Summary:
- The advent of generative AI has forced organizations to rethink how they can use artificial intelligence to create, curate, and manage media.
- With this adoption, many ethical considerations need to be kept in mind to use the technology responsibly and effectively.
- AI provides many new opportunities for the M&E industry to accelerate content creation and simplify workflows and media management, leading to an even more competitive landscape.
Across the media and entertainment sector, AI is increasingly becoming core infrastructure rather than a standalone tool. Media and entertainment companies are using AI algorithms and machine learning models trained on historical data to support media production, content distribution across streaming services, and audience engagement strategies shaped by consumer behavior. This shift reflects a broader move toward AI-native media operations, where automation, personalization, and data-driven decision-making underpin how new media experiences are created and delivered to a global audience.
The Future of AI in Media – Business Models & Media Economics
Beyond new experiences and workflows, AI is reshaping the business models that underpin the entertainment economy. Media and entertainment companies are experimenting with new media monetization strategies, data-driven marketing campaigns, and AI-enabled production pipelines that lower costs while opening opportunities for more personalized and interactive content offerings. As AI becomes core infrastructure across the media and entertainment sector, the ability to adapt business models to this new operating reality will play a central role in how organizations compete for attention, revenue, and long-term customer relationships in a rapidly evolving media landscape.
Artificial intelligence (AI) has transformed the media and entertainment industries, revolutionizing how content is created, curated, and analyzed. This transformation is reflected in the market itself: the global AI in media and entertainment ecosystem is projected to grow from roughly $67.5 billion in 2025 to $85.4 billion in 2026, with continued long-term expansion expected.
With this growth, AI’s impact on media strategy and operations is increasingly measurable. Around 95 % of gains in audience engagement are now attributed to AI-enabled personalization and on-demand recommendations, while nearly 90 % of new content-related initiatives leverage generative AI tools to some degree.
Companies are starting to extract greater value from their data by making it useful for AI. After data is ingested into an AI workflow, tokenization and enrichment convert content into machine-readable tokens—capturing faces, objects, speech, sentiment, events, and contextual signals—to create a structured, searchable, and interoperable data foundation that enables a variety of workflows.
For example, with the help of AI, particularly generative AI, media companies can analyze audience data and predict their preferences, enabling them to create hyper-targeted content and apply automation to media workflows, thereby improving their productivity and profitability. It’s also impacting the day-to-day lives of workers at media companies by increasing editing efficiency and reducing the number of routine tasks, enabling them to focus more on the creative and less on asset management.
In this article, we will explore the various applications of AI in media and examine its potential for continued development. We’ll cover the following topics, specifically how AI impacts:
- AI in Content Production
- AI in Content Curation and Personalization
- AI in Content Curation and Personalization
- Artificial Intelligence in Media Analysis and Decision Making
- Content provenance, authenticity, and trust
- Ethical and Other Considerations in AI and Media
- The future of media
AI in Content Production
AI-generated content is becoming increasingly popular in the media industry. With the ability to analyze data and create content based on trends and user preferences, AI-generated content can save media companies time and money while still delivering high-quality content.
AI Across the Media Production Lifecycle
Beyond content generation, AI is now embedded across the full media production lifecycle. Media production teams use AI to assist with script analysis and storyboarding in pre-production, automate post-production tasks such as color grading, audio quality optimization, and visual effects, and support live production workflows through real-time captioning, graphics, and highlight creation. These capabilities help entertainment companies reduce production timelines, improve streaming quality, and scale creative output without sacrificing consistency.
AI in Journalism, Video, and Audio Production
For example, news organizations can now use generative AI to create articles from data sets, such as financial reports, sports scores, and weather forecasts. Namely, The Associated Press uses machine learning to gather, produce, and distribute news. Tools that help automate journalism can help organizations scale article production and leverage data to inform their content, increasing quality in guiding AI more effectively. Today, these tools are increasingly paired with human editorial oversight to ensure accuracy, tone, and brand consistency while still enabling scale.
AI doesn’t just help organizations create written content; it also significantly impacts audio and video. AI-powered video editing tools are already helping automate tedious tasks like color correction, audio syncing, and text animations. This saves time, enabling teams to focus their efforts elsewhere to scale their creation and stay on top of new trends in consumption. More advanced systems are now being used to assist with real-time production tasks, such as generating highlights during live broadcasts or rapidly adapting long-form content into short-form clips for social distribution.
