Most companies we speak to don’t fail at implementing AI because the tech is bad.
They fail because they try to automate a process that no one has actually written down and looked to decompose the steps. It’s such a common problem, but one that is so often overlooked. We spend a good amount of time helping customers get prepared for this step and what the best practices are as they approach solving this initial problem.
Before you even think about LLMs, the perfect prompts, or which tool is best for “workflow automation,” there’s one step that matters more than any of that:
Mapping your real-world process into small steps, showing how they are connected and who the people or teams are involved in this process.
It sounds simple and somewhat obvious when you read it and think about it. But it’s the step most teams skip, and it’s the reason many AI pilots don’t deliver anything useful. They think that AI can just get it and will organize it magically. Even at Spiral Scout, I have been guilty of overlooking this step in the past.
If you’re a product manager, team lead, operations director, or founder looking to apply AI to your business, this post is for you. You don’t need to be technical, but that can help. You just need to be curious about how work actually happens inside your team and start to practice breaking these steps down.
We’ll show you how to take a process that currently lives across spreadsheets, inboxes, and spread across multiple people’s brains on your team and get it ready for workflow automation using AI agents.
Why Mapping Comes Before Automation
Let’s say your team responds to customer inquiries, reviews legal contracts, builds custom product quotes, or processes client invoices.
You probably already know the process works, but you also know:
- It’s slow
- It’s inconsistent
- It lives across five tools
- It depends on one or two people who “just know how to do it”
So someone says, “Can we use AI to automate this?”
That’s a great instinct, but also a trap. If you skip directly to trying out tools like ChatGPT, Claude, or a prompt template, you’ll quickly hit a wall and face frustration.
Here’s what we’ve seen happen again and again:
Teams try dropping messy files or scattered data into a chatbot and expect a magic answer. The results are chaotic, confusing, and unusable. Why? Because the AI can’t figure out what problem it’s solving or what the process actually looks like or is supposed to look like.
AI is a pattern recognition machine. If there’s no pattern to recognize, it doesn’t know what to do.
So the first step is for you to provide clarity.
That’s what workflow mapping gives you.
What Is Workflow Mapping, Really?
Workflow mapping is not a technical process. You’re not building a flowchart. You’re not drawing endless diagrams (think UML).
You’re simply writing down:
- What people do and how they work together
- What tools do they use
- What decisions do they make at each step
- What steps follow which
- Where delays, handoffs, or human guesswork exist
Think of it as documenting the actual muscle memory of how work gets done at your company. Not how it should work, or what’s in the SOP from 2020, but how it’s actually done today. It’s going to be messy and painful to start.
Once you write it down, you’ll start to see:
- Which parts are consistent and repeatable
- Which parts rely on judgment or experience
- Where people lose time searching, waiting, or redoing work
- Which steps could be owned by an AI agent
Let’s walk through how to do it.
Step 1: Watch Someone Do the Work
The best way to start is simple: observe someone doing the task.
Seriously. Sit next to them. Or screen share. Best if you can actually record the steps and process.
Watch how they open the files, click through tabs, scroll through messages, or check multiple tools to get the full picture.
Ask:
- “Where do you start?”
- “What info do you need before you can begin?”
- “What slows you down the most?”
- “What part of this feels manual or repetitive?”
Write it all down, step by step. You’ll likely discover steps and edge cases that aren’t documented anywhere, and you want to document those to help AI.
This is gold.
Real example:
We worked with a manufacturing company whose quote response process lived in an Excel doc, a CAD file, two shared folders, and three people’s memories who worked in different departments that were busy. Nobody had ever written it down from start to finish.
Until they did.
That was the turning point where AI could be useful.
Step 2: Write the Steps in Plain Language
Turn what you saw into a clean list. Here’s an example from a typical quote workflow:
- Open the quote request email
- Download the attached BOM spreadsheet
- Compare it to past quotes in a shared folder
- Check material pricing from a supplier
- Email the finance team to verify margins
- Build the quote in Word
- Submit the final version through a portal
You don’t need fancy formatting. You just need a clear outline of what actually happens.
