Almost daily, I have conversations with content leaders struggling to solve complex challenges in their organizations. They’re trying to help employees find critical information scattered across multiple systems, scale content creation without ballooning costs, measure the business impact of their content investments, and ensure consistency across thousands of content assets. Many are under pressure from management to use AI as a solution, but aren’t sure where to start or how AI can actually address these problems.
Here are seven ways AI can solve key content challenges in your organization:
- Locate: find relevant content across disparate systems
- Generate: create content faster and at scale
- Analyze: measure content performance and impact
- Optimize: ensure quality and consistency
- Personalize: deliver the right content to the right audience
- Repurpose: adapt content efficiently for different uses
- Secure and Govern: maintain compliance and protect assets
1. Locate Content
Many organizations are using AI to locate relevant content within their ecosystem. The content can be stored in a Content Management System (CMS), a Digital Asset Manager (DAM), a Learning Management System (LMS), or other repositories. AI locates content by understanding the semantic meaning of the request and suggesting content that matches.
AI is a great tool for locating content. It can save you significant time by reducing the need for manual searches. It can help surface underutilized content that often remains hidden from a standard search algorithm. It also provides a way to search across multiple repositories – something that conventional search has a difficult time doing. Granted, there are still challenges to overcome when you link multiple repositories, but AI technology can help us get there.
On the other hand, there are some potential dangers of using AI to locate content. AI algorithms may misinterpret user queries, leading to irrelevant or inaccurate results. This is especially true if your content is inconsistent, which can result in errors. Here are some applications of AI for search:
- Semantic search and Natural Language Processing (NLP) for content management systems
- Automated metadata tagging
- Knowledge graphs to map relationships between pieces of content
- Federated search
Search and automated metadata tagging tools include Graphwise (Poolparty Semantic Suite) and Progress Semaphore. Federated search tools include ElasticSearch, Coveo, Squirro, and Algolia.
2. Generate Content
Another use of AI in your content ecosystem is content generation. AI tools can create new content by leveraging existing content resources and user inputs. AI can generate a variety of content types including text, images, audio, and video. Using AI to generate content accelerates the process of creating content. It also can reduce the time to a first draft to speed up cycle time and enable scalability.
On the other hand, most of us have experienced disappointing results of AI-generated content. AI-generated content is often verbose. It lacks originality, creativity, and nuance. In addition, AI-generated content runs the risk of being inaccurate or made up if the source content or data is inconsistent or flawed.
We also need to be careful of the ethical concerns regarding ownership and attribution of AI-generated content. Just ask The New York Times, which is suing OpenAI for copyright infringement right now. Many industries are issuing AI guidelines. For example, the draft guidance for the Considerations for Use of AI to Support Regulatory Decision-Making for Drug and Biological Products released by the FDA.
AI tools that generate content include ChatGPT, Claude, Canva, Descript, Lumen5, and many others.
3. Analyze Content and Interactions
One of the very first uses of AI tools was for analysis. You can use AI to analyze content and how people interact with content. For many years, the content industry has sought sophisticated tools to provide data-driven insights so we can make good decisions. AI helps identify trends and patterns in audience behavior and measures business impact such as ROI on content initiatives.
As with all uses of AI, we need to be careful that the AI algorithms are accurate and as free of bias as possible. Otherwise, we run the risk of basing decisions on inaccurate data or of misinterpreting the data we receive. We also need to be cognizant of privacy concerns when we track and analyze user data. And let’s not forget that AI does not have “human intuition,” nor does it have decades of prior experience. AI forms an analysis based solely on the information we provide.
Analysis tools include MonkeyLearn, Google Analytics (with AI features), and Optimizely, among others.
4. Optimize Content
Natural Language Processing (NLP) has been in use for a very long time to improve content quality. NLP tools enforce corporate terminology, style, and grammar, performing repetitive mechanical edits at a scale that cannot be achieved by teams of human copy editors. The result is content that adheres to your organization’s style guide and branding guidelines, making it more effective, more standardized, more accessible, and more personalized than non-optimized content.
Improving content quality provides exponential returns by also lowering translation costs and improving the quality of AI solutions.
There are several AI content optimization tools available, including Grammarly, Writer.ai, Congree, and Acrolinx. SEO tools such as SEMrush and Surfer SEO also rely on NLP capabilities to help you optimize content for your business
5. Personalize Content
We’ve been talking about personalized content for many years. The ability to deliver the right content to the right person, at the right time, on the right device, in the language of their choosing has been a promise and a dream for seemingly decades. Now that we can incorporate AI into the content ecosystem, the goal of delivering tailored content in a dynamic way is finally achievable.
The benefits of delivering personalized content are many. Personalized content increases customer engagement and “stickiness,” builds stronger customer relationships, reduces customer frustration, saves money through reduced customer support calls, and more.
Personalization is not without its drawbacks. We need to be careful not to take personalization too far. Over-personalizing content can lead to user discomfort (“creepiness”), and it can also lead to privacy concerns. Personalization is subject to algorithms that could reinforce bias.
Most of us have experience being targets of various attempts at personalization (some good, some lacking). For example, how accurate are the recommendations from your Netflix or Spotify accounts?
Many tools provide personalization capabilities to help you deliver content, including Optimizely, HubSpot, and Marketo.
6. Repurpose Content
AI helps transform existing content into new formats or adapt content for different audiences, increasing the value and reach of the original asset. We call adaptations of content, derivative content.
Derivative content is particularly useful in situations where the verb tense needs to be changed (future, present, past), such as in pharmaceutical content that describes the intent of a clinical trial (future tense), the current situation in clinical trial results (present tense), and provides final results from a clinical trial (past tense).
Other uses of derivative content include:
- Providing chatbot answers that use the terminology of your customer’s query rather than the terminology present in the source content
- Extracting information from instructions to create knowledge checks in learning content
- Making complex technical or scientific information accessible to lay people
- Providing synopses of long works for people who just need to understand the key takeaways
- Extracting executive summaries and presentations from reports
There are different ways of storing and organizing derivative content. It is especially important to designate and maintain a single source of truth from which derivative content can be generated. Otherwise, you risk ending up with derivatives of derivatives of derivatives, which quickly becomes a content management nightmare. A single source of truth enables you to keep information current and ensures all derivatives receive the correct updates when the source is updated.
Using structured content authoring best practices combined with natural language processing is a good way to ensure you get the best of both worlds – a single source of truth that can be provided in derivative ways, based on the need.
Structured content management tools include RWS Tridion Docs, Fonto Integrated Authoring Platform, MadCap Flare, MadCap IXIA CCMS, Heretto, GlobalLink Vasont, and others.
7. Secure and Govern Content
Companies are using AI to ensure that content remains compliant with regulations, secure from unauthorized access, and properly managed across its lifecycle. AI can be used to enforce compliance with legal standards and protect intellectual property using content governance best practices. These best practices enhance efficiency in governing content.
There is a danger of false positives or false negatives in using AI for compliance checking. It is important to keep a human in the loop. Humans are also critical in interpreting nuanced regulatory requirements that are not obvious to a machine.
Some applications of AI in governance include plagiarism detection apps such as Turnitin and Copyscape, secure content management platforms that are enhanced using AI integrations, and compliance monitoring tools such as DocuSign for AI contracts.
How Do You ‘Use AI’?
When your management tells you to “use AI” in your content ecosystem, remember that there are many ways to use AI. Depending on your specific needs, you may find multiple applications of AI technology that will help move your department forward in a new and exciting way.
If you need assistance figuring out which AI applications are best for you and preparing your content to be AI-ready, contact us for a free consultation.
