AI reshapes real-time workflows in Perforce 2026 report

Perforce Software 2026 State of Real-Time Workflows Report looks at a practical question now facing development and production teams: what happens when generative AI increases the amount of code and digital content faster than organizations can confidently review, manage, and reuse it? Produced with Amazon Web Services and based on responses from 608 practitioners, the report covers game development, media and entertainment, automotive, manufacturing, aerospace, and defense.

The results describe meaningful gains, but they also show why speed cannot be the only measure of progress. AI is helping teams create more material and shorten parts of the production cycle. At the same time, organizations are being forced to think more carefully about quality, ownership, compliance, and the history of every change.

The productivity gains come with real concerns

Job insecurity was the most common AI-related concern, cited by 50 percent of respondents. Content quality followed at 49 percent, compliance at 48 percent, and reduced creativity at 36 percent. Those concerns sit beside measurable AI productivity gains. In media and entertainment, 48 percent of respondents said productivity increased between 11 and 50 percent after adopting AI. Automotive and manufacturing followed at 41 percent.

Regional differences were also pronounced. APAC reported the strongest AI-driven acceleration at 74 percent, while concern about AI-related job loss was highest in LATAM at 83 percent and in North America at 56 percent. The numbers do not suggest that AI is failing. They suggest that adoption is moving faster than many organizations' governance, workforce planning, and quality controls.

AI reshapes real-time workflows across industries

The report also tracks the continued movement of game technology beyond entertainment. Real-time 3D simulation is now used for film and television, visual effects, automotive design, manufacturing, and other technical work that benefits from immediate visual feedback. Fifty-five percent of media and entertainment respondents use game engines for film and television production, while 32 percent use them for visual effects.

Version control adoption reached 94 percent, up from 86 percent in 2025. That increase makes sense. As teams create more code, textures, models, animation, and other large assets, they need a reliable record of what changed, who changed it, and which version is approved for production. A shared repository is no longer merely a convenience for programmers. It is part of the operating foundation for multidisciplinary teams.

Provenance becomes part of the product

The most important issue in the report may be provenance. When AI can generate many variations quickly, producing another asset is easy. Establishing where it came from, how it was modified, and whether it can be trusted is harder. The same pressure is already changing artifact management and software supply chains, where traceability and policy enforcement increasingly matter as much as storage.

This is where AI governance and controlled development environments become practical engineering concerns. Teams need reviewable histories, repeatable automation, access controls, and policies that apply consistently across code and digital assets. If an organization cannot explain what changed and why, it will struggle to certify quality at scale.

Hybrid infrastructure becomes the common choice

Infrastructure is changing with the workflow. Twenty-seven percent of respondents now use a hybrid cloud and on-premises model, up from 10 percent in 2025 and 16 percent in 2024. That makes hybrid the most common setup in this year's results, ahead of cloud-only and on-premises-only deployments.

Large creative and engineering assets help explain the shift. Teams want elastic compute and global collaboration, but latency, security, cost, and data control can make a single deployment model impractical. A consistent hybrid cloud operating model gives organizations more freedom to place each workload where it makes the most sense without separating teams into incompatible toolchains.

The report also shows where integration remains difficult. Fifty-five percent of media and entertainment respondents expressed concern about poorly produced content entering their pipelines. In automotive and manufacturing, 37 percent cited difficulty integrating AI tools into established workflows. Buying an AI service is the easy part. Connecting it to production systems without weakening quality controls is the real work.

What development teams should take away

Teams should use AI where it produces clear value, including drafts, variations, repetitive work, and early exploration. They should pair that speed with version control and asset management that preserve lineage without creating unnecessary friction. Productivity should be measured in terms that include quality, review effort, reuse, and trust, not just the number of items produced.

The human side also deserves direct attention. Job insecurity is not an abstract concern when half of the respondents identify it as a problem. Clear processes, honest communication, and transferable skills give people a better chance to understand how AI is being used and where their judgment remains essential.

Perforce is releasing the research in three sections. The first covers real-time workflows and 3D technology trends. The second addresses AI trends and challenges, including automotive, aerospace, and defense. The third focuses on media and entertainment and the changing role of digital creation tools.

The practical conclusion is straightforward. Competitive advantage will not come from generating the most content. It will come from building a workflow that can produce useful work quickly while preserving the evidence needed to understand, review, and trust it.