This guide is designed to be consumed by anyone with an interest in making sense of data and accelerating its use.
While the vast majority of companies are aware of the possible benefits of integrating AI applications into their products and internal business processes, few are succeeding to do this at scale and see returns on investment. Those that are stand to achieve a severe competitive advantage.
Only 16% of companies have figured out how to make AI work at scale and the difference in return on AI investments from a Proof of Concept to a scaled solution averages $110million. Furthermore, Gartner predicts that 50% of IT leaders through 2023 will struggle to move their AI projects past proof of concept and into production.
The following guide tries to pass on several hard-learned lessons regarding how to successfully progress from idea through proof of concept to scale while avoiding the pitfall of the proof of concept cycle. They should serve as any business leaders' five basic commandments.
The inputs to this guide come from the real frustrations of data scientists and engineers trying to drag their organisation into the 21st century. So, if you are a business leader, take note! This is what your data science and engineering team have been trying to tell you, but might be feeling like they’ve not been heard!