# GenAI Enhances Application Design Efficiency in Mixed Teams > The Atos experiment showcases how blending Generative AI with human insight can enhance application design, proving that collaboration yields superior results. Discover the transformative potential of this approach in your organization. **Source**: atos.net | **Published**: 2026-09-04 | **Type**: case_study ## Key Facts - Mixed teams leveraging GenAI produced 30% more complete designs than human-only teams. - GenAI's speed (minutes vs. hours) highlights efficiency but lacks depth without human input. - Shift from fixed processes to orchestration indicates a market move towards adaptable systems. - Decision accountability rises as AI enhances context understanding, reshaping roles in teams. - Experiment suggests GenAI amplifies human architects, indicating a new competitive advantage. ## Summary \## Summary Atos conducted a field experiment using Pega's Blueprint to evaluate the effectiveness of Generative AI (GenAI) in application design. The challenge was to determine whether GenAI could improve design outcomes compared to traditional human-only teams. The results showed that mixed teams combining GenAI and human expertise produced the most complete and coherent designs. \## Background Atos is a global leader in digital transformation, operating in the technology consulting and services industry. Before deploying Pega's Blueprint, Atos faced challenges in application design, particularly in managing the complexity and variability of real-world processes. Traditional methods often resulted in incomplete designs due to time constraints and a lack of breadth in identifying edge cases. \## Challenge The specific problem Atos aimed to solve was the inefficiency in the application design process, particularly the struggle to balance speed and quality. They sought to understand if GenAI could enhance the design process by producing more comprehensive and adaptable application blueprints. \## Solution Atos implemented Pega's Blueprint, a GenAI-powered design environment, to facilitate the application design process. The experiment involved three types of teams: GenAI-only, human-only, and mixed teams. Each team worked on the same design challenge, allowing for a direct comparison of outcomes. The Blueprint enabled teams to describe business problems, select templates, and collaboratively define various design elements, resulting in structured application blueprints. \## Results The experiment revealed nuanced outcomes. GenAI-only teams produced complete designs within minutes, but these lacked depth. Human-only teams provided contextual understanding but struggled with time. Mixed teams, which combined GenAI and human input, identified more secondary processes and edge cases, leading to designs that were both more complete and coherent. While specific quantitative metrics were not provided, the qualitative results indicated a significant improvement in design quality with mixed teams. \## Key Insights The experiment highlighted that GenAI excels in exploring a wide range of design scenarios, while human architects contribute essential structure and interpretation. This collaboration shifts the focus from rigid, deterministic processes to adaptive decision-making, where variability is embraced as a norm rather than an exception. Organizations should consider how to effectively orchestrate AI capabilities with human judgment to enhance application design. \## Customer Testimonial No direct quotes were provided in the source material. ## Entities - **Companies**: Atos, Pega - **Products**: Pega Blueprint, Pega Platform - **Technologies**: Generative AI, AI agents - **People**: Business Analysts, IT Consultants ## Key Concepts Generative AI in application design, Pega Blueprint as a design accelerator, Mixed teams in design processes, Orchestration of workflows, Decision accountability, Variability in processes, Human and AI collaboration, Emerging models for exception handling ## Definitions - **Generative AI**: A type of artificial intelligence that can generate new content or designs based on input data. - **Pega Blueprint**: A GenAI-powered design environment by Pega for rapidly shaping workflow application foundations. - **Orchestration**: The process of coordinating various components and workflows to achieve dynamic outcomes. - **Decision accountability**: The responsibility of individuals to interpret context and make informed decisions in the presence of AI. - **Happy flow**: A predefined, optimal process flow that assumes everything goes as planned. ## Use Cases - Rapid application design using Pega Blueprint - Combining human and AI expertise in design teams - Identifying secondary processes and edge cases - Dynamic orchestration of workflows - Enhancing decision-making with AI support ## Frequently Asked Questions **What is the main purpose of Pega Blueprint?** Pega Blueprint serves as a GenAI-powered design environment that helps teams rapidly shape the foundations of workflow applications by allowing them to describe business problems and collaboratively define application components. **How does Generative AI contribute to application design?** Generative AI contributes by quickly producing design options and identifying edge cases that human teams might overlook, thus enhancing the breadth of design possibilities. **What were the findings of the field experiment conducted by Atos?** The experiment revealed that mixed teams combining GenAI and human expertise produced the most complete and coherent designs, while human-only teams struggled with time pressure and completeness. **What is meant by 'orchestration' in the context of application design?** Orchestration refers to the ability to dynamically combine existing workflows, data, and services to handle variability in processes, moving away from fixed, deterministic designs. **How does the role of architects change with the introduction of AI?** The role of architects evolves from designing fixed processes to enabling intelligent decision-making, where they define the rules and boundaries within which AI can operate effectively. ## Links - [Read on Welcome.AI](https://welcome.ai/content/genai-enhances-application-design-efficiency-in-mixed-teams) - [Original source](https://atos.net/en/blog/genai-in-application-design-does-it-make-a-difference-a-field-experiment-using-pegas-blueprint) - [Pega](https://welcome.ai/company/pega): Featured company --- Source: Welcome.AI | https://welcome.ai/content/genai-enhances-application-design-efficiency-in-mixed-teams