The Applied AI Economy
A sampler of ways to make money applying AI. This list is not exhaustive. That's the point.
The Economy Is Bigger Than You Think
Most people hear "make money with AI" and think of one thing: building chatbots or automating workflows. That's real work and it pays. But it's one corner of a much larger economy that is forming right now.
The applied AI economy includes consulting, coaching, training, tool building, culture transformation, and entirely new startups that didn't exist two years ago. New categories are emerging every month. If you only see one path, you're not looking wide enough.
Our advice: don't be too picky early in your applied AI career. Try different types of work. Find out what you're good at, what pays well, and what you actually enjoy. Those three circles may not overlap immediately, and that's fine. You need reps to figure it out.
Workflow Automation Consulting
This is the entry point most people know about. It's the one everyone's heard of, so we'll start here. Tools like Clay, n8n, Make, and Zapier have made it possible for non-developers to build powerful automations. Think: automating a receptionist's intake process, syncing CRM data across platforms, generating reports from raw inputs.
Who it's for: People who are detail-oriented, enjoy systems thinking, and can translate a messy business process into a clean automated flow.
The reality: This work is genuinely valuable. Businesses need it. But be aware that the market is getting crowded, and many clients in this space can only pay a few hundred dollars per project. That's fine for building your portfolio. Just know that it takes roughly the same amount of time and emotional energy to onboard a client paying $300 as one paying $3,000. Choose your engagements wisely.
Intrapreneurship: Becoming the AI Person at Your Company
You don't have to go independent to make money in the applied AI economy. One of the fastest paths is becoming the person inside your current organization who figures out how to apply AI. Every company needs this person, and very few have one.
This looks like: identifying workflows that AI could improve, running small experiments, presenting results to leadership, and gradually becoming the internal champion for AI adoption. You're not quitting your job. You're making yourself dramatically more valuable within it.
Who it's for: People who are already employed somewhere and see AI opportunities their colleagues are missing. You like stability but want to be on the cutting edge of how your organization evolves.
Why it works: Companies will pay more (raises, promotions, new titles) for the person who can lead AI adoption from the inside. You already understand the business, the culture, and the politics. An outside consultant has to learn all of that. You already know it. That's a massive advantage.
The trajectory: Many intrapreneurs eventually spin out into consulting, coaching, or startups once they've built enough expertise. But the intrapreneur phase is a low-risk way to get reps, build credibility, and get paid while learning.
Executive Coaching and AI Transition Support
There are people making very good money helping executives and professionals learn how to use AI tools effectively. Not building custom software for them. Teaching them to use what already exists.
This means helping someone get real value out of ChatGPT, Claude, Google Workspace with Gemini, or specialized tools in their industry. For high-level executives, AI becomes a strategist, a sounding board, a sense-maker that helps them identify blind spots. That's powerful, and many leaders will pay well for someone to show them how to unlock it.
Who it's for: People with strong communication skills who can meet executives where they are. You need patience, emotional intelligence, and the ability to translate technical capability into practical business value.
What to watch for: Some clients will ask you to build an "OpenAI wrapper" for them when what they actually need is to learn how to use the tools directly. You'll have to decide whether to push them toward the higher-leverage path or just build what they think they want. There's no universally right answer. Just be honest with yourself about the trade-offs.
AI Culture Transformation Consulting
Companies know they need to adopt AI. Most of them have no idea how to create a culture where people actually use it. This is the gap that AI culture transformation consultants fill.
The work looks like: running internal hackathons, leading company-wide AI training sessions, designing adoption programs, and helping leadership model the behavior they want to see. It's very hard for companies to find someone internally who can do this well, which is why they bring in outside help.
Who it's for: People who understand both technology and organizational behavior. You need to be comfortable facilitating groups, navigating internal politics, and measuring outcomes that are often qualitative (like "people actually started using AI in their daily work").
Why it matters: This is some of the highest-leverage work in applied AI right now. A single culture shift inside a company can unlock far more value than any individual automation. The Canon says efficiency is a tool, not the goal. Culture transformation is how you make the goal stick.
Building Custom Tools
Some clients need something that doesn't exist yet. A custom dashboard, an internal AI agent, a specialized workflow that connects multiple systems. This is closer to traditional software development, but with AI capabilities woven in.
Who it's for: People with engineering skills who enjoy building things from scratch. You need to scope well, set clear expectations, and be realistic about maintenance (someone has to keep this running after you deliver it).
The opportunity: Custom tools command higher prices than automation consulting because the work is harder to commoditize. But the engagement is also deeper. You'll need to understand the client's business, not just their tech stack.
Vertical AI Startups
Here's where it gets interesting. When you help one person in a specific industry (accounting, construction, marketing, YouTube strategy) and the solution works, ask yourself: is this person a singleton? Almost certainly not. There are thousands of people with the same problem in the same industry.
That's the seed of a vertical AI startup. You pair up with a domain expert (someone who knows the industry cold) and build a product together. They bring the knowledge and the network. You bring the AI implementation skills.
Who it's for: Practitioners who have done enough client work to recognize patterns across an industry, and who want to go from services to product.
What makes it work: The specialist partner is essential. You cannot build a great vertical AI product for construction if you don't deeply understand construction. Find the right partner first. The technology is the easy part.
Command Center Setup
A new category of work is emerging around setting up AI command centers for individuals and organizations. These are centralized systems where AI agents, data sources, and workflows come together in one place.
OpenClaw is a personal/life command center. OpenTeams' Nebari OS is a corporate/government command center. Both are powerful, both are open, and both need someone to set them up and configure them for the person or organization using them.
Who it's for: People who enjoy technical setup, configuration, and making complex systems accessible to people who didn't build them.
The model: You set up and configure the command center, then train the user or team on how to operate it. Recurring revenue comes from ongoing support, customization, and expansion as needs grow.
A Note on Trade-offs
Every one of these paths involves real trade-offs. When you take on a client, you're committing intellectual and emotional energy. You will naturally care about the well-being of the person you're helping. You will feel the weight of their business outcomes alongside your own.
Set expectations well. Be honest with yourself about what a project will mean for you in terms of time, emotional labor, and whether the compensation makes it worthwhile. Ramping up to have serious influence over someone's business is not a casual commitment.
This List Will Grow
The applied AI economy is evolving fast. New categories of work are emerging that nobody has named yet. We will continue updating this page and linking to case studies as practitioners in our community share what's working for them.
If you're doing applied AI work that doesn't fit neatly into any of these categories, that might mean you're early to something. Tell us about it.
Don't overthink the path. Start somewhere. Get reps. The economy is big enough for all of us.