2025 Gartner® Report

Emerging Tech Impact Radar: Generative AI

GenAI will be embedded in 90% of conversational AI offerings, up from 50% in 2024, as predicted by Gartner.1

Discover how organizations are leveraging generative AI to accelerate innovation, optimize workflows, overcome key challenges, and enable customers to reach new heights of value.

Download this complimentary report, provided by Weights & Biases, for:

Industry analysis of 22 generative AI technologies
Practical recommendations for implementing genAI engineering tools
Trends and strategies shaping the future of AI adoption in the workplace

Whether you’re a leader in AI or digital transformation, we believe this report will help you unlock the full potential of generative AI for your organization. Fill out the form to get started today!

1 Gartner, Emerging Tech Impact Radar: Generative AI. 14 February 2025, Annette Zimmermann, Danielle Casey, Aakanksha Bansal, Annette Jump, John Santoro, Roberta Cozza, Radu Miclaus, Jim Hare, Vibha Chitkara, Eric Goodness, Alfonso Velosa, Reuben Harwood, Ray Valdes, Bob Johnson, George Brocklehurst, Anushree Verma, Tom Coshow.

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Trusted by the teams building state-of-the-art LLMs

Heinrich Kuttler
Research Engineer – Facebook AI Research

“For us, Weights and Biases was a game-changer. No other MLOps tool available allows for rapid iteration of AI experiments with the same ease of sharing results, annotating interesting behavior, and long-term storage of logging data.”

Peter Welinder
VP of Product- OpenAI

“We use W&B for pretty much all of our model training.”

Ellie Evans
Product Manager- Cohere

“W&B lets us examine all of our candidate models at once. This is vital for understanding which model will work best for each customer. Reports have [also] been great for us. They allow us to seamlessly communicate nuanced technical information in a way that’s digestible for non-technical teams.”

Scalable and Secure

We offer solutions that scale up with massive distributed training, and can be hosted in our secure hosted cloud or on your own private cloud in a self-hosted deployment.

With Weights & Biases you can:

Focus critical developer resources on your core business

Launch new machine learning models faster, with less back and forth

Safeguard IP with a central system of record

Onboard new ML engineers fast, and avoid duplicated work

A Case Study with TRI

Overview

Toyota Research Institute’s mission is to build the safest mobility in the world. Machine learning teams at TRI are pursuing autonomous driving, and they use the Weights & Biases system of record to make their models reproducible.

Company size: 300+
Industry: Autonomous vehicles

Problem

Led by Adrien Gaidon, the ML team built up world-class infrastructure for training models, but lacked a good way to track and version the valuable results.

They quickly realized the need for a central system of record, but building a solution internally was a distraction from the team’s core goals.

“It’s really hard for machine learning right now to provide any guarantees, statistical or otherwise, on how reliable it’s going to be. Putting in a safety critical system, it really has to work. How can we make it safe enough so that we can put it in cars and save lives instead of endanger lives.”

Adrien Gaidon

Toyota Research Institute

Solution

The TRI team compared different solutions for their experiment tracking problem, and settled on Weights & Biases as the best platform to coordinate machine learning projects.

Instead of tinkering with brittle internal tools and ad-hoc solutions for experiment tracking and prediction visualizations, the ML team was able to standardize with Weights & Biases’ lightweight experiment tracking and visualization solutions.

The Weights & Biases dashboard gave machine learning practitioners a command center to compare across dataset and model versions, maintaining a reliable record of every experiment and result. ML engineers are now free to focus on the valuable work of model development, accelerating project progress.

“You have to define the metrics clearly when you have a robotic system or a self-driving car that is extremely hard to test on the public roads for instance because the safety standards are very high, but at the same time you want continuous deployment and you want rapid iteration.”

Adrien Gaidon

Toyota Research Institute

Emerging Tech Impact Radar: Generative AI

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Trusted by the teams building state-of-the-art LLMs

Scalable and Secure

With Weights & Biases you can:

A Case Study with TRI

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Solution

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