Workflow AI in HighLevel

Updated: February 4, 2026  | 
Author: Bill Stilwell

This post covers how Build Using AI works, what it generates versus what you still configure manually, how to write effective prompts, and how to use conversational follow-up to refine the workflow after generation.

Reading time: about 8 minutes.

What Is Workflow AI in HighLevel?

Workflow AI – called Build Using AI inside HighLevel – is a feature that generates a complete automation workflow from a plain-language description.

Instead of dragging triggers and actions onto the canvas one by one and wiring them together manually, you describe what the workflow should do and the AI builds the structure for you.

The generated workflow appears on the canvas as a standard editable HighLevel workflow. You review it, configure the specific details the AI left blank, adjust anything that does not fit, and publish.

It lives inside the Advanced Workflow Builder – you need to enable Advanced mode before the Build Using AI option appears.

How Does It Work?

You open the Advanced Workflow Builder, click Build Using AI, and describe the automation in the prompt field.

The AI reads your description, identifies the appropriate triggers and actions, determines the logical flow and branching, and places everything on the canvas. The result is a visual workflow you can see, inspect, and modify immediately.

You can then describe changes conversationally in the same interface – “add a branch after the second SMS that routes contacts who replied to a different pipeline stage” – and the AI updates the canvas based on your instruction.

The whole process is iterative. You are not locked into the first generation.

What the AI Builds

Given a clear prompt, the AI generates the structural backbone of the workflow.

It selects the correct trigger type – Contact Created, Form Submitted, Appointment Status, Tag Added, and so on – and places it at the entry point. It adds the appropriate action types in sequence – Send SMS, Send Email, Wait, If/Else, Add to Pipeline, Add Tag, and others.

It sets up logical branching when the prompt describes conditional logic.

Wait durations are set based on the timing described in the prompt – “wait 10 minutes” produces a 10-minute Wait action; “wait 1 day” produces a 24-hour Wait.

For common workflow patterns – lead follow-up sequences, appointment reminders, re-engagement campaigns – the AI often produces a structure that is 80 to 90% ready with relatively little manual adjustment needed.

What You Still Configure Manually

The AI builds the structure but does not fill in every detail. You are still responsible for configuring several things after generation.

Message content. SMS and email action nodes are placed in the right positions but the message text is blank or placeholder.

You write the actual content for each message.

Phone number and sender selection. SMS actions need a specific phone number assigned.

Email actions need a sender name and email address.

Tag names and pipeline stages. If the workflow uses Add Tag or Move to Pipeline Stage actions, the specific tag names and stage names need to be entered.

Workflow settings. Stop on Response, Allow Re-entry, Time Window, and Timezone are not configured by the AI.

You set these in the Settings tab after reviewing the canvas.

Trigger filters. If the trigger needs filters – for example, Form Submitted filtered to a specific form – those are not set automatically and need to be configured on the trigger node.

How to Write Effective Prompts

The quality of the generated workflow is proportional to the quality of the prompt. A vague prompt produces a generic structure.

A specific prompt produces something close to what you actually need.

A weak prompt: “Create a lead follow-up workflow.”

A strong prompt: “When a contact submits the contact form, immediately send an SMS thanking them and telling them we’ll call within 5 minutes. Wait 5 minutes.

If they haven’t replied, send a second SMS with a direct booking link. Wait 1 hour.

If still no reply, send an email follow-up with a different subject line. Add a tag called ‘lead-follow-up-sent’ after the first SMS.

If at any point they reply, stop the workflow.”

The second prompt gives the AI everything it needs: trigger, channels, timing, conditional logic, a tagging action, and a stop condition. The generated workflow will match the intention much more precisely.

Useful elements to include: the trigger event, communication channels (SMS, email, call), timing between steps, conditional branches (if replied / if not replied), tags to apply, pipeline movements, and the stop condition.

Conversational Refinement

After the initial generation, you can keep describing changes in the Build Using AI interface and the AI will update the canvas accordingly.

This is useful when the first generation is close but not quite right. Instead of manually locating nodes and reconfiguring them, you describe what needs to change.

“Move the email action to after the second SMS instead of after the first” or “Add an If/Else after the wait that checks whether the contact has the tag ‘already-called’ and skips the call action if they do” are both valid instructions the AI will apply to the existing canvas.

The conversational approach works best for structural changes. For content changes – editing the actual text of a message – it is usually faster to click directly into the action node and edit it manually.

What Can You Do With It?

• Build workflows faster without starting from scratch: Get a complete structural draft in seconds rather than spending 20 to 30 minutes manually placing and connecting every node for a complex workflow.
• Lower the barrier to building complex automations: Someone unfamiliar with the full HighLevel action library can describe what they want in plain language and get a functional structure – then learn by examining and editing what the AI built.
• Speed up client onboarding for agencies: Generate the standard workflows every new client needs – lead follow-up, appointment reminders, re-engagement – in a fraction of the time it would take to build each one manually from a blank canvas.
• Prototype new workflow ideas quickly: Use Build Using AI to generate a draft of a new automation concept, test whether the structure works, and iterate from there rather than investing significant build time before validating the approach.
• Refine workflows conversationally: Describe changes to an existing AI-generated workflow in natural language rather than navigating the canvas to find and reconfigure specific nodes manually.
• Use generation as a learning tool: New HighLevel users can describe a workflow and examine what the AI built to understand which triggers and actions apply to which situations – a faster way to learn the platform than reading documentation alone.