Avoid abstract terms like “process data” or “review request.” Write what they literally do and get as granular as you can.
Step 3: Highlight the Repetitive and Slow Parts
Now that you have a map, scan it for patterns:
- What steps happen every time? What’s a one-off use case?
- What steps depend on human judgment?
- Where do people or departments wait on others? (Pro tip: you can use the wait time to show the value that AI can have in cutting down that time)
- What involves digging through old files or emails?
These are important clues for where AI can help.
Here’s how to spot different categories of opportunity:
Task Type | AI Opportunity |
Repetitive/manual task | Automate it entirely |
Rule-based decision | Use an AI agent or rule engine |
Search-heavy step | Use AI-powered search or embeddings |
Requires memory | Build a persistent agent memory or a knowledge base of data |
Needs approval | Keep it human-in-the-loop |
Remember: you’re not trying to replace people. You’re trying to free them from low-value work to focus on creating successful outcomes.
Step 4: Define What Good Looks Like
Now, imagine the ideal version of the workflow:
- What should never require a human again?
- What steps could AI suggest that a person should approve?
- What parts of the process need better documentation or structure?
- What info do people always wish they had at the right time, i.e., didn’t have to wait for others to share with them?
This future state becomes your design blueprint.
We often ask clients:
“If your best team member designed a tool to help them do this job 10x faster, what would that tool do?”
That’s what you want to build with AI agents. Not just a faster version of the current workflow, but a smarter, more helpful one that persists in your company’s knowledge.
Step 5: Turn the Map into an Agent
At this point, you’ve got:
- A written list of real-world steps
- Clarity on pain points and inefficiencies
- A vision for what an AI helper could do
Now you can start shaping that into an actual AI agent.
If you’re using a platform like Wippy, here’s what happens next:
- Define the agent’s goal: “Help me respond to RFQs 60% faster.”
- Break it into steps: “Extract BOM, find past matches, write quote draft…”
- Identify tools: File parser, pricing API, historical database
- Write prompts or logic: Use traits like “cost-conscious” or “conversational.”
- Design outputs: Quote drafts, internal memos, alerts for approval
None of this requires coding. You’re defining behavior. And once it’s built, that agent can run 24/7, scale to other teams, and learn over time.
What a Final Output Looks Like
Here’s what a finished workflow-to-agent might look like:
Agent Name: Quote Assistant
Input: New RFQ upload
Steps:
- Parse the BOM
- Search historical quotes by part match
- Calculate price range with margin logic and find past examples where there could be cost improvements
- Draft a proposal paragraph
- Flag low-margin items for review
Output:
JSON + ready-to-send quote in a Word document that can be converted to a PDF.
One process. One agent. Saved hours and human mistakes every time.
Common Pitfalls to Avoid
- Trying to automate everything at once
Start with one process. One agent. One win. Pro tip: start small and find those wins that save 30 minutes to a few hours each time. - Not involving the person who does the actual work
Your SME knows what works and what breaks. They’re your co-designer and who you need at the start. - Thinking AI will “figure it out”
AI can’t do anything useful if it doesn’t understand the structure, steps, and concept of what you are trying to solve. - Assuming you need clean data before starting
You just require a consistent structure. Perfection is optional. Clarity is not. Please note that there is a rule that “garbage in, produces garbage out,” so having clean data is a great starting point.
Why This Matters More Than the Model You Choose
The best AI agent in the world is useless if it doesn’t know what to do.
Workflow mapping isn’t just step zero. It’s the foundation for:
- Agent accuracy
- Faster time to value
- Consistent outputs
- Real-world scalability
When you skip this, you get stuck in AI jail. When you do it well, you can build once and reuse everywhere and over and over again.
Ready to Map Your First AI Agent?
We work with product teams, operators, and innovation leads every week to map out and launch AI agents that actually work.
If you’re wondering where to start, here’s how:
We’ll walk through your process and show you how to turn it into an agent, without code, stress, or unnecessary tools.
AI is powerful. But structure is what makes it useful.
Let’s help you build that structure.
