Workflows in the Age of AI: How Design & Development Workflows Changed in 2025—and What Comes Next
A look back at how AI reshaped collaboration in 2025—and what it means for teams, tools and roadmaps in 2026.
Key Highlights
Reality is not simple—and the paradox is real. AI has made teams faster—but also messier. While productivity is often higher at the individual or small-team level, those gains rarely scale cleanly across teams and organizations. This gap makes the path forward clear: AI must move from ad-hoc usage into systems, standards-aware UI generation and production-ready workflows designed to scale.
use or experiment with AI I their workflows
avoid AI entirely
report a positive productivity impact
still fix low-quality AI output
say collaboration is smooth in 2025
said the same in 2024
prioritize implementing AI effectively in 2026
expect AI to deliver incremental improvements next year
Overview
Teams are no longer just figuring out how to collaborate. They’re figuring out how to collaborate with AI — at scale, with governance and with impact.
This is more than a design–developer collaboration report. It’s a story about productivity, velocity, cost and execution at scale.
was about the collaboration gap.
was about the AI shockwave across workflows.
will be about AI operationalization and measurable business outcomes.
Methodology
Workflows in the Age of AI survey was conducted through an online self-completion questionnaire.
We recruited people that were web developers, designers or leaders with a stake in the design-developer collaboration process. Respondents were invited to take the survey through various channels—being prompted on the telerik.com website, through social media and blog post promotion and through paid promotion in newsletters and on social media.
The survey was open for submissions for the period September 29 — December 05, 2025, and the total number of responses is 225.
The AI Reality Check: Help, Hype or Both?
2025 was the year AI stopped being a side-project and became part of everyday work. Four out of five teams now use AI somewhere in their workflow but speed gains haven’t translated into trust. Teams are learning that AI accelerates work, as long as humans still own quality, consistency and governance.
Adoption Pattern—
AI is Now Standard
use or experiment with AI in some form
follow an AI-first approach across their process
still avoid AI entirely
How Would You Describe Your Team's or Organization's Current Level of AI Adoption?
How Teams Use AI Today
The most common AI use cases in 2025 focus on task acceleration, not workflow transformation.
This suggests that AI is currently helping teams move faster within roles but has not yet meaningfully reduced friction between roles.
Teams Utilize AI for Code-Related Tasks 4 Times More Than for Collaboration and Handoff
use AI for code-related tasks (generation, debugging, optimization) and this is the most common use case, outperforming the rest by far.
are using AI to improve the collaboration and handoff between roles.
How Is Your Team Currently Using AI Tools?
What prevents you or your team from adopting AI tools?
Security and privacy issues
Regulatory and compliance concerns
Quality and reliability concerns
Unclear return on investment
Cost of tools and subscription
Impact on Workflow & Productivity
AI is delivering productivity gains—but mostly incremental, not transformational.
report a positive impact on design-to-dev workflow and overall productivity (18% “significantly positive”, 51% “moderately positive”).
say AI had a negative impact (8% “moderately negative”, 4% “significantly negative”).
For a deep dive into all the insights and fascinating cross-sections, download the full report.
What Impact Has AI Had on Your Team's Design-to-Development Workflow and Overall Productivity?
Where AI Helps—and Where It Creates Work
AI delivers its strongest value in speed-oriented activities: prototyping, code suggestions and documentation support. These gains are immediate and visible.
The implication for 2026 is clear: AI needs to be embedded into systems that enforce quality and consistency - not layered on top of them.
Top Value Areas
faster prototyping & iteration
contextual code suggestions
testing & debugging
documentation and specs
Friction Points
struggle with inconsistent or low-quality AI output requiring fixes
say outputs don’t align with their design systems or standards
lack AI standards
perceive the cost of AI tools too high
Trust, Review and Governance
Trust in AI output remains cautious. Most respondents describe a “human-in-the-loop” approach, where AI accelerates work but does not replace review, testing or judgement.
Adoption is high. Autonomy is not.
fall into the “use with extensive review” camp: 19% generally trust, 50% somewhat trust but require thorough review and testing.
generally or highly distrust AI outputs and require extensive verification or major modifications.
How Much Do You Trust the Accuracy of AI-Generated Code or Designs?
Collaboration & Efficiency: What AI Did and Didn't Fix
For leadership, collaboration and AI only matter if they move delivery, quality and risk. The 2025 data shows a clear pattern: AI amplifies whatever foundation is already there. Teams with clearer processes and stronger collaboration see faster delivery and fewer redesigns; teams with weak foundations get more noise, more cleanup and the same old delays.