Key Definitions

Use Cases by Industry

Marketing Agencies – Client Onboarding

An agency onboards a new client and needs to build six standard workflows: lead follow-up, appointment reminder, no-show re-engagement, post-service review request, reactivation campaign, and new client welcome sequence.

Using Build Using AI with a specific prompt for each workflow, the agency generates all six structural drafts in under 30 minutes. A team member then configures the message content and settings for each.

Total build time drops from a full day to a morning.

Result: Standard client onboarding workflow builds that previously took 6 to 8 hours are completed in 3 to 4 hours – and the quality of the structural logic is consistent across all six.

Home Services

A plumbing company owner wants to automate lead follow-up but has never built a HighLevel workflow. The blank canvas is intimidating – they do not know which triggers to use or how to set up conditional branches.

They describe what they want in plain language: “When someone fills out the contact form, send a text in 5 minutes, call them in 10 minutes, send a follow-up text in 2 hours if no reply, send an email the next day if still no reply.” Build Using AI generates the full structure. The owner fills in the message content and publishes.

Result: A business owner with no automation experience builds a functional lead follow-up workflow in 20 minutes instead of giving up on the blank canvas after 10.

Real Estate

A real estate team has a complex buyer nurture sequence they want to rebuild. The original workflow was built manually over several months and is difficult to understand because the canvas is cluttered.

They describe the sequence to Build Using AI with the full logic included. The AI generates a clean version of the same structure on a fresh canvas – readable, properly named, and with the Advanced Builder’s tidy layout.

The team reviews and configures the message content, then publishes the rebuilt version.

Result: A cluttered, hard-to-maintain legacy workflow is rebuilt in a clean, documented structure in under an hour – with the AI handling the structural translation from description to canvas.

Online Education

A course creator wants a drip onboarding sequence for new students: welcome email on day 1, first module prompt on day 3, mid-course check-in on day 7, completion push on day 14, review request on day 21.

The prompt includes all five touchpoints with their timing and channel. Build Using AI generates the full 21-day sequence on the canvas in one step.

The creator writes the email content for each node and publishes.

Result: A 5-email 21-day sequence that would take 30 minutes to build manually takes 5 minutes to generate and 20 minutes to configure – a significant time saving on a workflow the creator builds for every new course.

Dental Practices

A dental practice needs workflows for three scenarios: new patient welcome, appointment reminder (24 hours, 2 hours, 1 hour), and missed appointment re-booking.

Three prompts, three generated workflows. Each workflow is reviewed, message content is added, and workflow settings are configured.

The practice manager who has no technical background completes all three in one session.

Result: Three functional patient communication workflows are live in a single afternoon – a task previously outsourced to an agency because the practice team felt unable to build it themselves.

Who Is This For?

How to Use Workflow AI

Step 1: Open or create a workflow

Go to Automation, then Workflows. Open an existing workflow or click Create Workflow.

Step 2: Enable the Advanced Builder

In the top-left corner of the Workflow Builder, toggle on Advanced Builder mode.

This switches the canvas to the freeform layout and makes the Build Using AI button available.

Step 3: Open Build Using AI

Click Build Using AI in the left sidebar or toolbar.

The AI prompt interface opens alongside the canvas.

Step 4: Write a specific prompt

Describe the workflow in plain language. Include the trigger event, communication channels, timing between steps, conditional branches, tags, pipeline movements, and stop conditions.

The more complete the description, the less manual configuration is needed after generation.

Step 5: Review the generated workflow

Read through every node the AI placed on the canvas. Confirm the trigger type, action types, wait durations, and branching logic match your intention before configuring any details.

Step 6: Configure action details

Click into each action node and fill in the specifics – SMS message text, email subject and body, phone number, tag names, pipeline stages.

These fields are blank or placeholder after generation and must be completed before the workflow is functional.

Step 7: Refine with conversational follow-up

If the structure needs adjustment, describe the change in the Build Using AI interface rather than manually reconfiguring nodes.

“Add a second If/Else branch after the 24-hour wait that routes contacts with the tag ‘VIP’ to a separate SMS message” updates the canvas based on your instruction.

Step 8: Configure workflow settings

Open the Settings tab and configure Stop on Response, Allow Re-entry, Time Window, and Timezone.

These are not set by the AI and are critical to how the workflow behaves in production.

Step 9: Test and publish

Use Test Workflow with a test contact to verify every action fires correctly.

Check Execution Logs after the test. When confirmed, click Publish to make the workflow live.

