Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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Brace yourself, these are the bot fails that brands would rather you forget
Published: September 17, 2025
Charlie Mitchell
By 2027, conversational AI is expected to handle 70 percent of customer service interactions, up from about 50 percent today, according to Gartner.
The driver? Generative and agentic AI. The catch? We’ve already seen two years’ worth of high-profile AI misfires, and they are a warning shot of what’s to come.
Some bot blunders made headlines for laughs, like Virgin Money’s getting flustered over the word “virgin” or DPD dubbing itself the “worst delivery company in the world,” but others cut deeper.
For instance, one ChatGPT-powered agent on a car dealer’s site agreed to sell a $75k Chevy Tahoe for just $1, a “deal” that was treated as binding, but the next three examples had far bigger consequences, each one worse than the last.
In February 2024, Jake Moffatt, grieving the loss of his grandmother, turned to Air Canada’s chatbot for information about bereavement fares.
The bot incorrectly told him that he could purchase tickets at full price and then apply for a refund within 90 days after travel. Trusting this, Moffatt bought the tickets.
When he later tried to claim the refund, Air Canada denied it, explaining that bereavement fares don’t apply to completed travel. Frustrated, Moffatt sued and won, collecting $650.88 plus interest and fees.
As part of the ruling, a Civil Resolution Tribunal member noted:
“In effect, Air Canada suggests the chatbot is a separate legal entity that is responsible for its own actions. This is a remarkable submission.”
While $650.88 may sound minor, the real damage wasn’t financial; it was reputational. Stories like this spread quickly, raising questions about trust, accountability, and brand reliability.
The case underscores a critical truth: if your AI isn’t trained and monitored properly, it can misinform customers, damage brand credibility, and turn what should be a helpful tool into a liability.
From a lawsuit to lawbreaker: New York City’s “MyCity” chatbot misled small business owners with dangerously inaccurate advice and, at times, effectively instructed them to break the law.
The bot told shop owners that they could go cashless, contradicting a 2020 law requiring NYC stores to accept cash. It also gave a flat “no” when a landlord asked if they had to accept tenants using rental assistance, despite it being illegal in New York to discriminate based on sources of income.
A local housing policy expert called the tool “dangerously inaccurate,” while critics blasted the approach as “reckless and irresponsible.” Even Mayor Eric Adams faced backlash after defending the bot in a tense press conference.
Here’s the hard truth that they don’t tell you: If an untrained AI doesn’t know the answer, it doesn’t stay quiet, it makes one up. When that happens in high-stakes contexts like housing or compliance, the fallout isn’t just confusion; it creates legal risk, public outrage, and long-term damage to brand reputation.
In August 2025, Lenovo’s AI chatbot “Lena” was tricked into exposing sensitive company data with nothing more than a 400-character prompt.
Researchers at Cyber News discovered that this tiny input could leak live session cookies, which were enough for an attacker to bypass logins, hijack active chats, and sift through past conversations. The exploit combined an innocent product query with a hidden HTML switch, a fake image link, and a push to display it, effectively turning Lena into an insider threat.
Lenovo patched the flaw quickly, but the damage could have been far worse. A breach of this kind could have opened the door to massive data exposure, compromised customer trust, regulatory investigations, and an enduring black mark on Lenovo’s reputation.
The lesson is clear: AI chatbots aren’t just helpful, they’re vulnerable. Their likelihood to comply can be weaponized, and without rigorous safeguards, what starts as a customer service tool can spiral into a brand’s biggest liability.
As Jeff Blair, Chief Growth Officer at Transcom, summarized:
“AI chatbots have huge potential, but they need the proper boundaries to deliver effective value. What separates a bot that strengthens customer trust from one that undermines it is simple: clear guardrails, cultural context, and functional monitoring and training. Without those, even the smartest AI can quickly go off track.”
The reality is that chatbots aren’t plug-and-play. Without training, guidelines, and smart escalation paths, they can quickly become liabilities. A self-learning system is powerful, but only if it’s taught what not to do, and knows when to pass the conversation to a person.
The challenge? Few in-house teams have the time, tech depth, or risk appetite to design, test, monitor, and retain bots at scale. Every missed escalation, every hallucination, every compliance slip doesn’t just hurt experience; it hits revenue and brand trust.
That’s why more brands are turning to BPO partners. A modern BPO doesn’t just provide people, it brings best-in-class frameworks, monitoring, and safeguards that most enterprises can’t build alone. It means:
In AI at work: the hype, the truth, and what’s next, Transcom explores why 85 percent of AI projects fail, and how the right partnership flips the odds in your favor. Deploying AI in today’s landscape without expert support and oversight isn’t just risky. It’s reckless.
The latest models from Anthropic and Open-AI are jaw-dropping, but they are not built for CX. They’re generic, not tuned to customer outcomes, and prone to misinformation. That’s the obvious risk. The subtler risk is bias.
These systems learn from massive datasets scraped from the web, most of which are skewed toward English-speaking, Western European perspectives. The answers that they generate, no matter how fluent, often reflect those cultural defaults.
One study from Shav tested leading models against cultural values from 107 countries. The result? They all echoed the same assumptions: Western European norms. That’s a huge problem for Global CX, where cultural nuance isn’t a “nice to have,” it’s the difference between building loyalty and burning it.
As Tey Bannerman, former McKinsey Partner, stated on LinkedIn:
This is what bias looks like in practice. It’s not always misinformation. It’s culturally tone-deaf. And unless an AI model is trained on your customers, in your markets, with escalation paths to humans when it’s unsure, these missteps will multiply.
The fix is training and continuous retraining: teaching bots cultural context, embedded escalation rules, and stress-testing them in sandbox environments before customers see them. Without that, global brands risk launching “smart” AI that alienates the very people it’s meant to serve.
The reality is this: most AI failures don’t come from the tech itself, but from how it’s applied. Bias, blind spots, and brittle training loops can undo even the most advanced model. The real challenge isn’t whether AI can transform CX. It’s whether you can make it work in your environment.
Gartner predicts that half of businesses will end up walking back plans to shrink their service teams with AI. Why? Because too many deployments fail before they scale. The usual culprits: choosing generic models not built for CX, skipping guardrails, and underestimating the complexity of cultural nuance and compliance.
The better path is training AI where it matters, and that is on real-world conversations, regulations, and cultural contexts that define your customers. That’s where BPO-trained AI stands apart. By combining anonymized sector data, regional context, and human-in-the-loop safeguards, these models don’t just “handle” interactions; they elevate them.
Some organizations are already doing this well. For instance, companies like Transcom take a tech-agnostic approach, working with best-in-class  AI platforms in close partnership with their clients, keeping their business objectives front of mind. They then ensure the proper frameworks, simulations, and compliance checks needed to make chatbots customer-ready. They also draw on large, anonymized datasets across industries and geographies to reduce cultural bias, helping ensure AI responses land appropriately, whether the customer is in the US, Berlin, Tokyo, Dubai, or some other region of the planet.
The takeaway is clear. Businesses sidestepping embarrassing bot failures are the ones pairing AI with rigorous guardrails and cultural intelligence. For those that don’t, the risks aren’t just technical, they’re reputational, regulatory, and financial.
Ready to go deeper? Download the whitepaper: AI at work: The hype, the truth and what’s next for a practical look at why 85 percent of AI projects fail and how to flip the odds by aligning the right AI to your KPIs, building guardrails that prevent missteps, and scaling solutions that deliver ROI.
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