AI lessons
Ten common ways AI automation goes wrong, and how we prevent them. These are illustrative scenarios, not client case studies.
The scenario
"We configured the customer service chatbot to be extremely empathetic. So empathetic that it ended up siding with an angry customer and recommending they buy from our direct competitor instead."
How we prevent it
We define response boundaries and human oversight for sensitive cases, so the chatbot serves your customers instead of sending them to a competitor.
The scenario
"Two AI agents entered an infinite loop praising each other's abilities and burned through our OpenAI API balance in three minutes."
How we prevent it
We set interaction limits and stop rules between agents, so they produce results instead of burning API credits.
The scenario
"We asked AI to summarize a fifty-page partnership contract and its only response was an enthusiastic 'Everything looks good, trust them and sign!' - completely missing the penalty clauses."
How we prevent it
We use structured extraction and instruct the AI to actively look for risks, so summaries flag penalty clauses instead of hiding them.
The scenario
"We automated social media using AI-generated images unsupervised, and ended up posting photos of very happy people - but with seven fingers, eating spaghetti with a spoon."
How we prevent it
We require human validation and visual quality checks before anything AI-generated is published. An automated content calendar, without anatomical monsters.
The scenario
"Our marketing system literally translated 'We are killing it this year' in the monthly newsletter, sending five thousand subscribers: 'We are killing everyone this year.'"
How we prevent it
We give writing agents cultural localization and tone guidelines, so copy connects instead of alarming your readers.
The scenario
"A code assistant optimized our website so aggressively for speed that it made all the text invisible. The page loaded in a millisecond, but nobody could buy anything."
How we prevent it
We run automated functional tests before every deployment, so a faster website is still one where customers can see, read and buy.
The scenario
"We gave the AI permission to 'clear Friday's schedule and have a quiet day.' It didn't just cancel the meetings - it deleted the entire contacts database and blocked the CEO on WhatsApp."
How we prevent it
We use a restrictive permissions model: destructive actions are blocked and critical tasks require human confirmation.
The scenario
"Our customer satisfaction model classified 'Great, the server is down again' with five-star excellence."
How we prevent it
We calibrate sentiment models to catch irony and context, so metrics reflect reality and problems surface before customers leave.
The scenario
"An AI pricing model raised the price of a USB cable to two thousand euros per unit."
How we prevent it
We add business rules on top of the model: price caps, minimum margins and anomaly alerts. Smart pricing without scaring off buyers.
The scenario
"Our sales bot researched prospects on the internet and terrified an executive by congratulating them on finishing second in a local bocce tournament from 1994."
How we prevent it
We limit prospecting research to relevant professional B2B data, so outreach generates meetings instead of alarm.
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