AI vs Machine Learning: Why the Difference in Meaning Matters for Marketers

AI vs Machine Learning: Why the Difference in Meaning Matters for Marketers

Nowadays, the majority of SaaS tools that business ues for marketing rely on AI and ML. People often use these two terms interchangeably, although they are very different in nature. Specifically, machine learning is a subset of artificial intelligence, which is why it’s much more limited in terms of its functionality. 

AI and ML tools have become vital for staying relevant in a turbulent marketing landscape. These programs can generate engaging content within seconds, make a prediction of customer churn, gauge customer intent, optimize content, and perform various other tasks. Even the most common applications will boost customer satisfaction and help you dominate various search engines.

In this article, I’ll explain the difference between artificial intelligence and machine learning and how this technology can affect your marketing campaigns. 

AI vs. Machine Learning: Embracing the Nuances for Smarter Marketing

Artificial intelligence is a phrase robotics engineers use for technology that can process information in a similar way as humans. These machines should be able to learn on their own, without any external assistance. Although true AI still doesn’t exist, we’re seeing a glimpse of things that might come to pass. 

AI is an umbrella term involving various subsets like neural networks, machine learning, natural language processing, robotics, etc. I also need to mention deep learning, which is a subset of machine learning. 

Unlike artificial intelligence, machine learning models are computer systems driven by algorithms that allow them to perform certain tasks. The unique thing about ML is that it can tap into huge amounts of data to find patterns. The most common machine learning use cases include fraud detection, predictive modeling, virtual assistants, and sentiment analysis. 

The main difference between machine learning and artificial intelligence is that AI is a much more comprehensive concept. In most cases, artificial intelligence software uses technology that combines ML and its pattern recognition, but also deep learning algorithms, natural language processing, and neural networks. For example, generative AI tools use ML and NLP to create all sorts of business content. 

Demystifying the Jargon: AI vs. Machine Learning

To truly understand these two technologies, you should start by learning about their capabilities. In this section, I’ll break down their key aspects and how they work in practice. 

AI (Artificial Intelligence)

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Artificial intelligence is a computer science field that works on the creation of intelligent, self-sufficient robots and systems. These machines should be able to learn from their experience and make decisions without human intervention. Perhaps the best example of AI is self-driving cars, which use various transportation applications and other programs to get a person from point A to point B.

Key Aspects:

  • Problem-solving: AI systems have a wide range of features that will help solve complex problems. These intelligent machines can analyze data, automate tedious tasks, generate fresh ideas, and, overall, mimic human intelligence
  • Learning: Unlike traditional programs that use a limited amount of pre-provided data, AI is never static. The machines use constant human input to improve their functions and decision-making over time
  • Reasoning: AI systems have a sophisticated learning process. They can use vast amounts of data to draw valuable insights. Furthermore, the machines can understand the context behind the words, ensuring better, more relevant outputs
  • Goal-oriented: Companies use technology to achieve all sorts of objectives, from boosting their marketing to improving their sales, finances, logistics, and other processes

Machine Learning (ML)

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The fantastic thing about ML is that it can improve outputs without human assistance. You can feed a system with specific datasets and allow it to detect patterns, or it can improve itself over time through constant external inputs. Among others, ML can predict fluctuations by analyzing previous trends and provide actionable insights by finding correlations between data points.

It’s also worth noting that ML doesn’t require specific programming to execute specific tasks. As long as these machines have access to enough data, they can answer various questions you might ask.

Key Aspects:

  • Data-driven: ML algorithms can tap into datasets to find patterns and determine relationships between data and clusters. By relying on these machines, marketers can eliminate some tedious manual processes that would take them hours and hours to finish
  • Prediction: The technology can detect trends over time, which could be used for predictive analysis. ML can assess successful and unsuccessful marketing campaigns and compare them with your content to determine whether or not you’ll succeed or fail
  • Adaptability: Another cool thing about machine learning models is that they can change their outputs based on new data. They will also change outputs based on whether or not you were satisfied with the results
  • Automation: The majority of marketers use ML to save time on various tedious tasks. For example, specialized tools can scrape thousands of contacts, generate content, and perform competitor analysis within minutes. Besides process automation, the technology also ensures fewer mistakes when processing complex datasets

Further Reading: 23 Free AI Tools for Marketing to Try Out Today

Why the Distinction Matters for Marketers

As a marketer, it’s vital you learn more about the two concepts, as there’s a big difference between tools advertised as AI-driven and those that are ML-based. Specifically, machine learning mainly focuses on detecting patterns and doesn’t exhibit the same learning potential as artificial intelligence. AI, on the other hand, can tackle a much wider range of tasks. 

Generative AI

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Generative AI programs use a combination of natural language understanding and machine learning. These tools can comprehend written prompts, and by tapping into various online resources, they can generate textual, image, and video content based on your requirements.

Entrepreneurs love using these tools as they reduce the time needed to create engaging posts. The outputs usually vary based on the language model that the tool is using, as well as the marketer’s specific inputs. Even better, some tools give you several variations of output so you can choose the most suitable one.

