As ever, the organisational and human element is where technology fails.
In AI, it is crucial that there is clarity on what you are trying to achieve as well as the types of AI you are deploying and why.
If you want machine learning make sure you understand the difference between symbolic, statistical and deep learning otherwise you could squander a lot of money and get in serious trouble. And if you don't understand why you need Transparent AI rather than Opaque AI in a regulated industry, then you are just not ready to start the journey.
AI is not straightforward if you don't have a trusted partner to advise you.
Only one in three AI projects are currently succeeding, according to a new study from market researcher IDG. Data incompatibilities and organizational frictions are at the root of the problem, say the 200 U.S. and European IT executives surveyed. They indicate that 96 percent of businesses with 1,000 or more employees face data-related issues such as silos and inconsistent data sets. At 80 percent of the businesses surveyed, tensions between data scientists and data engineers are undermining the level of collaboration needed for a successful AI deployment.