Four years ago, Gartner predicted that by 2022, 85% of AI projects would fail to deliver tangible outcomes. But eventually, according to the IBM Global AI Adoption Index, around 66% of tech companies either execute or plan to apply AI today. This means the market is still growing, and there is no other way to stay competitive but to adopt artificial intelligence.
The prosperity of AI products, in turn, makes it easier for new applications to grow by producing technical resources. However, publicly available assets don’t make it any easier to adopt. As Gartner reports, only 53% of AI projects go from prototypes to production.
Technical complexity might seem an obvious reason for an initiative to fail, but that’s not always the case. As in my observation, the majority of AI product ideas die before any development has even started. This has to deal with the approach entrepreneurs try to apply and misconceptions businesses have about technology in general.
Where should an AI project start?
Any application has front-end and backend components, with some business logic laid underneath. That’s what basically dictates the terms when we plan a roadmap for future development by setting milestones, goals and feature lists.