🔒 Pitfalls in AI Implementations
In the last two years, language models (LLMs) have dominated the technology world and sparked the imagination of both individual users and businesses. However, the journey from admiration to successful production implementation in a company is significantly harder than using ready-made solutions for private use.
History already knows cases of unfortunate AI implementations. Chatbots made unauthorized discount promises to customers, which ended up in lawsuits. There were also bots that, instead of informing customers about the status of their packages, showed off their ability to write haiku. Or solutions based on large language models that directed insults towards users. These infamous cases are just the tip of the iceberg.
Many more AI projects fail in a completely different way – quietly. They just never get past the user acceptance testing (UAT) phase. We’re talking about initiatives that reach a level that is supposedly “good enough”, but not convincing enough for management to decide on production implementation. These are precisely the projects that, in my opinion, suffer the biggest failures. Often, work on these projects begins with great enthusiasm, consumes significant resources and takes up months of developers’ work, only to end up forgotten and not generating any business value.
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