Welcome

Welcome to the June third edition of Business AI — where we break down what Agentic AI means for your business and how to turn this opportunity into real results.

What’s Inside

✅ Agentic AI vs. Generative AI — What Business Leaders Need to Know

✅ $3 Trillion Opportunity — Why Business Leaders Can’t Ignore Agentic AI

✅ Real-World Example — How AI Transformed Customer Feedback

💡Agentic AI vs. Generative AI — What Business Leaders Need to Know

The AI landscape is evolving fast. You’ve likely heard of Generative AI and, more recently, Agentic AI. But what’s the real difference? And why does it matter for your business?

Let’s break it down — here’s how they differ in practice:

Understanding this distinction is key — and global research shows that businesses are already preparing for the next wave of AI adoption.

INSIGHTS: 🌍 $3 Trillion on the Table — Why Business Leaders Can’t Ignore Agentic AI

While many companies are still experimenting with GenAI, another transformation is already underway: Agentic AI. These autonomous AI systems don’t just assist humans — they take meaningful, independent action to deliver business outcomes.

And the numbers speak for themselves:

• KPMG estimates Agentic AI could unlock $3 trillion in corporate productivity annually, based on their global analysis of over 17 million companies.

• Nearly every business sees the potential — 99% of companies plan to put AI agents into production, according to KPMG’s latest AI Pulse Survey.

• But there’s a clear execution gap: only 11% have done so already.

KPMG predicts that within the next 12 to 36 months, Agentic AI will reshape competitive advantage across industries — and the early movers will define the landscape.

Types of AI Agents — KPMG’s TACO Framework

KPMG classifies AI agents into four types:

1. Taskers — Handle repetitive tasks (e.g., invoice validation)

2. Automators — Manage end-to-end workflows (e.g., supply chain processes)

3. Collaborators — Work interactively with humans (e.g., product development)

4. Orchestrators — Coordinate complex, multi-agent systems (e.g., global procurement optimization)

 Four Ways AI Agents Unlock Value

✔Always-On Productivity — Agents work 24/7, boosting output

✔Expanded Automation — They tackle more complex processes

✔Knowledge Into Action — Agents act on organizational expertise

✔Built for Change — They adapt to shifting business needs

🟢 Six Practical Next Steps for Leaders

1. Define your Agentic AI vision

2. Launch focused pilot projects

3. Prepare to scale within key functions

4. Update AI governance frameworks

5. Evaluate AI performance & risks continuously

6. Adapt workforce roles and change management

Following the global perspective, let’s turn to a concrete example — continuing our series on practical AI applications.

Real-World Case Studies:

Use Case: Real-Time Customer Feedback Analysis

Problem:
A hotel chain received thousands of open-text survey responses. Manual review by 10 FTEs led to delays, poor categorization, and limited insight.

AI Solution:
An AI model now classifies sentences from customer feedback into structured categories in real time — even when a single response contains multiple topics. The right insights are instantly routed to the relevant departments — whether it's service quality, facilities, or pricing. This enables better insights across the organization, real-time reporting for management, and faster action where it matters most.

Results:

• 96%+ classification accuracy

• No more manual tagging

• Employees can now focus on improving customer satisfaction instead of repetitive review tasks

• Better insights routed to the right departments

• Traceable, real-time reporting for decision-making

• Better internal responsiveness

💡 Pro Tip:
Text-heavy processes like surveys are perfect for early wins. AI adds clarity, speed, and structure — where humans struggle with scale.

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