Case Studies

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This page indexes real-world deployments of AI — what organizations actually built, what it cost them, and what it returned. Use the libraries below for documented implementations, and the industry index further down to jump to PRIMO.ai coverage of a specific domain.

Case Study Libraries

Searchable collections of documented, named-organization deployments.

Research & Benchmarks

For adoption rates, investment figures, and measured outcomes rather than individual deployments.

Historical (pre-generative AI)

Retained for the analytical frameworks, which hold up better than the specific examples. Treat the company examples as period documents.


What challenge(s) can your AI investment solve?

Industry & Domain Index

Consumer Products

Analysts' Lists

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Identifying AI Use Cases


If a typical person can do a mental task with less than one second of thought, we can automate it using AI … - Andrew Ng


The sessions below date from 2018–2020 and predate generative AI. The selection frameworks — problem-type matching, data-readiness assessment, portfolio prioritization — transfer well; the technology examples do not.

MIT Bootcamps: How to identify business opportunities with AI
AI is a fundamental, transformative technology like the microprocessor or the Internet. In the coming decades it will impact nearly all aspects of business. Unfortunately, few good resources exist for our future business leaders to learn about practical AI solutions and how to integrate them into their businesses. This is a E-Seminar for interested individuals to (1) understand how AI can generate revenue in business applications; (2) identify and develop AI use cases within your business environments; and (3) mitigate risks on any new AI initiative. By the end of this session, you will be able to put these new skills to immediate use at your company or startup. This is a business skills training and not a technical training. Participants won’t learn how to build a neural network or perform feature engineering. Instead, they will learn how to use fundamental AI concepts such as training data, machine learning, deep learning in customer conversations and proposals. They will learn how to identify emerging business opportunities for AI solutions like computer vision and natural language processing.

How To Identify AI/ML Use Cases
How do you identify AI/ML use cases in your company?

1. Assess internal products and business processes. 2. Enhance B2C software and hardware products. 3. Explore B2B products and AI consulting.

If you know where to look and what questions to ask, you will develop the skill and intuition of AI/ML opportunity assessment. If you believe artificial intelligence is the future and you want to capitalize on the new career growth opportunities, consider breaking into AI through the non-technical career path.

Applying AI to Real World Use Cases - MIT AI Conference 2019
Julie Choi - Intel Corporation, VP of Artificial Intelligence Products and Research Marketing. The 2019 MIT AI Conference, the 3rd edition of this annual conference, focused on the Future of Computing - the rise of Artificial Intelligence and how innovators are leveraging AI to drive new use cases and achieve better outcomes across industries.

Identifying Machine Learning Use Cases
In this Video we will see how to identify good candidates for machine learning. Machine learning is not a solution for all problem. If problem can be solved with simpler techniques like rules or statistics that must be the way to go. But there are reason where simple techniques might not be effective or hard to manage and maintain in this rapidly evolving data space as well as in big data era

AI Simplified: What Makes a Good Machine Learning Use Case?
Hear from Jake Shaver, Director of Strategic Initiatives, as he walks through a checklist of steps to help you determine a good machine learning use case for your specific organization.

MFML 003 - Advice for finding AI use cases
Cassie Kozyrkov. Welcome to a machine learning course for everyone! This video features a neat trick for finding an AI/ML use case.

From Industrial AI Ambition to Operational Reality: Choosing the Right Use Cases
OpenAI's GPT-6 Astra jumped from 18% to 41% on a real workplace task automation benchmark in just 8 weeks, yet 41% still means the AI makes more mistakes than it gets right. Maddie Zeng of NeoAI explains why that gap matters: if a vendor promises high precision across everything, the best funded labs on earth are publishing numbers that say otherwise. The panel maps out what has changed in industrial AI, from sensor monitoring to autonomous closed-loop decision making, and what traps wait between a successful pilot and a production deployment.

AI Use Cases Pattern Across Industries
“It’s really challenging to identify AI/ML use cases. How to identify?” A project manager asked this question. I said – “the easiest way to find opportunity is to look for common patterns across industries.”