We won best AI team (& what we learned) 🎉

We won best AI consulting team in Charlotte this week!! Honestly pretty mind blowing after ~18 months of StealthX. It’s also funny because we consider ourselves the anti-consultants, but still.. pretty wild.

I'm not gonna pretend like we had this all figured out from the start because we didn't. We’ve made a lot of mistakes and tried stuff that didn't work. We’ve changed direction more times than I can count but I think what made the difference was just.. moving. Learning, trying things, not letting perfect be the enemy of great. Progress > motion.

A year ago, we didn't have half the stuff we're using now. Vision-to-Value wasn't a thing. Betting Table cycles weren't part of how we worked. Claude Code didn’t exist. We were just building for clients and running into the same walls everyone else was hitting.

The difference is that instead of accepting the walls, we started trying stuff. Some of it worked and a lot of it didn't. But we/I learned faster than I have in the last decade.

Here's what some of that that looked like.

Here's what we kept seeing. Client comes to us after spending several months and serious money with a bigger firms or wresting internally. They've got this beautiful AI strategy deck, maybe a spreadsheet with a bunch of use cases, and a 12-month roadmap.

And literally they have little/no progress. No actual value, no team buy-in, just expensive docs that executives can't explain and engineers can't build from. Or the other version where a company bought an AI platform, has run some training sessions, and then.. crickets. Tool sits there gathering digital dust and no one uses it. No processes change and no value created.

Both paths leave leaders stuck somewhere between "we should do something with AI" and "this AI thing actually made/saved us money." And even when companies got past that mess, they’d hit another wall.

Individual people are getting scary good with AI. Like, really good. Building custom workflows, tailoring personal assistants, getting 10-100x the output they used to get.

But when they tried to scale that to a team? Brutal.

Those workflows didn't port over. The mental models didn't translate or someone would gloss over critical nuance and the AI newbies wouldn't get it, wouldn't internalize it, and wouldn't actually use it.

Teaching someone the buttons to press is easy. Teaching them how to think about AI is the hard part, and I don't think enough people are talking about that gap.

As we observed all of this, we started building what eventually became our Vision-to-Value framework, though we didn't call it that at first. We were just trying to solve a problem.

The problem? So many companies are just talking about AI and leaders are stuck with nothing that actually works and no real progress.

What we figured out is that you need both vision and fast execution. Speed matters because you have to get momentum but it has to produce ACTUAL value.

Honestly, it's pretty simple when you strip away the jargon. Show people what you're building instead of writing about it. Build prototypes and proofs of concept, not slides/docs/decks. Get to something tangible fast. Make sure you're measuring business impact, not just whether you turned something on.

The full framework has five phases but here's what it really means. We validate different things at different stages so we don't waste time building the wrong thing or building the right thing the wrong way.

We focus on speed to value to get momentum. Not rushing, but not wasting time. We focus on a highly visible, acute pain that isn’t dependent on internal consensus building or protected data.

This has been the biggest unlock for unlocking a company with AI.

Here's the thing about planning in 2026.. it's kind of ridiculous.

Before companies would plan quarterly roadmaps and by month two, half the features were already obsolete. Not because they planned wrong, but because one of the big AI companies just shipped something that made the approach look silly. Or a new model dropped that completely changed what was possible.

We started experimenting with 6-week cycles instead and focused on one bet per cycle. That's it. No detailed backlogs. No Agile sprint planning ceremonies. Just this.. what's the one thing worth doing for the next 6-weeks?

At the start of each cycle, we pick one bet. Not three priorities, not a ranked list. One thing that matters most given what we know today. Then we define our appetite.. how much time and how many people are we willing to invest? That's the constraint. Shape the solution to deliver meaningful value within that box.

And once we start, we don't interrupt. It’s 6-weeks, no scope changes, no "quick wins" that derail focus.

This approach probably isn’t perfect for every team, but for teams building AI-forward stuff where the tools change monthly? It's the only thing that makes sense.

As we built these frameworks and worked with companies, certain patterns just kept showing up. Operators kept asking the same question: "How should we actually use AI?" Not "what's possible with AI" but "What can we do this quarter that shows up as profit/revenue?"

The answer that keeps working is to start small with productivity wins. AI-powered meeting transcription, document processing, and stuff that helps get efficiency in 30 days. Prove value there, build confidence, then go bigger with stuff that becomes a competitive advantage.

Also, the nature of work itself has started shifting in ways I didn't expect. Software feels sillier by the day. Like, we should just be talking to AI that stores structured data, then asking it to create disposable UI when we need to see something. Permanent software seems kind of hilarious now.

Being in feels like it’s becoming the moat. It's wild to talk to folks who eat, sleep, and breathe AI and then talk to friends who barely use GPT and are getting laid off. The dichotomy is nuts. Everyone's scared, so just show up in person. People trust people.

What I don't think enough people talk about is the compounding effect of context. The output I'm getting now versus 45-60 days ago in Claude Code is crazy. The knowledge stacks and it starts suggesting stuff that makes me think "wait, how did you know that?"

Career paths are changing too. I've talked to dozens of students and early career folks over the last six months, and most of them are doing it wrong. They're building the wrong things, applying the wrong way, relying on credentials that don't matter anymore.

Stop applying to jobs on LinkedIn. Build something that solves real business problems. Create case studies that show value. Get introductions and go through the side door. Show what you can do in ways that grab attention. The traditional path doesn't work anymore. That's just the reality.

If you're trying to figure out where to start with your team, here's what's worked for us.

The world is moving faster than it ever has.

I truly believe that the teams that win with AI are the ones that keep shipping. They know the difference between vision and execution and they do both. They validate assumptions through building, not debating. They measure business impact, not just deployment. That's what this award represents to me. Not perfection but learning velocity.

The gap between teams that get this and teams that don't is widening every week. Good news is you don't need a transformation program or a dedicated AI team. You just need one smart bet that delivers tangible value in the next 6-weeks. That's enough to build momentum, the rest follows.

Onward & upward 🤘
Drew

P.s. Huge shout out to the whole squad at StealthX for the hard work, it's been an absolute blast! And to the AI Innovation Council and the Charlotte AI/tech community! Thanks for being amazing builders and supporters. So grateful to be running with y'all.