What is Artificial Intelligence?

Artificial intelligence (AI) is perhaps the most buzzworthy technology of the 21st century. AI has attracted unprecedented news coverage and billions of investment dollars for several years now. Let’s take a closer look at what artificial intelligence is and why it matters for content-curious leaders and content professionals.

What is Artificial Intelligence?

Chances are you can find many different definitions of AI. At Content Science, we view artificial intelligence this way:

Artificial intelligence is a technology that simulates human intelligence and decision-making. Using advanced algorithms, artificial intelligence substitutes and supplements processes traditionally performed by people.

Another way of looking at it is AI mimics operations of the human mind, using trained algorithms to “think” like a person would.

Artificial Intelligence Types 

Artificial intelligence is an umbrella term applicable to any algorithm using machine or deep learning to execute its tasks. 

Machine learning was the first major form of modern artificial intelligence. With machine learning, an AI algorithm uses structured data sets to “learn,” making it capable of categorizing data and finding patterns.  

• Facial recognition: By supplying a person’s face as training data, a machine learning algorithm can pick that same face out of a crowd. 
• Spam filters: A machine learning algorithm searches for patterns in an email to determine whether it’s spam or not. 
• Predictive modeling: Trained on large datasets, machine learning algorithms analyze data to predict future data. 

Deep learning is the successor to machine learning. With deep learning, an AI algorithm uses a complex series of neural networks to predict and generate outcomes based on its training data. 

• Generative AI: After being supplied with a prompt, generative AIs create everything from text to videos based on similarly categorized examples in their enormous training datasets. 
• Thumbnail Personalization: Streaming services use what a subscriber has previously viewed to determine what content thumbnails would encourage a user to click on a show or movie. 
• Chatbots: Developers train chatbots to use certain tones and phrases to make a human-sounding response to a huge variety of user questions, prompts, and answers. 

Colleen Jones describes the difference this way in the latest edition of The Content Advantage:

So, the difference between machine learning AI and deep learning AI is a bit like the difference between the Star Wars droids C3PO or R2D2 answering a query and the Jedis Luke or Rey tapping into the Force. One is less powerful but more predictable, and the other is potentially very powerful but also much less predictable.

Why Has AI Become So Popular?

In 2012, breakthroughs in deep learning greatly advanced the practical applications of AI. For example, 2012 was the first year neural networks, a fundamental element of deep learning AI, outperformed traditional AI models in speech recognition. It was breakthroughs like this that eventually led to publicly available platforms like ChatGPT receiving huge levels of investment from tech giants like Microsoft. Between advancements in the field and increased commercial interest, AI’s evolution into what it is today has been one of the most rapid developments of technology in the 21st century. 

Automation requirements, whether providing personalized content or analyzing enormous data sets, have spurred rapid adoption of AI tools and platforms. According to Content Science’s research on content operations, only 15% of content operations used AI in 2017. This number grew to 22% in 2022 and 29% in 2023. 

Why Does Adopting AI Correctly Matter? 

Every year, more companies adopt AI tools and platforms to benefit their content operations. Deloitte reports over 73% of companies have “started on the path to AI,” making AI adoption a requirement to stay competitive. 

And given the benefits of AI, it’s understandable that so many companies are considering it as a solution to their content problems. Increasing demands for content personalization, struggles for SEO prominence, and providing omnichannel content delivery are difficult to provide manually. A focus on artificial intelligence while expanding an end-to-end content initiative helps a business meet these challenges.

However, correctly adopting AI has a long list of requirements requiring substantial research and internal analysis. Adopting AI works best when an organization already has modern content strategies, governance standards, and departmental alignments. 

Correctly adopting AI as a company or enterprise takes time. Even though each of us, as individuals, can now start using generative AI within minutes by signing up for an account with ChatGPT, Claude, or Gemini, a business has much more at stake. In The Content Advantage, Colleen Jones explains the contrast like this:

Individual adoption of the publicly available deep learning tools such as ChatGPT has been lightning fast. And while organizational adoption of deep learning AI has increased, the pace is slower than a Star Wars sandcrawler in comparison—and slowing. Recent reports from a range of sources (including Microsoft, which has a vested interest in advancing AI due to its relationship with OpenAI) show that, while curiosity about the potential of this AI remains high, concerns about accuracy, predictability, security, privacy, intellectual property, and other risks as well as pressure to show ROI are leading organizations to shift from a “me-too” approach to a more strategic one.

Slowing down to accommodate strategy for deep learning AI is a smart move for an organization—and a great opportunity for content curious leaders and content professionals.

Training is another important consideration for implementing AI. Recent research found that “a lack of uptake” of new technologies causes enterprises to overspend by more than $32 million on their digital transformation initiatives to meet their strategic goals. The same research found that properly educating and training employees on the full value of new technologies can save enterprises struggling with uptake almost $100 million. 

Communicating and training employees on the value of newly adopted AI is critical to realizing an AI’s full value. At best, failure to execute this step properly leads to a reduced ROI. At worst, it produces no values and becomes a wasted cost. 

