Power Platform: Generative Pages Scaled

Key insights

• Generative Pages
  From the video, this feature lets makers describe a page in plain language and generate React-based pages inside model-driven Power Apps. It creates editable, production-ready UI that ties directly to the app's data model.
• Orbit
  Brian Hodel describes Orbit as T‑Mobile’s large-scale go-to-market platform that combines Canvas apps with Dataverse. The episode shows how Orbit supports thousands of employees with integrated, user-focused experiences.
• Dataverse
  The team designs data models in Dataverse to handle scale, permissions, and common CRUD patterns across apps. Generative Pages only works with Dataverse tables, so planning tables and relationships up front is crucial.
• Development workflow
  Brian demos a workflow that mixes AI prompts, manual code edits, and GitHub Copilot inside Visual Studio Code to build interfaces fast. Pages remain source-controlled and extensible, enabling multi-developer collaboration and DevOps practices.
• Enterprise scale & governance
  He emphasizes performance, security, and accessibility when rolling out Power Platform at scale, using centralized governance, tenant controls, and testing patterns to keep solutions reliable. Design choices focus on maintainability and user adoption across large teams.
• Limitations & practical tips
  Practical constraints include Dataverse-only data sources and tenant feature requirements, so validate environment and Copilot settings before starting. Use sketches, clear prompts, and iterative previews to speed delivery while keeping manual edits for complex needs.

Overview

Microsoft published a new episode of its Keeping It Real series titled "Building enterprise‑scale Power Platform apps with Generative Pages," and the video centers on practical guidance for large organizations. In this installment, host Leon Welicki interviews Brian Hodel of T‑Mobile to explore how teams can design and run Power Platform solutions at scale. The conversation mixes high-level strategy with a live demonstration, making the episode useful for both architects and hands‑on developers.

Importantly, the video highlights how AI‑assisted tools are changing routine app development, while also stressing the need for governance and testing. Consequently, viewers get a sense of both the upside and the operational work required to adopt these tools in production environments. The episode also clearly frames where Generative Pages fits into existing app portfolios.

T‑Mobile’s Orbit: A Real-World Example

Brian Hodel shares the evolution of T‑Mobile’s Orbit platform, which began as experimentation with Power Apps and expanded into a go‑to‑market system used by thousands of employees. He explains how Orbit blends Canvas apps with model-driven capabilities and the Dataverse data model to support a wide set of scenarios. As a result, Orbit demonstrates how different app types can interoperate inside the same enterprise platform.

Moreover, the episode shows that scaling an internal platform requires clear design patterns and repeatable practices, rather than ad‑hoc development. For example, Brian emphasizes consistency in data modeling and reuse of components to reduce maintenance overhead. Thus, the story of Orbit offers a practical template for organizations aiming to centralize app delivery while preserving flexibility.

Finally, Brian’s account makes clear that cultural adoption matters as much as technology. He notes that success depends on training, governance, and a support structure that allows citizen makers and professional developers to collaborate. Therefore, the technology alone is not sufficient without an operational model that encourages responsible use and continuous improvement.

How Generative Pages Changes Development Flow

The video’s demo focuses on using Generative Pages inside model-driven apps to speed interface creation from prompts and sketches. Brian walks through creating pages by selecting Dataverse tables, issuing natural‑language prompts, and then refining the generated React-based code. He also shows how to switch to Visual Studio Code for manual edits and to integrate with GitHub Copilot, which together enable rapid iteration and developer oversight.

Consequently, teams can move from concept to a working UI much faster than traditional hand‑coding would allow, and they can still apply standard DevOps practices. However, the video emphasizes that generated pages are a starting point; developers often refine behavior, accessibility, and integration points after generation. Therefore, the flow becomes a hybrid of AI-assisted creation followed by targeted engineering work.

Additionally, the episode showcases practical features that matter in production such as charts, file uploads, and dark mode, while reminding viewers that some limitations currently apply. For example, the capability is tied to certain data sources and tenant configurations, so organizations must plan around those constraints. Thus, teams should evaluate readiness and roadmap implications before wide rollout.

Tradeoffs and Operational Challenges

Adopting generative tools brings clear speed benefits but also introduces tradeoffs around control, accuracy, and governance. On one hand, rapid prototyping reduces time to value and helps non‑developers contribute more directly; on the other hand, it can create uneven quality or hidden technical debt if organizations skip design and testing steps. Therefore, teams should balance empowerment with guardrails.

Security and compliance also surface as practical challenges when scaling AI‑assisted development. The episode stresses the need to enforce permissions, monitor data access, and validate outputs against accessibility standards. Moreover, limitations such as data source support and environment portability mean that large enterprises must invest in integration and migration planning.

Finally, the human factors are nontrivial: reliable prompts and AI outputs depend on clear requirements and skilled reviewers. Consequently, successful programs combine maker training, developer checkpoints, and automated testing to keep pace with accelerated development cycles. In short, the promise of speed requires complementary investments in people and processes.

Guidance for Enterprise Teams

For organizations considering this approach, the video recommends starting small and measuring impact, while progressively expanding governance and reuse. Teams should begin with pilot projects that map to clear business value and then capture patterns for wider adoption. This incremental strategy reduces risk and produces reusable components for future projects.

Furthermore, the episode advises combining AI generation with established DevOps practices and manual code review to preserve quality. In practice, that means using source control, automated testing, and role‑based access controls as part of the delivery pipeline. Ultimately, by blending Generative Pages with engineering discipline, enterprises can accelerate innovation without sacrificing reliability.

Keywords

enterprise-scale Power Platform apps, Generative Pages Power Platform, enterprise Power Apps best practices, scaling Power Platform apps, Power Platform governance and security, low-code enterprise app development, Microsoft Power Platform architecture, Generative AI for Power Pages