How to do AI content analysis: A full guide

Editor’s note: This article was first published on December 21, 2023, and has since been revised for clarity and accuracy.

We’re all seeing the power of AI, and its time-saving potential is providing users with many benefits. 

Thomson Reuters’ Future of Professionals Report now estimates that optimizing work with a genAI assistant saves 200 hours of manual work a year per professional.

Many applications and work processes are already tapping into AI's efficiencies. And content analysis is where AI may have the most to offer.

Synthesizing huge swaths of different forms of data into actionable insights just became much easier. 

For example, your organization can incorporate AI content analysis protocols to glean insights from customer data, industry stats, and market information. 

Keep reading to learn more about types of AI analysis, how to get started, and the right way to do it as ethical AI usage comes to the forefront.

AI content analysis means using artificial intelligence technologies to process and analyze large volumes of content. AI enables faster, more structured, and often automated content analysis. 

Organizations collect various types of data every day, including numerical, textual, and visual formats.

Traditionally, it has been categorized into two broad types: quantitative (such as statistics) and qualitative (descriptive data, such as text and images).

Earlier computational systems primarily focused on quantitative data analysis. However, modern AI-powered tools are increasingly capable of extracting patterns and insights from qualitative data as well.

AI content analysis offers several advantages over conventional techniques. Most notably, AI can process vast amounts of data quickly, identifying patterns in near real-time.

Additionally, AI models can detect nuanced insights humans might overlook, such as subtle linguistic patterns, sentiment shifts, and intent signals. AI can also test hypotheses and scale, helping organizations validate findings more comprehensively.

AI applications can:

• Use natural language processing (NLP) to glean understandings from types of text data

• Use machine learning and algorithms to parse unstructured content data for patterns

• Work with complex neural networks and artificial intelligence to: 

  • Ask questions

  • Fine-tune analysis queries

  • Discover and verify patterns

  • Prioritize insights to deliver results to key stakeholders

Across industries, we can use many types of AI content analysis for ongoing research. 

You can use AI for many tasks depending on your organization's needs. If you need to sift through data for very specific answers, AI has you covered: 

• Product teams can analyze thousands of user reviews to prioritize feature development.

• UX researchers can use AI to detect shifts in user sentiment after a product update.

Ultimately, anyone in the enterprise can use AI in content analysis for efficiency and to dig deeper into the data. However, the process is not yet robust enough to work without human oversight.

AI can analyze a wide range of data types, including both structured and unstructured data. 

Some of the different formats AI analytics tools can process include:

• Diagrams, graphics, and other visual content

• Text (including transcripts, reports, news articles, product descriptions, narratives, how-to guides, and more)

• Numerical data (including research data, financial information, customer usage data, etc.)

• Video and audio

AI can analyze these formats from human-generated sources, historical records, real-time information, and even AI-generated sources.

Four of the most popular types of analysis are text, diagram, video, and cross-modal.

Text analysis

AI tools can 'read' text passages and pull out meaningful insights based on the tool's understanding of the text and the user's intentions. 

This process involves extracting explicit and implicit text information. Explicit could be a summary of the information, while implicit could be the tone or intent of the text. 

Some examples of using text analysis include:

• Summarizing white papers or large research documents into short, tailored summaries

• Using AI-generated content detection tools to check for human language and creators

• Learning the context in which a document was written based on language patterns

• Create high volumes of text, like product descriptions and definitions

• Generate engaging social media posts based on audience preferences across platforms

• Transforming the information from a block of text into a helpful outline or mind map

• Learning a text source's human writing style to create a language model and generate additional AI-generated text

Diagram analysis

Diagram analysis uses AI to extract information from graphs, charts, organizational diagrams, and other pictorial data types. 

There are several different layers of analysis made possible through AI, including:

Diagram identification

When AI tools scan PDFs and use optical character recognition (OCR) to recreate a digital copy, they can recognize diagrams within the text to flag them for a human operator.

Translation of diagrams

More sophisticated AI tools can convert the image into data. They translate the labels and text fields of the diagram into text through OCR. From there, they capture and relay the known values in the diagram to a diagram creator. 

Example: Imagine a prefilled 'Create a Chart' popup in Excel that pulls all the figures from an image.

Generating new content based on diagrams

After digitizing and configuring diagrams into data, AI tools can generate various content from the information, including summaries, valuable insights, and new diagrams.

Video analysis

AI tools can analyze human behavior, objects, and events within videos.

They can:

• Identify objects: Detect and track specific objects captured in video footage.

• Monitor interactions: Track and report on interactions between objects, such as determining the speed of cars on a highway or identifying potential causes of a traffic accident.

• Detect unusual behavior: Spot irregular activities that may raise concern, like abnormal movement on a baby monitor or atypical patterns in factory equipment.

Video analytics, powered by AI, has advanced across all industries. It can identify objects, predict their expected behavior, and flag when something deviates from the norm.

Cross-modal analysis

Cross-modal analysis uses mutiple types of data (modalities) to extract insights. When one modality doesn't provide enough information, cross-modal AI tools supplement the data from different sources to draw more complete conclusions. 

