AI Readiness Report

1st edition of the annual AI Readiness Report

At Scale, our mission is to accelerate the development of AI applications to power the most ambitious AI projects in the world. That’s why today we’re introducing the Scale Zeitgeist: AI Readiness Report, a survey of more than 1,300 ML practitioners to uncover what’s working, what’s not, and the best practices for ML teams and organizations to deploy AI for real business impact.

Today, every industry – from finance, government, healthcare, and everything in between – has begun to understand the transformative potential of AI and invest in it across their organizations. However, the next question is: are those investments succeeding? And are the organizations set up in the right way to foster meaningful outcomes? Is your business truly “AI-ready?”

We designed the AI Readiness Report to explore every stage of the ML lifecycle, from data and annotation to model development, deployment, and monitoring, in order to understand where AI innovation is being bottlenecked, where breakdowns occur, and what approaches are helping companies find success. Our goal is to continue to shed light on the realities of what it takes to unlock the full potential of AI for every business. We hope these insights will help empower organizations and ML practitioners to clear their current hurdles, learn and implement best practices, and ultimately use AI as a strategic advantage.

“In the world of AI, there are a lot of people focused on trying to solve problems that are decades away from becoming a reality, and not enough people focused on how we can use this technology to solve the world’s problems today. It can feel like a leap of faith to invest in the necessary talent, data, and infrastructure to implement AI, but with the right understanding of how to set ML teams up for success, companies can start reaping the benefits of AI today. Thanks to all the ML experts who shared their insights for this report, we can help more companies harness the power of AI for decades to come.”

Alexandr Wang — Founder & CEO, Scale

01. Data Challenges

02. Data Best Practices

03. Model Development, Deployment, and Monitoring Challenges

04. Model Development, Deployment, and Monitoring Best Practices

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01 - Data Challenges

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