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10 Reasons Why AI Projects Fail or How You Can Easily Sabotage Your Own AI Project- Part 1
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10 Reasons Why AI Projects Fail or How You Can Easily Sabotage Your Own AI Project- Part 1

by Adriana Cacoveanu on Jan 11 2019

Running an AI startup? Or just planning to implement AI technology into your next software product? Then you sure don't run short on AI advice, right? Everyone's telling you why you should adopt AI, how to successfully incorporate AI into your business processes... But no one tells you why AI projects fail.

What mistakes you should avoid to foolproof your AI implementation.

What are those gotchas —  going from obvious pitfalls to more subtle traps —  that can easily change your AI project's results from success to major failure?

It might not be as resounding as Amazon Echo's nasty blunder:

The Alexa-powered device decided to throw a “surprise party”, with loud music and all that jazz, once it got a house in Berlin all for itself...

Yet it would still mean flushing all your high hopes for AI and the invested resources down the drain...

Now, back to the most common reasons why so many fail with AI. Or, better said:

The 10 AI project mistakes to avoid.

 

Mistake #1: You Start Big and Spread Yourself Too Thin

In other words:

Don't bite off more than you can chew!

I know you might be overexcited about the incredible AI potential right now. But jumping on a too complex AI project, with long time horizons, is the perfect “recipe” for failure.

For, let's face it:

Expecting AI to instantly transform all your business processes, to go from no value to 100% value for your AI project is as realistic as... checking off all your New Year's resolutions on the 1st of January.

Instead, start small and grow big.

Take your time to learn more about the technology you're implementing. To gradually gain all the needed expertise, to fail fast and organically grow your AI project.

Rather than artificially pumping it up.

 

Mistake #2: You Keep Your R&D Expenses to a Minimum

Failing to see research & development spending as an investment is one of the most common AI project mistakes.

In short, getting stingy when it comes to investing in:

 

  • training programs for your employees
  • research on advanced algorithms
  • heavy experimentation with those cutting-edge AI technologies that you expect your team to develop
  • computing infrastructure

     

… is not a way to save money. It's the shortest path to AI project failure, actually.

 

Mistake #3: Vague Goals, The Key Reason Why AI Projects Fail

What's your vision? 

What short-term goals have you set for your specific application of AI, in your... specific industry (be it health care or finance or...)? 

Make sure you articulate those goals crystal clear and share them with your team.

Oh, you don't have a vision? Not just yet? Only high hopes and expectations about how AI will completely transform your business?

Or is it just a few ambiguous, fragmented goals and vague objectives that you have at hand?

Then I'm sorry to break it to you: no clear vision means no great value that you could “reap” from your AI project.

 

Mistake #4: Your AI-Powered Software Doesn't Meet Any Real Need

Your new AI technology needs to be usable.

And that says it all:

Building AI for AI's sake is as profitable as... designing bathing suits for Eskimos.

Above all things, your AI project has to meet real business needs.

Therefore, make sure you don't fall into the “technology bubble” trap. Do your research, identify the current needs in the finance, health care, disease research or any other field that you target and adapt your new tech accordingly.

The main reason why AI projects fail? They're built to awe, not to serve.

They put outstanding, revolutionary technology before real people's needs. Instead of aligning it to them.

 

Mistake #5: You Rely on AI Newbies Only

If you ran a 5 star Michelin restaurant would you ask your cashier to cook that exquisite dish recently added to your menu? 

See my point? One of the most common reasons why AI projects fail is because startups hand their projects to AI enthusiasts with great potential and zero work experience.

Be better than that!

Aim for AI expertise when you're recruiting for the team that will be working on your new AI project. Don't just expect AI newbies to... turn into AI experts overnight or your internal staff to jump on a totally new technology and turn your project into the... next new AI companion robot!  

Enthusiasm won't compensate for all those imminent mistakes and fails to execute your AI strategy.

And these are just 5 of the most common reasons behind AI fails. Stay tuned, for we have 5 more to expose to you in all their “glory” in our next post...

Photo by Rock'n Roll Monkey on Unsplash. 

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