Excel: Autofill Columns with AI Lists

Key insights

• In a community demo Chris Kent (Takeda) showed how Autofill Columns in SharePoint can auto-populate metadata and link to AI image generation, turning document content into searchable, visual entries.
  
• The feature uses natural language prompts and LLMs to read files and extract, summarize, or generate column values — for example, client name, policy owner, or audience classification.
  
• Setup is simple: add a new column (or Content AI), choose a column type (Text, Choice, Number, Managed Metadata), enable Autofill, and write a plain-language prompt; you can run autofill per file or plan bulk runs later.
  
• Combine list formatting (JSON) to change how results display without altering data, and pair outputs with image APIs like Pollinations.ai to generate visuals driven by metadata for richer library views.
  
• Administrators keep control via org settings: they can limit which sites use Autofill, disable the feature, and manage billing under a pay-as-you-go model; note current limits (no Person columns, encrypted files, or some library types).
  
• Key benefits include faster, consistent tagging, stronger governance with managed term sets, and scalability for large libraries — ideal for compliance, client tracking, and audit preparation.
  

Introduction

The Microsoft-led demo, presented by Chris Kent of Takeda during a Microsoft 365 & Power Platform community call, shows how to combine Autofill Columns, JSON list formatting, and Pollinations.ai to generate images and metadata automatically from documents. Consequently, the video frames a practical workflow that moves metadata from static values to dynamic visual elements within SharePoint libraries. Moreover, the demo exemplifies how natural language prompts can help populate columns and then render those results with custom list formatting for richer, visual experiences.

What the Demo Covered

First, the presenter demonstrated configuring an Autofill Columns prompt to extract or generate metadata from uploaded files, such as client names, policy owners, or audience classifications. Then, he showed how to apply JSON list formatting to those columns so SharePoint can display icons, links, or embedded visual outputs without changing the underlying data. Finally, the demo explored using an external generative-image service, Pollinations.ai, as a downstream step to convert extracted metadata into contextual images that appear in the list view.

Benefits and Practical Gains

This approach promises clear efficiency gains by reducing manual tagging and improving consistency across large libraries. For example, teams that previously relied on human review can now extract structured values from unstructured documents and surface them at scale, thereby improving searchability and governance. In addition, list formatting enhances readability and user experience by turning raw metadata into clickable links or visual cues that help users scan and act on content faster.

Tradeoffs and Cost Considerations

Nevertheless, organizations must balance automation benefits against cost and control. While Autofill Columns operates under a pay-as-you-go model that helps predict spending, integrating external image-generation services adds separate usage and licensing considerations. Furthermore, automated outcomes may require human review, so time savings can vary depending on the complexity of documents and the accuracy required for compliance or legal scenarios.

Governance and Security Challenges

Administrators retain control through tenant settings and can limit the feature to specific sites or disable it altogether, which helps manage risk. However, challenges remain: some column types like Person fields are not supported, and encrypted or unsupported files cannot be processed automatically. Moreover, routing metadata to external AI image services introduces privacy and content-safety concerns that organizations must address through policy and vendor agreements.

Implementation Tradeoffs and Best Practices

Practically, teams face tradeoffs when deciding how much to automate. On one hand, broad automation accelerates tagging and supports consistency; on the other hand, strict governance and manual checks reduce the risk of misclassification or hallucinated outputs. Therefore, a phased approach usually works best: pilot with non-sensitive libraries, validate prompts, and then expand while monitoring accuracy and cost.

Technical and UX Considerations

From a technical perspective, integrating JSON list formatting lets teams present results in flexible ways without touching source data, but writing and maintaining JSON can be error-prone for non-developers. Consequently, organizations should document common patterns and provide templates to reduce maintenance work. Additionally, rendering images generated by external services requires attention to performance and bandwidth so that list views remain responsive.

Future Directions and Community Value

The demo hints at expanding capabilities, such as bulk processing and additional column support, which would further scale automation for large repositories. Furthermore, community calls and shared samples help practitioners learn real-world patterns and avoid common pitfalls, making it easier for teams to adopt these features responsibly. Lastly, integrating richer AI outputs with governance controls will be critical as organizations balance innovation with compliance.

Conclusion

In summary, the YouTube demo presents a practical pattern for turning document metadata into dynamic visual experiences in SharePoint by combining Autofill Columns, JSON list formatting, and external image generation. While the workflow can deliver efficiency and improve UX, it also introduces tradeoffs around cost, governance, and accuracy that require careful planning. Ultimately, organizations that pilot thoughtfully and tighten governance can realize strong benefits while keeping risks under control.

Keywords

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