From The New York Times and The Washington Post to Bloomberg and Reuters, industry giants are using AI to move faster, work smarter, and cut costs. But AI in the media industry isn’t just for the big players — smaller media companies are also tapping into its power to streamline operations and stay competitive.
In fact, 61% of media and entertainment companies are already using AI to boost content production, while 55% are leveraging it to reduce operational costs. If you’re not exploring AI in your media company yet, you could be leaving serious opportunities on the table.
In this article, you’ll learn:
- How AI is reshaping the media and entertainment landscape
- The biggest benefits AI offers media businesses today
- Key risks and challenges you should be prepared for
Let’s dive right in!
AI and media companies: How AI is shaping the future of the industry
We’ve all heard about AI’s ability to generate content, create lifelike videos, and speed up media production. But not many people understand the benefits of automation behind the scenes.
AI use cases in media can be creative, like Warner Bros. using PhotoRoom to create a Barbie selfie generator for the movie’s marketing campaigns.
Many use cases involve technical and repetitive tasks like organizing content or using AI chatbots to streamline customer support. A great example of this is SAGE Publications, which uses AI to generate precise article tags for its large content database.
What once took hours can now be easily done with the help of automation.
Let’s walk through some of the most prominent examples of how AI is benefiting media companies today.
1. Improved operational efficiencies and less manual work
Artificial intelligence and media companies go hand in hand to automate repetitive work and free up human staff to focus on complex tasks. According to McKinsey, AI can help people automate as much as 70% of their work by eliminating repetitive and manual tasks like data entry or content categorization.
AI in the media industry can automate:
- Fact-checking and verification: AI tools can quickly scan large volumes of content, cross-check facts, and detect misinformation, improving accuracy and saving time in the editorial process.
- Translation: AI-powered translation tools can localize content for global audiences faster and with fewer errors, making it easier to distribute media across multiple regions.
- Content summaries: AI can generate concise summaries of articles, videos, or interviews. Editors can repurpose content more easily for audiences to enjoy.
- Content categorization and rating: Machine learning models can automatically tag and organize content by topic, sentiment, or target audience, and even assign age-appropriateness or sensitivity ratings for better curation and compliance.
- Content moderation: Artificial intelligence systems in media can flag inappropriate or harmful content, such as hate speech, nudity, or violence, across comments, videos, and user submissions. This helps media platforms maintain safety and uphold community guidelines.
A great example of repetitive task automation is Bloomberg. The company employs AI to assist journalists in generating financial news stories based on earnings reports.
What used to take hours to research now takes minutes.
Media companies also benefit from automation when optimizing their internal and external customer support. Tools like Capacity offer a comprehensive AI solution for media companies. Features like Agent Assist help support teams find answers faster through prompts. Instead of searching for information in FAQs and documentation, agents can just enter a question, and Agent Assist gives a direct answer in seconds.
2. New content development at scale
AI isn’t just about supporting human work—it can also create content itself. Generative AI tools like ChatGPT or image generators can help:
- Produce scripts
- Write articles
- Generate visuals
- Compose music
Although most media companies don’t rely on 100% AI-generated content, it can help writers and journalists boost creativity and productivity.
A great example is when a writer needs to produce a research-heavy article on a topic they aren’t closely familiar with. They can use AI tools to generate article outlines and find resources faster, saving time and making the final piece more comprehensive.
A great example of AI-powered content development and personalization is The New York Times. The publication uses automation to help writers come up with article outlines and find relevant research. It also uses AI to personalize its mobile app’s “For You” feed.
3. Increased audience engagement rate
The moment you turn on your favorite streaming service, log into your social media accounts, or open your favorite news outlet, you’re bombarded with content. People often feel overwhelmed by the quantity of content they receive each day.
One of the ways media companies help people find relevant information is by personalizing and creating interactive experiences that resonate with them. A study by Buffer found that AI-assisted social media posts, on average, received a 5.87% engagement rate compared to 4.82% for non-AI-generated posts.
Another great example of this is from the Washington Post, which recently launched a new feature called Ask The Post AI.
How it works: It scans their archives dating back to 2016 to generate summary answers to reader questions. Just by entering simple prompts like “Tell me the weather updates in New York,” users receive relevant articles. This enhances readers’ experience and boosts their engagement.
4. Fast insights from unified data
Having the right data is key for media companies to deliver relevant content to their audiences and drive business growth. AI software solutions for media help businesses make smarter, faster decisions by turning data into actionable insights.
Media companies use AI for:
- Automated reporting: AI tools can generate real-time performance reports for content, campaigns, and user metrics, saving manual work and enabling quicker decision-making.
- Data analysis: AI can process and analyze massive data sets from social media, streaming platforms, and web traffic to uncover audience trends and behavior.
- More accurate predictions: By recognizing patterns in consumption, AI helps predict what content will perform well, aiding in planning and content investment decisions.
However, how you handle and organize the data you have is just as crucial as the data itself. Many media companies have a lot of siloed data they can’t put to use. That’s where AI tools like Capacity come in handy.
Capacity’s AI analytics feature unifies data across internal drives, third-party integrations, group chats, and other business units to create a centralized data hub with clear insights and predictions.
You can use AI insights to:
- Analyze and predict trends
- Exceed customer expectations
- Find new ways to grow your business
Risks and ethical considerations associated with AI in media
When talking about AI in creative fields, it’s important to acknowledge the risks and ethical considerations.
One thing is clear: AI shouldn’t replace writers, journalists, designers, and other creatives you employ to deliver a quality experience for your audience.
