I read the entire post, and I think this company has no expertise at all. So I'd rather they just used AI writing instead.At least Frontier model AI doesn't make such overblown claims.

They proudly claim that every AI project they've observed over the past year and a half had a 0% success rate, and that they've rejected all AI implementation work. While this is evidence that the market is crazy, at its core, it's a painful confession that they have no engineering expertise to implement and control modern AI architectures like RAG, Agentic Workflow, and context window optimization to meet business requirements. I find it fascinating how they're packaging that. It's basically saying, 'We're behind the times.'

There are already products that have achieved results by using AI as part of their development process, yet lumping all different types of AI usage into a single failure category is not only inaccurate but also misleading.

Same goes for the Snowflake Cortex anecdote. Even a freelancer like me can explain technical limitations and distinguish between what's possible and what's not, especially when clients are eager.

There's no engineering analysis in this entire post about why AI fails. No mention of technical bottlenecks like vector DB retrieval quality degradation or prompt injection failures.

I've also worked on RAG for a specific company. For internal knowledge chatbots, it often fails depending on document collection rates and chunking. But none of that is mentioned.

So I understand that AI projects and related things are bad. But there's no analysis of why.

For example, regarding Snowflake, I'm not sure, but did they discuss accuracy in terms of what query set or what ground truth they were using? You're consultants, aren't you?

Honestly, I don't understand why people are excited about this. I'd rather they just used AI. TIt's not about whether human writing is good or bad. It's that this kind of writing feels like a deception of the reader.

When making overgeneralizations, there's a basic minimum standard required.

Saying that making token usage a KPI makes it hard for employees to report is just an 'obvious' fact that's already appeared in far too many essays. Wake up. You're 'consultants.' Consultants are supposed to provide metrics and directions, but all you're doing is shouting into an echo chamber and asking for agreement.

If a significant portion of corporate AI investments are shoddy, you could at least propose specific metrics like document collection rates or user evaluation scores using the very skills you claim to have. I really don't get it.

Just use AI. I wish the OP had used AI. Let me be realistic.