Highlevel AI Automation 2024 AI Awards: Features, Use Cases & Practical Guide

Highlevel AI Automation 2024 AI Awards refers to HighLevel's recognition as a finalist in the inaugural 2024 A.I. Awards, where the platform was selected in the Best AI-Powered Workflow Solution category.

HighLevel announced the recognition on September 17, 2024. The A.I. Awards, operated by The Cloud Awards organization, evaluates artificial-intelligence products and implementations from companies around the world. HighLevel reached the finalist stage specifically for its AI-powered workflow capabilities.

That distinction is important.

The accurate statement is:

HighLevel → 2024 A.I. Awards Finalist

in:

Best AI-Powered Workflow Solution

It should not be rewritten as:

HighLevel Won the 2024 A.I. Awards

unless a separate source supports that claim.

HighLevel later included the recognition in its 2024 year-end recap alongside other company awards and milestones.

The award is useful historically because it captured an early stage of a direction that became much more significant afterward:

Traditional Workflow Automation

↓

AI-Assisted Automation

↓

Conversational AI

↓

AI-Generated Workflows

↓

Autonomous AI Actions

For a broader explanation of HighLevel triggers, actions and workflow architecture, see our GoHighLevel Automation guide.

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The A.I. Awards launched in 2024 as an international awards program from the organization behind The Cloud Awards.

The program was designed to recognize innovation in artificial intelligence and machine learning across multiple industries and technical categories.

HighLevel's finalist announcement says the program attracted organizations from:

The broader awards program included 21 AI categories covering areas such as industry applications, natural-language processing, cybersecurity and advanced AI environments.

HighLevel competed specifically in:

Best AI-Powered Workflow Solution

That category aligns closely with the platform's automation strategy.

HighLevel was already known primarily as an agency-focused CRM, marketing and automation platform.

The 2024 AI recognition indicated that the company was increasingly trying to combine:

CRM Data

Automation

Artificial Intelligence

rather than keeping AI as a separate content-generation tool.

This distinction matters.

Basic AI might create:

Email Copy

A more integrated AI system can participate in:

Lead → Conversation → Qualification → Workflow → Booking

That is operational AI.

HighLevel's 2024 year-end recap highlights several developments that help explain why AI automation was becoming more central.

Among them was AI Employee, which HighLevel positioned as an always-available assistant capable of helping with activities such as answering questions and supporting appointment scheduling.

The company also continued expanding automation, funnel generation and AI-assisted marketing functionality during the year.

The larger direction was:

AI Should Do Work Inside the Platform

rather than:

AI Should Only Generate Text

The difference is significant.

In 2024, HighLevel's AI automation story was still developing around:

By 2026, users can work with a much broader set of AI automation capabilities.

These include:

Workflow AI Builder

Conversation AI Workflow Actions

AI Agent Actions

AI Extract Data

Ask AI Workflow Creation

and other AI-assisted workflow features.

The award recognition therefore makes more sense as an early milestone than as a description of HighLevel's current AI environment.

Before discussing AI, it helps to understand normal automation.

A standard HighLevel workflow might look like:

Form Submitted

↓

Create Opportunity

↓

Send SMS

↓

Notify Salesperson

↓

Wait

↓

Follow Up

Every step is predetermined.

If:

X happens

then:

Do Y

This is deterministic automation.

It remains useful even as AI becomes more powerful.

Many business rules do not require artificial intelligence.

For example:

Appointment Booked → Send Confirmation

There is little reason to ask AI to decide whether a confirmation should be sent.

Likewise:

Invoice Paid → Mark Customer

can usually remain deterministic.

AI becomes more valuable when the task requires:

A strong automation strategy uses both.

HighLevel's current Workflow AI Builder can create complete workflows from natural-language instructions.

Instead of manually selecting every trigger and action, a user can describe the desired automation.

For example:

Create a workflow that sends an email when someone submits a consultation form, waits one day, and sends an SMS if they have not booked.

Workflow AI Builder can generate the structure, after which the user can inspect and modify it.

The current version supports conversational refinements and multi-step changes.

That changes workflow creation from:

Know Every Trigger → Know Every Action → Configure Everything

toward:

Describe Goal → Generate → Review → Refine

AI generation should not eliminate QA.

