Gokan's Studio

The digital transformation of enterprise content management has reached a critical inflection point in 2026, transitioning from a passive document-storage era to a knowledge-orchestration era, and finally to an agentic era.

Organizations operating within the 365 ecosystem are increasingly moving beyond traditional, SharePoint, rule-based metadata management—which relied on deterministic structures such as calculated columns and fixed content types—toward a more fluid, AI-driven framework powered by LLMs.

The necessity of accurate SharePoint metadata is no longer an exercise in organizational hygiene; it is the foundational requirement for regulatory compliance, enhanced searchability through semantic search, and the execution of complex, automated business processes that drive modern business outcomes. As document volumes continue to grow exponentially –where Jeff Teper consistently highlights its growth, where SharePoint plays an immense role and becomes crucial for Microsoft 365- manual file tagging is a persistent bottleneck, and despite years of administrative efforts to enforce metadata standards, end-users frequently bypass these requirements, leading to information silos and a degradation of organizational findability.

The introduction of SharePoint AI and the SharePoint Knowledge Agent represents Microsoft’s comprehensive response to this “information neglect,” providing tools that automatically classify, extract, and generate metadata directly from the narrative content of documents. My blog post today can be seen as a report-analysed, tested, and automated within the manufacturing and military environment. We’ll see the technical architecture, third-party performance benchmarks, and strategic implementation pathways for supercharging SharePoint Autofill through the integration of native Microsoft features and advanced frontier models, such as Anthropic’s Claude 4.5 and OpenAI’s GPT-5.2.

The Evolution of Document Intelligence in Microsoft 365

The trajectory of intelligent document processing (IDP) in Microsoft 365 began with the launch of SharePoint Syntex in October 2020, which emerged from the conceptual foundations of Project Cortex. Syntex initially offered basic machine-learning content, but its branding and capabilities have since matured. In November 2023, Microsoft transitioned this offering from Microsoft Syntex to SharePoint Premium, a broader platform that encompasses content experiences, processing, and governance. By 2024, the pay-as-you-go services within this suite were consolidated under the functional designation of “Document Processing for Microsoft 365,” emphasizing a shift from per-user licensing to a more flexible, consumption-based model that eliminates upfront financial friction.

• If you want to learn more about Document Processing Era: Whitepaper: Document Processing Methods in SharePoint, Microsoft 365, and Power Platform
• If you want to compare the IDPs and learn about the future: The Future of Intelligent Document Processing in 2026: Autofill vs. AI Builder vs. Document Processor Agent

This strategic evolution reflects a fundamental realization: for AI to be effective, data must be high-quality, structured, and governed.

• AI must be effective: GPT (LLM in 365)
• Data must be high-quality: IDP (Autofill, Document Processing Agent, …).
• Data must be governed: SharePoint Advanced Management (SAM)

Tools like Microsoft 365 Copilot are not remediation mechanisms for decades of data disorder; rather, they are accelerators for organized knowledge. The “SharePoint plus Copilot Advantage” lies in the native integration, where the AI can “speak SharePoint fluently,” reasoning over not just the text within files but also the metadata, site structures, and permissions that provide essential business context

Branding and Functional Evolution of SharePoint AI Services

Era	Product Name	Primary AI Capability	Licensing Model
2020-2023	Syntex	Basic Document Understanding / Pattern Matching	Per-user Subscription
2023-2024	Premium	Advanced Content Processing / eSignature	Hybrid (Subscription + PAYG)
2025-2026	Document Processing for M365	LLM-powered Autofill / Agentic Curation	Pay-as-you-go ($0.005/page)
2026+	Knowledge Agent + Agents Ecosystem	Proactive Site/Library Orchestration	Included in Copilot License

Mechanics and Constraints of SharePoint Autofill

SharePoint Autofill columns represent one of the most accessible entry points for document intelligence. By associating a natural language prompt with a library column, users can instruct the underlying large language model (LLM) to perform specific extraction tasks. For example, a prompt might ask, “Extract the total contract value and specify the currency used,” which the AI then populates into the corresponding library fields upon file upload.

The native Autofill feature is powered by OpenAI’s GPT-4 Turbo and, more recently, the GPT-5 series. While these models are highly capable, Microsoft’s official documentation outlines specific performance boundaries. To maintain optimal throughput and accuracy, libraries are recommended to use no more than 10 autofill columns and process files with no more than approximately 65 pages per column. Documents exceeding this threshold may suffer from context window saturation, leading the model to “guess” or hallucinate metadata values for subjective fields such as “Confidentiality Level” or “Regulatory Risk”.

