Why Most AI Companies Never Find Their Sweet Spot
The AI industry has a focus problem.
Every week, there is a new model, a new framework, a new use case. And most AI companies respond the same way: they chase everything. They build chatbots AND computer vision AND recommendation engines AND predictive analytics. They serve healthcare AND finance AND retail AND manufacturing.
The result? They are mediocre at everything and exceptional at nothing.
Jim Collins studied this problem for five years. His research team analyzed 1,435 companies over 40 years to find what separated the truly great companies from the merely good ones. The answer was not technology, not talent, not capital.
It was the Hedgehog Concept.
What Is the Hedgehog Concept?
The Hedgehog Concept comes from an ancient Greek parable: "The fox knows many things, but the hedgehog knows one big thing."
Foxes are clever, pursuing many strategies simultaneously. Hedgehogs are simple, focusing on one thing they do extraordinarily well. Collins found that companies that made the leap from good to great were hedgehogs, not foxes.
The Hedgehog Concept sits at the intersection of three circles:
Circle 1: What can you be the best in the world at? Not what you want to be the best at. Not what you are currently good at. What you can be the best at — given your unique capabilities, history, and position.
Circle 2: What drives your economic engine? What is the single economic denominator that has the greatest impact on your economics? Collins calls this your "profit per X" — the one ratio that matters most.
Circle 3: What are you deeply passionate about? Not what you think you should be passionate about. What genuinely energizes your team and keeps them going when things get hard.
The sweet spot — where all three circles overlap — is your Hedgehog Concept.
Applying Circle 1: What Can Your AI Company Be Best At?
This is the hardest question, because it requires brutal honesty.
Most AI companies answer this wrong. They say things like "we can be the best at AI" or "we can be the best at machine learning." That is not specific enough to be useful.
The right approach: Define your unique capability stack.
Your unique capability is not a single technology. It is the intersection of:
- Domain expertise: What industry or problem domain do you understand better than anyone?
- Technical depth: What specific AI/ML techniques have you mastered?
- Data advantage: What unique data assets or data pipelines do you have?
- Delivery model: How do you deliver value in a way others cannot easily replicate?
Example: A manufacturing AI company
- Domain: 15 years of manufacturing process optimization experience
- Technical depth: Time-series anomaly detection on noisy sensor data
- Data advantage: Proprietary dataset of 500M+ sensor readings from 200+ factories
- Delivery model: Edge deployment that works with legacy PLCs and no cloud dependency
Their "best in the world" is not "AI for manufacturing." It is "real-time anomaly detection on legacy manufacturing equipment without cloud connectivity."
That is specific. That is defensible. That is a Hedgehog Concept.
Questions to find your Circle 1:
- What do customers come to us for that they cannot get elsewhere?
- What do we do that our competitors consistently struggle to replicate?
- What problems have we solved that nobody else has?
- If we had to bet the company on one capability, which would it be?
- What would our best customers say we are the best at?
Applying Circle 2: What Drives Your Economic Engine?
Collins' key insight: great companies identify a single economic denominator — their "profit per X" — and obsess over it.
For AI companies, the economic engine question is critical because most AI business models are broken. They build custom solutions for every client, which does not scale. They charge by the hour, which caps revenue. They give away AI features for free, hoping to monetize later.
Finding your "profit per X" for AI companies:
The "X" should be the single unit that most directly drives your economics. Common options:
- Profit per deployment: How much do you make each time you deploy your solution?
- Profit per data point processed: What is your margin on each unit of data you process?
- Profit per customer retained: What is the lifetime value impact of keeping a customer?
- Profit per decision automated: What is each automated decision worth?
Example: The manufacturing AI company
They tried several economic denominators:
- Profit per customer: Too broad, did not drive behavior
- Profit per project: Led to chasing big custom projects (fox behavior)
- Profit per machine monitored: This was the one
Why? Because "profit per machine monitored" drives the right behavior:
- It incentivizes standardized, repeatable deployments (more machines = more profit)
- It incentivizes retention (keep monitoring = recurring revenue)
- It incentivizes product improvements (better monitoring = more machines per customer)
- It creates a clear scaling metric (10,000 machines → 100,000 machines → 1,000,000 machines)
Questions to find your Circle 2:
- What single metric, if improved, would have the biggest impact on our business?
- What is the unit of value we deliver to customers?
- How do our most profitable engagements differ from our least profitable?
- What business model allows us to scale without proportionally scaling headcount?
- What would make our revenue predictable and recurring?
Applying Circle 3: What Are You Deeply Passionate About?
Collins is clear: you cannot manufacture passion. You discover it.
Passion matters because building an AI company is brutally hard. Models fail. Customers churn. Competitors emerge. The market shifts. The only thing that keeps a team going through years of hard work is genuine passion for what they are doing.
Common passion traps for AI companies:
- "We are passionate about AI": Too generic. Everyone in AI is passionate about AI.
