artificial intelligence robot
artificial intelligence robot

5 common pitfalls when implementing AI, and how to avoid them

When it comes to AI implementation, the greatest challenges stem from organizational cultures, silo mentalities and old practices. This blog identifies five common AI implementation challenges, along with suggestions to overcome them.
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Guiding clients on their artificial intelligence journeys

In helping clients benefit from the power of AI, we start with an idea, identify the critical data and rapidly turn that idea into a minimum viable product. We then help scale the solution, and continuously manage and improve it. In doing so, we leverage cloud-based and on-premise AI accelerator platforms, including access to high performance computing. We also help clients transition to an industrialized and mature AI operating model.

We work with clients locally, while bringing the strength of our global insights and end-to-end skills. We also provide a rich global and local ecosystem of AI specialty and academic partners. Our data scientists, together with our domain experts, identify realistic use cases. In addition, our method of experimenting, learning and applying human psychology and empathy enables us to deliver pragmatic and responsible AI innovation and transformative experiences.

Using machine learning to predict cracks in steel

Steel manufacturer Uddeholm turned to CGI to increase steel quality and reduce unnecessary waste due to cracks in steel. CGI developed a solution for predicting steel cracks that uses big data and IoT to capture relevant data, along with machine learning and advanced analytics to generate insights. With the solution, cracks can be predicted with 70% accuracy.

Watch the video on cgi.com.

Combining responsible AI with human creativity to propel you to new horizons

AI spark session AI data science – Use case validation AI MVP / POV / POC Production and scale Enterprise AI operating model Continuous improvement
Half to one day workshop to demystify AI, with two goals—inspire what’s possible with AI and envision AI use cases 1-2 weeks of data scientist-led use case validation 4-6 week project typically involving a design lab and rapid prototyping and/or an MVP sprint Leverage AI platforms to scale prototypes / MVPs into production Assess AI operations and offer recommendations for an industrialized operating model Leverage production platform for continuous model improvement and life cycle management

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