But AI can and should become a tool for your teams to maximize creativity and productivity. However, finding the balance between the two is where the biggest risks lie.
Bias and misinformation
AI systems are only as good as the data they’re trained on. When this data reflects societal biases or misinformation, AI can unintentionally perpetuate bias in news reporting, content recommendations, or even the hiring process.
When generative AI creates convincing but factually incorrect or misleading content, it can erode public trust and damage your brand’s reputation.
There have been cases where companies used biased AI in hiring processes, with systems favoring male applicants over women due to the biased training data.
That’s why you should always:
- Use tools trained on large, high-quality data sets to reduce misinformation
- Treat AI-generated content as the first draft and work your way through the facts
- Curate data training and avoid using social media comments or threads for your business tools
- Always have people who can audit AI output
Need for transparency
There’s a growing concern about how and when AI is used, especially since regulation is still catching up. Media companies must decide on internal policies around AI use, such as when to disclose that a piece of content was AI-generated or AI-assisted.
For example, platforms like TikTok, Instagram, YouTube, and many others allow creators and companies to disclose when content they upload is AI-generated or altered. This is crucial to maintain audience trust and ensure editorial accountability, especially in journalism or documentary work.
Copyright and ownership issues
AI-generated content raises complex legal questions: Who owns AI-generated work? The tool developer, the user, or the company?
Generative models trained on copyrighted material might infringe on existing intellectual property, creating legal risks. This is especially concerning in industries like film, music, and publishing. For example, a few years ago, The New York Times sued OpenAI for training its large language models (LLMs) on the content published by the newspaper.
That’s why it’s important to always double-check AI-generated content and run it through plagiarism detection tools.
Job security
One of the most discussed risks of AI in media and entertainment is its potential impact on job security. As AI tools become more sophisticated, they’re increasingly able to support tasks like:
- Copywriting
- Video editing
- Voiceover work
- Data entry
- Translation and subtitling
While this boosts efficiency, it can also create uncertainty about how these tools might shift the demand for human talent.
It’s important to recognize that AI isn’t replacing creativity—it’s reframing it. In most cases, AI handles the repetitive or labor-intensive parts of creative production, freeing professionals to focus on higher-value, more strategic work.
But, that doesn’t mean people aren’t concerned about their jobs and being replaced by AI. A World Economic Forum survey showed that 41% of employers plan to reduce their workforce due to advances in AI. Many companies are already cutting costs by reducing staff numbers.
Companies that adopt AI without a clear strategy for human collaboration may erode trust among employees. The path forward requires thoughtful integration: upskilling workers, clearly communicating how AI will be used, and ensuring that efficiency gains don’t come at the expense of creative integrity.
Lack of originality
AI tools often generate content by remixing what already exists. This can result in formulaic, derivative output that lacks the depth, nuance, and unpredictability of human-created work.
Some experts argue that both humans and AI can lack originality, with so much content being created every second. But the difference is that AI also lacks the ability to use art and content to connect people. That’s why media companies should aim to use AI to support and enhance creative work, not replace it.
Culture of productivity over creativity
One of the main uses of artificial intelligence in media is to speed up content creation at scale. With AI, you can produce multiple articles, news stories, and videos in hours that used to take days. But there’s a risk of prioritizing speed and volume over artistic quality.
This can pressure creators to keep up with algorithmically driven trends. It may also lead to a decline in human-driven storytelling, where emotion, lived experience, and originality matter most.
Studies show that AI boosts human productivity significantly, but the downside is that people don’t feel motivated. After implementing AI, people report their motivation dropping by 11% and boredom increasing by 20%.
However, when used correctly, AI solutions in media can support your business at every step.
Improve your media business efficiency and cut costs
When used correctly, AI in the media and entertainment industry can help businesses harness benefits such as:
- Faster and more personalized content creation
- Reduced operational costs
- Routine task automation
- Unified business insights
- Assistance with customer support, subscriptions, and audience retention
Tools like Capacity help media companies tackle two of the toughest challenges: unifying data for business growth and empowering customer support teams. Heavy support functions and complex lead capture and subscription flows can be easily automated with an AI-powered front line.
If increased efficiency, lower costs, and happier customers sound good, book a demo with us and start reaping these benefits.
Increase agent efficiency with AI
FAQs
Artificial intelligence for media companies offers many benefits, like:
– Automated workflows
– Data-based insights
– Faster content development
– Personalized user experiences
The future of AI in media lies in more personalized, efficient, and data-driven content experiences. We’ll likely see increased use of generative AI for content creation, personalization, and curation, as well as the automation of customer support tasks. At the same time, ethical guidelines, transparency, and human–AI collaboration will become crucial to maintain trust and creative integrity.
In digital media, AI powers:
– Content recommendations
– Search optimization
– Ad targeting
– Automated content generation
– Audience engagement tools
For example, social media platforms use AI to surface relevant posts, while publishers use it to tailor newsletters or headlines for individual readers.
AI assists newsrooms with:
– Fact-checking
– Automated reporting
– Translating news into multiple languages
Some outlets use AI to write short reports on sports or finance based on data inputs. It’s also used to analyze audience engagement and tailor news delivery across platforms.
AI is unlikely to fully replace journalists, but it’ll transform how they work. While AI can handle repetitive or data-heavy tasks like earnings reports, it lacks the judgment, ethics, and investigative thinking required for in-depth reporting.
AI is reshaping the media industry by enabling faster production, smarter distribution, and richer audience insights. It empowers companies to scale content creation, personalize user experiences, and optimize monetization strategies.