How Media and Entertainment Went from AI Ground Zero to Industry Blueprint

In our 2025 report, we suggested that Media and Entertainment (M&E) was "Ground Zero for Generative AI." The 2026 data confirm that framing and suggest that M&E has now become the blueprint for AI execution. No other industry in this year's survey demonstrates broader adoption, stronger productivity gains, or more diverse AI usage. 

TL;DR: Media & Entertainment has pushed AI adoption farther than most industries and is reporting stronger productivity gains. The next challenge is scaling creative infrastructure and addressing integration frictions around trust, ethics, and creative ownership. 

M&E isn’t an industry without its challenges, but we see real-time technologies, creative pipelines, and modern collaboration systems evolving together in a way that no other industry has managed to achieve. With 93% of M&E respondents using GenAI and over 50% of those saying it has accelerated their production and development, it appears that this is the industry that has cracked the AI integration problem. What’s next? The upcoming challenge will be scaling their success while avoiding the pitfalls that have stymied others. 

Table of Contents

• Insight #1: Media and Entertainment Has Become the Blueprint for AI Adoption
• Insight #2: Despite Creative Strides, AI Friction Still Exists
• Insight #3: M&E Is Turning Real-Time 3D Into a Cross-Channel Production Engine

AI has quickly reached near-universal adoption in the M&E sector, the highest of any industry segment we polled. However, the bigger story is how this implementation gets reflected in the tools used and the workflows being impacted. 

The AI Tool Stack for Media & Entertainment

A notable point from our data is that M&E has diversified its AI toolbox more quickly than other industries. We’ve seen a general trend away from a single application (ChatGPT) in favor of a variety of tools, but M&E’s wider range of outputs (text, images, video, concepts, animation, and prototyping) may have pushed it towards faster adoption. Other industries might learn from this example and use AI to accelerate their workflows more intentionally. 

Looking at the data, ChatGPT is still the preferred LLM for M&E organizations in 2026, but other applications made more significant gains. Claude made a noteworthy 31-point jump and Adobe’s incorporation of AI quickly won over an additional 17% of respondents.  

How M&E is Using AI in 2026  

While M&E organizations are expanding their tool stack, they remain selective around where they are increasing AI use. The biggest gains appear around decision making and imaging: 

Data analysis and R&D use nearly doubled since last year. This suggests that M&E teams have moved beyond content creation or coding and are now using AI as a planning and strategy tool.  

AI Acceleration Rates

Measuring acceleration rates was a new data point for the 2026 survey. In looking at M&E specifically, we see strong productivity gains well above the survey average: 

Nearly half of M&E respondents reported workflow acceleration of 10% or more, while only 10% saw no measurable gains. Across the full survey population, only one in four respondents reported similar acceleration, and nearly one in three reported none at all. M&E appears to be turning AI adoption into AI acceleration more quickly than anyone else.

Why This Matters

With a more diverse approach to AI, utilizing multiple tools across the production pipeline (Imaging/prototyping, content creation, R&D, code, data analysis), M&E is providing a blueprint for AI adoption that other industries should follow. Their success offers a glimpse of where other sectors may be headed as AI implementation matures.  

Concerns around job security, poorly produced AI-generated content, and ethical use show that M&E teams still have deep reservations around AI despite strong creative gains. Issues around using people’s likeness or voice without permission have sparked debates and legal challenges. For example, when Dutch studio Xicoia unleashed AI “actress” Tilly Norwood, it immediately led to backlash from actors and acting guilds who questioned the legitimacy of the creation and the ethics around the real faces composited in her appearance.  

These and other stories suggest that the biggest challenge may be ensuring that AI-generated work meets the same creative quality standards as human-created content. AI can rapidly generate concepts, create alternatives, summarize research, and develop prototypes, but at the end of the day humans want to maintain final decisions around creative direction, quality, and ethical output. 

Why This Matters

The AI conversation often assumes creative industries will be among the most disrupted by generative AI. Yet the M&E sector presents a more nuanced picture.  While AI adoption has reached 93% and many organizations report measurable productivity gains, enthusiasm is mixed. Concerns about ethical use, copyright ownership, creative authenticity, and the impact of AI on creative roles leave many feeling uncertain. The challenge will be to address ethical concerns and build trust while leveraging the hard-won creative gains.  

Running parallel to this evolution, we see an increase in version control adoption. In 2025, 26% of M&E respondents reported using version control as their primary method for storing and sharing assets. In 2026 that number grew to 36%. This shift in technology allows M&E teams using game engines and real-time 3D to move beyond a single production format and repurpose 3D assets in trailers, social content, interactive experiences, marketing campaigns, streaming graphics, and more.  

Why This Matters 

As film, streaming, VFX, advertising, and interactive content increasingly share the same underlying workflows, organizations can reuse assets, accelerate iteration, and bring new experiences to market faster.  

At the same time:  

• 39% of M&E respondents cite large file movement as their primary collaboration friction 
• 35% report difficulty integrating AI tools into existing workflows  

Expanding these AI-powered pipelines will require new infrastructure to manage the growing volume of assets, files, and collaborators moving through them.