How Does It Connect to HighLevel?

• Advanced Workflow Builder: Workflow AI is exclusively a feature of the Advanced Workflow Builder. The Advanced Builder must be enabled before Build Using AI becomes accessible.
• Workflow Version History: Every time a workflow is saved – including after AI generation – a version is recorded in Workflow Version History. If the AI generates something that needs to be discarded, you can restore a previous version without losing prior work.
• Tag-Based Automation: Workflows built with AI frequently use tag-based triggers and actions. The tagging infrastructure managed in Tag-Based Automation determines which tags are available to reference in AI-generated workflows.
• AI Email Generation: Use AI Email Generation to write the email content for workflow email actions after the AI has built the workflow structure. Both AI tools complement each other – Workflow AI builds the frame, AI Email Generation fills in the content.
• Workflow Builder and Automation Engine: The Workflow Builder and Automation Engine is the full system that Workflow AI operates within. Understanding the underlying trigger and action library helps you write better prompts and configure the AI-generated output more effectively.

Common Questions

What is Workflow AI in HighLevel?

A feature called Build Using AI inside the Advanced Workflow Builder that generates a complete automation workflow from a plain-language description. The AI creates the triggers, actions, wait steps, and conditional logic.

You configure the specific details and publish.

Where do I find Workflow AI in HighLevel?

Inside the Advanced Workflow Builder. Enable Advanced mode from the top-left toggle in any workflow, then click Build Using AI in the left sidebar to open the prompt interface.

What can I build with HighLevel Workflow AI?

Any workflow type – lead follow-up sequences, appointment reminders, onboarding flows, re-engagement campaigns, review request automations, pipeline-based sequences, and more. The AI builds the structural foundation and you configure the specifics.

Does HighLevel Workflow AI require the Advanced Builder?

Yes. Build Using AI is only available in Advanced Builder mode. Toggle Advanced mode on from the top-left corner of the workflow interface before looking for the AI generation option.

Does Workflow AI configure all the action details automatically?

No. The AI generates the structure – trigger types, action types, wait durations, and branching logic.

Message content, sender selections, tag names, pipeline stages, and workflow settings must all be configured manually after generation.

Can I edit the AI-generated workflow?

Yes. The generated workflow is a standard editable canvas workflow.

Add, remove, or modify any node. Rename actions.

Adjust timing. The AI-generated output behaves exactly like a manually built workflow.

Can I describe a workflow conversationally to the AI?

Yes. The prompt interface accepts natural language.

You do not need to use HighLevel terminology. Describe what you want in plain terms and the AI translates it into the appropriate triggers and actions.

Can I ask the AI to modify an existing workflow?

Yes. After generation, describe changes in the Build Using AI interface and the AI updates the canvas accordingly.

This works best for structural changes – adding branches, moving actions, inserting new steps.

How specific should my Workflow AI prompt be?

As specific as possible. Include the trigger event, communication channels, timing, conditional logic, tags to apply, and stop conditions.

A detailed prompt reduces the amount of manual configuration needed after generation.

Is Workflow AI available on all HighLevel plans?

Workflow AI is available in the Advanced Workflow Builder. AI features may require a configured AI subscription or credits depending on plan level.

Check the Advanced Builder in your account for the Build Using AI option.

To Wrap It Up

The Workflow Builder is one of the most powerful features in HighLevel and also one of the most time-intensive to use well. Building a 20-step workflow with conditional branches, multiple channels, and proper stop logic manually takes experience and time – even for people who know the platform well.

Build Using AI compresses the structural design phase dramatically. The part of workflow building that requires the most knowledge – which trigger to use, what action types to chain together, how to wire the conditional branches – is handled by the AI in seconds.

What remains is the part that requires your judgment: the actual message content, the business-specific details, and the settings that make the workflow behave correctly in production.

The prompting skill is worth developing. A prompt that includes the full logic of the workflow – trigger, channels, timing, branches, tags, stop condition – produces something close to production-ready after one configuration pass.

A vague prompt produces a skeleton that still requires significant manual work.

For agencies, the compounding benefit over time is real. Every standard workflow that starts as a Build Using AI draft rather than a blank canvas saves 15 to 30 minutes.

Across a client base of 20 accounts, each needing 5 to 8 standard workflows, that adds up to hours of reclaimed build time per onboarding cycle.

Here is how to get started:

1. Go to Automation, then Workflows and create a new workflow
2. Toggle on Advanced Builder mode from the top-left corner
3. Click Build Using AI in the sidebar
4. Write a specific prompt – include trigger, channels, timing, conditional logic, and stop condition
5. Review the generated canvas carefully before configuring anything
6. Use conversational follow-up to adjust the structure if needed
7. Click into each action node and fill in message content, sender details, and specific field values
8. Open the Settings tab and configure Stop on Response, Time Window, and Timezone
9. Test with a test contact, review Execution Logs, and publish when confirmed

The AI builds the frame. You build the content.

Together they produce a finished workflow significantly faster than either approach alone.