How AI SaaS is Transforming Marketing and Healthcare

American folklore has it that John Henry was the best railroad tie layer in the country.

In competition, he could lay railroad ties two to three times faster than others. 

But along came the steam engine. 

The steam engine could lay railroad ties without manual labor, exhaustion, or a lunch break.

So the railroad company decided to test John Henry and the steam engine. A battle pitting man versus machine.

The two raced to lay the most railroad ties in a day. 

In a neck-in-neck finish, John Henry defeated the steam engine. But before he could claim victory as the superior railroad tie layer, he collapsed and passed away from a heart attack.

However, what if John Henry worked alongside the steam engine rather than competing? How much faster would they be?

This folklore illustrates a point about humans working alongside machinery. 

Humans will always be better at particular tasks. Artificial Intelligence (AI) can’t understand nuances in which humans excel. But the truth is AI can excel in several areas like automation, data analytics, and problem-solving. 

Now, apply this to today in two areas where AI SaaS applications can be game changers: marketing and healthcare. 

Whether or not you’re tech-savvy, there needs to be a resource exploring what’s happening right now and what the future holds. Especially in marketing and healthcare.

By the end of this article, you’ll learn an AI use case you hadn’t heard of — we guarantee it. 

Which brings us back to the story. 

If the parable about John Henry wasn't enough to convince you that we'll all soon be working alongside AI...

This article will.

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What are the benefits of AI SaaS? 

AI software will revolutionize the way we do our jobs and think about solutions to problems. 

Before we get into the nitty-gritty explanation of how AI software works, we need to briefly cover why this information is practical to you. Even if you don't work in marketing or healthcare, AI software is probably a part of your workplace. 

And the benefits of smart automation will help you:

• Automate menial tasks that sap productivity
• Integrate with software you're already using
• Maximize efficiency and workflows
• Predict behavior
• Automate analytics reporting
• Incorporate automated personalization 
• Improve user experience

Regardless of your industry, job title, or expertise with computers, an AI time-saving solution exists. For instance, we’ve included an AI mechanism that helps SaaS companies drastically reduce churn rates. And reducing churn is significantly less expensive than acquiring new customers.

In addition, the primary benefit of AI implementation is that it enables you to stay ahead of your competitors. And as AI becomes more mainstream, you want to be at the forefront of change to reap the most benefits. However, some argue the risks outweigh the benefits. 

The risks associated with AI

Many people have noted that AI is not without flaws and isn’t necessarily a godsend to any industry. As with any new technology, there are costs and tradeoffs to every decision. 

One of the prevalent downsides of AI advancement is scamming. Now scammers are using AI to automate their initiatives, sometimes even automating phone calls with AI voices selling people services. On the contrary, there is a countermovement against these scammers by startups and large corporations including Apple. For example, you can turn on “mute potential scam calls” in settings which helps alleviate the issue. 

Of course, you can find negative implications in nearly any situation. And with each negative comes a solution. For now, let’s focus on understanding the underlying mechanisms behind AI.

What Is AI?

Artificial intelligence (AI) is the process of programming a computer to make decisions for itself based on inputs, experience, or experimentation. 

The term AI was first coined in 1956 by Dartmouth professor John McCarthy. But that doesn't really help us understand AI. 

A simpler way to define AI would be this: a computer's ability to mimic human decision-making and task completion without human intervention. 

Understanding the mechanism behind the AI programs allows you to use them more effectively. 

For example, let's think about a system like chess. Currently, the highest-rated chess player would be crushed by a computer. Yet it's with the assistance of computers that the world champion could even attain his world-record rating. 

And to learn from computers, chess players had to understand how computers thought about chess positions. 

In other words, they had to learn the mechanisms behind the process itself. 

But not all AI works the same, right? 

Are there different types of AI?

The short answer is yes. You can classify AI and machine learning in several ways.

Essentially, AI is a collection of systems with differentiating classifications based on particular objectives. 

What is most useful (and easy) to understand is how these machines adapt, learn, and analyze datasets. 

     1. Machine Learning 

It makes the most sense to start with machine learning because it's an integral process to nearly all AI systems. Without machine learning, AI would be unable to adapt and improve.

Probably the most relevant example of machine learning is the predictive text feature on Google search and Apple iPhone messages. 

What differentiates machine learning from other AI types AI is that it doesn’t receive detailed instructions from humans — only structured data. And a program may have one or multiple goals. 