The correct process is:

Prompt

↓

AI Generates Workflow

↓

Human Reviews Trigger

↓

Human Reviews Actions

↓

Test

↓

Publish

HighLevel's own documentation instructs users to review generated workflow structure and configuration before relying on it.

This matters because the AI does not automatically know every business rule.

Suppose a dental practice wants to recover leads who did not book.

A useful prompt could describe:

Trigger: Consultation form submitted.

Action: Create opportunity.

Action: Send confirmation.

Wait: Two hours.

Condition: Has appointment?

If no:

Send follow-up.

The AI can generate the starting structure.

The agency still needs to verify:

HighLevel expanded this concept further in September 2026.

Ask AI can now create workflows directly from a natural-language conversation.

The generated workflow appears in a live preview where users can inspect triggers and actions, request changes conversationally and then open the automation in Workflow Builder for final testing and publishing.

That creates another path:

Ask AI

↓

Describe Automation

↓

Preview

↓

Refine

↓

Open in Workflow Builder

↓

Test

↓

Publish

Another important evolution is the Conversation AI Workflow Action.

This allows a workflow to start an AI-powered conversation, wait for a contact response and route the workflow based on the answer.

For example:

Lead Submitted

↓

Conversation AI: "Which service are you interested in?"

↓

Wait for Reply

Then:

Service A → Branch A

Service B → Branch B

No Reply → Timeout

The workflow no longer needs every possible customer answer to be predefined manually.

HighLevel's current Conversation AI Workflow Action can incorporate information such as:

It can then evaluate the response and route the contact according to workflow branches.

This is a strong example of:

AI Interpretation + Deterministic Workflow

The AI understands the response.

The workflow determines what happens next.

HighLevel currently documents the Conversation AI Workflow Action across selected channels including:

Channel availability can differ between a specific workflow action and HighLevel's broader Conversation AI product.

Therefore, agencies should check the current action configuration rather than assuming every supported bot channel works identically everywhere.

Suppose a roofing company receives leads.

The workflow could begin:

Lead Form

↓

Conversation AI

Question:

What type of roofing project do you need help with?

The answer can be interpreted and routed.

For example:

Repair

→ Repair pipeline.

Replacement

→ Replacement pipeline.

Commercial

→ Commercial team.

This can reduce manual triage.

HighLevel now has an Appointment Booking Conversation AI workflow action that can interact with a selected calendar.

The AI can help contacts find availability and book an appointment, then route the workflow according to outcomes such as:

The flow can therefore become:

Lead

↓

AI Conversation

↓

Calendar Availability

↓

Appointment

↓

Confirmation Workflow

This moves AI closer to real operational execution.

Imagine a service business needs several pieces of information before assigning a lead.

Traditional workflow:

Form With 15 Fields

An alternative model:

Short Form

↓

Conversation AI

↓

Ask Follow-Up Questions

↓

Collect Information

↓

Route

This may create a more conversational customer experience.

It still requires careful configuration and testing.

HighLevel now also provides an AI Agent workflow action.

Unlike a simple fixed action, the AI Agent can decide how to complete a task using selected tools and plain-language instructions.

HighLevel describes it as useful when a workflow requires:

The AI can plan part of the execution itself.

Consider a process:

Find Contact

↓

Generate Booking Link

↓

Format Date

↓

Prepare Follow-Up

A traditional workflow could use separate actions for each task.

The AI Agent action can potentially handle multiple connected tasks according to its instructions and available tools.

That can reduce workflow complexity.

However, flexibility introduces another requirement:

Review and Monitoring

HighLevel currently identifies AI Agent as a premium workflow action that can incur additional execution charges.

That means agencies need to evaluate both:

Automation Benefit

and:

Cost Per Execution

A flexible AI action that costs money every time it runs should be used where the value justifies it.

Do not use premium AI for tasks a simple free workflow action can handle reliably.

Another practical AI automation capability is AI Extract Data.

This workflow action turns unstructured text into structured information that later workflow steps can use.

For example, a message might say:

Hi, I'm Sarah. My order number is 58321 and my preferred appointment is Friday afternoon.

AI Extract Data could be configured to identify:

Name → Sarah

Order ID → 58321

Preferred Time → Friday afternoon

Those values can then be used downstream.

