Download your Guide on How to Scale AI:
Navigate From Proof of Concept to Production and scale the $100m gap


While the vast majority of companies are aware of the possible benefits of integrating AI applications to their products and internal business processes, few are succeeding in delivering AI at scale and seeing returns on their investment. Those that are, stand to achieve a severe competitive advantage.


Find out how you can bridge the $100m adoption gap with our 5 key lessons to take machine learning to production at scale.


This guide is intended to be consumed by anyone with an interest in making sense of data and accelerating its use.
Data scientists, engineers or chief data officers will find this guide rewarding and insightful.


The +$100m Gap

The impact of machine learning at scale is only realised by 16% of companies according to Accenture.

Furthermore, Gartner predicts that 50% of IT leaders through 2023 will struggle to move their AI projects past proof of concept and into production.


CEO insight

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!

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