And I'm back, as promised, with a handful of reasons — ranging from “the usual suspects” to more nuanced ones — why AI projects fail.
- getting too absorbed in keeping your technological assets up to date to the point of skipping to put together a solid business strategy for your AI implementation
- to getting overly excited about AI and trying to “force integrate” it into... everything
- to skipping to further maintain it, once launched
… there are quite a few mistakes that you can easily avoid and thus foolproof your AI project.
So, let me expose these hidden “traps” to you:
Mistake #6: You Put Technology Before a Solid Business Strategy
AI-powered or not, it's still a... business that you're running, right?
So, getting focused on technology only, turning it into the unique driver of “business” value is simply... non-realistic.
First, you need to build your solid business strategy. One to include:
- thorough research of your target markets
- all the technological assets needed to reach your AI project's goal
- ... along with all the resources to be invested, of course
Technology, no matter how advanced, never comes before business strategy.
That, of course, if it's business value that you try to achieve and not just... building AI for AI's sake.
Mistake #7: You Cut Down on Testing Time
Probably one of the most common reasons why AI projects fail:
You get all too eager (and over-confident) to release your AI-powered software out into the wild and you deliberately skip some major debugging phases.
To avoid this trap, make sure to include, while setting up your business strategy, the due resources for properly testing your AI project before “setting it free”.
For, placing it into the spotlight prematurely, faulted and vulnerable to future bugs, will “doom” your AI solution to years of... public distrust.
Mistake #8: You Get Stuck in a Never-Ending Development Cycle
Now, mind you don't avoid a pitfall only to... fall into the next one:
A never-ending design-develop-design-develop... process.
For, yes, one of the worst AI mistakes is to release a buggy, poorly tested AI-enabled software product.
But it's equally risky to keep postponing its launch and get tangled up in this loop of continuously polishing it and testing it.
You just risk having your competition leverage all the AI opportunities out there while you're constantly updating your software.
Instead, consider launching the best possible version of your AI software. Then, collect the relevant data and the message coming from your target market to iterate and release an updated version.
Mistake #9: Baking AI into Everything- Why AI Projects Fail
Trying to turn AI into an “all-purpose” tool is yet another frequent mistake behind many AI fails.
I know you must be infatuated with AI (we, too, are infinitely excited with the still unexplored opportunities of artificial intelligence). Yet, do keep in mind that the right sequence is the following:
You first identify the specific need/problem in the market and then come up with the suitable AI solution for it!
You don't just jump on the latest AI technology and... force-fit it into any software product.
For, let's face it:
There are tasks where AI rocks and tasks where human staff's emotional intelligence is needed (take certain customer service scenarios, for instance).
So, don't try to bake AI into... everything or your project will only swell the ranks of failed AI projects.
Mistake #10: You Skip Further Maintaining Your AI Solution
A “launch and run” strategy won't propel your AI project too far ahead...
Just like any other product, an AI-powered software product needs periodical maintenance. Regular “infusions” of new methods, new models and training data.
The more complex it gets, the more crucial a solid maintenance strategy becomes.
So, mind you do not underrate this phase. It's another too common reason why AI projects fail.
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