Wit Jakuczun,

Your cool AI project might be missing its true purpose – here’s why

AI is now on everyone's agenda, but you should pay attention to one very important issue: not every cool AI project solves real-world problems. In fact, some could be missing the point entirely.

AI is now on everyone’s agenda, but you should pay attention to one very important issue: not every cool AI project solves real-world problems. In fact, some could be missing the point entirely.

From Amazon’s infamous face recognition algorithm that misidentified members of Congress, to a SaaS company that built an unused but technically sound AI feature, we have some cautionary tales.

How do you avoid falling into the same traps? Here are some questions to ask yourself before diving into your next AI project.

1. Can you explain why you must use AI for the project?

  • Consequences of Not Knowing: If you can’t articulate the need for AI, you may be risking resources on a tool that’s not right for your problem. This is not just a tech question; it’s a business question that impacts your ROI.

2. Can you estimate the benefits of the model implementation?

  • Using Comparative Analyses: Examine past or similar projects and their ROIs as benchmarks. If your project doesn’t promise to outperform or at least match these, it might not be as beneficial as you think.

3. Can you estimate the current performance measure of the optimized process?

  • Importance of a Baseline: Before diving into a project, you need a baseline to compare the before and after. This will allow you to measure whether your project brings about a significant improvement.

4. Can you explain why you believe the improvement is possible?

  • Learning from Others: Case studies, testimonials, or historical data can provide essential validation that the improvement you seek is feasible.

5. If asked by the CEO about expected results, would they be impressed?

  • Alignment with Business Objectives: Make sure that the outcome you expect will genuinely impress stakeholders and align with the company’s objectives.
Why do AI projects fail

6. If you were to pay for it from your pocket, would you do this?

  • The Personal Investment Test: If you wouldn’t invest your own money, consider why you think the company should.

7. Would you feel good if the challenge was addressed by a simple AI model or even without AI?

  • The KISS Principle (Keep It Simple, Stupid): Sometimes the simplest solutions are the best. If the problem can be solved without AI, that may be the route to take.

8. Is the goal to solve the challenge or build AI?

  • Technology as a Means, Not an End: Focus on solving the business problem first. If AI helps, great, but it should not be the driving force of the project.

By considering these questions and aligning your AI projects with real business needs, you significantly increase your chances of success and the likelihood of delivering genuine value. Don’t fall into the trap of building AI for AI’s sake; always start with the challenge you’re trying to solve.

Behind F33
Greg Bigos, CEO

Greg Bigos is the CEO of F33, bringing over decades of experience in delivering ERP solutions for manufacturing, logistics, and retail industries.

Contact Greg
Behind F33
Wit Jakuczun, CTO

Wit Jakuczun is the CTO/Chief Data Science Officer at F33 with a PhD in Applied Mathematics and over 18 years of experience in mathematical modeling, data analysis, and simulations.

Contact Wit

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