That means the machine must learn from experimentation, data trends, and trial and error. Machine learning is an ongoing process. Developers can input parameters to ensure the program increases its precision and accuracy. 

And unlike humans, machines don't need to sleep; they just continue learning. 

What's most practical to understand are the different objectives of machine learning. Learning about the objectives will allow us to better understand what’s happening in marketing and healthcare. 

• Predictive: Based on past data, this system will predict the probability of an event, like a customer making a purchase. You can see how this might be useful in marketing, which we'll discuss later!
• Prescriptive: A prescriptive system must predict what might happen but will also recommend a course of action. And if you think this might be useful in healthcare, you'd be 100% correct. What if AI software could diagnose patients based on millions of past data from other patients?
• Descriptive: Descriptive systems offer conclusions based on previous data. For instance, AI marketing software can identify trends and generate reports. 

Further, we can identify how these AI programs are invaluable. 

Returning to the chess analogy, the computer allowed top players to look at positions in a different light. Specific moves that were never considered suddenly appeared to be the best move.

What if there are specific "moves" we aren't able to see in healthcare or marketing? AI might be the catalyst we need to find cures in healthcare, test new marketing initiatives, or unlock further creativity. 

An additional type of AI programming adds to AI’s benefits and uses.

     2. Deep Learning 

Like machine learning, deep learning relies on data instead of human intervention. However, deep learning takes it a step further by imitating the workings of a human brain. 

Nope. Not joking. 

The goal of deep learning is to literally recreate the human brain in an artificial form. Where machine learning excels at understanding structured data, deep learning excels with unstructured data. 

Unstructured data includes images, videos, and natural language processing (NLP). As a result, deep learning applications are far wider than standard machine learning.

Additionally, the benefits of deep learning are two-fold. First, it offers a much higher level of accuracy when the data set is large enough. Of course, if an unstructured set contains minimal data, machine learning will be more precise. 

Why is that important? 

Let's say you're a hospital using AI to predict patient outcomes. An 80% accuracy rate might seem decent. However, a 95% versus 80% accuracy could mean the difference between life and death.

The second advantage of deep learning is its ability to learn abstract concepts through pattern recognition, like a human brain. Some of your favorite algorithms are using this technology (think Netflix, Instagram, TikTok, and YouTube). And in marketing, thinking like a human would help in scenarios with chatbots and other customer service software. 

Did you know that performing reCAPCHTA's is helping deep learning AI systems identify bridges, animals, and more? 

If you have an acute eye, you may have noticed that reCAPCHTA's have increased in difficulty over the years. At first, it was a mix of obfuscated letters and numbers. Now it's almost exclusively geographical pictures. 

And if you're thinking about practical application, then the field of radiology might have popped into your mind. After all, wouldn't it be amazing if an AI system could identify a tumor on an MRI? 

The answer is yes. Companies are already working on this application. 

And there are even more types of AI coming to or already in the marketplace. 

     3. Narrow AI

Narrow AI is typically the most common form of AI and is included in many of the systems we've discussed. 

Also known as weak AI, narrow AI systems are created for specific tasks. For example, if you were to ask Siri about the weather, it would give you an accurate response based on your location. 

However, if you ask her about something else outside of its scope, you won't get a response at all (or a very inaccurate one). 

Narrow AI serves the role of automating tasks that humans do. It also marginally improves itself each time. 

The more advanced the task is, the more time it takes to perfect it. For example, a healthcare image recognition system relies heavily on narrow AI. Even with thousands of datasets per day from hospitals, perfect accuracy hasn’t been achieved. But because narrow AI continues to improve itself, though slowly, its accuracy also improves over time. 

     4. General AI

If you've read Isaac Asimov's "The Last Question," you're likely familiar with general AI.

General AI, also known as Strong AI or artificial general intelligence (AGI), is the Holy Grail of AI development. This is the kind of system that could pass the Turing test with ease. In other words, it would be difficult to distinguish between humans and machines. 

There isn’t a need to speculate about general AI; however, science fiction authors like Charles Stross and Isaac Asimov do an excellent job. 

So here are the facts. 

To date, no system has been able to achieve AGI. 

Why?

When asked about the barrier to AGI, Dr. Ben Goertzel emphasized, "10 years ago, the biggest issue was lack of funding for AI. Now the biggest issue is lack of funding for serious AGI approaches, as opposed to narrow AI systems that mine large numbers of simple patterns from big datasets, like most current deep neural net systems."

