Last-Mile AI: Why Most AI Projects Fail Before Production
What is last-mile AI?
Last-mile AI is the final stretch of an AI project — the part where teams have to actually get the thing into production and into people's hands.
The term borrows from logistics, where last-mile delivery is often the hardest part of the whole journey.
As Michael LaVista, founder and CEO of Caxy, puts it: you can get a package all the way from China to Long Beach, then on a truck to Chicago — and you still have to get it to the person's home, and that's most of the work.
AI projects behave the same way.
They tend to run out of steam right at the end.
Caxy specializes in exactly this stage, because it's where the work that determines whether an AI project wins or dies actually happens.
Why do most AI projects fail before they reach production?
When people build AI tools on their own, they usually hit the same walls right before the finish line.
According to Caxy, the most common failures are: No problem solved.
The single biggest issue — teams picked AI first instead of picking the problem first.
The tool works in a demo but isn't usable in the real workflow.
It can't handle real volume.
It isn't safe to put in production.
There's also a change-management failure that runs in parallel: not everyone in a company is excited about AI.
Some people are worried about their jobs, some simply aren't technology people, and — notably — younger people can now be the ones more resistant to the technology.
Managing that resistance has to go along with any transformation.
“last-mile delivery is often the hardest part: you get a package all the way from China to Long Beach, California, then on a truck to Chicago, and you still have to get it to the person's home”
Why is "the board said use AI" a recipe for failure?
The thing Caxy hears most often from companies is "the board told us" or "the CEO told us we have to use AI." So someone just buys AI — with no strategy behind it.
The pattern repeats: companies lurch back and forth.
They decide they have to get AI, everyone gets AI, then they spend too much money and yank it out, and then they put it back.
One organization handed out Claude Pro to everyone.
A city bought Microsoft Copilot for every employee — and then everyone sat around asking "okay, now what are we supposed to do?" Answering that "what are we supposed to do now" question is the work Caxy spends the most time on, because buying a tool is not a strategy.
“In an organization of 5,000 people, at $200 each, you're spending $100,000 a month on AI tokens, and there's really no accountability for what they're supposed to be doing with them.”
Why should you solve the problem before choosing AI?
The fix for last-mile failure is to reverse the order: start with the problem, not the technology.
Companies go about it backwards when they start with AI first and say "let's do something with AI." It also matters which problem you pick.
If you simply focus on automating or making something you already do faster, you'll run out of enthusiasm fast, because it didn't make that much of a difference.
The payoff is in value creation — doing something you couldn't even dream of doing before.
That's the moment a company realizes it has a competitive advantage.
“if you simply focus on automating or making a thing you already do faster, you're going to run out of enthusiasm gas quick, because it just didn't make that much of a difference”
What does it cost to roll out AI with no accountability?
Buying AI for everyone without a plan is expensive in a way that's easy to overlook.
Caxy gives a concrete example: in an organization of 5,000 people, handing everyone a $200/month seat means spending $100,000 a month on AI tokens — with no accountability for what people are supposed to be doing with them.
That cost pressure is also reshaping how buyers behave.
As tokens have become more expensive, everyone needs to be more accountable about how much they're spending on use cases, so teams are being forced to get more pragmatic about what AI is actually for.
Frequently asked questions.
- What does "last mile" mean in AI projects?
- It borrows from logistics, where last-mile delivery — getting a package the final stretch to someone's home — is often the hardest part of the journey. In AI, the last mile is the final stage of getting a project into production. It's where projects tend to run out of steam, because building the model is not the same as shipping something usable.
- Why do AI projects built in-house often fail?
- When companies build AI tools on their own, they usually lack good UX, lack scaling, and lack security. But the most common problem is that they didn't actually solve a problem — they picked AI first instead of picking the problem first. Change management is the parallel failure: not everyone is excited about AI, and that resistance has to be managed alongside the technology.
- Should I start with the problem or the AI?
- Start with the problem. Companies go about it backwards when they start with AI first and say "let's do something with AI." Beyond that, pick a problem that creates value rather than just automating something you already do faster — speeding up an existing task runs out of enthusiasm quickly because it doesn't make much of a difference. Value creation is what produces a competitive advantage.
- How much can buying AI for everyone cost?
- It adds up fast. In a 5,000-person organization, giving everyone a $200/month seat means spending $100,000 a month on AI tokens — often with no accountability for what people are supposed to do with them. As tokens have become more expensive, teams need to be more accountable and pragmatic about spending on use cases.
- Why isn't buying a tool like Copilot or Claude Pro a strategy?
- Because a purchase isn't a plan. Caxy hears "the board told us" or "the CEO told us we have to use AI" most often, so someone just buys AI. One city bought Microsoft Copilot for everyone and then sat around asking "now what are we supposed to do?" Companies lurch back and forth — buy AI, overspend, yank it out, put it back — with no strategy behind it.