The Ultimate Guide to AI Marketing Automation for 2024

AI has taken the world by storm. 

Companies are rushing to implement artificial intelligence technologies in the hopes of gaining a competitive edge.

We hope that this guide to AI marketing automation will help you harness the power of artificial intelligence to grow your business!

Beware of the AI Hype Cycle!

The field of artificial intelligence is notorious for its hype cycles.

A well-known computer programmer Glyph wrote a great article called “A Grand Unified Theory of the AI Hype Cycle” in which he goes over the 13 stages of the AI hype cycle. 

We would summarize the process that he described like this:

1. There’s a big technological breakthrough that gets blown out of proportion.
2. Investors start pouring money into everything that is even remotely related to artificial intelligence because they believe that the big breakthrough will revolutionize everything. 
3. After several years it becomes clear that the big breakthrough was overhyped and the funding dries up.
4. At that point, “AI winter” sets in until the next big breakthrough, when the cycle starts again. 

People who aren’t familiar with the history of AI and aren’t old enough to have witnessed the previous cycles are prone to falling for the hype because they aren’t aware that what they are seeing has happened before. 

Glyph pointed out that neural networks and symbolic reasoning went through a hype cycle in the 1950s, theorem provers in the 1960s, expert systems in the 1980s, fuzzy logic and hidden Markov models in the 1990s, and deep learning in the 2010s.

He emphasized that each of these breakthroughs produced genuinely useful technologies, it’s just that they didn’t live up to the unrealistic hype cycle expectations.

It seems quite likely that the same fate awaits large language models that are currently being touted as a miracle technology that will revolutionize everything. 

If you want to use artificial intelligence to grow your business, you need to be aware of AI hype cycles, keep a cool head if you find yourself in the midst of an AI craze and take time to understand the capabilities, limitations and practical applications of AI technologies!

Here’s What You Need to Know About AI

What is AI?

The term “artificial intelligence” – “AI” for short – refers to a machine’s ability to perform cognitive tasks. 

There’s an entire field of computer science dedicated to researching, designing, and improving artificial intelligence systems.

The greatest breakthroughs in the recent decade have come out of its machine learning subfield, particularly the area of research known as deep learning. 

Business consultant Eric Siegel who helps companies deploy machine learning has argued that the term “AI” is misleading because in the public consciousness, it’s inextricably linked with the concept that computer scientists call “AGI”.

“AGI” stands for “artificial general intelligence”: sentient machines that are either comparable to humans or surpass us in terms of general intelligence.

Here’s how he put it in his Harvard Business Review article “The AI Hype Cycle Is Distracting Companies”:

In his view, it would be better to drop the vague term “AI” altogether and simply use the more precise term “machine learning” when talking about technologies that implement it. 

We believe that adopting this approach when it comes to your own use of AI can help you stay grounded in reality, manage expectations, and make better business decisions. 

Get in the habit of using more precise computer science terms when referring to specific technologies and you will have a much easier time seeing past the AI hype!

What is Generative AI?

Generative artificial intelligence is a type of deep learning system that is trained on a lot of data and “learns” to produce novel content in response to user queries. 

Note that “content” in this context can mean:

• Text (e.g. ChatGPT)
• Visual content (e.g. Dall-E)
• Audio content (e.g. AudioCraft)
• Video content (e.g. Sora)
• Computer code (e.g. Copilot)

What are Large Language Models (LLMs)?

Large language models are a type of generative AI that works with textual data and can generate novel text in response to user queries. 

LLMs are moving beyond just text, though. For example, ChatGPT also has vision abilities (can recognize what you are showing it) and speech abilities (can talk to you in audio). 

What is the Black Box Problem?

Large language models are black boxes:

Computer scientists who created them can see the data that goes into them and the data that comes out of them but they cannot see what is happening inside of them.

Consequently, no one really knows how LLMs work. 

This is called the black box problem. 

What are the Practical Implications of the Black Box Problem?

The black box problem isn’t just some interesting computer science trivia.