Traditional automation works best when information already arrives in structured fields.

But customers often communicate through:

AI can convert that unstructured information into data the workflow can use.

The architecture becomes:

Human Language

↓

AI Extraction

↓

Structured Data

↓

Automation

This is one of the most practical AI use cases.

Customer writes:

My payment went through twice and I need someone to check the second charge.

AI can potentially extract or classify information such as:

Topic → Billing

Then the workflow can:

Assign Billing Team

↓

Create Task

↓

Notify Staff

This reduces manual categorization.

A new inquiry might say:

I need a website and Facebook ads but I'm not ready for CRM yet.

AI could identify:

Website Interest

Paid Ads Interest

CRM = Not Ready

Then automation can update relevant fields or route the opportunity.

The workflow responds to meaning rather than exact keyword matches.

The most useful architecture often combines several layers.

For example:

Facebook Lead

↓

Workflow Trigger

↓

Conversation AI

↓

Qualification

↓

AI Extract Data

↓

Update CRM

↓

Appointment Booking AI

↓

Sales Team

This is a much more sophisticated use of automation than:

Send Email After Form

The 2024 A.I. Awards finalist recognition can therefore be seen as an early milestone toward the architecture HighLevel now supports.

The recognition aged reasonably well because HighLevel continued investing heavily in AI automation after 2024.

HighLevel's 2025 year-end report says its AI tools became increasingly embedded in conversations, follow-up and execution rather than operating as isolated add-ons.

By the end of 2025, HighLevel reported:

These are company-reported platform figures, not independent performance benchmarks.

Still, they indicate substantial AI usage across the platform.

Suppose a local service business receives a new inquiry but the salesperson does not respond immediately.

A workflow could:

Lead Created

↓

Immediate Confirmation

↓

Conversation AI

↓

Collect Service Need

↓

Booking

↓

Notify Sales

The business gets a faster response without requiring staff to manually start every conversation.

Traditional process:

Lead Arrives at 11 PM

↓

Wait Until Morning

AI-assisted process:

Lead Arrives

↓

AI Acknowledges

↓

Answers Basic Questions

↓

Collects Information

↓

Books or Routes

↓

Human Follows Up

This can improve response coverage.

It does not mean the AI should handle every scenario.

Suppose a database has old leads.

The agency could trigger:

Inactive Lead

↓

Outreach

↓

AI Conversation

↓

Interested → Qualification

Not Interested → Stop

Needs Information → Continue Conversation

This creates more flexible reactivation than a rigid sequence alone.

AI automation is not only customer-facing.

An agency may use workflows internally.

For example:

High-Value Opportunity Created

↓

AI Summarizes Contact Context

↓

Create Internal Task

↓

Notify Account Executive

The sales rep receives better context before the call.

A useful decision framework is:

The rule is predictable.

Example:

Appointment Booked → Confirmation

Interpretation is required.

Example:

Read customer's explanation and determine intent

Risk or nuance is high.

Example:

The strongest system combines all three.

Every customer-facing AI workflow should answer:

When should AI stop?

A practical model is:

AI Handles Routine

↓

Confidence Low / Sensitive Topic / Qualified Opportunity

↓

Human

Without escalation rules, automation can create poor customer experiences.

Weak instruction:

Talk to the customer.

Better instruction:

Determine whether the customer wants residential repair, replacement or commercial roofing, collect the ZIP code, then route the contact to the appropriate branch.

The second provides:

Better instructions generally produce more controllable automation.

Before publishing:

Customer gives expected response.

Customer gives incomplete answer.

Confirm timeout.

See how AI reacts.

Confirm escalation.

Determine whether the contact can trigger the process again.

AI testing should include edge cases, not only the perfect conversation.

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HighLevel's AI Builder can generate automation quickly, but prompt quality matters.

Include:

What starts the workflow?

What outcome should occur?

When should actions happen?

What changes the path?

When should automation stop?

For example:

When a consultation form is submitted, create an opportunity in New Lead, send a confirmation SMS, notify the assigned salesperson, wait two hours, then send another message only if no appointment has been booked.

That is much more useful than:

Build follow-up workflow.

After generation, inspect:

AI can accelerate setup.