- "We are passionate about making money": Not a passion, it is an outcome.
- "We are passionate about disrupting X industry": Often performative, not genuine.
The real test: What would your team work on even if the market did not reward it? What problems keep your engineers up at night — not because of deadlines, but because they genuinely want to solve them?
Example: The manufacturing AI company
Their passion was not "AI" or "manufacturing." It was "making factories safer and more efficient for the people who work in them."
This passion showed up in how they built products:
- They spent weeks on factory floors understanding operators' workflows
- They designed interfaces for people wearing gloves, not sitting at desks
- They celebrated when their system caught a bearing failure before it caused an injury
- They hired engineers who had worked in factories, not just data scientists from academia
Questions to find your Circle 3:
- What work energizes our team the most?
- What customer problems do we find most compelling to solve?
- What would we continue doing even if it was not the most profitable path?
- What gets our engineers excited in stand-ups?
- What stories do we tell at company all-hands that make people proud?
The Hedgehog Concept in Action: A Framework for AI Companies
Here is a practical framework for finding your Hedgehog Concept:
Step 1: Council Meetings (Not Brainstorming)
Collins recommends creating a "council" — a small group of 5-8 senior people who meet regularly to debate and discuss the three circles. This is not a one-day offsite. It is an ongoing dialogue that typically takes 6-12 months to crystallize.
Rules for council meetings:
- Lead with questions, not answers
- Engage in vigorous debate, not polite consensus
- Use data and evidence, not opinions and analogies
- Make decisions based on understanding, not politics
- Conduct autopsies without blame when things go wrong
Step 2: The Brutal Facts
Before you can find your Hedgehog Concept, you must confront the brutal facts of your current situation. Collins calls this the "Stockdale Paradox" — maintain unwavering faith that you will prevail, while simultaneously confronting the most brutal facts of your current reality.
For AI companies, brutal facts might include:
- "Our technology is not actually differentiated — three competitors have similar capabilities"
- "Our biggest customer accounts for 40% of revenue — we are one phone call away from crisis"
- "We have been in business for three years and still cannot articulate what we do better than anyone else"
- "Our best engineers are leaving because they are bored building custom integrations"
Step 3: The Flywheel
Once you find your Hedgehog Concept, you build a flywheel around it. The flywheel is a self-reinforcing loop where each turn builds momentum for the next.
Example flywheel for the manufacturing AI company:
- Deploy anomaly detection on machines → 2. Collect more sensor data → 3. Improve model accuracy → 4. Catch more failures before they happen → 5. Customers add more machines → 6. More deployments → (back to 1)
Each turn of the flywheel makes the next turn easier. After thousands of turns, the flywheel has enormous momentum — and competitors cannot replicate it because they do not have the accumulated data, domain expertise, and customer trust.
Common Mistakes AI Companies Make
Mistake 1: Confusing core competence with Hedgehog Concept
Core competence is what you are good at today. Hedgehog Concept is what you can be the best at. These are different things. You might be good at NLP today, but if you cannot be the best at it, it is not your Hedgehog Concept.
Mistake 2: Skipping the economic engine
Many AI founders are passionate technologists who ignore unit economics. If your economic engine does not work, passion and capability are irrelevant. You will run out of money.
Mistake 3: Changing the Hedgehog Concept every quarter
The Hedgehog Concept is not a strategy pivot. It takes years to discover and decades to execute. If you are changing your core focus every quarter, you are a fox pretending to be a hedgehog.
Mistake 4: Thinking the Hedgehog Concept limits growth
The opposite is true. By focusing on one thing, you go deeper, build more defensible advantages, and eventually expand from a position of strength rather than spreading thin from a position of weakness.
How to Start Finding Your Hedgehog Concept This Week
You do not need to solve this in a week. But you can start the process:
- Schedule the first council meeting: Get your senior team together for 90 minutes. No slides. Just three questions on a whiteboard.
- List your brutal facts: What are the uncomfortable truths about your business that nobody wants to talk about?
- Interview your best customers: Ask them: "Why did you choose us? What do we do that nobody else does?"
- Analyze your economics: Which engagements are most profitable? Which ones scale? What is the pattern?
- Watch your team: What work energizes them? What work drains them?
The Hedgehog Concept is not something you invent. It is something you discover through disciplined inquiry and honest self-reflection.
How Hyperion Consulting Helps AI Companies Find Their Sweet Spot
At Hyperion Consulting, we facilitate the Hedgehog Concept discovery process for AI companies and tech startups. Our strategy workshops help leadership teams:
- Confront brutal facts about their competitive position
- Identify their unique capability stack (what they can truly be best at)
- Define their economic engine and unit economics
- Discover their authentic passion and purpose
- Build a flywheel that creates compounding competitive advantage
The companies that find their Hedgehog Concept do not just survive — they dominate their niche and expand from a position of strength.
Ready to find your strategic sweet spot? Book a free strategy consultation to start the conversation.
