Why do more than 70% of all digital transformations project fail?

?? Insufficient domain expertise: Let's say your company wants to start a business or product solution in a field you know nothing about. It's like trying to swim with lead weights on your feet. Without the right expertise and support from those who really understand the ins and outs of that industry, your AI project won't make any waves. You need people who can navigate the industry's challenges and seize the opportunities. Otherwise, you'll just be throwing cash into a pit with no lifeguard in sight.

?? Lack of quality data: Picture this, you're trying to make an intelligent investment, but the information you have is like a sketchy tip from a random person on the street. If the data you rely on for your AI project is all messed up, it's like throwing your money into a pit. Insufficient data means terrible results, and that's a waste of time, effort, and money. You want your AI project to bring in some dough, not sink it!

?? Inadequate project planning: Imagine you're throwing a party, but you forgot to invite anyone and didn't buy any snacks or drinks. That's a party that's going to flop! Similarly, if you don't plan your AI project properly, you'll end up wasting resources and missing out on potential benefits. Your company needs a solid plan considering costs, timelines, and expected returns. Otherwise, your project will be a party for one, and you'll be left with a hefty bill.

?? Lack of communication and collaboration: Scenario: You're playing in a band, but nobody knows which song to play or when to start. It's just a bunch of noise! Similarly, if your team doesn't communicate and collaborate effectively, your AI project will be out of tune. You need everyone on the same wavelength, sharing ideas, and working together to hit the right notes. Otherwise, you'll end up with a cacophony of confusion.

?? Unrealistic expectations: It's like buying a scratch-off lottery ticket and expecting to win a million bucks every time. It's fun to dream big, but let's be honest, AI isn't a guaranteed ticket to instant success. If you expect your AI project to bring in mountains of cash overnight, you're setting yourself up for disappointment. You need to realistically weigh the potential ROI against the costs, OPEX, and TCO. Otherwise, you'll be left with shattered dreams and an empty wallet.

?? Model interpretability and trust issues: Imagine you're getting advice from a fortune teller, but they refuse to explain how they're making their predictions. Would you trust them with your life savings? Probably not! If AI models can't explain their reasoning, it's like a shady fortune teller—hard to trust. You need transparency and interpretability to build trust with users, stakeholders, and customers. Otherwise, your AI project will be seen as a snake oil salesman.

?? Data privacy and ethical concerns: You have a top-secret recipe, but if you're not careful, someone might steal it and sell it as their own. When it comes to AI, mishandling sensitive data or neglecting ethical considerations is a recipe for disaster. You must follow privacy regulations, protect people's data, and ensure your AI project operates ethically. Otherwise, you'll have legal trouble, a damaged reputation, and customers fleeing like rats from a sinking ship.

?? Rapidly evolving technology landscape: AI is like an all you can eat game. New tools, algorithms, and frameworks keep popping up left and right. If you're not staying up-to-date, it's like playing an outdated version of the game. You must be on top of the latest trends, technologies, and best practices to keep your AI project relevant and effective. Otherwise, you'll be left in the dust while your competitors whack away at success.