It has very real, practical implications when it comes to using large language models in the business context:

Large Language Model Output Cannot be Trusted

LLMs are known to “hallucinate: if you ask them a question, they might make up the answer. 

And since no one knows how they generate their output, to begin with, computer scientists cannot solve hallucinations and ensure their accuracy. 

What this means in practice is that you cannot use large language models for research, content creation or data analysis if you don’t have the means to then manually verify their output.

That is the primary reason why complete AI marketing automation is not possible at this point in time.

In theory, you could create an entirely automated workflow where ChatGPT would produce content for your business that would then be automatically published on your social media, your company blog, and your email newsletter.

In practice, though, that content would be riddled with hallucinations and it would be just a matter of time until people noticed that you are publishing inaccurate, misleading, or even outright dangerous information. 

Needless to say, that would be terrible for your brand image!

Large Language Model Behavior is Unpredictable

Since no one really knows how large language models generate their output, computer scientists cannot ensure that their behavior remains consistent, appropriate, and in line with social norms.

Say, ChatGPT once had an “episode” where it started spouting gibberish at its users completely out of nowhere.

For example, one person shared a screenshot of this incoherent response to a prompt asking about the Jackson family’s history:

While one should always remain skeptical of the veracity of any given screenshot since they are so easy to fake, OpenAI did acknowledge the issue in response to a flood of complaints from their users and scrambled to fix it. 

It’s also worth noting that incoherent responses are not the worst-case scenario when it comes to unhinged AI bot behavior. 

Back in 2016, Microsoft released its AI Twitter bot Tay but then had to take it down just 16 hours after its launch because it started tweeting offensive stuff, most notably denying the Holocaust. 

Amusingly, the company managed to accidentally release it again, which led to Tay tweeting about smoking pot in front of the police. Needless to say, it was quickly shut down, for good this time.

While large language models such as ChatGPT are much more advanced than the early AI bots like Tay, you never know what they might say. 

In practice, this means that using LLM-powered chatbots as shopping assistants or customer support agents is out of the question because they might start acting crazy out of the blue. 

Never allow them to interact with your customers directly!

Large Language Models are Vulnerable to “Jailbreaking”

In this context, the term “jailbreaking” means getting a large language model to do something that it’s not supposed to do.

That can mean getting it to say offensive stuff, producing misinformation on controversial subjects, giving you instructions for manufacturing illegal substances, and so on. 

Here’s how security expert Dr. Tim Muller explains jailbreaking with a real example of getting ChatGPT to produce flat earth misinformation:

Since no one really knows how LLMs work, computer scientists cannot predict their vulnerabilities in advance, which means that these apps are easy to jailbreak. 

In fact, there are a lot of AI enthusiasts out there who jailbreak LLMs as a hobby. They get to work the moment a new model is released and start posting screenshots on Twitter once they successfully jailbreak it, which typically happens in a matter of hours.

This is another reason why you should not use LLM-powered chatbots as shopping assistants or customer support agents: they are vulnerable to jailbreaking, especially to the so-called prompt injection attacks. 

That makes them a serious security risk!

Prompt Engineering: the Secret to Getting the Most out of Large Language Models

What is Prompt Engineering?

Prompt engineering is the process of designing large language model prompts in a way that elicits the desired output.

Keep in mind that the quality of the prompt will determine the quality of the output. Garbage in, garbage out!

Jeff Su’s Prompt Engineering Formula

It’s probably safe to say that everyone who uses large language models on a regular basis eventually develops their own unique prompt engineering style.

But if you don’t have much experience with these tools yet, we recommend starting with Youtuber Jeff Su’s prompt formula:

It consists of six prompt components ranked in descending order of importance:

1. Task: mandatory 
2. Context: important 
3. Exemplar: important
4. Persona: nice-to-have
5. Format: nice-to-have
6. Tone: nice-to-have

Let’s take a closer look at each of these prompt components:

#1: Task

This component is the most important one.

