You’ve seen it before. A new technology rolls in, promising speed, efficiency, and intelligence. Leadership gets excited. The team is hopeful.
But then, something happens. Instead of making things better, it makes them worse—faster, more often, and on a scale no human could ever achieve. Because doing the wrong thing faster and more often is always bad. Poorly trained agentic AI isn’t just inefficient. It’s dangerous.
When Agentic AI Fails, It Fails Spectacularly
A human making mistakes is frustrating. A poorly trained AI agent making mistakes is catastrophic. A single undertrained employee might misclassify a few tickets daily; an AI agent trained on bad data could misclassify every single one, 24/7, without hesitation. It doesn’t get tired. It doesn’t second-guess. It just executes—scaling failure at the speed of light.
And yet, we expect automation to fix inefficiency instead of first fixing the process.
AI Learns Faster Than You Think
The irony? AI has the potential to outperform humans—if we train it right. The best AI isn’t just a rule-follower; it’s an optimizer. Given clear best practices, defined outcomes, and real-world lessons from past successes and failures, AI can refine its approach in ways no human can match.
When trained correctly, AI doesn’t just do the job—it finds better ways to do it. You might be surprised at how quickly it re-engineers workflows, identifies inefficiencies, and exceeds your expectations.
Avoid “Paving the Cow Path”
A broken process, when automated, only fails faster. AI won’t fix inefficiency—it will amplify it. This is what we call "paving the cow path"—using AI to automate a bad process instead of redesigning it first.
Imagine a bloated, bureaucratic approval system. Employees might navigate the workarounds, but AI won’t. It will instantly enforce inefficiency, making bottlenecks permanent.
Before deploying AI, ask: Are we optimizing the right things? If not, stop. Redesign first. Then deploy.
Human Expertise Comes First
AI should not define how work gets done—humans should. The most successful AI implementations start with collaborative process mapping to establish best practices before automation begins.
What does “best” look like?
Where have humans struggled before?
What failures should AI learn from, not repeat?
Skipping this step is like handing AI a mess and expecting it to sort things out. That never ends well.
Training AI with Value Stream Principles
The best AI follows Value Stream principles—prioritizing outcomes over output. To ensure AI delivers real value, organizations should:
Map the flow of value—so AI accelerates the right things, not just anything.
Eliminate waste—so AI avoids redundant, low-value work.
Continuously improve—so AI evolves based on real-world feedback, not static rules.
Focus on impact, not activity—so AI isn’t just “doing more” but delivering more value.
Final Thought: How Well Are You Training Your AI Agent?
AI isn’t inherently good or bad. It’s only as effective as the training, processes, and values you embed in it. If trained well, it can optimize beyond human capability. If trained poorly, it becomes your biggest liability—scaling mistakes at an alarming rate.
So, the real question isn’t whether you should use AI.
It’s this: Are you training it well enough to trust it?
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