Collaboration Health & Efficiency
The cost of poor collaboration is not abstract. When alignment breaks down, teams report delays, rework, wasted time, quality issues and team morale and retention. AI does not eliminate these costs or replace people. In fact, without clear processes, it can accelerate teams towards the same problems as before, but faster.
AI improved the workflow. It didn’t (magically) fix the foundation.
describe design implementation as “very efficient - smooth handoffs, minimal issues”
say results are mixed
report ongoing inefficiency and regular delays/rework
How would you rate the efficiency of the design implementation process?
Among respondents who reported poor collaboration as an issue:
experienced delayed launches or slower time-to-market
face increased rework costs
report inefficient use of team time/resources
For a deep dive into all the insights and fascinating cross-sections, download the full report.
AI Impact on Outcomes
report that AI has had a significantly or moderately positive impact on design-to-dev workflow and productivity.
When zooming into the differences between different roles, it turns out the developers have experienced less positive impact than the rest.
still spend time fixing inconsistent AI output, and 36% say results don’t align with their design system standards.
What Impact Has AI Had on Your Team's Design-to-Development Workflow and Overall Productivity?
Hybrid Roles & Skills
AI blurred the boundaries between human and AI responsibilities
Hybrid roles are now common—reflecting blurred boundaries between design, development and AI-assisted work.
Hybrid roles reduce handoffs and speed up iteration—but without orchestration and clear boundaries, they shift coordination work onto individuals, increasing burnout and role ambiguity instead of eliminating friction.
Benefits of Hybrid Roles
see faster iteration and reduced back-and-forth
get broader skill coverage
report fewer handoffs
Downfalls of Hybrid Roles
warn about role overload and burnout
see skill depth sacrificed for breadth
note career-path confusion
2026 Outlook: From Experimentation to Production
If 2024 exposed the collaboration gap and 2025 was about AI disruption, 2026 is about operationalizing AI. Teams are moving from “try this new tool” to “how do we govern and integrate AI into our stack, roles and development process?” The survey shows cautious optimism—most teams expect AI to help but they’re planning for governance, hybrid skills and better foundations, not magic.
The 2026 to-do list is clear: AI + people + process have to move together.
Top priorities for improving collaboration and workflow efficiency in 2026:
Implementing AI tools effectively
Building hybrid skill sets across the team
Improving cross-team communication
Strengthening our design system
Expectations for 2026
The center of gravity is “AI will help—but we still have work to do.” Most respondents expect AI to deliver incremental improvements rather than dramatic disruption. This signals a shift from hype to accountability.
Adoption Pattern—AI Is Now Standard
expect AI to deliver incremental improvements to their workflow.
believe AI will revolutionize design-dev collaboration.
only expect minimal impact, 4% predict more complexity than benefits and 7% are too uncertain to call it.
Looking Ahead 12 Months, Which Statement Below Best Describes Your Expectations for AI's Impact on Your Workflow?
2026 Priorities = AI + Systems + Governance
Companies will need to build a more stable AI foundation. Organizations will invest in transforming their AI infrastructure into a scalable, flexible, and secure basis for business outcomes.
Accelerating AI-Powered Knowledge & Insights with Agentic Infrastructure
Enterprise-grade RAG + autonomous AI agents
AI Observability & Operational Governance
Trust, explainability and risk control across AI workloads
Prompt-to-Code Workflows and IDE-Integrated AI Development
AI-assisted UI code generation with MCP servers
Prompt-to-Page & AI-Driven Content Experiences
Generative CMS for content-first digital experiences
AI-Assisted Consistency & Design System Governance
Embed UI standards into AI workflows—design systems, branding, accessibility
Conclusion
In 2024, we learned that designers and developers often weren’t even seeing the same reality. In 2025, AI stepped into that gap—speeding some teams up, slowing others down and forcing everyone to rethink trust, governance and what “good enough” looks like. 2026 will be less about experimenting with AI and more about proving which AI-powered workflows and tools actually deliver better quality, faster releases and healthier teams.
Collaboration is still at the heartbeat of great products.
AI alone won’t replace or fix broken workflows. But teams that embed AI into structured, standards-based workflows will deliver faster, with less rework and more confidence.
About Telerik and Kendo UI
Progress Telerik and Progress Kendo UI help teams move from individual AI usage to production-ready, team-scaled AI workflows—where speed is matched with consistency, trust and measurable outcomes.