If you remove everything else except for the task, the LLM will still attempt to complete it despite the vagueness of the prompt. 

Jeff Su advises always starting the task sentence with an action verb that describes the task: “write”, “analyze”, “generate”, etc.

He also recommends clearly articulating what is the end goal of that task!

#2: Context

Jeff Su advises asking yourself these three questions in order to figure out what information you should provide as context:

1. What is your background?
2. What does success look like?
3. What environmental conditions need to be taken into account?

This should help you provide the LLM with enough context to generate a useful response. 

#3: Exemplar

LLMs tend to perform better if the prompt includes an example. 

You can provide concrete examples of the type of output that you want by uploading or copy-pasting a job resume, an email, a sales page, etc.

Alternatively, you can provide a general direction, such as a specific framework that the LLM should follow in their response. 

#4: Persona

Prompt engineering has a role-playing aspect to it: 

If you tell the LLM that it is an expert in a particular field, you are more likely to get helpful answers to questions related to that field. 

Jeff Su advises thinking of someone who you wish you could have immediate access to while working on this task and then telling the LLM that it is that person.

Note that LLMs can take on generic personas (e.g. a hiring manager), personas of famous individuals (e.g. Warren Buffet), and even personas of well-known fictional characters (e.g. Batman).

#5: Format

You also want to specify how exactly you want the end result to look like. 

Simply describe how the LLM should format its response: a 500-word article, a summary with three key takeaways, a spreadsheet with specific categories, etc. 

#6: Tone

You can also ask the LLM to use a specific tone in its response: casual or formal, serious or playful, etc.

Jeff Su advises telling it the feeling that you are going for if you can’t remember the exact adjective to describe the tone that you have in mind. 

Prompt Example: Work Email

Here’s a prompt example from Jeff’s video that includes all six prompt components:

If you want to see how prompt engineering affects the LLM output, you can start with just the task and then add other components one by one. That will help you understand why well-designed prompts are so important!

What Marketing Tasks Can You Automate With AI?

#1: Coming Up With Ideas

Large language models can be a great brainstorming tool.

If you know that you want to make money online but aren’t sure what to sell, you can ask them to help you come up with product or service ideas.

And if you already have a product or service, you can ask them to help you come up with ways to turn it into an irresistible offer. 

Finally, if you already have an irresistible offer that you want to promote, you can ask them to help you come up with ideas for marketing campaigns.

#2: Developing a Marketing Strategy

You can also ask large language models to create a marketing strategy for your business.

The more parameters you include in your prompt, the more viable that marketing strategy is going to be.

So make sure to specify things like the timeframe that you have in mind, the end goal that you hope to achieve, the resources that you have at your disposal, etc. 

Ideally, you want to get not just a high-level strategy but also a step-by-step action plan that tells you what you need to do every day in order to grow your business. 

Just make sure to fact-check any claims that large language models might make, exercise common sense when evaluating their suggestions, and rely on your own judgement regarding what to implement and what to discard. 

Keep in mind that in all likelihood, LLMs are stochastic parrots that don’t understand what they are saying, so you should never outsource your decision-making to them!

#3: Producing Marketing Content

Once you have a detailed marketing strategy, you can ask large language models to help you produce marketing content such as:

• Social media posts
• Blog articles
• Video scripts
• Lead magnets
• Ad copy
• Landing page copy
• Email copy
• Sales copy

…etc. 

Since large language model output cannot be trusted due to hallucinations, it’s important to treat the content that they produce as the first draft, not the final one.

Once you have that first draft, you need to manually edit it, fact-check it, and proofread it before you publish it!

#4: Producing Marketing Visuals

Once you have your marketing content, you can produce visuals for it with text-to-image generators.

That might include:

• Social media images 
• Blog article images 
• Lead magnet images
• Ad images
• Landing page images
• Sales page images

…etc. 

We want to caution against using popular tools like Dall-E, Midjourney, and Stable Diffusion for this, though. Why?

