Automation can be brilliant, until it is not. In April 2025, dev-tool startup Cursor discovered this the hard way when its AI support bot “Sam” emailed users about a fabricated “one-device-only” policy. Developers were locked out, Reddit lit up, and the co-founder posted a public apology within hours.
Cursor’s misstep echoes Air Canada’s 2024 tribunal loss, when its website chatbot invented a bereavement-fare refund, and the airline tried to blame the bot as a “separate legal entity.” The court disagreed and ordered compensation.
Both stories prove the same point: when AI hallucinates, we own the fallout. A thoughtful AI escalation management plan turns those slip-ups into trust-building moments.
Why AI stumbles (and what that means for us)
- Incomplete or biased training data
Edge-case scenarios, such as post-travel refunds, rarely appear in historical tickets, so models guess. Customers treat the guess as gospel. - Ambiguous intent without a safety brake
If a chatbot cannot decide whether “cancel” means stop my subscription or void my order, it may choose the wrong path unless confidence thresholds route to a human. - Model drift after updates
Post-deployment tweaks can shift response patterns. Cursor’s security update triggered unexpected logouts, and the bot justified them with an imaginary policy.
Quick exercise: Pull ten recent escalations and tag each to one of these three roots. Patterns appear quickly, and so do your next fixes.
Early signals your AI is slipping
Watching real-time data helps you act before social media does:
- Agent-takeover rate climbs, a spike in “escalate to human” triggers is often the first sign the model cannot parse new intents.
- Negative-sentiment bursts, phrases such as “bot useless” in social or ticket notes surface one or two days before CSAT slides.
- Repeat tickets citing the bot, when customers start messages with “Your chatbot said …,” misinformation is already spreading.
Hold a fifteen-minute “voice of customer” huddle every Tuesday to review these indicators.
Building an escalation plan that puts people first
A sturdy escalation framework rests on three interlocking layers:
- Guardrails
List high-stakes intents, for example billing disputes and legal threats, and set conservative confidence thresholds so anything uncertain goes straight to a human. - Context-rich handoffs
When the bot taps out, it should pass the conversation, classification scores, and suggested next steps, so agents start at line ten, not line one. - Closed-loop learning
After resolution, agent corrections feed back into training. Over time the model handles more without losing accuracy, the core of augmented AI.
Assign clear owners for each layer, such as bot trainer and escalation QA lead. Clarity beats scramble when volume spikes.
Weaving empathy into every fallback
An effective handoff balances speed with sincerity. When a customer has already repeated themselves to a bot, they need assurance the next interaction will be different. Start by acknowledging effort.
A line such as “Thank you for walking through those steps” shows you recognise the time they have spent. Follow with an emotional validation. Saying “I understand how frustrating that must feel” signals that a human, not another script, is listening.
Next, provide a clear path forward. Outline what you will do and give a realistic timeframe, for example “I will review your account details right now and circle back within fifteen minutes.” Close by inviting any additional context.
This invitation turns a one-way apology into a dialogue and helps agents collect the details that improve training data later. Empathy in these moments does not slow service; it accelerates resolution by restoring trust.
Trust and safety checkpoints
A proactive trust-and-safety program prevents the surprise headlines that Cursor and Air Canada faced. Begin with a quarterly bias and fairness review.
Pull a statistically meaningful sample of conversations from different customer segments and examine whether certain groups receive less helpful or slower responses. Document findings and feed corrections into model retraining.
Run a security sweep in the same quarter. Pen-test the bot for prompt injection, verify that access to conversation logs is role-based, and audit retention settings to ensure compliance with privacy policies.
Finally, schedule a compliance roundtable whenever you roll out a major model update. Product, legal, and CX walk through new intents, confirm approved language for regulated topics, and sign off on escalation triggers.
Keeping these checkpoints rhythmic and well documented means no single team carries the risk alone and issues are caught long before customers notice.
Lessons from the field
Even the savviest developer tools can stumble when automated answers outrun human oversight. Cursor’s AI support agent, Sam, confidently told subscribers they were suddenly limited to a single device. The policy did not exist, yet churn spiked as outrage spread across Reddit and Hacker News. To stem the damage, Cursor labeled every bot reply as “AI-generated,” imposed strict confidence thresholds before sending answers, and routed anything policy-related to a human reviewer. The incident proved that clear disclosure and firm guardrails matter more than flashy tech.
The stakes rose even higher in aviation. Air Canada’s website chatbot promised a bereavement refund that the airline’s real policy forbids, so a grieving passenger followed the advice, then sued when the refund was denied. A tribunal ordered the carrier to compensate him and rejected the claim that the bot was a separate legal entity. Air Canada took the bot offline, tightened its prompts to block policy guidance, and now routes all refund requests straight to live agents. Cursor and Air Canada regained trust only after spelling out who owns each answer and shrinking the space where their bots can improvise, a playbook worth adopting before reputational dents become craters.
Conclusion
Empathy is the thread that ties it all together. Technology can scale, but only people can repair trust. Prioritising compassion in design and response language turns unavoidable AI misfires into memorable recovery moments.
Want to learn more about AI, CX, and more? Check out our blog or reach out to our team. Together, we can blend the best of humans and AI for support experiences your customers will love.
