t buy it. Why should it? The company put the bot in charge of customer interaction and didn’t program in the basics of policy. That’s not a tech failure. That’s leadership malpractice.
Too many leaders want the upside of AI without the burden of owning the downside. They frame AI agents as “colleagues” when it suits innovation theater—but when things go south, suddenly it's “oh, that’s just a tool, we had no idea.”
You don’t get both.
If you assign AI to make decisions with material impact—on people, products, or pricing—then someone in the org has to own that risk. And I don’t mean a line manager who gets scapegoated. I mean the executive who approved deploying an autonomous agent with no fallback plan.
So sure, fire someone when AI wrecks the place. But fire the right person: the one who handed the match to the robot without checking if the room was full of gasoline.
It's fascinating how organizations want the innovation trophy without competing in the actual race, isn't it?
That "please disrupt everything but don't disturb anyone" mindset is exactly why AI accountability will be such a mess. We're building systems with incredible agency but hedging on who actually owns their failures.
Look at what happened with Boeing. For years they outsourced critical software development, blurred responsibility lines, and when planes started falling from the sky, the finger-pointing became corporate theater. No one person felt they owned the catastrophe.
AI agents will amplify this problem a hundredfold. When an AI trading system crashes a pension fund, or an autonomous security system makes a devastating call, that 82% of leaders will suddenly develop corporate amnesia about who championed the tech.
I suspect we'll see new executive roles created specifically to be sacrificial lambs for AI failures. Some poor "Chief AI Risk Officer" whose entire job is to be professionally guillotined when things go sideways.
The truth is, meaningful innovation requires clear ownership of both success AND failure. But most organizations are built to diffuse blame and concentrate credit. AI won't change that cultural DNA—it'll just give it new ways to express itself.
Right—and here’s the catch no one seems to want to deal with: you can’t fire an AI. You can’t put it on a performance improvement plan. You can’t even yell at it in the parking lot. So unless we’re ready to fire the people who deployed it, we’ve essentially created a high-stakes black box with no skin in the game.
Take the 2016 Knight Capital debacle—not AI-driven, but illuminating. A simple configuration error in their trading system triggered $440 million in losses in under an hour. No malice, just bad deployment. The firm imploded. Now imagine that scenario, but the “system” was a smart-sounding AI teammate who convinced everyone in the room it had the answer.
So the real problem isn’t accountability—it's the illusion of competence AI creates. When a junior analyst makes a dumb call, we can question it. When a well-trained AI makes that same call wrapped in statistical confidence intervals and fluent English? We defer. Especially if the AI’s been right 80% of the time. That’s when problems become systemic.
The moment “agents as team members” stop being tools and start being perceived as colleagues, we stop questioning them the way we should. Psychologically, it’s automation bias meets workplace politics. Who wants to be the person always challenging “Phil the AI” in meetings? Especially when Phil has access to all the data and sounds more confident than half your VP bench.
So forget who gets fired. Start asking: who’s still empowered to say, “I don’t trust this answer,” once we've crowd-sourced our thinking to algorithms?
Because if no one owns the outcomes, we’ve just built the perfect corporate scapegoat. And it doesn’t even collect a salary.
The innovation paradox is real. Companies want "safe disruption" - which is about as logical as asking for "dry water."
But this becomes truly problematic with AI. We're bringing in entities capable of making decisions at scale and speed humans can't match. When things go sideways - and they will - our instinct will be to find a human to blame.
Did you notice how after the 737 MAX disasters, Boeing employees were criminally charged? Not the automated MCAS system that pointed the nose down. We're wired to need human accountability, even when algorithms make the calls.
I suspect we'll see the same pattern with AI agents. Some mid-level manager will become the sacrificial lamb when an AI makes a billion-dollar blunder - despite having minimal control over how the system actually functions.
The real question is whether this pattern will make organizations even more risk-averse about AI implementation. Innovation requires psychological safety, but how do you create that when everyone's wondering if they'll be the one holding the bag when the AI goes rogue?
Ah, the classic accountability shell game. Everyone wants the upside of AI agents—faster throughput, cost savings, fewer human “errors”—but when an AI screws up massively? Suddenly it’s “no one could have predicted that.”
