2. You’re Not Ready to Automate with AI Until You’ve Mapped Your Decisions
AI doesn’t scale without decision architecture. Here’s where to start.
We’re all in a race to automate something.
Companies are piloting AI for customer service. Sales teams are using AI to write emails. HR is experimenting with auto-generated job descriptions and resume screening. And across every function, the question is the same:
"What tasks can we automate with AI?"
It’s a logical question — if what you’re looking for is incremental improvement. But if you want real transformation… the kind that drives bold, not just incremental, improvement, you need to ask a better question:
“Where — and how — are decisions being made in our organization?”
Because before you automate anything, you need to understand how work thinks —
How decisions are made,
Where judgment is applied
What logic guides the flow from input to action
The truth is, most orgs have no clear map of how their decisions get made, or by whom — and that is the quintessential first step to being AI ready.
The Real Bottleneck is Decision Friction
If you read my last post, you’ll know I’m pushing for a shift: from viewing AI as a tool, to viewing AI as a Cognitive Operating Model — a way to reengineer workflows to take advantage of the point where people and AI meet.
But that shift doesn’t start with a Copilot license or a shiny chatbot. It starts by identifying the points of friction in your organization’s ability to think, decide, and act.
Because that’s where AI creates leverage.
Most processes don’t break down because a human can’t type fast enough.
They break down because:
People wait for approvals.
No one knows who owns the final call.
Decisions are inconsistent or undocumented.
Knowledge lives in people’s heads, not systems.
AI won’t fix that by plugging chatbots into your workflow.
You need to redesign the decision layer.
Step 1: Identify High-Friction Decisions
Start by asking:
Where are we slow?
Where do we keep revisiting the same issues?
Where does inconsistency hurt us — in quality, customer experience, or missed opportunities?
Common examples:
“Should we escalate this customer issue?”
“Is this deal qualified enough to move forward?”
“Which website pages need a content refresh next?”
“Is this vendor in compliance with our new policy?”
These aren’t tasks — they’re decisions, that shape the tasks, that drive the outcomes.
Step 2: Deconstruct the Decision Flow
For each one, unpack the how:
Who currently makes this decision?
What information do they rely on?
Is there a playbook or is it tribal knowledge?
Are there consistent rules, or is it all gut feel?
You’re not looking to automate this — yet.
You’re modeling how cognition flows, so you can eventually delegate some of it intelligently. This often involves mapping out the many places, people, and processes that are involved along the way, and requires the ability to deeply dive in to accurately paint a full picture of the situation.
Step 3: Assess the Cognitive Load
With a full picture, we can begin assessing the critical decision structures. Not all decisions are equal: some are high-stakes, some are low-risk. Some are frequent and repetitive. Others are rare but consequential.
Evaluate:
How long does the decision take?
How often does it require follow-up or clarification?
Is it bottlenecked by a single person?
Could an AI reasonably handle a first-pass, flag exceptions, or route by category?
Here’s where we peel back the onion, seeking out the high-frequency, high-friction decisions that drain time and attention, but follow a consistent pattern. That’s your sweet spot.
Step 4: Sketch the AI-Augmented Path
Now — reimagine the flow to address the opportunities to optimize your work.
Ask:
Could an LLM summarize key info before a decision is made?
Could it draft a proposed recommendation based on prior examples or known data?
Could it classify decision categories or risk levels to speed triage?
Could it surface exceptions for human review?
In other words: don’t give the AI the final say. Give it the first move. Done well, this creates a hybrid system:
AI handles the low-cognition, high-frequency load.
Humans focus on nuance, judgment, and edge cases.
Operating on AI — not just integrating it — means designing systems that deliver value and intelligence earlier, faster, and with more reliability. It also means humans maintain control over the outcomes, ensuring accountability.
Why a COM approach Changes the Game
Two organizations dealing with similar customer support issues implemented the same AI solution for customer support.
Company A installed it like a plugin. It drafted replies, giving Agents information that could be useful on a call or response, but Agents seemed to largely ignore it.
Company B started by mapping its escalation decisions, determining when and how calls were funneled up the ladder. They realized Agents were spending most of their time figuring out which queue a ticket should go to — instead of responding to the problem itself.
Company A scrapped the project as a failure.
Company B redesigned the workflow:
AI tags intent and urgency.
Flags VIP customers.
Routes low-risk issues directly to tier-one.
Escalates with recommended language for edge cases.
Same tool. Different outcome.
Why? Because when we map cognition first, it uncovers hidden opportunities to apply AI to drive better outcomes. Throwing tech at the task simply removes the human from the task — but doesn’t change the process. Rethinking the system enables us to reimagine how the system can work better.
Final Thought: Decisions Are the Foundation of Work
Before there’s a workflow, before there’s a task — there’s a decision.
AI is most powerful when it supports or augments that decision.
So if you’re serious about transforming your business with AI, don’t ask “What can we automate?”
Ask:
“What decisions are we making — and how can we make them smarter, faster, and more scalable?”
That’s the first step to building your Cognitive Operating Model.
That’s where AI becomes infrastructure — not just interface.
Want help mapping your organization’s decision flows? Or frameworks to identify high-friction judgment points? That’s what Be AI Ready is here for. Subscribe, share, and let’s build smarter systems — from the inside out.