OLIVIER BLAIS - RESPONSIBLE AI LEADER

Despite the growing media buzz around AI, very few organizations are planning to implement their first AI systems. Moreover, only about 15% of AI projects developed today are actually put to use.

This is indeed alarming.

Why is it that so many companies are falling behind in terms of planning and implementing AI systems, while only a few innovative ones are achieving success stories?

Olivier Blais will guide you through the steps of the AI readiness cycle to plan, implement, and successfully adopt your first and future AI projects.

A data scientist once told me that model training and selection represent only 20% of a machine learning project. This may seem anecdotal, but it should not surprise data science practitioners who have already delivered a machine learning project.

So, what does the other 80% represent...

And how do we go from an idea for a machine learning project to a proof of concept, then to adoption, and finally to a return on investment?

User needs, data collection and cleaning, model validation, user training, governance... And we haven't even touched upon the vast complexity of the infrastructure needed around your models.

In the end, algorithms don't implement themselves, and we need rigorous processes and project management to achieve the value we seek in our AI projects.

Olivier Blais will guide you through the stages of a machine learning project cycle to successfully implement your next AI project.

Only 15% of AI projects will yield results in 2022. This is concerning. The good news: there is a better way. We can deliver high-quality AI systems that meet business objectives and drive their adoption.

In this presentation, Olivier Blais, Head of Decision Science at Moov AI, a consulting company specialized in AI and machine learning projects, and project editor for the "ISO/IEC TS 25058 - Guidelines for the evaluation of AI system quality," will share best practices and modern techniques to enhance the evaluation of AI system quality. This session is targeted at AI practitioners, project managers, and stakeholders, as AI system quality is crucial in every AI project, team processes, and expert toolkits.

Did you know that only 15% of all AI projects developed today are actually used? Developing a functional and useful AI solution is indeed a complex task.

Drawing from concrete examples of projects deployed in production and lessons learned from the field, Olivier provides an up-to-date overview of AI in the business world and offers a new perspective. You will discover best practices and appropriate steps to adopt AI in your company.

Did you know that only 15% of all AI projects developed today are actually used? Popular case studies circulating on social media and in the media showcase the giants of this world, such as Google, UPS, and Facebook.

They are rapidly developing AI projects thanks to their army of data scientists and billions invested in research and development.

But don't try this at home: replicating their success in your company, with your context, is a challenging task.

Copying their recipe and waiting to meet all the winning conditions before launching an AI project will lead you to failure. You will waste time and be left behind your competitors.

But don't worry, all hope is not lost.

With full transparency, Olivier Blais offers a conference on the current state of AI in the business world and provides a new perspective based on his on-the-ground learnings. You will discover best practices and the right steps to adopt AI in your company.

If you have data and a business objective, don't miss this conference.