The account owner remains responsible for production behavior.

A common mistake is adding AI because it sounds more advanced.

Consider:

Add tag.

Use:

Add Contact Tag

not:

AI Agent determines how to add tag

when no decision is required.

Simple automation is:

Use AI when it provides actual value.

Some AI and premium workflow actions can create additional usage charges.

Therefore, evaluate:

Cost Per Execution

×

Monthly Execution Volume

For example:

$X × 10,000 workflow runs

can materially affect agency margins.

A workflow that saves a few seconds but runs thousands of times may not justify a premium AI action.

AI usage itself is not a KPI.

Do not report:

We ran 8,000 AI actions.

Measure outcomes such as:

Lead Response Time

Qualification Rate

Appointment Rate

Sales Opportunities

Support Resolution

The technology should improve the process.

Before AI:

1,000 Leads

↓

300 Responses

↓

80 Appointments

After AI:

1,000 Leads

↓

450 Responses

↓

120 Appointments

Now the comparison is meaningful.

You still need to account for other variables before attributing every improvement solely to AI.

Agencies can use HighLevel AI in two ways.

Improve agency operations.

Package AI automation as a service.

For example:

24/7 Lead Qualification & Booking System.

The technical stack may use:

But the offer should describe the business outcome.

Instead of:

We implement AI workflows.

consider:

We install a missed-lead recovery system for HVAC businesses.

Then define:

Missed Lead

↓

AI Follow-Up

↓

Qualification

↓

Booking

↓

Sales Team

Specific outcomes are easier to sell and fulfill.

The A.I. Awards recognition is particularly relevant because the category itself focused on AI-powered workflow solutions.

HighLevel was not recognized in that announcement for generic AI content creation.

It was recognized for the intersection between:

AI

and:

Workflow Automation.

That is still one of the platform's most strategically important AI use cases.

This deserves repeating because award claims often become exaggerated over time.

HighLevel's official September 17, 2024 announcement says:

Finalist

in:

Best AI-Powered Workflow Solution.

HighLevel's own year-end recap also describes the recognition as:

Cloud AI Awards: Finalist for Best AI-powered Workflow Solution.

Therefore, publishers should preserve the finalist wording.

Writing:

Award-winning AI workflow platform

can become ambiguous.

Writing:

Finalist in the 2024 A.I. Awards' Best AI-Powered Workflow Solution category

is precise.

Accurate wording improves:

It also avoids turning a real accomplishment into an unsupported claim.

Do not automate chaos.

First map:

Current Process

Then simplify.

Then automate.

A lead who books should not continue receiving booking messages.

Define escalation.

Give the AI a clear objective.

Test unexpected responses.

Simple rules should stay simple.

Premium AI actions may create usage charges.

Measure business outcomes.

Generation is not QA.

HighLevel changes quickly.

Use current documentation for current implementation.

AI automation has tradeoffs.

Customer messages can be ambiguous.

Poor instructions create inconsistent results.

An AI system using inaccurate business information can produce inaccurate responses.

Certain AI actions are premium.

An AI workflow may be harder to troubleshoot than a simple deterministic workflow.

Especially for:

HighLevel's current workflow landing experience allows users to:

Build Using AI

Browse Templates

or:

Create Workflow From Scratch.

This is useful because not every user needs the same building method.

A beginner may prefer:

AI Builder

An experienced automation architect may prefer:

Manual Builder

A repeatable common process may begin with:

Template

The best method depends on the workflow.

HighLevel's current Advanced Builder provides a visual freeform canvas for complex automations, including branching paths and collaborative workflow documentation features.

This becomes useful when automations evolve beyond simple linear sequences.

However:

More Visual Complexity ≠ Better Automation

Architecture should remain as simple as the process allows.

For advanced scenarios, HighLevel can generate JavaScript inside the Custom Code workflow action.

HighLevel distinguishes this from Workflow AI Builder:

That can help with tasks such as:

Custom Code remains a premium action and should be reviewed and tested before publishing.

Before adding AI to an agency workflow, answer five questions.

Example:

Leads wait too long for a response.

Because customer replies are open-ended.

After qualification or whenever the request falls outside scope.

Lead-to-appointment conversion.

This framework keeps technology connected to business outcomes.

