Getting Effective AI

It's no secret that generative AI is the hot new thing in the corporate world. Companies are pouring money into pilot programs, hoping to strike gold and revolutionize their business. But what if I told you that most of these efforts are falling flat? 📉

A new report from MIT's NANDA initiative has some eye-opening findings: a whopping 95% of generative AI pilots are failing to deliver significant revenue growth. So, what's going on?

You might think it's a problem with the AI models themselves, but that's not the case. The report points to a much bigger issue: a

"learning gap" and flawed integration. In other words, companies are struggling to figure out how to actually use these powerful tools effectively.

The report's lead author, Aditya Challapally, notes that the success stories are mostly found in startups and younger companies. Why? Because they're focusing on one specific problem and aren't afraid to partner with others who already know the ropes. They're not trying to reinvent the wheel; they're simply using the best tools available to solve a business problem.

This highlights a major lesson for larger organizations: instead of trying to build everything in-house, they should leverage the expertise of those who have already built successful AI solutions. The data from the MIT report backs this up—companies that purchased AI tools from vendors had a 67% success rate, while in-house builds had a much lower success rate.

Trying to build a complex AI system from scratch is not only expensive and time-consuming, but it's also more likely to fail. Instead, companies should embrace a more strategic approach: identify the business need, find a proven tool or partner to address it, and focus on integrating that solution seamlessly into their workflow. In the world of AI, it seems that collaboration and smart partnerships are the key to success. 🔑