Why You Should Stop Looking for AI Use Cases

🔗 https://blog.tobiaszwingmann.com/p/why-you-should-stop-looking-for-ai-use-cases

Tobias Zwingmann’s article challenges conventional wisdom by urging organizations to focus on profit, not technology, when embarking on AI projects. This profit-first framework ensures that AI initiatives drive tangible value.

1. The Traditional AI Approach (That’s Burning Money)

Many organizations approach AI by brainstorming dozens of use cases, hiring consultants, and creating extensive lists. However, this often leads to wasted resources with little to no impact on profitability. Zwingmann illustrates this with a real-world example: a retail company spent $500,000 on an AI-powered inventory system that saved only $200,000 annually—a poor ROI.

2. A Better Way: The Value-First Framework

Instead of starting with AI, start with value opportunities. The goal is to identify business challenges worth solving and then determine whether AI is the right tool for the job.

• Key shift: Focus on initiatives that drive massive value, with AI as a component—not the centerpiece.

• Think of AI as part of a business solution, not a standalone goal.

3. The 10K Rule

A practical rule: Only pursue AI projects that promise at least $10,000 in monthly impact (cost savings or revenue). This threshold ensures projects are worth the investment.

• For large enterprises, this value might scale to $10K per day or more.

• Setting a "value filter" helps prioritize high-impact opportunities while avoiding trivial initiatives.

4. Practical Examples

Zwingmann applies the value-first lens to common business scenarios:

• Sales: AI reducing proposal-writing time by 50%, saving $20K monthly while improving proposal quality and response times.

• Customer Support: AI chatbots handling 20% of tickets, saving $15K monthly while offering 24/7 availability.

• Manufacturing: AI reducing defects by 1%, saving $66K monthly and improving sustainability.

  
  

5. Making It Work: The People Factor

While profit justifies AI projects, people determine their success. Zwingmann advises framing projects in terms of benefits for employees:

• From "cutting costs" → To "eliminating tedious tasks."

• From "boosting productivity" → To "enabling impactful, stress-free work."

  
  

6. Your Action Plan: From AI to Profit

The article concludes with a call to action: Start with profit-first thinking. Identify challenges worth solving, quantify their impact, and align AI as a tool for achieving tangible results. The goal is simple: Transform AI projects from costly experiments into reliable revenue drivers.

Why This Matters

AI is a powerful tool, but it’s only effective when tied to clear, measurable business outcomes. Value-first framework ensures that organizations invest in AI projects that matter, driving both short-term gains and long-term growth. For deeper insights, read the full article here.