AI use cases that actually work (not the ones from LinkedIn fever dreams)

Last week we told you why 95% of AI projects fail.

This week we're telling you what the other 5% are building.

This isn't theory. This isn't "imagine if we could..." This is based on 200+ projects we've shipped. Real problems. Real solutions. Real ROI. The kind where CFOs stop asking questions because the numbers actually work.

Welcome to 2025, where every executive has been told they need an "AI strategy" but nobody can quite explain what that looks like beyond "ChatGPT, but for our company." Let us help.

Make Your Data Accessible to Humans

Natural Language Data Querying - Your team asks questions in English instead of writing SQL queries they half-remember from a 3-day course in 2019. Turns out "show me Q3 sales by region" works better than making everyone pretend they're database experts.

Intelligent Document Processing - Stop paying people to manually type information from PDFs into spreadsheets. AI reads invoices, contracts, and forms with 95% accuracy. Probably better than Karen from accounting after her second coffee.

Domain-Specific Search & Knowledge Retrieval - Build a search engine that actually knows your business. Ask it specialized questions and get answers from your proprietary content without bothering the one person who's been here since 1997.

Let AI Talk to Your Customers (So You Don't Have To)

AI-Powered Support & Advisory Chatbots - Handle the same 12 questions everyone asks while your human team focuses on problems that actually require a human brain (for now). Works 24/7. Never calls in sick. Terrible at office gossip.

Hyper-Personalized User Experiences - Show people content and recommendations based on what they actually care about. Not whatever your marketing team decided was important last quarter.

Automate the Tedious Stuff

Automated Content Generation with Quality Control - Generate high-quality content at scale without the hallucinations. Yes, really. The trick is building quality assurance into the system instead of hoping for the best.

Computer Vision for Workflow Automation - Teach AI to look at floor plans, architectural drawings, or images and extract the information your team currently squints at for hours. It doesn't get eye strain. (See last week's newsletter—this one reduced quoting from 3 weeks to 22 minutes.)

Expert Knowledge Replication - Turn decades of "only Janet knows how to do this" into automated systems before Janet retires to Boca Raton.

Entity Recognition & Classification - Automatically identify and categorize the important stuff in mountains of unstructured data. Like having an intern who never gets bored.

Smart Routing & Assignment - Send work to the right person based on skills and availability. Not whoever answered the phone first.

Make More Money

Dynamic Pricing & Yield Management - Stop leaving money on the table because your pricing strategy was set in a meeting six months ago. Let AI adjust prices based on actual demand while you sleep.

Automated Proposal Generation - Turn multi-week quoting processes into multi-minute ones. Your sales team will love you. Your competitors will not.

Lead Scoring & Sales Intelligence - Tell your sales team who's actually going to buy instead of making them chase every tire-kicker with a LinkedIn profile.

Predictive Analytics for Decision-Making - Get warnings before bad things happen instead of detailed reports after. Revolutionary concept, we know.

Supply Chain Optimization - Figure out how much inventory you actually need without relying on Bob's gut feeling or last year's Excel spreadsheet.

Catch Problems Before They Cost You

Fraud Detection & Anomaly Identification - Catch the bad stuff before it becomes a really expensive problem. Works while you're asleep, which is when the fraudsters prefer to work anyway.

Predictive Maintenance - Fix things before they break instead of after. Turns out preventive is cheaper than emergency.

Automated Compliance Monitoring - Catch regulatory violations before the regulators do. Considerably cheaper than fines and legal fees.

Contract Analysis & Risk Assessment - Read every contract you've ever signed and tell you what's actually in there. Useful for due diligence, vendor management, and realizing what you agreed to in 2019.

Understand What People Actually Think

Sentiment Analysis & Brand Monitoring - Know what people actually think about you by analyzing feedback at scale. Sometimes ignorance is bliss, but it's bad for business.

Voice of Customer Analysis - Synthesize thousands of support tickets, surveys, and complaints into actionable insights. Like having a focus group that never ends and costs less than pizza.

Make Quality Scale

AI-Enhanced Assessment Tools - Automate evaluations and provide personalized feedback at scale. Particularly useful if you're drowning in things that need grading, reviewing, or assessing.

Adaptive Learning & Personalized Content Delivery - Identify gaps in knowledge and serve them exactly what they need to learn next. Stop teaching everyone the same thing at the same pace and pretending that works. AI figures out what isn’t understand and delivers the right content at the right time. Scales 1-to-1 tutoring without hiring thousands of tutors.

Real-Time Performance Analysis - Use AI to watch, listen, and analyze performance in real-time. Think sports analytics, but for your actual business processes.

Automated Code Review & Testing - Let AI find bugs and security holes before your customers do. Your developers will still complain, but at least they'll complain about different things.

Optimize the Boring Operational Stuff

Workforce Planning & Scheduling - Optimize staffing without the weekly arguments about who has to work Sunday. Jeremy can let an AI know about his cat’s dentist appointment instead of you.

Synthetic Data Generation - Create fake data that looks real for training and testing when real data is too expensive, too sensitive, or doesn't exist yet.

The Part Where We Tell You the Truth

None of these are magic. A magical version of these and billions of other applications can be stitched together by my toddler vibe coding in cursor for a few hours. These will look great in a demo and will check a box if you’re trying to AI-wash, but will not actually produce real results.

Getting real results is harder and takes significant effort and iterative refinement. Its not worth doing if these results don’t matter. But if you have a problem worth solving, these initiatives can be highly effective.

These are things we've actually built. Things that actually work. Things companies actually use every day. Things where AI delivers measurable results through saved time, increased revenue, reduced errors, or expanded capacity without adding headcount.

Not the sci-fi version. Not the "imagine if we could" version. The version that saves money, makes money, or prevents your competitors from eating your lunch.

The organizations winning with AI aren't doing "AI transformation" for the sake of having an AI strategy. They're the ones who identified a real problem, found a use case that fit, and built something that actually works.

Your move is simple: Pick one problem that's costing you real money or limiting your growth. Find the use case that fits. Build something that actually solves it.

The "transformation" part happens when you do that ten more times.

Next Steps

Look at this list and ask yourself: Which of these solves a problem we're actually having?

Not "which sounds cool" or "which would look good in the board deck."

Which one would save us real money or make us real money?

If you found one, reply to this email or schedule a free strategy session here. We'll tell you honestly if it's a good fit or if you should try something else first.