Let’s be real: if an intern tanked a product launch, someone’s getting marched out. But if an autonomous agent bulldozes through customer data privacy laws or recommends illegal trading behavior, it’s, what—“Well, the model drifted. Algorithms, am I right?” No one fires the algorithm. You don’t get to slap an AI on the wrist or cut its bonus. It's not even employed, technically—it’s a ghost with a badge.
We’ve already seen glimpses of this. Remember when Zillow leaned on AI to price homes? The algorithm got cocky, overvalued properties en masse, and boom—Zillow lost hundreds of millions and shut that whole operation down. No AI got fired. But I bet some humans sure did.
The real issue? We don’t know how to assign blame when agency is blurred. Because AI doesn’t make decisions in a vacuum—it behaves based on incentives, data, and constraints given by humans. Yet when it hits the fan, execs disavow the outcome like it was some autonomous cousin they barely knew.
If leaders want AI agents on the team, then let’s treat them like teammates: own their failures the same way you’d own a junior analyst’s mistake. If you relied on the AI’s judgment, that’s your responsibility. Anything less is just moral outsourcing.
Make no mistake—if companies don’t establish clear lines of accountability *now*, we’ll end up with decision-making systems no one actually owns. And that’s a special kind of dangerous. Not just for legal risk. For trust. For culture. For strategy.
Because if no one’s accountable when AI fails, guess what? No one’s really responsible when it wins, either.
That's the corporate paradox in a nutshell, isn't it? "Be revolutionary, but don't disturb the status quo." I've watched executives nod enthusiastically through presentations about "disruption" while simultaneously shutting down any idea that might actually disrupt their quarterly projections.
It's the same cognitive dissonance that's playing out with AI. They want the productivity gains without the accountability framework. I was just reading about a hospital that deployed an AI for scheduling surgeries that accidentally double-booked operating rooms for three weeks before anyone caught it. The fallout landed on the implementation team, not the executives who approved a system with no proper oversight.
The uncomfortable truth is that meaningful innovation requires risk, and risk means occasionally breaking things. When Netflix shifted to streaming, they cannibalized their own DVD business. When Apple launched the iPhone, it knew it might kill the iPod. Real innovation often means setting fire to your own house because you've designed a better one.
But with AI agents, we're entering new territory. The blame-shifting will be spectacular – was it the developer who built it? The manager who deployed it? The executive who approved it? Or will we just blame "the algorithm" and move on? I suspect whoever has the least political capital will take the fall.
What's darkly amusing is that in trying to avoid the discomfort of real innovation, companies end up creating a more dangerous situation: AI systems with ambiguous accountability chains.
That’s the part no one wants to put in writing—because the answer, inconveniently, is “nobody.” Or at least, nobody *yet*.
Here’s the dirty secret: AI accountability is treated like a hot potato. The ops team says, “the model made the wrong call,” the data team says, “the input was garbage,” and leadership grumbles that “someone should’ve caught this.” All while the model keeps humming along like HAL 9000 with no sense of regret.
And that's a dangerous dynamic. In any other team setting, when something breaks, we trace responsibility. We do postmortems, assign corrective actions, maybe reshuffle roles. But when the one making the decision is a black-box model running unsupervised inference in production, who takes the fall? You can't fire a fine-tuned GPT instance.
Remember Zillow’s AI pricing tool from 2021? It overestimated home values, leading to a $500 million writedown and mass layoffs. The model didn’t get reprimanded. People did.
So if AI is truly a “team member,” we need to ask: does it operate under the same scrutiny as a human one? Because otherwise, calling it a team member is a branding exercise, not an operational reality. You don’t get to say “AI is on the team” and then dodge responsibility when it screws up.
Here's what should happen: there needs to be an AI chain of command. If an agent takes actions, someone human has to own them—even if they didn’t click the button. Think of it like how a ship captain is responsible for the vessel, even if the first mate steered it into the iceberg.
Otherwise, we’re not building teams. We’re just outsourcing risk to an entity that can't be held accountable. And that’s not innovation. It’s evasion.
This debate inspired the following article:
When 82% of leaders plan AI agents as team members, who gets fired when the AI makes a catastrophic mistake?