> ## Content Index > Fetch the complete content index at: https://www.livain.com/llms.txt > Use this file to discover other available public pages before exploring further. # The industry column was wrong on two-thirds of the list. I checked every one. - URL: https://www.livain.com/blog/the-industry-column-was-wrong-on-two-thirds-of-the-list/ - Published: 2026-07-14T07:00:00.000Z - Updated: 2026-08-29T07:16:13.000Z - Description: A sales list arrived neatly tagged by industry. Two-thirds of the tags were wrong. Why the confident-looking label is the base rate to distrust — and why AI makes checking cheap, not optional. - Author: Remco Livain - Tags: AI, Sales, Data Quality, Prospecting A sales list landed on my desk this week — just under five hundred companies, already tagged by industry, ready to call. Someone had done the sensible thing and put a label on every row. Cybersecurity here, recruiting there, a bank, a manufacturer. All I had to do was write the pitch. So before I did, I pulled a dozen names I happened to already know and checked the labels against reality. Several were wrong. Not close-but-off wrong — different-planet wrong. A company tagged as a drinks brand turned out to be a cybersecurity firm. A "recruiting" business was actually industrial engineering software. That's when I stopped trusting the column and decided to check all five hundred. ### The label is the easy lie Here's the uncomfortable part. A pitch is only ever as good as the tag it's built on. If I greet a cybersecurity founder as though he sells fizzy drinks, the email is dead on arrival and I've burned the one impression I get. A wrong label doesn't feel like a risk when you're staring at a tidy spreadsheet. It feels like a head start. It isn't. This is the same trap I keep coming back to — [a number or a category that looks authoritative right up until you audit it](https://www.livain.com/blog/why-your-direct-traffic-is-a-lie-and-how-to-audit-it/). The neat field lulls you. The work is in refusing to believe it. ### Five hundred checks used to be a no Ten years ago, verifying what five hundred companies actually do would have been a week of someone's life, so nobody did it. You pitched the label, accepted the miss rate, and moved on. That's the bit that has genuinely changed. I ran the domains through a company-data lookup to pull the firmographics, then sent a research agent after the ones with no website and the ones where the first answer smelled wrong. Bulk classification by machine, targeted second looks by hand. The whole pass cost a rounding error in credits and an afternoon — not a week. But — and this is the entire point — the machine got the confident-looking ones wrong too. Industry codes lump a boutique consultancy in with the giant it once served. A name with "cyber" in it turns out to be an industry association, not a vendor. The tool gets you eighty percent of the way at a hundred times the speed; the last stretch is judgement about which answers to distrust. [That judgement is the actual leverage](https://www.livain.com/blog/the-death-of-generic-ai-why-deep-domain-expertise-is-the-only-real-leverage-left/), not the tool. ### What the recount showed Of the rows that arrived with a confident industry tag, only about a third survived. Two-thirds were reclassified. Whole categories the original tags never even had — training providers, public-sector bodies, data and AI shops — surfaced once I looked properly. > The spreadsheet wasn't lying on purpose. It was just never checked. Most spreadsheets aren't. None of this is exotic. Bad records are the quiet tax on every business — Thomas Redman's much-cited estimate is that poor data quality costs the US economy [around three trillion dollars a year](https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year?ref=livain.com), and his follow-up work found that [only three percent of company data meets basic quality standards](https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards?ref=livain.com). B2B contact data rots especially fast because people change jobs; a meaningful chunk of any list is stale within a year. The label being wrong isn't the exception. It's the base rate. I've made this mistake the expensive way before — [trusting a tidy summary that turned out to be missing the one thing that mattered](https://www.livain.com/blog/the-ai-said-nobody-replied-the-reply-was-message-28/). The reflex I've built since is boring and it works: when a field decides how I'll spend my time, I verify the field before I spend the time. The list is in good shape now. The pitches are built on what these companies actually do, not on what a column claimed they do. And the part I'd underline for anyone leaning on AI to move faster: speed makes the checking cheap. It doesn't make it optional. Trust the reps, not the label. ### Sources & further reading **External** Thomas C. Redman, ["Bad Data Costs the U.S. $3 Trillion Per Year," Harvard Business Review](https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year?ref=livain.com) ["Only 3% of Companies' Data Meets Basic Quality Standards," Harvard Business Review](https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards?ref=livain.com) **Related posts** [The Death of Generic AI: Why Deep Domain Expertise Is the Only Real Leverage Left](https://www.livain.com/blog/the-death-of-generic-ai-why-deep-domain-expertise-is-the-only-real-leverage-left/) [The AI handed me a number that was wrong in the most convincing way](https://www.livain.com/blog/the-ai-handed-me-a-number-wrong-in-the-most-convincing-way/) [Claude Cowork said nobody replied. The reply was message 28.](https://www.livain.com/blog/the-ai-said-nobody-replied-the-reply-was-message-28/) [Why your direct traffic is a lie (and how to audit it)](https://www.livain.com/blog/why-your-direct-traffic-is-a-lie-and-how-to-audit-it/)