Here are a few good reasons why you should use generative AI software for your next marketing campaign:

  • Increased efficiency: No matter the type of content you need, AI-driven tools can significantly boost your productivity. They are especially fantastic for creating product descriptions, emails, generic social media posts, and any other type of content that doesn’t require that much creativity
  • Personalization: Some tools allow you to set a target audience and brand voice. That way, you can ensure that each piece targets the right prospects with the right message. Generative AI programs are fantastic when you write corporate emails, as these tools can extrapolate prospects’ data and enrich messages with it
  • Scalability: Unlike the traditional content creation process, where you need to hire additional writers for scaling, AI tools aren’t limited by quantity. Even if you need hundreds of articles for your blog, these programs can generate them in an extremely short amount of time

Alas, generative tools aren’t as great as you might think. The technology is still in its early days, so there are numerous things to be desired. Among others, you might have issues with the following things:

  • Quality issues: AI outputs are nowhere as exciting or engaging as human-written posts. Although the technology understands spoken language, it often fails to connect all the dots that will provide cohesion to texts
  • Data bias and inaccuracy: Artificial intelligence systems tap into specific data, which means they’re susceptible to information bias and obsoleteness
  • Data training: The quality of a tool is also dependent on training data. Ideally, you should use programs that utilize the most advanced, recently updated language models

Further Reading: Can Google Detect AI Content? Here’s What We Know, and Why It’s Important

Content Marketing

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Artificial intelligence and machine learning work in conjunction to help you create better content marketing campaigns. While AI is vital for analyzing audience data and improving personalization and conversion, ML is mainly used for finding relevant formats and topics.

  • Focus on audience needs: With artificial intelligence software, you can find the ideal audience for your brand. On top of that, these tools provide a plethora of demographic and behavioral data, allowing you to focus on their pain points and capitalize on their interests
  • Data-driven content strategies: Machine learning is vital when ideating and scheduling content. These programs can find the best topics for your potential clients while also suggesting the best formats 
  • Optimize content performance: AI tools can compare your pieces to similar blog posts and content found online. They can provide a list of suggestions for improving your articles and personalizing them for the target audience. After posting content, you can use AI-driven solutions to tweak the posts so they achieve higher conversions 

Further Reading: AI in Content Marketing: Benefits, Ways to Use, and 5 Case Studies

SEO

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Through machine learning, we can accrue numerous valuable insights regarding competitors’ performance. The technology can analyze datasets to help us create more viable SEO strategies and optimize our content. On the other hand, AI is vital for understanding semantics and performing voice and visual searches. 

  • Keyword research: AI software can check all the keywords in a specific search engine to determine their search volumes, difficulty, and CPC. The software can provide numerous insights that will assist you when planning content strategy 
  • Content optimization: By comparing your piece with numerous other blog posts, the software can tell whether the article is optimized. An AI-driven program generates a list of suggestions that you need to follow to boost your chance of ranking within Google for that particular phase. Specifically, it will increase content relevance while answering user intent
  • SEO analytics: By utilizing ML and AI technology, entrepreneurs can assess their website performance over time. They can track various analytical data, including website visits, impressions, click-through rates, time spent on a page, and more. These tools can also provide lots of nifty information about users’ demographics, financial state, and origin 

Further Reading: 15 AI SEO Tools to Help Your Content Rank

Real-World Examples of AI vs. ML in Marketing

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Due to the nature of modern marketing, there’s a lot of data to parse through. So, by relying on AI and ML technology, entrepreneurs can sift through enormous quantities of data and extrapolate valuable insights. More importantly, they can compile all this information in one spot, extract it, and repurpose it. 

But the benefits don’t stop there. Artificial intelligence can also predict buyers’ trends, recommend products based on past shopping, and provide answers to users’ questions by using knowledge bases. The technology will usher in a new era of personalized web experience by modifying browsing so that it fits every individual who visits the platform. 

  • Chatbots powered by ML: Chatbots have completely changed the way we perform customer service. Instead of relying on large teams of trained staff, companies now use AI tools to provide premium experience to their past and future customers. Artificial intelligence uses knowledge bases to find answers to all questions that social media and website visitors might ask. That way, you can serve your clients regardless of the time zone and without relying on human assistance
  • Product recommendations by AI: Specialized AI tools can tap into data from various shopping sources. They can track users’ movements across the web and, specifically, analyze what kind of commercial content they reacted to. Based on this information, online shops, as well as other websites, can provide product and service recommendations 
  • Dynamically generated website content: Dynamic content changes as soon as the user opens a page as a way of accommodating his specific needs. This type of content is powered by AI apps and scripts, and it works in conjunction with static content on the page. Artificial intelligence can perform all sorts of modifications based on the user’s location, preferences, and devices. For example, it can automatically translate a page, change measurement units, showcase the number of people reading the page, and more 

Often, the best way for me to explain the differences in AI vs machine learning is with examples. After all, many of us use high-tech tools without realizing that they are ML or AI based! Yet, whether we realize it or not, these new technologies are increasingly critical to marketers.

Further Reading: 15 Powerful AI Content Generators to Test Out

Conclusion 

Although, in theory, entrepreneurs don’t have to understand the difference between AI and ML, it’s much better if they do. That way, they can precisely assess the quality of tools’ features and determine if there’s a better solution on the market. For example, it’s much better for software to rely on an AI model than an ML algorithm, as it presumes more complexity and better data processing.

Hopefully, after reading this piece, you will have a better understanding of what each one of them pertains to. Although the boundaries are somewhat blurred, you can ask software manufacturers what kind of technology they use for their products. 

Hero Photo by Mohamed Nohassi on Unsplash

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Neal Schaffer
Neal Schaffer

Neal Schaffer is a leading authority on helping businesses through their digital transformation of sales and marketing through consulting, training, and helping enterprises large and small develop and execute on social media marketing strategy, influencer marketing, and social selling initiatives. President of the social media agency PDCA Social, Neal also teaches digital media to executives at Rutgers University, the Irish Management Institute (Ireland), and the University of Jyvaskyla (Finland). Fluent in Japanese and Mandarin Chinese, Neal is a popular keynote speaker and has been invited to speak about digital media on four continents in a dozen countries. He is also the author of 3 books on social media, including Maximize Your Social (Wiley), and in late 2019 will publish his 4th book, The Business of Influence (HarperCollins), on educating the market on the why and how every business should leverage the potential of influencer marketing. Neal resides in Irvine, California but also frequently travels to Japan.

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