Hi there,

A proud manager recently told me: "We've identified over 50 AI use cases!". I said: "That’s great! Which one will get you the most profit?" He paused. "We haven't gotten to that part yet".

This is backwards. And it's why so many companies end up with impressive AI projects that look good in presentations but do nothing for the bottom line.

Today, I'll show you why chasing AI use cases is the wrong approach, and how focusing on profit first will transform your AI initiatives from cost centers into revenue generators.

Let's dive in!

The Traditional AI Approach (That’s Burning Money)

Here's a scene I witness too often: A company decides they need to "do AI". They bring in expensive consultants, form innovation teams, and start listing every possible place they could use AI. Each workshop generates dozens of "promising use cases".

Six months and $200,000 later, they have beautiful PowerPoint decks but zero impact on their P&L.

Let me share a recent example (details changed for privacy):

A retail company spent $500,000 building an AI-powered inventory management system. The technology worked great - it could predict stock levels with 94% accuracy. Impressive!

But they skipped the main question: How much were stockouts and overstock actually costing them?

The answer: About $200k annually.

Even if their AI system worked flawlessly (it didn't), they'd need ~2.5 years just to break even. Meanwhile, they had other processes leaking $50,000 per month that could have been fixed with simple AI augmentation.

This is what happens when you lead with AI instead of value.

A Better Way: The Value-First Framework

When you kick off your AI initiative, immediately stop looking for ways to use AI. Instead, look for significant value opportunities, and then see if AI can help to unlock them.

It sounds simple, but this flip in thinking changes everything.

Think about it. Would you rather have:

• An AI project that works perfectly but delivers minimal impact, or

• A business initiative that drives massive value, where AI is just one piece of the solution?

The second option wins every time. But to make this work, you need a clear framework for defining "massive value".

The 10K Rule

Here's a simple rule I use with my clients: If a business problem isn't worth at least $10,000 per month in value (either cost savings or additional revenue), it's probably not worth building an AI solution for.

Why $10K? Let's break it down:

• That's about $500 per business day

• Or roughly $60 per hour

• Or $1 per minute

This might sound like a lot, but remember - implementing AI solutions comes with significant costs:

• Development time

• Ongoing maintenance

• Training and change management

• Transitioning from existing processes

Plus, there's the opportunity cost. Every hour your team spends on a small AI project is an hour they can't spend on potentially bigger opportunities.

Finding Your Number

The $10K threshold works for many mid-sized businesses, but your number might be different. For a large enterprise, it could easily be $10K per day or even per hour. For a smaller business, it might be $10K per year.

Whatever your number, you need a "value filter" that helps you:

1. Set realistic expectations

2. Quickly identify worthy opportunities

3. Filter out low-value processes

Do this in the phase where you look for Pain Points & Bottlenecks. Ignore processes that are below your value filter - no matter how painful they seem. This gives you the necessary clarity at the start of your AI journey.

Practical examples

Let's look at some common business scenarios through our profit-first lens. What's crucial here is starting with the value, not the AI.

Example 1: Sales

❌ Wrong approach: "Let's use AI to help our sales team write better proposals"

✅ Profit-first approach:

• 20 sales reps × 2 hours daily on proposals

• Hourly cost: $50

• Monthly proposal cost: $40,000

• AI reduces proposal time by 50%

• Profit impact: $20,000 monthly savings

• Equivalent to hiring 2 more sales people

• NOW we're talking!

Besides these hard metrics, your AI initiative will further support the following "softer", or more strategic benefits:

• Higher quality, consistent proposals

• Faster response times to RFPs

• Knowledge preservation when top performers leave

"Deal-Sweeteners" like these are great, but you can't rely on them alone to get you along your (likely bumpy) road to AI success.

Example 2: Customer Support

❌ Wrong approach: "Everyone's building AI chatbots, we need one too"

✅ Profit-first approach:

• Current cost per ticket: $15

• Monthly tickets: 5,000

• Total support cost: $75,000

• AI handles 20% of tickets

• Profit impact: $15,000 monthly savings

Supporting Benefits:

• 24/7 customer availability

• Consistent service quality

• Rich customer interaction data

• Improved employee satisfaction (handling more interesting cases)

Example 3: Manufacturing

❌ Wrong approach: "We should use computer vision AI for quality control"

✅ Profit-first approach:

• Current defect rate: 3%

• Monthly defect costs: $200,000

• AI reduces defects by 1 percentage point

• Profit impact: $66,000 monthly savings

Supporting Benefits:

• Environmental impact reduction

• Innovation leadership

This also means that your AI solution must not cost more than ~$1.5m if you want ROI in 12 months (assuming a 2x risk multiplier).

Notice what we're doing here:

1. Starting with actual costs and revenue numbers

2. Breaking everything down into monthly profit impact

3. Including both direct and indirect benefits

4. THEN considering if AI is the right solution

Making It Work: The People Factor

While profit gets your AI project started the right way, people determine its implementation success. But most employees don't wake up thinking about company profits. They care about their daily work experience.

So once you've identified a profitable opportunity, translate it into what matters for your teams:

• From "reducing costs" → to "eliminating mind-numbing tasks"

• From "improving efficiency" → to "focusing on interesting work"

• From "increasing productivity" → to "having more impact with less stress"

• From "staying competitive" → to "building future-proof skills"

Think about it: A 50% efficiency gain isn't just $20K monthly savings - it's also 4 hours of tedious work eliminated from someone's day.

Remember, the next time someone suggests an AI project, ask a simple question: "What profit are you looking for?" If you're pitching an AI project yourself, ask that question in the mirror first. If you can't give a straight answer, it's probably not worth pursuing yet - no matter how impressive the technology sounds.

Your Action Plan: From AI to Profit

I've already been helping three folks get to 10K additional profit with AI in their business.

This November, I set up the goal for myself to help 5 more people and make it easy for them to add 10K profit to their business with AI.

If you’re interested, reply “10K” and I’ll send you the details.

Keep innovating (profitably)!

See you next Friday,
Tobias