The promises of artificial intelligence (AI) have been around for decades. But despite many technological advancements over the past few years, AI has only recently started to deliver on those promises. For example, AI can now translate text between languages and recognize images in a way that even the most skilled human experts cannot match.
Machine learning and artificial intelligence (AI) are often used interchangeably. In fact, many people don’t even realize that they’re two different things. Both machine learning and AI are subsets of the broader concept of artificial intelligence, but they mean different things.
Machine learning is a subset of AI in which systems are programmed to learn from data. The more data your system has access to, the better it will become at making predictions based on historical trends — but machine learning can’t create new knowledge or ideas on its own; it can only draw conclusions from what’s already been fed into it.
AI is much broader than machine learning: It encompasses computers that can perform cognitive tasks (such as visual perception) without being explicitly programmed to effectively “think” with their own minds like humans do. Examples include self-driving cars that navigate through traffic without human intervention, mobile apps like Siri or Alexa, chatbots built into messaging apps like Facebook Messenger and WhatsApp, virtual assistants such as Cortana on Windows 10 computers, virtual assistants such as Siri on iPhones (and Google Assistant for Android devices), etcetera…
AI is a great tool for businesses, but it will never replace the human touch. AI is here to stay, but it will take time to get there. Artificial Intelligence (AI) is still in its infancy, but it is improving every day. Accessibility of AI has made it more available for business use than before and many companies are already implementing AI into their marketing strategies.
While AI may seem like it will make all of your jobs obsolete, it can help you do your job better. And in the process of becoming a better marketer, you’ll be able to use AI to become a better human being. The two go hand-in-hand: by using AI, you’re able to make more informed decisions and use those insights to become an even better marketer. You’ll have the knowledge necessary to improve your marketing strategy and approach.
That said, not everyone is ready for this change — especially when they’re so accustomed to traditional methods of learning about their customers’ needs and wants. If you find yourself falling behind with this new technology that seems like it has been designed specifically for marketers (it has), then it’s time for us all to ask ourselves some tough questions about how we want our future selves — and our businesses — to look like post-AI era.
AI is all about the data. This may sound obvious, but it’s easy to forget that AI is just an algorithm without data. The data that makes it intelligent and its ability to learn from you and adapt over time gives it value.
Without access to quality, relevant data sets, your AI will be severely limited in its capabilities and usefulness as a tool for marketers.
You’ll also need to ensure you have access to good metrics if you want your AI-powered campaigns or programs to succeed; otherwise, how can you be sure what works?
When you think about it, SEO is really just a form of optimization. Search engine optimization (SEO) is the process of improving your website’s visibility on a search engine results page (SERP) by increasing the rank of your website on SERPs, which leads to more organic traffic. Essentially, it involves analyzing your content and then making adjustments based on what you learn from that analysis. Search engines want users to find relevant information quickly — so they reward sites that are well optimized for user experience.
But AI can also help optimize your content for search engines as well as social media, email marketing and mobile devices (and all other digital channels).
Digital advertising has been revolutionized by AI. Automated bidding, targeting, and optimized ad delivery are becoming more common in marketing — and it’s not hard to see why. With AI-powered advertising platforms, marketers can target consumers based on their individual interests, needs, and behaviors.
AI has also helped create ads more relevant to users’ interests. This will help improve engagement rates across all digital media channels because users will find the ads they see in their news feeds or email inboxes more interesting than before (when these ads were created using traditional methods).
AI is a powerful tool for content marketing. It can help you find the right content for your audience to share, engage with, and consume. AI can also use data from their past experience to learn what types of things they respond best to — and then create more of that stuff in the future.
AI can even help you determine which topics are most popular with potential customers — so you know what kinds of posts will get them excited enough to click through and buy something from your website or social media page.
In a 2018 report by Gartner Inc., the market intelligence firm predicted that by 2021, companies would spend over $1 billion on chatbots alone. And it’s easy to see why: Chatbots can automate customer service interactions, allowing businesses to save money and provide better customer service. As more companies adopt these tools for their own needs, yours are likely to follow suit if you want your customers — and your competitors’ — businesses to use them too!
AI is a branch of computer science that studies the design and development of intelligent agents (systems), where an intelligent agent is a system that perceives its environment and takes actions that maximize its chance of success. John McCarthy, who coined the term in 1955, defines it as “the science and engineering of making intelligent machines.”
In simple terms, AI refers to all technologies that enable machines to mimic human behavior by learning from experience. In other words, AI-driven solutions learn from data they receive over time after exposure to various inputs and outputs. Machine learning algorithms are behind most AI applications today, including speech recognition systems (e.g., Siri), image analysis software (e.g., self-driving cars,) and bioinformatics projects (e.g., cancer diagnosis).
So there you have a few predictions about the future for artificial intelligence in marketing. Clearly, this technology has a bright future, but we can only truly understand how it will shape our world when it becomes more widespread and mainstream. What do you think? Are we on the cusp of something big?