9 Principles For Turning AI Into Profit

Hi there,

I've been hearing it more and more from my clients: Using AI for personal productivity is nice, but it's not what justifies big investments. Business leaders want to see hard dollars and cents.

People are tired of being promised "the bright AI future" and getting a reality that leaves them with more complexity than they had before.

That's why when I approach new AI projects, I aim to add at least $10K recurring profit – whether that's per year, month, week, or even day depends on the business. This gives everyone an aligned expectation of why (or why not) they should embark on their AI journey at all.

Today, I'll show you the key principles that guide my decisions to drive that $10K number home.

Because in the end, it's not about what AI tools you buy – it's about the profit you create with them.

Let's dive in!

The AI Reality Check

Here's a something few talk about: While basic AI model costs go down, the total cost of implementing AI solutions is actually going up.

When you hear about AI getting cheaper, that's usually about raw model inference costs. But building production-ready AI systems also requires expensive guardrail systems, rare expertise, and high-touch services.

This widening gap between costs and value means one thing: Your AI initiatives need to deliver real, measurable profit to justify their growing expense.

As I wrote previously, a vague promise of "innovation" or "efficiency" won't cut it anymore.

3×3 Principles for Profitable AI Initiatives

Over the years working with clients on their AI initiatives, I've identified a set of 9 key principles that really worked well to drive tangible profit home.

These principles fall broadly into 3 areas: mindset, strategy, and execution.

Here they are:

Mindset

1. Treat AI as a journey, not a destination. AI must be driven by business and needs organizational alignment along your AI maturity journey. This means continuous learning, constant adaptation, and gradual improvement – because AI is only as good as the people who use it.

2. Do not treat AI as just another IT project. AI initiatives require deep business integration and continuous refinement. Success depends on a strong link between technical and organisational readiness at different stages. If you outsource AI to IT, it will ultimately fail to deliver value if your organisation isn't ready for it and expects a plug-and-play solution or, even worse, something that 'learns' and gets better over time.

3. Avoid the shiny toy syndrome and prioritize business needs. Don't do things with AI that are fun but don't have value. And on the other extreme, not every business problem is an AI problem. In fact, few are. Finding the intersection is where the true AI opportunities lie.

Strategy

4. Consider long-term AI impact. AI will reshape every market force: suppliers, customers, competitors. Position yourself accordingly. Even your suppliers might gain more power through AI capabilities, potentially knowing more about your business processes than you do.

5. Balance quick wins vs. strategic bets. Launch initial use cases in areas where you have a deep problem understanding and the tech is relatively mature as well. Don't try to disrupt markets by applying new tech to problems unfamiliar to you. Grow your risk appetite over time as you get more familiar with tech and new problem domains.

6. Think in roadmaps, not isolated projects. You build AI capabilities over time, where smaller projects should unlock bigger ones. Each project should build on the last to create a coherent journey. Look for connections in terms of data, technology, or process steps — these connections will become your roadmaps.

Execution

7. Start with augmentation, not automation. The path goes Assistants → Copilots → Autopilots → Agents. But many try to do this in reverse. This will only burn trust and money. Instead, enhance human capabilities first, then build automated capabilities as your AI maturity grows.

8. Follow the 20/20 rule: When you begin prototyping and incrementally deploying AI use cases, aim to ship increments that take less than ~20 days and less than ~$20k. This approach acts as a forcing function, ensuring your increments are small enough to implement quickly, big enough to matter, and clear enough to measure success. I call these Atomic AI Use Cases.

9. Iterate and improve. As Mike Tyson famously said, "Everyone has a plan until they get punched in the face." That's especially true for AI. Have a plan, but be ready to adapt along the way. The path isn't straight, and that's okay — learn and adjust.

Conclusion

Creating profit with AI isn't about having the latest and greatest tools or the biggest budgets. It's about having the right mindset, following proven execution principles, and keeping an eye on the future while delivering value today.

Start applying these principles to your AI initiatives, and you'll be surprised how quickly that $10K profit target becomes achievable.

Keep innovating – and stay profitable.

See you next Friday
Tobias

PS: If adding profit is on your roadmap for 2025, don't miss the $100K AI Profit Package which I'm offering only once through the end of the Black Friday weekend.