According to Goertzel, our society is on the "brink" of achieving general AI. Because of the shift from narrow AI to the development of deep learning and AI neural networks, the idea of benevolent AGI is on the horizon. 

     5. Robotics

Robotics is one of the first types of AI to pop into your head when you think about conscious AI, which is why it’s positioned last on the list. Remember that a "conscious, aware AI" would mean combining several classifications of AI with robotics to complete tasks. 

One of the more rudimentary examples of robotics is the self-sufficient AI vacuum. There isn't much else to discuss regarding robotics in today’s marketplace. However, the potential of robotics combined with other AI classifications makes the use cases endless. AI could replace many jobs at fast food restaurants, grocery stores, and other places where machines are already used.

Plus, robotics are already being used with great success in healthcare, which we’ll discuss later on. 

How AI SaaS is transforming marketing

AI in marketing is now worth $17.46 billion, according to the Global Marketing Report.

AI SaaS has become the new norm in marketing. If you're not utilizing this software, it's likely your competitors are already ahead of you. 

And it makes sense. AI systems are simply better at analyzing data and automating tasks than humans. 

     1. Data-Driven Insights for Marketing

As we previously noted, machine learning is centered around data. The more data, the better for having a robust AI. And with modern technology, we have an unprecedented amount of user data that machine/deep learning and narrow AI systems can crunch.

This allows companies to be proactive instead of reactive with their marketing efforts. 

In other words, they can anticipate the needs and wants of their customers and deliver relevant content before the customer even knows they need it. 

Plus, AI is more effective at churning data into useful predictions, prescriptions, and personas. 

For instance, companies like Amazon and Target offer customers coupons and other product recommendations anticipating their needs. These "recommended sections" are now found in nearly all online stores, your Netflix feed, and podcast apps. 

Additionally, email service providers (ESPs) like Klaviyo, ActiveCampaign, Zembula, and Moveable Ink incorporate AI into their platforms. Predictive analytics, automated A/B testing, and individualized (not segmented) emails top the list of AI features these platforms offer. 

Now we've learned how data-driven marketing can increase revenue. So how does it decrease costs? 

One of the most effective ways to reduce costs (right now) using AI marketing in SaaS startups is churn prediction, according to Dataiku.

Let's assume your business has a monthly churn rate of 1% – the rate at which you lose subscribers, customers, or entities that stop doing business with you.. Churn rates compound so that 1% compounds into 12% by the end of the year.

Because it costs significantly more to acquire new customers (compared to retaining them), companies need to dial in on reducing their churn rate. You'll avoid financial holes, keep more customers, and have a predictable growth rate.

AI SaaS can help you by constantly monitoring user behavior, segmenting audiences for increased personalization, and creating churn scores.

With advancements in machine learning and more data volume, the results can only improve.

Currently, you'll need to hire data scientists to manage these AI systems. But if general AI makes its way to marketing, those costly data scientists may become obsolete.

     2. Automated Marketing Solutions (and personalization) 

As a freelancer, one of my favorite examples of the use of AI is personalized, automated cold email outreach. Although it may not be the most impressive form of AI, it's highly pragmatic. 

The software can save freelancers and marketing agencies (or any business owner) hours per day, which will compound. And if your employees are currently completing these tasks, their time can be used more efficiently too. 

Above all, automating monotonous (yet vital) tasks in any industry is highly beneficial to your workforce and your bottom line. 

We've seen AI further personalize and automate our marketing efforts through chatbots. 

Another AI process you may not be aware of is dynamic pricing. AI can automatically adjust pricing for products and services using data analysis. 

According to a study done by Minderest, Amazon offers price changes of around 20% when competitors offer discounts and promotions. Factors like demand volume, stock volume, specific days of the year, total product impressions, and other data analytics go into dynamic pricing — a process better suited for AI and not humans. 

All in all, dynamic pricing is best for the consumer and for Amazon. It helps keep inventory moderate while maintaining mutually beneficial prices. 

So, what can we expect the future of AI marketing to hold? 

Although general AI applications are more logically applied to healthcare and hospitality, AGI would benefit marketing in a few ways. 

First, chatbots, digital assistants, and other forms of customer service would be streamlined by AGI. They'd be highly efficient, cost-effective, and beneficial for companies and consumers. 

Additionally, what if your CMO or data scientists were highly adept AGI systems? You might think  that's too advanced for where we are now. 

But with AI’s uses in healthcare, that possibility is becoming more of a reality.