Image recognition is another area in which AI is having a significant impact on media. With the ability to recognize objects, people, and even emotions in images and videos, AI is helping media companies better understand and engage with their audiences. For example, image recognition technology can be used to track how many people are in a particular shot, where they are looking, and what they are reacting to, all of which can be used in accelerating content search.
AI is also used to create musical compositions and jumpstart mastering. AI-generated music composition services use algorithms to create original music based on user input, such as genre, mood, and tempo. AI-powered tools can analyze audio tracks and optimize them for the best sound quality to a certain degree. These capabilities are increasingly used to generate variations, previews, or localized versions of audio assets at scale.
These use cases only show where this technology will change the media industry. It’ll also impact how organizations curate and personalize content to audiences.
AI in Content Curation and Personalization
AI-driven content recommendations can help improve the user experience. Streaming platforms already use AI to personalize newsfeeds and playlists, catering to users’ unique preferences and behaviors. But with generative AI, search has the potential to become more of a conversation, querying based on specific things they are looking for in something they want to watch (for example, asking Bing Chat to “find me a black and white movie that’s highly regarded by critics and is in the action genre”). This shift toward conversational and semantic search allows users to explore content libraries more naturally, even when they don’t know exactly what they’re looking for.
Personalization Across Streaming Services and Platforms
As streaming services continue to shape how audiences discover and consume content, AI plays a central role in curating experiences tailored to individual preferences at scale. By analyzing consumer behavior across platforms, AI systems help media and entertainment companies surface relevant content to a global audience, balancing premium catalog discovery with the growing influence of user-generated content as a primary entry point to new media experiences. This ability to curate content tailored to individual tastes is becoming essential as content libraries expand and competition for attention intensifies.
AI-Driven Engagement, Discovery, and Operations
In advertising and marketing, AI is already being used with conversational AI and chatbots. It enables potential buyers to converse with brands to quickly find information, book a meeting, or make a purchase. This same technology is also being applied in recruitment, combining with AI voice to enable media brands to hire talent more effectively, impacting the future of hiring across industries.
Personalization of content using AI doesn’t just benefit the audience; it can also help media companies save time and increase efficiency in editing. Technologies like Veritone’s aiWARE platform, which orchestrates hundreds of AI models across over 20 cognitive categories, enable the portfolio of applications built on top of it to analyze tens of thousands of hours of unstructured media, such as audio and video, each day. This helps organizations gain deeper insights into media files, automate the processing of this media (such as AI auto-tagging), and scale production, distribution, and monetization. As media libraries continue to grow, this ability to turn unstructured content into structured, searchable data has become critical to unlocking long-term value. And the data insights AI provides can help organizations make smarter decisions.
Artificial Intelligence in Media Analysis and Decision Making
AI can identify trends and patterns in audience behavior, allowing teams to make informed decisions about their content. With sentiment analysis and audience insights, creators can refine their work by measuring audience reactions to the content.
Audience Insights and Performance Analytics
Underpinning these insights are AI algorithms and machine learning models trained on vast volumes of historical data, enabling media organizations to analyze audience behavior, identify emerging trends, and forecast content performance with greater precision. By combining behavioral signals, engagement patterns, and contextual data, these media models support more informed programming, marketing campaigns, and distribution strategies across the media and entertainment sector.
For example, AI can analyze social media activity and identify popular topics or keywords that can be incorporated into content to improve visibility and engagement. AI can also help creators understand their audience’s emotions and opinions, enabling them to tailor their content accordingly.
Real-Time and Predictive Decision Making
Today, AI technology is already used in broadcasts to measure the performance of ads with a target audience. It also helps track all over-the-air content, indexing it to make airchecks easier to pull, reuse content, and repurpose content for other channels such as social media. This same analysis increasingly supports real-time decision making, helping teams adjust distribution strategies or promotional tactics while content is still live.
We’ll see predictive analytics continue to improve and help forecast content performance, guide strategic decisions, and improve visibility. This information can guide strategic decisions and help creators optimize their content for maximum reach and engagement.
Content Provenance, Authenticity, and Trust in AI-Generated Media
As AI-generated and AI-assisted content becomes more prevalent across news media, entertainment, and social platforms, questions of content provenance and authenticity are becoming increasingly important. Media and entertainment companies are under growing pressure to demonstrate how content is created, modified, and distributed, particularly as deepfake technology and low-quality automated content challenge public trust in digital media.
