AI Fails : The Mistakes we learn from..

~ By Parth Hatwar

AI is a Future of our world. Our world is going for the full Electrical and
technological future. At every blink of an eye we saw the new technology growing on our world. Its most like our future is likely depends upon technology. If you want to build your fucture in the world of technology you have to consider AI it’s a very big growing part of technology.AI operations and processes is one factor but there are many other reasons that lead to failure of data science projects. These include: Absence of comprehension about AI tools and methodology. Lack of investment in employees who know data very well. Not opting the right tool. Poor Data Quality. Bad Strategy from top management.

Why the AI fails (and how to fix it).

1. You have poor data quality Data is key for AI to become successful. One of the best analogies is that a car can not…

2. You don’t solve the right problems As a company, every investment you make is to solve a business problem. Building a…

3. General e-commerce solutions don’t work for jobs, real estate, and car websites. A common mistake is that a lot of…

4. You don’t have enough data scientists.

We always hear about Artificial Intelligence being the next big thing for a lot of industries due to the many possibilities that it provides. Yet when we look at the numbers, we see some very worrying statistics. The success rates for many AI and data related projects seem to be hitting an all-time low. And the numbers haven’t improved in almost a decade. But what exactly are companies doing wrong? First, let’s take a look at the numbers:

• VentureBeat AI reports 87% of data science projects never make it into production

• NewVantage survey reports 77% of businesses report that “business adoption” of big data and AI initiatives continues to represent a big challenge for business.

• Gartner says 80% of analytics insights will not deliver business outcomes through 2022 and 80% of AI projects will “remain alchemy, run by wizards” through 2020.

• A report from Dimensional research states that 8 out of 10 AI projects had failed while 96% ran into problems with data quality, data labelling, and building model confidence.

• 25% of organizations worldwide that are already using AI solutions report up to 50% failure rate according to IDC

Those are some disturbing numbers, right? It seems that a lack of skilled staff and an inability to pinpoint exact customer needs are the main reasons for failure. In this article we will go more in-depth in the reasons why those AI projects are failing and give you tips on how to avoid making the same mistakes. Here are the main reasons why your AI project will probably fail:

1. You have poor data quality

2. You don’t solve the right problems

3. General e-commerce solutions don’t work for jobs, real estate, and car websites.

4. You don’t have enough data scientists.

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