If AI technology is your organization’s path toward improving its content operations, then it must be ready to adopt one correctly. Failing to properly prepare for a technology adoption on this scale will set your business back further than where it started. 

Potential Benefits of AI for Content Operations 

Despite its rigorous requirements, an artificial intelligence initiative is capable of yielding significant, long-term results.

Automating Content Operations 

In the age of needing to do more with less to fuel growth, what a business can automate it should automate. 

Customer demands for personalization grow constantly. However, providing this personalization requires expert data analysis, the manpower to create and distribute personalized content, and various back-end IT and omnichannel optimization processes. Doing all of this manually drastically reduces the ROI of personalization. Automating these processes ensures that the return on investments for personalization stays as profitable as possible, all while freeing up your staff to focus on other worthwhile ventures. 

Formstack, a premier workplace productivity platform, found repetitive tasks cost an average of $14,560 a year for each employee. McKinsey estimates that generative AI may automate 60 to 70 percent of current employee tasks. Identifying opportunities for automation and freeing up employees to focus on high-value responsibilities improves the ROI of any labor budget and keeps staff focused on creating and optimizing high-quality content. 

Improving Future Content 

When used with effective content governance, AI stands to improve the quality of your organization’s content. 

Writing assistants are popular AI-powered tools that analyze content to ensure adherence to grammar and spelling requirements, style guidelines, legal compliance requirements, and audience needs. These tools automate much of the review process and allow editors to focus on the substance of the content rather than the nuts and bolts of its readability. Content Science’s research on content operations has found 29% of businesses are already adopting AI tools and 36% of those users employ AI for content composition. 

AI is also highly effective at improving a content operation’s data analysis capabilities. Machine learning AIs are phenomenal at discovering patterns in data and can quickly evaluate a set of user data to determine what forms and elements of content are most effective. Getting this information on-demand helps content operations formulate effective content strategies and avoid producing low-value content. 

Generative AI 

Generative AI is a relatively new application of deep learning and has garnered the most attention recently. Examples include ChatGPT, Claude, and Gemini. 

For most purposes, generative AI alone is insufficient to create and manage a content asset. But it can provide great jumping-off points for content operations, creating rough cuts of content that require careful editing and review but still reduce the time your professionals spend on the “grunt work” aspects of producing. 

In our experience, getting the most out of a generative AI requires a content operation to be mature and for the staff using the AI to be knowledgeable about how it works and what it requires. While no replacement for living, breathing content professionals, generative AI has potential to be fantastic partner to streamline or scale content operations. 

Considerations for AI in Your Content Operations 

Using AI to aid content operations benefits from many considerations of the potential risks and rewards. We’re highlighting two.

Your Level of Content Maturity 

Content operations maturity is the key factor determining when an organization is ready to begin an AI initiative. Adopting an AI solution before you’re ready makes your solution more of a hindrance than a help. 

We developed a comprehensive understanding of the correlation between content operations maturity levels and AI adoption success. Adjust your AI focus depending on your level of maturity. Here’s an example:

• Level 1: Develop standardized processes before turning to AI. 
• Level 2 + 3: Use AI to speed up ideation and scale. 
• Level 4 + 5: Use AI to optimize personalizing content experiences, analyzing content performance, and building out significant pieces of content. 

Transparency and Trust in Using AI 

The average consumer is fully aware of the dangers AI poses for misinformation, unethical use, and the exploitation of content creators. Because of these potential risks, companies using AI solutions must be upfront and transparent about their use. 

This is especially true of generative AI solutions. According to a Business Wire survey, 94% of respondents want transparency and regulation for generative AI’s use in marketing. Between generative AI’s unlicensed use of its “training materials” and its contribution to misinformation, many people view this technology with caution.  

Creating and maintaining a high level of transparency around your organization’s AI use may seem unnecessary, but building credibility and trust with audiences is crucial to growing and retaining your customers or audiences. Toni Mantych, former senior director of ServiceNow, says this about AI and consumer trust: 

We’re seeing the use of AI expand from turbo-charging intelligent content discovery and delivery to synthesizing new content. To realize the potential of AI-generated content, development teams will need to build user trust in the content. 

So, artificial intelligence is a powerful technology with a range of potential benefits and pitfalls for content-curious leaders and content professionals. Learn more about adopting AI from these resources.

Recommended Resources:

• The Content Advantage: Succeed at Digital Business with Effective Content
• Is Your Company Really Ready for AI? Workshop
• AI for Content: Fundamentals Course
• Artificial Intelligence and Content White Paper 
• Artificial Intelligence Fact Sheet
• Artificial Intelligence Training and Intellectual Property Lawsuits
• Checklist to Maximize Value from a Generative AI
• How Useful is Generative AI for Content Professionals?
• Prompting Text Generative AI Course
• Purpose, Prompts, and Polish: How to Get the Most Out of a Text Generative AI