Cross-modal analysis techniques include:

Binary-value representation: AI uses binary values to identify and compare data from different modalities.

Real-value representation: AI integrates multiple data types to form a comprehensive conclusion.

Example:

AI might incorporate traffic data from previous years or metadata from Wi-Fi-enabled dashcams to enhance its analysis. This combination can help identify more robust patterns and reach more accurate conclusions.

AI content analysis should include the following:

• A specific query or objective to guide the analysis

• A specific sample set of data or content to analyze

• Detailed workflows for generating and sharing the results of the analysis

• Disclaimers and best practices for using the results of the analysis so audiences understand the limitations of the insights and how to use them

For data you can trust, thorough preparation is your best friend. 

Follow these seven steps for consistent results:

1. Define the objective of the analysis

Create a clear question or research objective for the project. This will help you select the right data sources and content to add to the analysis. It’ll also guide the AI in identifying the most valuable insights and conclusions. 

Some example objectives for analysis might include:

• Determining the tone: Analyzing text content using sentiment analysis tools to identify the tone.

• Identifying business inefficiencies: Uncovering patterns or trends in business data (such as sales reports or customer feedback) that highlight areas for improvement or cost-saving opportunities.

• Improving marketing content: Identifying weaknesses in marketing materials for revision or rewriting.

2. Collect and prepare AI-generated content

Next, identify the collection of content to analyze. Restricting the content set is just as important as creating a specific objective. For example, you might select:

• Communications from a single author

• Reports generated during a specific period

• Customer interactions through a chatbot or a specific sub-domain of your company website

3. Choose appropriate analytical tools and methods

Dozens of analytical tools are available, each incorporating AI and machine learning to varying degrees, often using proprietary algorithms. 

Your organization may already have licenses for specific tools, or you may need to purchase new tools with the specific capabilities you require.

Make sure the tool supports the analysis method you need, whether a simple text-based analysis or a complex cross-modal approach.

4. Analyze the content for patterns and insights

The AI application should do most of the heavy lifting here. The program will have a library of algorithms to translate the content into usable data, identify patterns, and generate conclusions or insights. 

Some AI tools answer queries or follow instructions—similar to ChatGPT—while others have a menu of functions to work with.

5. Interpret the results in the context of the objective

Once the program analyzes the content and creates an accurate analysis, it generates its findings for review. 

Outputs could include:

• Summaries of the content, including insights, conclusions, and recommended actions

• Answers to direct queries

• Quantitative reports

• Personalized recommendations

• Notes and highlights added to the original media

You and other stakeholders can review these outputs for accuracy and act on the insights.

6. Document the findings and conclusions

The outputs can act as documentation for your findings and subsequent actions. 

However, it’s vital to have a human intermediary. They should: 

• Review the AI’s findings

• Determine what actions to take as a result of the analysis

• Intervene if the AI appears to be malfunctioning or making incorrect assumptions

You can move forward with your business objective based on your judgment of the analysis and its conclusions.

7. Present the analysis to relevant stakeholders

Communicate the outcomes of the analysis to stakeholders, such as:

• Leadership who want the analysis as a foundation for strategy decisions, budgetary changes, or new programs

• Employees, especially if the results indicate a need for new workflows or work processes

• Teams and departments who can use the insights to be more productive and make more informed, data-driven decisions

AI content analysis is a powerful tool with exciting developments. 

Organizations of all sizes and industries can use AI tools to analyze information. Whether they want to examine product usage, employee output, or customer feedback, AI content analysis can help make informed decisions.

However, AI usage continues to pose several ethical challenges for all users, including:

Permission to use the data

Companies must obtain consent to use consumers' information, whether they collect it directly or acquire it from third-party sources. 

Many consumers worry about companies misusing their data, impacting brand reputation and customer trust. Staying on top of emerging data privacy laws is important.

Implicit biases within AI tools

As AI tools learn from human content, any bias in the original material can result in unethical, biased conclusions. 

Organizations must ensure people thoroughly vet results and recommendations before implementing them.

Dovetail enhances the content analysis process for product teams by offering a streamlined, AI-powered platform that automatically uncovers insights from various content types. 

By leveraging machine learning and natural language processing, Dovetail helps teams quickly identify trends, detect hidden patterns, and prioritize key findings across text, audio, video, and more. This enables product teams to quickly transform large datasets into actionable insights, ensuring that decisions are data-driven and focus on the most relevant customer feedback and market trends.

See how your team can save time, reduce manual effort, and improve their content analysis process's overall quality and speed. Request a demo.

The first step in high-quality content analysis is to determine your objective. 

Unfocused AI content analysis can lead to a scattergun approach and error-filled conclusions. 

In-depth analyses can also use a lot of resources and time, so structuring your objective to be as specific as possible plays a critical role in the project’s effectiveness.

Yes, AI can generate insights, conclusions, and recommended actions from the analysis. However, generative AI is constrained by the content samples it can access and how well-trained the programs are.