Parameter	Recommended Limit / Specification	Contextual Implication
Max Page Count	65 Pages per Document	Optimal accuracy threshold for GPT-4/5 architectures.
Max Columns	10 Autofill Columns per Library	Prevents excessive latency during bulk uploads.
Billing Threshold	Only charged up to 65 pages per file	Predictable costs even for very large documents.
Supported Types	Text, Choice, Date, Number, Currency, Yes/No	Managed Metadata is limited to 100 terms.
File Processing	Automatic on upload; Manual on modification	Reprocessing requires user intervention.

Source: Create an autofill column | Microsoft Learn

The economic model for Autofill was radically updated in March 2025. Microsoft reduced the transactional cost from $0.05 per page to $0.005 per page, a 90% decrease intended to facilitate massive scaling of metadata automation. This pricing adjustment allows organizations to process high-volume repositories—such as legacy invoice archives or historical policy folders—at a fraction of previous costs, significantly improving the return on investment (ROI) for information governance projects.

A transformative addition to the 2026 SharePoint environment is the Knowledge Agent, currently in public preview and scheduled for general availability in early 2026. Unlike traditional Autofill, which requires users to manually define extraction prompts, the Knowledge Agent serves as a proactive, intelligent curator. It is accessible via a “Floating Action Button” (FAB) in the SharePoint UI and adapts its capabilities based on the user’s role and location within the site.

The Knowledge Agent introduces the “Organize this library” capability, which uses AI to analyse existing files and suggest up to 3 new metadata columns to best enhance searchability and classification. This feature removes the technical barrier for site owners who may not know how to craft effective prompts. Furthermore, during the preview phase, the metadata generated through the Knowledge Agent is included in the Microsoft 365 Copilot license, bypassing the standard pay-as-you-go costs associated with standalone Autofill.

Gokan’s suggestion to the SharePoint AI team

Today, column creation is based on analyzing document content and identifying patterns. What I would like to see instead is the ability to reference a central knowledge source—such as the Managed Metadata Service or a dedicated list—where the knowledge already resides. The system could then cross-match against that source and propose only the three most relevant columns.

Beyond metadata extraction, the Knowledge Agent performs “site health” functions under the “Improve this site” menu. It can identify and retire inactive pages (de-prioritizing them in search results), detect broken links, and analyse search behavior to identify “content gaps” where users are looking for information that has not yet been documented. This proactive maintenance ensures the knowledge base remains fresh and accurate, which is critical for grounding Microsoft 365 Copilot’s responses.

Third-party benchmarking Frontier Models: GPT-5.2 vs. Claude 4.5 Opus

While native SharePoint tools are optimized for ease of use, sophisticated extraction scenarios often require the superior reasoning capabilities of frontier models. In the current market, OpenAI’s GPT-5.2 and Anthropic’s Claude 4.5 family (Sonnet and Opus) are widely regarded as leading frontier models for enterprise document analysis.

Anthropic’s Claude 4.5 series (Sonnet and Opus) offers a distinct architectural advantage for interpreting long-form documents.

1. Claude 4.5 Opus is engineered for “agentic” behavior, meaning it can maintain complex goal hierarchies over hours-long sessions without losing context or deviating from its objective. While GPT models are known for their raw execution power, they provide more exhaustive coverage of requested data points in long narratives.
2. Claude Opus 4.5’s most practical upgrade for SharePoint users is its massive 200,000-to 1,000,000-token context windows. This enables the model to ingest entire books, multi-hundred-page contracts, or complex research papers in a single session, bypassing the 65-page limitation of native Autofill. Furthermore, Claude 4.5 introduces the “Effort” parameter, allowing administrators to toggle between “medium” effort and “high” effort for high-stakes reasoning tasks
3. Claude Sonnet 4.5 is optimized for shorter, high-precision tasks and excels at understanding nuanced queries with a smaller context window. This makes it ideal for quick document summaries, coding assistance, or real-time collaboration where responsiveness and focused reasoning are critical. In contrast, Claude Opus 4.5 can maintain context across hundreds of thousands of tokens, enabling full-length books, research papers, or multi-department workflows to be processed in a single session without losing continuity.
4. Claude Sonnet 4.5 prioritizes accuracy and clarity over sheer scale, delivering highly reliable outputs for detailed yet contained queries. Opus 4.5, however, offers enhanced multi-agent coordination and adaptive effort settings, allowing it to tackle complex, multi-step reasoning tasks and dynamically adjust output style for different audiences. Essentially, Sonnet is built for precision in shorter engagements, while Opus is engineered for scale and extended goal-driven workflows.

