👋 Welcome

Welcome to the June edition of Business AI, your guide to practical, business-ready AI.

🚀 What’s Inside This Edition

🎯 Demystifying AI series — Clear explanations and hands-on tips without the jargon

📚 OpenAI Insights — Key takeaways from OpenAI’s latest enterprise AI guides

🏆 Real-World Case Studies — How companies like Johnson & Johnson and Abridge are deploying AI to drive value

Demystifying AI:

RAG — Securely connecting AI to internal knowledge

AI is only as good as the information it can access.

Public models like ChatGPT don’t know your company’s internal data — and that’s a problem.

Imagine you want to build:
✔️An internal knowledge assistant that taps into your company’s documentation — manuals, reports, PDFs on shared drives
✔️ A customer support tool that answers questions using your product FAQs, pricing sheets, and help guides

But how can you make company data searchable, useful, and monetizable — without exposing it to the outside world?

That’s where RAG (Retrieval-Augmented Generation) comes in.
It connects AI models to your internal data, so they can retrieve the right information before generating an answer — making your AI smarter, safer, and far more useful.

It combines the power of language models with your internal documentation, enabling fast, accurate, and secure responses—without training the model on your data.

Key Benefits for Your Business

✅ Business-Specific Answers — Speaks your company’s language
✅ Accurate answers based on internal knowledge, reducing Risk of Hallucination
✅ Faster Deployment, Lower Costs — No model fine-tuning needed; just upload and update documents

🧠 RAG doesn’t replace employees. It replaces time wasted searching for answers.

INSIGHTS

OpenAI recently published practical guides for organizations on Identifying and Scaling AI Use Cases and AI in the Enterprise. These guides, based on insights from over 600 real-world examples, help companies enhance productivity and drive value across departments.

Why It Matters

According to McKinsey, 92% of companies plan to increase their AI investments — yet only 1% believe they’ve reached full AI maturity. OpenAI’s guidance focuses on helping organizations bridge this gap by identifying ROI-driven use cases and scaling them effectively.

How to Identify the Right Use Cases

🔑 3 core principles:

1. “AI should be led and encouraged by leadership

2. Complex use cases can feel impressive, but often slow you down. Instead, empowering employees to find use cases that work best for them, and your company, is often a faster path to success

3. Encouraging adoption with hackathons, use case workshops, and peer-led learning sessions is a catalyst for many companies.”

Source: OpenAI, 2025, Identifying and Scaling AI Use Cases; AI in the Enterprise

Executive Action Prompt:

▶️ Which of your teams would benefit most from an internal hackathon or a use case discovery workshop?

NEWS: Enterprise Adoption Case Studies

1. Johnson & Johnson: Strategic Pivot in Generative AI Deployment
Johnson & Johnson has refined its generative AI (GenAI) strategy by shifting from broad experimentation to focusing on high-value applications. Initially, the company supported nearly 900 GenAI projects through a centralized governance board but found that only 10%-15% delivered 80% of the value. Consequently, J&J decentralized its approach, empowering individual corporate functions such as commercial, supply chain, and research to evaluate and manage GenAI initiatives more effectively.
WSJ+1Siemens Press+1

2. Abridge: Continuous Patient Records in Healthcare
Abridge utilizes RAG to create continuous patient records by transcribing doctor-patient conversations and incorporating them into medical records. This approach informs future care and enables multiple clinicians to access and contribute to a shared memory of patient information, improving coordination and treatment outcomes.
WSJ

📩 Stay in the Loop

Want more content like this?
This is just the beginning. Upcoming issues will cover:

• Agents and Multi-Step Automation

• AI for Tabular Data — Not Just for Text

• Fine-Tuning vs. Prompt Engineering — What’s the Difference?

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Reply to this email or contact us directly at [[email protected]]. We’re happy to help.

The Business AI Team
CMasterAI.com Business AI Consulting & Tools for Growth
🌐 [https://cmasterai.com]