Trying to automate everything at once
Start with one clear workflow. Don’t build an internal platform or "AI layer" until you’ve shipped a couple of successful flows.
Guide
Getting Started with AI Workflows
This guide is for teams who are new to AI but not new to work. You'll learn how to pick a good first workflow, design it clearly, and ship a small but real AI-powered process that saves time instead of generating more meetings.
You don't need a research background or a giant budget. Just a real business process, a small bit of focus, and the willingness to ship something slightly imperfect and improve it.
What we’ll cover
Use this as a checklist: if you can answer these sections, you can ship your first AI workflow.
Foundations
- • What an AI workflow actually is
- • Where AI helps vs. where it hurts
- • Picking a starter use case
Design & build
- • Mapping inputs, decisions, outputs
- • Human-in-the-loop steps
- • Drafting your first prompt & flow
Launch & iterate
- • What "good enough" looks like
- • How to collect feedback
- • Avoiding the classic traps
1. What is an AI workflow, really?
Forget the buzzwords. An AI workflow is just a process where AI helps transform inputs into useful outputs with humans still owning the outcome.
At its simplest, a workflow is just a repeatable series of steps that turn one thing into another. For example:
- • Incoming support email → triage → reply
- • New lead → qualify → next step
- • Recorded demo → notes → follow-up tasks
An AI workflow is the same thing, except some of those steps are handled or assisted by a model: summarizing, classifying, drafting responses, suggesting decisions.
Great AI workflows don't try to automate judgment. They remove the repetitive, text-heavy parts so humans can make better calls with more context.
Example: support triage workflow
- Customer emails support.
- AI reads the email and classifies: bug, billing, "how do I", or urgent.
- AI suggests a reply using your knowledge base + previous answers.
- Human agent reviews, edits if needed, and sends (or escalates).
The workflow is still human-owned. AI just moves you from "blank screen" to "reasonable draft" and flags the right priority.
2. Choosing your first workflow
If you pick the wrong starting point, you’ll either ship something no one uses—or never ship at all.
Your first AI workflow should live where pain is real but risk is low.
Look for processes that are:
- • Repetitive and text-heavy
- • Already happening today (no new behavior required)
- • Owned by a small, friendly group willing to try new tools
- • Time-consuming but not legally or financially catastrophic if something goes a bit wrong
Common good first workflows: drafting emails, summarizing calls, generating meeting notes, triaging support, drafting proposals, cleaning or structuring text data.
Shortlist exercise (30–45 minutes)
- Ask your team to list tasks they repeat weekly.
- Group similar tasks into a small set of workflows.
- For each workflow, estimate: time spent per week and "how bad is it if AI makes a weird suggestion?"
- Pick 1–2 workflows that score high on time and low on risk. That's your starting point.
Write this down somewhere shared. You're not just picking tasks—you're building the beginning of an AI roadmap.
3. Designing your first AI workflow
Before you touch any tools, sketch the flow in plain language.
The most common mistake is jumping straight into a prompt or a UI. Instead, design the workflow like you would any other process:
- • What is the trigger? (new email, new row, new form)
- • What inputs are needed? (text, metadata, attachments)
- • What decisions need to be made?
- • Where should humans review or approve?
- • What should the final output look like?
Once this is clear, it becomes much easier to slot AI into the right steps instead of trying to make it do everything.
Simple design template
- • Name of workflow: ___________________
- • Trigger: ____________________________
- • Inputs: _____________________________
- • AI assists with: _____________________
- • Human reviews: ______________________
- • Final output: ________________________
You can keep this in a Google Doc, Notion page, or ticket. If you end up working with a vendor, this one page will save everyone hours.
4. Building a small but real v1
You’re not trying to build The Platform. You’re trying to build something real people can use this month.
Your first version should look embarrassingly simple. That’s a feature, not a bug. Focus on:
- • One narrow workflow
- • One AI model (at first)
- • One place the output shows up
- • A clear way to give feedback or flag issues
This might be a shared inbox, a Slack command, a small web form, or a button in your existing tool. Don't over-think the UI. The value is in the workflow, not the chrome.
Example stack (one of many)
- • Trigger: new row in a Google Sheet or Airtable
- • Backend: Firebase Functions or a small Cloudflare Worker
- • Model: OpenAI or Gemini, called via API
- • Output: write back to the sheet, plus optional email
You can swap tools freely. The important part is: one clear trigger, one processing step, one obvious output.
5. Measuring if it’s actually working
If you don’t define “done”, your AI project will live in pilot purgatory forever.
Pick one or two metrics. That’s it. Common choices:
- • Minutes saved per task
- • Number of tasks handled per week
- • Deflection rate (tasks that don’t need escalation)
- • Error rate or rework needed
Decide up front what would make this workflow "worth keeping". For example:
- • Saves at least 5 hours per week
- • No increase in error rates
- • Users say it makes their job easier, not harder
Lightweight feedback loop
- • Add a quick 👍 / 👎 or "Was this useful?" option.
- • Keep a weekly 15–30 minute review with the people using it.
- • Log a few examples each week where the AI was great and where it struggled.
- • Use those to refine prompts, rules, or guardrails.
You don't need full-blown analytics at first. Consistent, small reviews beat giant dashboards that no one has time to read.
6. Common pitfalls when starting with AI workflows
Most teams run into the same few issues. You can skip them.
No clear owner
Every workflow needs a business owner who cares about the outcome and can make decisions. It can’t just belong to "the AI team".
Launching without training users
Even a simple workflow needs a 10–15 minute walkthrough and a short doc. If people don’t know when to use it, they won’t.
Treating AI like magic instead of software
AI workflows still need monitoring, logging, and versioning. Treat them like product features, not experiments.
FAQ: starting with AI workflows
Questions we hear from teams who are just getting started.
Do we need engineers to build our first AI workflow?
Not always. Many first workflows can be prototyped with no-code tools, spreadsheets, and off-the-shelf assistants. For anything that touches customer data at scale, having engineering involved is a good idea—but it doesn't have to be where you start.
How long should our first workflow take to build?
We like a 2–4 week target: a few days to pick and design the workflow, a week to build a small v1, and a week to pilot and adjust. If it drags on for months, the scope is probably too big.
What if our first attempt isn't very good?
That's normal. The point of the first workflow is learning and building muscle, not perfection. As long as you instrument, review, and iterate, even a rough v1 is a win.
Want help shipping your first AI workflow?
If you're a small business or startup and you'd like someone who has done this before to help you pick the right workflow, design it, and get it into production, that's exactly what we do at BotRidge.
We'll use this playbook, tailor it to your context, and build alongside your team so you keep the knowledge in-house.
Talk to us about your first AI workflowExplore services & engagement options
Typical engagements are 4–8 weeks, with a clear scope and a real workflow live at the end—not just slides.