July 22, 2026
AI hype meets spreadsheet hell
Six questions before you add an LLM
Tech worker tried to make AI do everything — and ended up cleaning the mess by hand
TLDR: A consultant tried using an AI system to automate lead hunting, but it messed up enough records that he had to manually check more than 800 entries himself. Commenters say the real lesson is simple: stop forcing AI into jobs that ordinary software can do more reliably.
The big mood in the comments is basically: just because a chatbot can do a job doesn’t mean it should. In Cameron Palmer’s post, the consultant admits he handed one all-purpose AI helper his entire lead-hunting process — finding people, checking them, removing repeats, and adding them to his customer list — only for it to botch 10–20% of the entries. That meant a very unglamorous finale: manually reviewing 800+ records one by one. Ouch. And the community reaction? Equal parts sympathy, validation, and a big flashing “we told you so”.
The strongest opinion came fast: companies are chasing AI like it’s a magic wand, when sometimes plain old software rules are faster, cheaper, and less chaotic. Palmer’s own comment reads like a public confession from someone who learned the hard way, while another commenter, jimbokun, dropped the calm-but-deadly take that many jobs can be done better with predictable code — and if AI helps at all, it should help write the tool, not sit in the middle of the process breaking things every day.
The drama here isn’t a screaming flame war — it’s that painfully familiar tech storyline where hype meets reality and reality wins. The joke hanging over the thread is brutal: the “smart” system created so much extra work that the human had to become the cleanup crew. In other words, the robot intern didn’t replace anyone; it just invented homework.
Key Points
- •The author built an LLM-based prospect discovery and qualification system that produced 10–20% irrelevant, duplicate, or improperly inserted CRM records.
- •Because the LLM agent handled the full pipeline and behaved variably, the system was difficult to debug and required manual review of more than 800 prospects.
- •The article argues that organizations should begin with the problem to solve rather than trying to apply AI everywhere.
- •It describes LLMs as offering flexibility for language interpretation, information synthesis, and reasoning, but at the cost of determinism, repeatability, speed, and cost.
- •The article introduces a six-question framework for deciding whether an LLM is appropriate, including whether a workflow can be fully specified in advance and whether identical inputs must yield identical outputs.