AI has revolutionized the way we think about Healthcare

Healthcare is one of several industries where AI is in its beginning stages, similar to the arts and hospitality industries (think AI imaging tools or robots bringing your food to you).

Butthe use of AI in the healthcare industry was valued at $7.9 billion in 2021. However, it's on pace to reach $201.3 billion in 2030. That's an increase of 2450%. 

When you extrapolate, the forecast makes sense. Technologies like AI diagnosing patients or providing preventative care are becoming more realistic daily. 

For instance, the rate of doubling medical knowledge was every 73 days in 2020. That number was every 50 years in 1950 and every seven years in 1980.

In 2022, that number is closer to one-and-a-half months and will only continue to decrease with AI integration. 

We've seen a few different applications of AI in healthcare so far, primarily in upper management and some in treatment.

     1. Administrative and diagnostic use cases

First, the benefits of automating administrative tasks are numerous. Clinicians deserve to spend more time helping patients rather than dealing with data, right? 

We've seen AI-powered software help with transcription, claim processing, fraud detection, and population health management. In addition to the time saved, these systems are often more accurate than humans. They don't get tired and can process and store data faster too. 

AI is also transforming how pharmaceutical companies operate. It's being used in target discovery, clinical trials, and target validation. 

In short, AI is making drug development cheaper, faster, and more effective. We can expect to see new cures and treatments in the coming years. 

AI is also providing decision support for clinicians. This includes identifying at-risk patients, providing personalized recommendations, and monitoring clinical events. 

For example, AI image recognition is used for both X-Rays and MRIs. The software works similarly to reCAPTCHA, as it's taught through leveraging millions of images as past experience, using machine learning. 

In one case study, researchers used AI to predict sepsis (a life-threatening condition) 12 hours beyond onset in ICU patients with high-predictive accuracy. In another example, AI was used to diagnose a form of leukemia with 98.38% accuracy. 

Right now, there are too few clinical studies available to offer a sound conclusion on the current state of AI diagnosis. More studies need to be performed. 

However, the potential is there for AI to serve as a second opinion or even a primary care physician in some cases.

     2. Treatment use cases 

But AI is already being used in some routine healthcare tasks. First, robots are used more and more frequently in hospitals for patient transport, disinfection, and even surgery. 

For instance, robots have been assisting in operating rooms since the 1980s. And currently, systems are being developed (and already in practice) for robots to take over medical situations where AI precision supersedes human skill.

Another example of AI being used in treatment is CT scans. Following a stroke, patients undergo a scan where computers are trained to identify issues. By saving time in analysis, the risk of a brain complication decreases. 

As all medical clinicians know, preventing a disease is better – less costly for everyone and less damaging to patient health – than having to cure one. With the use of AI predictive analytics, clinicians can anticipate the development of diseases like cancer or heart disease and help prevent the disease rather than having to treat it later. 

For example, researchers at the Georgia Institute of Technology created a model using AI that predicts cancer with 91% accuracy. Again, it should be noted that one study is not indicative of an all-knowing AI entity — just steps in the right direction. 

But AI is also affecting how we look at cures to respective diseases. With AI already saving thousands of lives in hospitals through predictive analytics and diagnosis, let's explore the ways AI will be used to discover new cures. 

One of the original startups founded with the above premise is Pharnext. They launched a Phase III study in 2021 (not yet peer-reviewed) showing outstanding results in curing Charcot-Marie-Tooth disease (CMT). 

This is one of the first positive results from AI formulating cures for diseases based on its knowledge of chemical make-ups. Instead of trying new drugs, the AI seeks to combine compounds of drugs based on previous data deemed successful by humans. 

Essentially, Pharnext is taking the first step toward finding cures with AI. 

Lastly, researchers around the world are also driving new approaches to AI discovery. At Michigan State University, scientists are exploring new ways to use old drugs. Using old drugs would reduce costs and benefit both producers and consumers. 

The Bottom Line

We are on the brink of another technological revolution — this time with AI. And although some argue AI integration comes with risks, we’re highly adaptable as humans. Solutions will always follow problems. Plus, the benefits of AI integration can’t be overstated.

AI will make personalized treatment at scale available in both healthcare in marketing. 

Not only are there tremendous benefits to this personalization, but the use of AI will free up time for medical practitioners and marketers to do the tasks AI can’t.

As you see, the use cases for AI in marketing and healthcare are already abundant. From predictive analytics to pure automation, the benefits are clear. All that’s left is further innovation and iteration. 

What do you think?