To address these risks, organizations are exploring methods to verify authorship, trace content origins, and disclose when AI systems are involved in content creation or transformation. Provenance frameworks, watermarking approaches, and authenticity signals can help audiences better assess the credibility of media in a dynamic world where synthetic content is becoming harder to distinguish from human-created work. Establishing transparent practices around provenance and disclosure is emerging as a foundational requirement for maintaining trust in the future of media.
Ethical and Other Considerations in AI and Media
While AI significantly benefits the media industry, ethical concerns remain to be considered. The underlying data set matters when it comes to creating content with AI. If you don’t provide AI platforms with quality training data, then the outputs will not be of quality. This has increased focus on curating trusted, well-governed datasets and monitoring model performance over time.
There are also concerns about false AI-generated content. A problematic concept that has arisen is that of hallucinations, in which large language models like ChatGPT create seemingly believable falsehoods in their content. Additionally, the rise of deepfake videos and images has raised concerns about the spread of false information.
To mitigate these issues, media companies must implement policies around how staff interact with these models, apply content moderation and review techniques to the responses , institute disclosure best practices, and educate their audience on identifying deepfakes. Even natural language processing, which allows computers to understand and respond to human language, also raises concerns about privacy and the ethical use of data.
It shows that despite AI’s technological advances, the technology still requires some degree of human oversight. And while AI can help scale output, whatever content is produced will need vetting to ensure that proper industry standards, tone of voice, and that human touch is present. Other technology-based tools, such as benchmarking model performance, can help prevent model drift and biases from developing.
For our part, Veritone believes in AI for Good, which means we do not create anything without the strict approval of the involved parties. We’re active IAB and Open Voice Network members dedicated to developing global best practices for synthetic content. In doing so, we aim to impact how AI is used, affecting the future of technology in media and across other industries.
The Future of AI in Media
The future of AI in media is promising, with the potential for AI-powered virtual and augmented reality experiences to transform media consumption. These technologies offer immersive and interactive experiences that can significantly enhance user engagement. AI can help support a safe online environment in social media content moderation by identifying and removing harmful content. It will also continue to play a larger role in real-time audience insights, live production workflows, and new monetization models built on media data.
However, as mentioned above, personalization of content using AI and privacy concerns must be balanced to prevent potential harm and misinformation. Ethical AI implementation is crucial, and collaboration between industry leaders, policymakers, and AI experts will be instrumental in shaping the future of AI in media.
Despite these challenges, it’s clear that AI is changing the media industry in a significant way. From content personalization to AI-assisted editing, media companies are finding new and innovative ways to use AI to improve their processes and better engage with their audiences. As technology advances, it will be exciting to see what new possibilities AI will bring to the media world.
Frequently Asked Questions (FAQs):
How is AI being used in the media and entertainment industry?
AI is revolutionizing the media sector with applications like natural language processing for content creation, computer vision for tagging visual content, and deep learning for personalized recommendations. AI and machine learning have become indispensable tools for media companies.
What is an example of AI in the entertainment industry?
AI-generated music is an example of AI in the entertainment industry. By analyzing patterns and styles, AI models can compose original pieces that cater to specific audience preferences, opening new creative avenues in the music industry.
How is AI transforming the media and entertainment industry?
AI is transforming the media landscape by streamlining content creation, offering personalized experiences, optimizing SEO, and automating repetitive tasks. AI applications empower media producers and company employees to focus on creative and strategic tasks.
How does AI improve content discovery?
AI-powered predictive models analyze user behavior and preferences, enabling more accurate and personalized content recommendations. This leads to increased engagement and satisfaction for users of media platforms.
Can AI create original content?
Yes, AI can create original content using natural language generation, image synthesis, and music composition techniques. However, AI-generated content often benefits from collaboration with human creators to ensure depth and nuance.
How does AI affect jobs in the media sector?
AI can both create and displace jobs in the media industry. Automation of tasks might displace some roles, while new opportunities arise for media company employees to focus on higher-level tasks and creative content development.
What are the ethical considerations of AI in the media?
Ethical concerns with AI in the media include privacy, data security, bias, and misinformation. Media companies must address these issues by ensuring transparency, securing user data, and investing in efforts to reduce bias and misinformation.
What role do AI models play in the media and entertainment industry?
AI models are crucial for developing cutting-edge content creation, personalization, and analytics applications. The growing AI market offers new opportunities for media companies to stay competitive and adapt to evolving consumer demands.
Resources:
https://www.businessresearchinsights.com/market-reports/ai-in-media-entertainment-market-122837