Facebook Lead

↓

CRM

↓

Workflow

↓

Conversation AI

↓

Qualification

↓

AI Extract Data

↓

Update Opportunity

↓

Booking AI

↓

Appointment

↓

Human Salesperson

This illustrates what AI-powered workflow automation can now mean in practical terms.

The system is not:

AI does everything.

It is:

AI and deterministic automation each handle the tasks they are suited to.

The official HighLevel announcement says it was a finalist in the 2024 A.I. Awards for Best AI-Powered Workflow Solution. HighLevel's 2024 year-end recap uses the same finalist wording.

HighLevel announced its finalist status on September 17, 2024.

HighLevel was selected as a finalist in the Best AI-Powered Workflow Solution category.

The A.I. Awards are an international artificial-intelligence awards program operated by the organization behind The Cloud Awards. The program launched in 2024 and includes multiple AI-related categories.

Yes. Workflow AI Builder can generate end-to-end workflows from natural-language prompts and allow conversational refinements before testing and publishing.

Yes. As of September 2026, Ask AI can generate workflows, display them in a live preview, accept conversational changes and then open them in Workflow Builder for testing and publishing.

Yes. The Conversation AI Workflow Action can ask questions, wait for contact responses and route the workflow according to configured branches and conditions.

Yes. HighLevel currently provides an Appointment Booking Conversation AI workflow action that can check selected calendar availability and help contacts book available times.

AI Agent is a premium HighLevel workflow action that can make flexible decisions and use selected tools to complete multi-step tasks according to plain-language instructions.

Yes. AI Extract Data can convert unstructured text from sources such as messages, email bodies or webhook payloads into structured values that later workflow actions can use.

No. Predictable business rules are often better handled by standard deterministic workflow actions. AI is most useful when interpretation, flexible decisions or natural-language interaction adds value.

It depends on the feature. Some AI workflow capabilities do not use the older AI credit model, while specific premium actions such as AI Agent and Custom Code can create additional charges.

No. AI-generated and AI-driven automations should be reviewed, tested and monitored before being trusted in production.

Highlevel AI Automation 2024 AI Awards is an important historical search topic because the award recognition highlighted a direction that later became central to HighLevel's product strategy.

On September 17, 2024, HighLevel announced that it had been selected as a finalist in the inaugural A.I. Awards under the:

Best AI-Powered Workflow Solution

category.

HighLevel's own year-end report later repeated that status as a finalist, so the recognition should be described accurately rather than upgraded into an unsupported winner claim.

What makes the recognition more interesting today is what happened afterward.

HighLevel's automation environment expanded from:

Trigger → Predetermined Action

toward systems that can combine:

Workflow Rules

AI Conversations

Natural-Language Workflow Creation

Data Extraction

AI Agents

Today, Workflow AI Builder can generate complete automations from plain-language requirements.

Conversation AI can participate directly inside workflows and route contacts according to what they say.

Appointment Booking Conversation AI can guide contacts toward available calendar times.

AI Extract Data can turn free-form language into structured workflow variables.

And AI Agent actions can perform more flexible multi-step work where simple predetermined actions are not enough.

But the practical lesson is not:

Put AI Into Every Automation.

It is:

Use deterministic automation for predictable rules.

Use AI for interpretation and context.

Use humans for judgment and high-risk exceptions.

For example:

Lead Submitted

↓

Workflow Creates Opportunity

↓

AI Understands Customer Need

↓

Workflow Routes Lead

↓

AI Helps Book Appointment

↓

Human Salesperson Takes Over

That is a stronger use of AI than simply replacing every normal workflow action with an AI equivalent.

The 2024 finalist recognition therefore matters less as a trophy and more as an early signal.

HighLevel was moving toward a model where AI would no longer sit beside automation.

It would increasingly become part of how automation itself was built and executed.

For agencies, the opportunity is to use that capability carefully:

Start With the Business Problem

↓

Map the Customer Journey

↓

Automate Predictable Work

↓

Add AI Where Interpretation Adds Value

↓

Create Human Escalation

↓

Test

↓

Measure Business Outcomes

That approach turns AI automation from an attractive feature into an operational system that can actually support leads, appointments, customer communication and agency fulfillment.

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