Because with some of these models, it’s unclear what data they have been trained on while with others, it’s already known that they were trained on unlicensed data. 

There are obvious ethical issues with training commercial text-to-image generators on the works of various visual artists without their consent.

Moreover, there are also potential legal implications of this questionable business practice that are important to consider. 

There have already been several class-action lawsuits by artists against the companies behind these tools.

As the law evolves to accommodate the advancements in artificial intelligence, the use of images generated with models that have been trained on unlicensed data might become illegal. 

That’s why we advise playing it safe and only using text-to-image models that have been trained on licensed data, such as Adobe Firefly. 

#5: Building Sales Funnels

We believe that the best way to maximize the ROI of your marketing efforts is to build a sales funnel for your business.

You can do that with ClickFunnels: 

1. Use our AI sales funnel builder to create a sales funnel. It only takes 10-15 minutes!
2. Edit your sales funnel with our drag-and-drop page editor.
3. Optimize your sales funnel for conversions with our A/B testing functionality.

This can help you make the most out of the traffic that you generate with your marketing campaigns!

#6: Marketing Personalization

Machine learning software can analyze vast quantities of customer data such as browsing behavior, purchase history, email marketing metrics, etc., make predictions based on the patterns that it uncovers, and personalize marketing in a way that is optimized for conversions.

You know how Amazon sends you personalized product recommendation emails, shows you personalized recommendations based on your purchase history, and makes suggestions based on what other people with similar interests have bought?

Now thanks to the advancements in machine learning, you can do the same in your business!

#7: Analyzing Business Data

You can also use machine learning software to analyze your business data.

That can help you:

• Uncover various optimization opportunities and enable you to get things done cheaper, faster and more efficiently. 
• Identify customers that are at a high risk of churning with predictive analytics and then take pre-emptive measures to retain them. 
• Improve the accuracy of your sales forecasts, set realistic sales targets, and stay on track until you reach them.

Consequently, machine learning data analysis might enable you to reduce your customer acquisition costs, increase customer lifetime value, and maximize both revenue and profit!

What Should You Include in Your AI Marketing Stack?

We would argue that a basic AI marketing stack should include:

1. A large language model
2. A text-to-image generator
3. An all-in-one marketing platform

Here are our recommendations:

Large Language Model: ChatGPT

ChatGPT only costs $20/month at the time of writing.

That will give you access to GPT-3.5, GPT-4, and the latest GPT-4o version which is considered to be state-of-the-art. 

You will get access not just to the chat functionality but also to additional features such as file uploads, web browsing, data analysis, and vision. 

The paid plan includes access to the OpenAI’s text-to-image generator Dall-E. 

There’s also a free plan that currently uses GPT-3.5 and includes limited access to GPT-4o.

Text-to-Image Generator: Adobe Firefly

As we discussed previously, we recommend using text-to-image generators that have been trained on licensed data and therefore do not present legal risks related to copyright. 

We would argue that Adobe Firefly is the best option right now. Not only has it been trained on licensed data, it’s also easy to integrate with Adobe’s other tools such as Photoshop. 

At the time of writing, there’s a free individual plan, paid individual plans that range from $4.99/month to $59.99/month, and a business plan that costs $89.99/license. 

You can also contact the company if you want to learn more about their custom enterprise plan. 

An All-in-One Marketing Platform: ClickFunnels

We already discussed the funnel building functionality of our software, including our AI sales funnel builder that enables you to create sales funnels for your business in just 10-15 minutes. 

Our software also features a website builder, an e-commerce store builder, an online course builder, a membership site builder, an email marketing functionality, a customer relationship management system, advanced business analytics, and more.

ClickFunnels is a complete marketing platform that has everything you need to launch, manage, and grow your business!

Try ClickFunnels Risk-Free!

Let’s be real:

We are biased in favor of our software. 

So we understand if you take what we say about it with a grain of salt.

Fortunately, we have a free 14-day trial, so you don’t have to take our word for it. 

You can go and see ClickFunnels for yourself without any risk!

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