• GPT-5.2 Review: Benchmarks (AIME 100%), Visual AI, SWEbench, and Competitive Analysis
• GPT-5.2 Review: Benchmarks vs. Gemini 3.0 & Claude 4.5
• https://podtail.se/podcast/the-top-ai-news-from-the-past-week-every-thursdai

Benchmark	Claude 4.5 Opus	GPT-5.2 Thinking	Gokan’s recommendations for SharePoint
SWE-bench Verified (software engineering)	80.9% (Leader)	80.0%	High-complexity code/doc analysis.
GDPval (knowledge work)	59.6%	70.9% (Leader)	Multi-step knowledge work execution.
GPQA Diamond (science questions)	87.0%	90.0%+	PhD-level scientific reasoning.
AIME (Math)	92.8%	100% (Leader)	Numerical logic and spreadsheet audits.
Context Window	200k – 1M Tokens (vendor messaging)	128k – 256k Tokens	Capability to process long documents.
Output Token Limit	64,000 Tokens	64,000 Tokens	Ability to generate long reports.

• Gemini 3 Pro vs GPT-5.2 vs Claude Opus 4.5
• GPT-5.2 Review: Benchmarks (AIME 100%), Visual AI, SWEbench, and Competitive Analysis

While such benchmarks may be plausible, they are not official and could quickly become outdated. I draw your attention to the fact that this should not be represented as the “current market state of the art,” as it is inherently speculative and should be clearly labelled as third-party data rather than fact, in case you want to show/mention it in official statements.

Advanced Architecture: The Claude-Powered Extraction for SharePoint

To achieve “best of the best” metadata performance, I think organizations, especially those with sophisticated data, will adopt a hybrid architecture that routes standard files through the Knowledge Agent and complex, long-context files through a custom Claude-powered agent. This pipeline relies on integrating Claude models into Copilot Studio and mapping the AI reasoning back to SharePoint columns.

The Performance Gap: Custom AI vs. Native Autofill

While native SharePoint Autofill is a convenient entry point for basic metadata, it often hits a “complexity ceiling” when faced with technical, long-form documentation. In my testing of a 165-page NATO doctrine, the difference between the out-of-the-box solution and a custom Copilot Studio orchestration powered by Claude was night and day.

• The “Wall of Text” vs. Structured Intelligence: Standard Autofill provides a functional but dense summary. In contrast, Claude transformed the 165 pages into a structured report, extracting specific “Joint Functions” and “Operations Themes” into high-fidelity tables that are immediately actionable.
• Breaking the 65-Page Barrier: Most standard models begin to lose coherence or skip details as documents exceed 65 pages. Claude maintained “full-text awareness” across the entire document, accurately capturing deep-nested references (like the Military Committee documents) that the native tool simply glossed over.
• Precision at Scale: The custom solution didn’t just summarize; it synthesized. By using a more robust model within Copilot Studio, we moved from simple keyword extraction to a deep understanding of the document’s architecture, proving that for high-stakes intelligence, custom orchestration is the only way forward.

SharePoint Autofill

Copilot Studio with Claude Opus 4.5

Lessons Learned 1 – Administrative Governance and Model Enablement

The prerequisite for this advanced workflow is the activation of Anthropic as a Microsoft sub-processor. As of January 7, 2026, Anthropic is officially onboarded into the Microsoft 365 security and compliance framework, meaning data processed by Claude is covered by Microsoft’s Data Protection Addendum (DPA) and Enterprise Data Protection (EDP).

However, administrators must be mindful of regional data residency. Anthropic models are currently excluded from the EU Data Boundary and in-country processing commitments. In the EU, EFTA, and UK, Claude models are disabled by default and require a conscious opt-in by a Global Administrator. Organizations in these regions must conduct thorough risk assessments to ensure that the use of models hosted by Anthropic (running on AWS infrastructure) aligns with their internal privacy policies.

Lessons Learned 2 – Orchestration via Copilot Studio

In Copilot Studio, a custom AI agent can be created using Claude Opus 4.5 as the primary reasoning engine. The agent is configured to trigger on file upload or modification. If writing results back to SharePoint is required, a Power Automate flow must be used. Alternatively, the output can remain within the agent itself or be written to Dataverse. The choice depends on how you plan to consume the AI-generated outcome and whether any data mashup or downstream integration is needed.

Lessons Learned 3 – Regional Data Residency and the 2026 Compliance Landscape

Working in a highly regulated environment has taught me one thing: sometimes the “where does it run” is more important than “how does it run”, and with the regulatory landscape for AI evolving rapidly, Microsoft is expanding its “in-country” data processing commitments. By the end of 2025, customers in Australia, the UK, India, and Japan will have the option for localized AI processing. In 2026, this will expand to eleven more countries, including Canada, Germany, the US, and Switzerland. These sovereign controls are vital for organizations in healthcare, financial services, and the public sector.

