AI Adoption
By: Olivier Penel
Tags: Resilience, Artificial Intelligence, Innovation, Mlops, Cloud Computing

Photo by Tim Graf on Unsplash

1. Thank you for taking out time to chat with us. To begin with, how do you see the landscape of AI and data analytics developing globally? What specific trends do you see that will shape up the future of its adoption?

Thanks for inviting me!

In recent years, the AI landscape has seen a dramatic acceleration of the level of adoption, globally. This is due to a combination of factors starting with some significant improvements in technology areas such as neural networks, deep learning, image recognition and natural language processing. In parallel, data processing has become commoditized through open-source and cloud technologies.

Organizations are also moving towards XaaS (Everything as a Service) consumption models so that they can innovate faster without having to massively invest in hardware, software and services. The world where organizations buy a software, then spend tons of money on hardware to install it and then months to configure their solutions, is over. The technology has become easily accessible, cheap, and innovation is achieved through short, agile iterations allowing to fail fast and try often. Analytics users want instant access to the latest versions of enterprise analytics software, they do not want to worry about upgrades, and they want to pay for what they use, or for the results they get. In effect, self-service analytics is fuelling the democratization of AI.

This evolution of the technology landscape and practices is making it possible and relatively easy for private and public organizations to implement innovative use cases and to leverage AI to build greater competitive advantage and develop new business models.

On one side, this is great because AI is proving to be a catalyst for digital transformation, and it creates many new business opportunities.

On the other side, organizations are now facing an increasingly complex ecosystem that is difficult to navigate. New technologies appear every day, with no readily available recipe. Lots of choices have to be made in order to decide what pieces are relevant and how they fit together. So, what was supposed to make it easy to access and adopt those technologies is having the opposite effect of making it difficult and not delivering the expected results.

As technologies are becoming more accessible, it is relatively easy to develop sophisticated models and to experiment with new use cases, but the real challenge lies in the operationalization of those models, embedding analytical insight into the decisioning fabric, at the front line of business operations.

The danger is for organizations to go down this ‘rabbit hole’ of never-ending experimentation, lack of business results, and increased technical debt.

Finally, growing awareness in the general population about the applications of AI technologies, as well as regulatory pressure, are raising the need for more governance, transparency and explainability in the way those technologies are used, how automated decisions are made, and the impact they have on people. I believe this question of the ethics of AI will have a significant impact on the adoption of AI technologies and their applications in the real world. This is likely to hit the headlines in 2021 as the European Union is cooking a new regulation for the responsible use of AI, along the same lines as the GDPR, and this will most likely have a ripple effect across the world.