Generative AI is accelerating how teams create code, 3D assets and other digital content, but a new global survey suggests the productivity boost is arriving alongside a less visible problem: organizations are struggling to understand what AI created, how it changed and whether the resulting work can be trusted. Perforce Software’s 2026 State of Real-Time Workflows Report, produced with Amazon Web Services, finds that AI adoption is delivering measurable gains across media, entertainment, automotive and manufacturing while increasing concerns around job security, content quality, compliance and workflow integration.
The enterprise AI conversation is increasingly shifting from how much work AI can produce to whether organizations can govern everything it produces.
That distinction is becoming especially important in industries where teams manage enormous volumes of code, 3D assets, visual effects, engineering files and other production data.
Perforce Software’s latest State of Real-Time Workflows Report, produced in partnership with Amazon Web Services (AWS), surveyed more than 600 practitioners globally to examine how generative AI and real-time technologies are changing production workflows.
The findings reveal a paradox. AI is making many teams significantly more productive, but the same organizations adopting it are increasingly concerned about quality, compliance and the provenance of AI-generated work.
Globally, 50% of respondents identified job insecurity as their leading AI-related concern, followed closely by content quality at 49% and compliance at 48%. Thirty-six percent cited reduced creativity as a concern.
Those numbers suggest that enterprise AI adoption is entering a more complicated phase. Initial productivity gains can be relatively easy to demonstrate. Establishing reliable governance around increasingly autonomous and AI-assisted workflows is considerably harder.
The problem becomes more acute when AI generates or modifies digital assets at a scale that traditional production processes were not designed to handle.
A designer can now generate multiple visual concepts in the time it previously took to create one. Developers can use AI coding assistants to produce and modify large amounts of code. Engineers can experiment with AI-generated designs and simulations. In media production, generative tools can contribute to everything from previsualization to asset creation.
That acceleration creates a new question for enterprise technology leaders: Can the organization reconstruct the history of an asset when AI is responsible for part of the production process?
Perforce argues that version control and workflow visibility become increasingly important under those conditions.
The report found that version-control adoption among game-technology respondents reached 94% in 2026, compared with 86% in 2025. While version control is hardly new, its importance increases when production teams are generating and modifying assets at a faster pace.
For AI-enabled development teams, version control can provide an audit trail for changes, help teams compare iterations and establish greater visibility into who—or what—modified a production asset.
That is particularly relevant for organizations operating under regulatory, intellectual-property or safety requirements.
The challenge extends beyond software development. Perforce’s research shows that real-time 3D technology and game engines are moving deeper into industries far removed from traditional gaming.
In media and entertainment, 55% of respondents use game engines for film and television production, while 32% use them for visual-effects work.
Game engines from companies such as Epic Games and Unity have evolved into general-purpose real-time production environments capable of rendering complex scenes, supporting virtual production and connecting interactive assets across workflows.
Automotive and manufacturing organizations are also adopting these technologies for visualization, simulation, product development and immersive experiences.
The convergence matters because AI is now operating inside an increasingly interconnected production environment.
A typical workflow may involve a game engine, source-control system, cloud infrastructure, AI coding or content-generation tools and multiple specialized applications. The more systems involved, the harder it becomes to maintain consistent governance and traceability.
Infrastructure itself is changing as well.
The report says 27% of respondents now use hybrid cloud-and-on-premises infrastructure, up from 10% in 2025 and 16% in 2024. That makes hybrid environments the leading infrastructure configuration among respondents.
The shift is unsurprising for organizations managing large media files, proprietary assets or computationally demanding workloads. Cloud platforms provide scalability and access to AI services, while on-premises infrastructure can remain attractive for performance, data control, latency or legacy-system requirements.
For enterprise AI teams, however, hybrid infrastructure introduces another governance layer. Data, models, assets and production workflows can cross environments, making identity, access controls, versioning and monitoring more important.
The strongest productivity gains in Perforce’s survey appeared in media and entertainment and automotive and manufacturing.
Forty-eight percent of media and entertainment respondents and 41% of automotive and manufacturing respondents reported productivity increases of between 11% and 50% after adopting AI.
Yet those same sectors face significant implementation challenges.
Fifty-five percent of media and entertainment respondents expressed concern about poorly produced content, while 37% of automotive and manufacturing respondents cited difficulties integrating AI tools into existing workflows.
The regional numbers are equally revealing. APAC reported the strongest AI-driven acceleration at 74%, according to the survey, while concerns about AI-driven job losses were highest in Latin America at 83% and North America at 56%.
These differences suggest that AI adoption is not progressing uniformly. Organizations may have access to the same foundation models and development tools, but workforce expectations, regulation, infrastructure and business processes can significantly influence how those technologies are deployed.
For enterprise technology leaders, the takeaway is less about choosing one AI platform and more about designing an environment in which AI-generated work can be governed.
That means knowing where assets originate, tracking changes, controlling access, evaluating quality and establishing accountability for decisions made with AI assistance.
Microsoft, Google, Amazon and NVIDIA are investing heavily in the underlying infrastructure and models required for enterprise AI. But the operational layer around those technologies may become just as important as model capability.
Perforce’s report points toward that emerging market. As AI increases the volume and velocity of digital production, workflow infrastructure becomes the connective tissue between creation and governance.
The next competitive advantage may therefore not come from generating the most content.
It may come from being able to prove which content can be trusted.
Market Landscape
Enterprise AI is increasingly moving into production environments where traceability, collaboration and governance matter as much as raw model performance.
Perforce’s survey captures three simultaneous trends:
AI acceleration: Media and entertainment and automotive and manufacturing respondents report substantial productivity improvements.
Real-time production: Game engines and 3D workflows are becoming mainstream production technologies beyond gaming.
Infrastructure complexity: Hybrid cloud and on-premises environments are becoming more common as organizations balance AI scalability with data, performance and operational requirements.
The combination creates a new category of enterprise AI infrastructure requirements. Organizations need tools that can manage not only models and applications, but also the assets, code, data and workflow changes produced by those systems.
This is where version control, DevOps, MLOps and AI governance increasingly overlap.
The competitive ecosystem includes cloud providers such as AWS, Microsoft Azure and Google Cloud, infrastructure companies such as NVIDIA, development-platform vendors and specialized workflow providers. For CIOs and engineering leaders, interoperability and auditability may become increasingly important procurement criteria alongside model performance and price.
Perforce’s findings also highlight a crucial adoption lesson: productivity improvements do not eliminate organizational risk. They can actually increase the importance of governance by multiplying the volume of material that needs to be reviewed and controlled.
Top Insights
- AI adoption is delivering major productivity gains in media, entertainment, automotive and manufacturing, but organizations increasingly worry about content quality, compliance and job security.
- Version-control adoption reached 94%, highlighting the growing importance of traceability as generative AI increases the volume and velocity of digital production.
- Game engines are expanding beyond gaming, with 55% of media respondents using them for film and television and 32% for visual effects.
- Hybrid cloud and on-premises infrastructure reached 27% adoption, reflecting enterprise efforts to balance AI scalability with performance, data control and existing workflows.
- The report suggests AI governance increasingly depends on tracking asset provenance, changes and accountability rather than simply controlling which models employees can access.
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