Don't Just Add AI Sprinkles: Why Your Old Policies Are Sabotaging Your Technology Investment
A client recently told me, "We bought AI licenses for everyone, but nothing's really changed. We're still doing everything the same way, just... with AI."
That's like buying a race car and driving it through a school zone—technically you have the horsepower, but your environment is designed to keep you slow.
Most companies are layering AI onto processes and policies that were built for a world of scarcity. Scarcity of information. Scarcity of processing power. Scarcity of real-time insights.
But AI operates in a world of abundance.
When you force abundant technology to work within scarce frameworks, you get expensive disappointment instead of transformation.
The Problem with "AI Sprinkles"
I call it "AI sprinkles"—that tendency to dust artificial intelligence on top of existing workflows and expect magic to happen. It's like putting a Formula One engine in a horse-drawn carriage and wondering why you're not winning races.
Your current policies were designed around three fundamental limitations that AI obliterates:
Limited Processing Speed: Your approval chains exist because humans needed time to review, analyze, and decide. When AI can process that same information in seconds, why are you still requiring three-day review cycles?
Information Scarcity: Your reporting structures were built when data was hard to gather and harder to analyze. When AI can synthesize insights from multiple sources instantly, why are you still waiting for monthly reports to make decisions?
Human Bottlenecks: Your workflows route everything through specific people because they had unique expertise or access. When AI can democratize that expertise across your organization, why are you still creating artificial chokepoints?
The Customer Support Wake-Up Call
Let me share a story that illustrates this perfectly. A B2B2C client came to me frustrated: "Our customers are getting frustrated. Their tickets aren't addressed promptly enough."
The knee-jerk reaction? Throw more technology and more people at the problem.
Instead, we stepped back and looked at the data. The real issue wasn't capacity—it was policy. They had decided that all help requests should immediately enter the support queue because they wanted to provide "personalized, human touch."
Noble intent. Terrible execution.
Most tickets were coming from end-users who should have been directed to their direct client, not us. A significant majority could have been resolved through better self-service education. But their policy created an indiscriminate flood that overwhelmed their team and diluted focus from their actual priority customers.
We didn't buy more AI tools. We didn't hire more people. We changed the policy to align with business strategy, then redesigned the data-enabled workflows to support that policy.
Result? Within a month, tickets dropped by 30%. Time-to-first-response dropped by half. For priority clients, response time improved by two-thirds.
No extra costs. We just needed the right focus: Strategy → Policies → Data-Enabled Workflows → Technology and People.
The Five-Step Framework for Policy Redesign in the AI Era
Here's how to systematically rethink your policies and processes for the age of AI:
Step 1: Audit for Artificial Scarcity
Start by identifying policies built around limitations that no longer exist.
Questions to ask:
Which approval processes exist because information was hard to gather or analyze?
Where do we route decisions through specific people because they had unique access to data?
What waiting periods exist because processing used to take time?
Which manual checks happen because automation wasn't reliable?
Example: A financial services client required three-level approval for any customer account changes. This made sense when changes required manual ledger updates and verification. With AI-powered real-time fraud detection and automated verification, most changes could be processed instantly with better accuracy than the old manual process.
Step 2: Map Your True Value Chain
Use the enhanced process mapping framework I've developed—but with an AI lens.
Don't just map what happens now. Map what should happen if you could have perfect information, instant processing, and unlimited analytical capacity.
Layer on these elements:
Information Flow: What decisions need what data, and how quickly?
Decision Points: Which choices require human judgment vs. pattern recognition?
Exception Handling: What percentage of cases are truly unique vs. variations on known patterns?
Value Creation: Where does human expertise actually add value vs. where it's just a bottleneck?
The key insight: Your current process map shows you where policies create artificial constraints. Your ideal process map shows you where AI can eliminate those constraints.
Step 3: Distinguish Between Judgment and Processing
This is where most companies get it wrong. They assume that because a human currently makes a decision, human judgment is required.
Wrong.
Many "decisions" are actually just data processing disguised as judgment.
True Judgment (Keep Human):
Ethical considerations
Stakeholder relationship management
Strategic trade-offs
Creative problem-solving
Handling genuine edge cases
Disguised Processing (Automate with AI):
Pattern recognition ("Is this normal?")
Data synthesis ("What does this mean?")
Rule application ("Does this meet criteria?")
Trend analysis ("Where is this heading?")
Risk assessment based on known factors
Step 4: Design for AI-Native Operations
Now comes the hard part: designing new policies that assume AI capabilities rather than human limitations.
Principle 1: Default to Real-Time If AI can provide real-time insights, why are you making decisions based on week-old data? Design policies that leverage continuous rather than periodic information.
Principle 2: Enable Distributed Intelligence Instead of centralizing expertise in a few people, use AI to democratize capability across your organization. Design policies that empower rather than control.
Principle 3: Build in Adaptive Learning Unlike static human processes, AI improves with data. Design policies that get better over time rather than staying fixed.
Principle 4: Plan for Exponential Scale AI doesn't get tired or overwhelmed. Design policies that can handle 10x volume without breaking.
Step 5: Implement with Staged Rollouts
Don't flip everything at once. Use a staged approach that builds confidence and captures learning.
Stage 1: Shadow Mode Run AI-enabled processes parallel to existing ones. Compare results. Build trust in the data.
Stage 2: Assisted Mode AI provides recommendations; humans make final decisions. This builds comfort with AI insights while maintaining control.
Stage 3: Supervised Automation AI makes routine decisions; humans handle exceptions. This is where you see dramatic efficiency gains.
Stage 4: Full Automation AI handles end-to-end processes with human oversight for quality and ethics.
The Courage to Let Go
The hardest part isn't technical—it's psychological. Your current policies aren't just processes; they're comfort blankets. They make people feel important, necessary, in control.
But here's what I've learned from helping organizations through this transition: The companies that thrive in the AI era aren't the ones with the best technology. They're the ones with the courage to reimagine their fundamental assumptions about how work gets done.
Your policies should be engines for your strategy, not museums for your limitations.
AI doesn't just change what you can do—it changes what you should do. But only if you're brave enough to let it.
Make the Right Thing to Do the Easy Thing to Do
This is my core philosophy, borrowed from my mentor Dr. Bror Saxberg: Make the right thing to do the easy thing to do.
In the AI era, this means designing policies that make intelligent decisions the path of least resistance. When your workflows are aligned with AI capabilities rather than fighting against them, your people naturally make better choices because better choices become easier choices.
Your AI investment isn't just about buying technology. It's about buying yourself permission to operate differently. The question is: Are you brave enough to use it?
Want to assess how your policies might be constraining your AI potential? Let's have a conversation about where your organization might be driving a race car through school zones. Sometimes the biggest transformation happens not by adding more, but by removing what no longer serves you.