However, there is a technical divergence between native Microsoft models and Anthropic models regarding the EU Data Boundary. Native Copilot chat interactions for EU users remain within the EU Data Boundary, whereas Anthropic models are currently excluded from it. This means that while prompts and responses for Claude are protected by Enterprise Data Protection (EDP), the physical processing may occur in data centers outside the EU. Administrators must explicitly document this in their Data Protection Impact Assessments (DPIA)

Service	EU Data Boundary	In-Country Processing
M365 Copilot (Web)	Included	15 Countries (Select)
SharePoint Autofill	Included	Available
Claude (Subprocessor)	Excluded	Unavailable
Government Cloud (GCC)	Excluded	Unavailable

• Anthropic as a subprocessor for Microsoft Online Services | Microsoft Learn
• Microsoft offers in-country data processing to 15 countries to strengthen sovereign controls for Microsoft 365 Copilot | Microsoft 365 Blog

Lessons Learned 4 – Claude vs. Copilot LLM: Strategic Selection Matrix

Capability	Native Copilot LLM (Autofill)	Advanced Claude AI (Studio Pipeline)	Gokan’s Recommended Usage
Integration	Native SharePoint UI	Custom Agent + Flow	Native for simple libraries; Claude for custom apps.
Max Context	~65 Pages	200k – 1M Tokens	Claude for books, long contracts, and manuals.
Output Type	Direct Column Writing	Structured JSON – a developer is required	Claude for multi-field, complex logic. Native Copilot LLM for Structured Data
Visual Reasoning	GPT-5 Strong	Moderate	GPT-5 for scanned documents and charts.
Reasoning Speed	Fast / Real-time	Medium / Sequential	Native for high-volume uploads.
Compliance	EU Data Boundary Ready	Subprocessor	Native for regulated EU sectors.

To successfully supercharge SharePoint Autofill at an enterprise scale, organizations should follow the “crawl, walk, run” deployment strategy advocated by industry leaders. Here is how we tackled this:

Step 1: The Crawl Phase (Controlled Pilots)

Begin with 10-20 users in a low-risk environment, such as a departmental marketing library or a project-specific workspace. Use the Knowledge Agent or Pay-as-you-go with Autofill to automatically suggest columns and organize the content. If you go with PAYG, please set a budget cap in the Azure resource group for SharePoint document processing to avoid financial surprises during experimentation. I do recommend setting spending alerts at 80% of the allocated monthly budget

Step 2: The Walk Phase (Advanced Automations)

Once the pilot demonstrates ROI, expand to high-value libraries such as legal contracts or HR policies. Implement the Claude-powered Power Automate pipeline for files that exceed the 65-page limit. Leverage the “Effort” parameter in Claude Opus 4.5 to manage costs—reserving “high effort” for final risk assessments and “medium effort” for routine data extraction.

Effort Parameter: Claude Opus 4.5 vs 4.1: 3x Cheaper & Better? – MPG ONE

Step 3: The Run Phase (Autonomous Orchestration)

If Microsoft proceeds as currently announced, by July 2026, traditional “SharePoint Alerts” will be fully retired and replaced by Power Automate notification systems. In the final phase, organizations should deploy autonomous, site-specific agents that use Autofill-generated metadata to provide AI-driven answers to employees. The focus shifts from manual oversight to “agent governance,” using the new Agents 365 Admin pane to monitor permission sprawl and site health organization-wide.

My Conclusions for SharePoint AI vs Claude AI

Supercharging SharePoint Autofill requires a nuanced balance between the ease of native Microsoft tools and the raw intelligence of frontier language models. The most effective enterprise architecture for 2026 is a hybrid one:

1. Use Autofill: It is the best-of-the-best for general site hygiene, proactive link repair, and creating basic metadata columns. Its inclusion in the Copilot license makes it the most economical starting point.
2. Deploy GPT-5.2: When accuracy in reading charts, tables, and financial spreadsheets is paramount, OpenAI’s native visual retrieval technology remains the industry leader.
3. Reserve Claude 4.5: For documents over 100 pages or tasks requiring sustained autonomous thinking (such as regulatory gap analysis or cross-document logic).
4. Prioritize Governance and Regional Residency: Administrators must be proactive in managing opt-ins for Anthropic models in the EU and ensuring that all AI actions are captured in audit logs for compliance.

By embracing this multi-model strategy and leveraging the proactive curation of the Knowledge Agent, organizations can transform their SharePoint environments from static document repositories into dynamic, AI-ready intelligence platforms that drive measurable productivity gains and strategic advantage.

Hope that helps!

Renewed Revolution!