most AI content is theater. it doesn't survive contact with real work. i've shipped plenty of slop myself. this page is for the people who build systems, run operations, and make key decisions from data. if this is your world, follow along.

companies are capping or killing AI subscriptions. the bills are huge and most usage is non-productive code that never ships. the answer isn't less AI. it's smarter usage: • scripts only call AI for the parts that need judgment • python handles the data, files, calculations (free) • more output per dollar than ad-hoc usage ever delivers.

What is some complex data you’ve been struggling with as far as analysis or even automation of reports? Comment below and let’s see if that’s solvable

in my years of experience with AI, i've seen usage fall into three tiers. most users are stuck in tier 1. only a few ever reach tier 3. do you agree?

waiting for someone at your company to show you how AI fits your role? they don't know either. the people figuring this out are figuring it out by trying. • the only way to learn what AI can do for your data is to point it at your data and see what comes back • you'll be more useful in three weeks than the people still waiting in three months • every script you build teaches you the next one. it compounds i post examples here every week. follow if you want to see what real attempts look like.

most sales teams have thousands of dollars in untapped data potential. a few python scripts unlock it. - every script runs on data already in your CRM and ERP — order history, inventory, customer master - these are time-saving compounders — you build once, they run forever here are five thought starters to get you going.

most purchasing teams know exactly what they should be analyzing. AI python is what gives them the hours back to actually do it. • every script runs on data you already pull regularly — POs, invoices, vendor master • these are time-saving compounders — you build once, they run forever here are five thought starters to get you going:

tens of thousands in savings sit between you and a few python scripts. • warehouse data is the most actionable data in your operation — every line ties to a real physical event with timestamps and locations baked in • the average mid-market warehouse leaves five figures on the table every year here are five thought starters to get you going:

the strongest opinions on AI almost always come from the people who've never run a real prompt. • 91% of companies have adopted AI in some form, but only 26% of employees use it weekly or more • among employees in AI-adopting organizations, 65% say it has improved their productivity • about half of all AI projects fail to make it from pilot to production if you're in the camp that hasn't tried it, the answer isn't a stronger opinion. it's an afternoon, a real problem, and a script you build yourself.

the report you keep describing to someone else? you can build it yourself. every custom report turns into a back-and-forth with IT. you ask for something specific. they build what they heard. you come back with corrections. they rebuild. the specifics never fully land, because half of what makes a report useful is context only the operator carries. claude python cuts out the loop. no waiting behind someone else's priorities. no translating your business logic into someone else's language. you describe what you need in your own words and build it on your own timeline. everyone wants to be more self-service and less dependent. this is how you get there. Follow along and check out my page to see actual use cases and problems you can solve.

how many hours a week do you spend turning reports into decisions the report should've told you? if you're running AWS Redshift, QuickSight, Athena, or any of the bigger query platforms, you know the gap. these tools are built to pull data. they stop at the reporting layer. you get numbers, not decisions. that's the last mile — the gap between data and action. most teams close it manually in excel. almost nobody builds scripts for it. claude python does. your query tools pull the raw data. python applies the business logic — thresholds, rankings, recommended actions. same data. different output. start a conversation if you want to see how to cut those hours and make clearer decisions.

there are thousands of hidden dollars in how you buy right now. most teams never see them. they're sitting in the gap between what your vendor's price break offers and what your team actually buys. buy too little, miss the discount. buy too much, tie up cash in inventory that sits for months. using claude python scripting, here's what you can do: take your historical sales data and your vendor's price break, paste the headers and a few sample rows into claude, then ask: "for each SKU in sales_history.csv and vendor_pricing.csv, calculate the optimal purchase quantity that maximizes net benefit while keeping days-of-coverage under 90. factor in a 20% annualized carrying cost. output as price_break_analysis.csv — SKU, recommended qty, new unit cost, gross savings, carrying cost, net benefit, days of coverage." claude writes the script. you run it. you get a ranked list of the SKUs where a quantity change actually pays — and the ones where it doesn't. this doesn't replace purchasing judgment. cash, vendor relationships, storage all still matter. but it surfaces the specific buys worth negotiating or committing to. run it monthly. the tiers that matter change with your demand.

you already know the feeling: sat through the enterprise SaaS demo, watched 50 features get walked through, and counted maybe 5 you'd actually use. six-figure annual contracts. months of implementation. integration headaches. all to run the handful of workflows that actually matter to the business. claude python scripting flips that equation. you build exactly the 5 functions you need — without the 45 you don't — for almost nothing. with a few weeks of practice, plus claude doing the heavy lifting, you're producing the specific tools your business actually runs on. SaaS still has its place when you genuinely need the whole platform. most of the time you don't. and the "we need a platform for that" reflex is often the expensive path to a cheap solution. feel free to start a conversation if you want to see whether your current tools or workflows are good candidates.