AI in Business: Why Results Stall—and the Agentic AI Advantage for CEOs
The Problem: We're Using AI Wrong
Most companies deploy chatbots and copilots because they're easy to set up. These tools help individual employees, but the benefits are spread too thin to matter to the business. The real value comes from AI that transforms specific business processes. But less than 10% of these projects ever get beyond testing.
Why do they fail? Six reasons:
Building everything from scratch instead of using proven solutions
Current AI tools just respond—they don't act independently
AI teams work separately from business teams
Company data isn't organized or accessible
No CEO leadership (only 30% of AI projects have direct CEO support)
Employees resist change, they don't understand
What Changes Everything: AI Agents
AI agents are different. They don't just respond to questions—they take action, remember context, and work toward goals without constant human input.
Three real examples from McKinsey research:
Banking: A bank cut its $600M software upgrade project time in half by using AI agents that write code, review each other's work, and document systems automatically.
Research: A market research firm increased productivity 60% with agents that spot data problems and explain market changes—often finding insights humans miss.
Credit: A bank sped up loan decisions by 30% using agents that pull data, write reports, and flag what humans should review.
The Key: Redesign How Work Gets Done
Here's what most leaders miss: you can't just add AI to existing processes. You need to rebuild the process around AI.
Example: Customer service
Adding AI tools to help agents = 5-10% improvement
Using AI for specific tasks = 20-40% improvement
Redesigning everything around AI = 80% of issues resolved automatically, 60-90% faster
What Works: Four Strategic Shifts
The companies winning with AI make these changes:
Focus on strategy, not experiments: Pick a few high-impact business processes instead of scattered pilot projects
Transform entire workflows: Don't ask "Where can we use AI?" Ask "How would this department work if AI handled most of it?"
Build mixed teams: Combine business experts, process designers, AI engineers, and data specialists
Plan for real deployment: Design for ongoing costs and maintenance from day one
What This Means for Your Organization
What This Means for Your Organization
At KHIA AI, we see organizations succeeding when they approach Agentic AI not as a technology deployment but as a fundamental reimagining of how work gets done.
There's a window of competitive advantage open right now. Companies that figure out how to redesign work around AI agents will dominate their industries.
The question isn't whether your competitors are testing AI—they are. It's whether they're fundamentally changing how work gets done.
Those who act first won't just improve what they do. They'll redefine what's possible.
How is your organization approaching the shift from AI experimentation to transformation? We'd be interested in your perspectives on where the biggest opportunities—and challenges—lie.
Source: McKinsey & Company, "Seizing the agentic AI advantage" (June 2025)
