The Real AI Opportunity Nobody Is Funding
Everyone's racing to build the next model. The money is in making the ones you already have actually work
Hey everyone, real John here. This week is based on a few recent conversations I have had with CTOs and recruiters. Everyone is jumping on the bandwagon of we are an AI first company, and most of them are opening ChatGPT and saying, “How can I use AI?”. Actually, not a bad starting point if you have never used AI in anything (personally, I would recommend Claude, but that’s me). Anyway, the real challenge is bridging the gap from having an AI tool to being useful for YOUR work. It takes time, it is not an overnight process to refine the prompts, create the right skills, but most importantly integrate with your day-to-day tools. That’s the missing piece that all these silly YouTube videos are glossing over. I laugh every time I see “I 10xed my productivity overnight!”. Let’s say that is kind of real, they 10xed their output but -20x their effectiveness. Creating garbage 10x faster is not improving anything. Ok, I’ll stop my rant to get into the newsletter this week. Its about integration with YOUR tools, not using AI. Five of Diamonds, if you know you know…
I want to start a new thing to share something to celebrate each week and shout out to people who made an impact on me, maybe recently, maybe years ago. This week - I want to thank Trevor Carnahan for all the support he has given me on being a great manager. I’ll never forget ShareBuilder and being one of my first experiences after Microsoft, you showed me how much fun I could have with an amazing manager and a great team.
I've been in enough boardrooms and engineering standups to recognize a pattern when I see one. And right now, the pattern is this: companies are drowning in AI tools and starving for results.
Last week I was talking to a CTO friend of mine — sharp guy, serious company — and he told me they're running four different AI tools across three teams, have a couple of pilots "almost ready for production," and exactly zero workflows that have been fundamentally changed by any of it. Sound familiar?
This isn't a one-off. Deloitte's State of AI 2026 report just dropped some numbers that should make every builder sit up straight. Access to AI tools is up 50% year over year — 60% of employees now have access to something. But fewer than 60% of those employees actually use the tools regularly. And only 25% of organizations have converted even 40% of their AI pilots into production systems.
Read that again. The tools are there. The budget is there. The access is there. And still — most of it is sitting in a staging environment, waiting for someone to figure out the last mile.
That last mile? That's the opportunity.
The Funding Gap Nobody Talks About
While everyone argues over which foundation model is best, OpenAI raises $110 billion, and Yann LeCun spins up a $1B seed-stage company before shipping a single line of production code — the real problem in enterprise AI isn't model quality.
It's integration.
Nearly 60% of AI leaders say legacy integration is their primary adoption challenge. Not the model. Not the cost. Not the regulation. Getting the AI to talk to the systems that already exist. That problem is unsexy, unglamorous, and absolutely everywhere.
This is where I've spent the last several years of my consulting work. Not building the next GPT competitor. Building the bridge between what a company already has and what they're trying to do with AI. Connecting the Jira tickets to the pull requests. Getting the agentic pipeline to actually fire in the right order. Making the output of one AI tool the input of another — reliably, in production, every time.
That work is hard. And companies will pay real money for it.
Here's How to Go Get It
This is practical, so let me give you actual steps.
1. Find the pilot graveyard.
Every company that's been "investing in AI" for the last 18 months has them — tools that got bought, demos that impressed the C-suite, proofs of concept that never made it to production. Ask a CTO or VP of Engineering where their AI pilots went to die. That answer is your roadmap.
2. Pick one workflow, not one tool.
The mistake most people make is going in and saying "let me help you use Claude" or "let me build you an AI chatbot." Wrong frame. Go in and say: "What does your team do every week that takes forever and shouldn't?" Pick one workflow. Make AI actually do it. Ship it.
3. Integration over innovation.
You don't need to build anything novel. You need to connect things that already exist. A language model that reads a Slack message and creates a formatted ticket. A pipeline that takes a customer email and routes it with context to the right team. A weekly report that writes itself. None of this is rocket science. All of it is incredibly valuable.
4. Governance is the new moat.
Here's what the Deloitte data also shows: only 21% of companies have proper governance in place for their AI agents. That means security, audit trails, model reliability, access controls. If you can come in and not just build the thing but also make it *safe to run in production*, you are in a completely different conversation than the consultant who just builds demos.
5. Charge for outcomes, not hours.
You reduced their weekly report from 4 hours to 20 minutes? That's value. Price it that way. The companies that are serious about AI adoption aren't nickel-and-diming integration work — they're desperate for someone who can actually close the loop from pilot to production.
The Builders Who Win in 2026
The race to build the biggest model is already over for most of us. That game requires billions of dollars and a PhD army. But the race to make AI actually work inside real companies? That race is just starting, and it's wide open.
I built Cash Critters on $50 a month. Not because I didn't want more resources — but because constraints forced me to make every decision count. The same principle applies here. You don't need a massive team or a venture-backed runway to solve an integration problem. You need to understand the workflow, pick the right tools, and ship something that actually runs.
The opportunity isn't in the models. It's in the mile between the demo and the deployment.
Go close that gap. Go build something amazing.
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John Mann is the founder of Startups and Code LLC and writes weekly about AI, startups, and tech leadership. Have an integration problem you're trying to solve? Reply and let's talk.