What's the biggest mistake companies make implementing agentic AI?

The biggest mistake is bolting agentic AI onto existing processes instead of redesigning workflows around outcomes. Companies chase technology, not results—they hire someone to 'handle CRM updates,' build processes around human availability, measure task completion. This fails. Successful implementation defines missions ('qualify and route every lead within 2 minutes'), appoints mission owners steering humans and AI, measures outcome achievement. Only 14% of companies exploring agentic AI have deployable solutions because they're thinking in the old paradigm.

How do you successfully implement agentic AI?

Successfully implement agentic AI by redesigning from scratch around outcomes: (1) Pick one high-value outcome, not task (e.g., 'every lead qualified within 2 minutes' vs 'update CRM'), (2) Redesign the entire workflow from the outcome backward, (3) Deploy, measure results, iterate based on data, (4) Scale only after proving the model works. The shift is from Task Management to Mission Ownership—treating AI agents like workforce members with clear outcomes, not software tools completing tasks.

What is agentic AI vs traditional automation?

Traditional automation closes the execution gap for predefined tasks. Agentic AI closes it for decision-making itself—it doesn't just complete steps, it determines the path. You can't layer intelligence onto broken systems; you must redesign the workflow around the outcome. Companies succeeding with agentic AI don't automate work—they compound leverage by having AI handle strategy execution while competitors are still hiring for capacity.

December 30, 2025

Implementing Agentic AI

Githui Maina

Founder & AI Systems Architect

Most companies are implementing Agentic AI wrong.

They bolt it onto existing processes. They chase the technology, not the outcome. They wonder why nothing changes.

Here's the problem: You can't layer intelligence onto broken systems and expect transformation. The entire promise of Agentic AI is that it closes the Execution Gap—the chasm between what you decide to do and what actually gets done. But to unlock that, you have to redesign the workflow around the outcome, not the task.

Hire someone to "handle CRM updates"
Build a process around their availability
Measure success by tasks completed

Define the mission: "Every lead gets qualified and routed within 2 minutes"
Appoint a mission owner who steers both humans and AI agents
Measure success by outcome achieved

This is the shift from Task Management to Mission Ownership. Most companies treat AI agents like software tools. The winners treat them like workforce members. Right now, only 14% of companies exploring Agentic AI have solutions ready to deploy. Not because the technology isn't ready. Because they're still thinking in the old paradigm.

If you want to implement Agentic AI successfully: Pick one high-value outcome (not task) Redesign the workflow from scratch around that outcome Deploy, measure, iterate Scale only after proving the model works The companies that figure this out aren't just automating work. They're compounding leverage while their competitors are still hiring.