Is Your SharePoint Ready for Microsoft Copilot? The AI Revolution Starts with Information Architecture

Microsoft Copilot is rolling out now, but most SharePoint environments aren't ready for AI. In this episode, Liza Tinker reveals why your current SharePoint setup will likely fail with Copilot and what you can do about it.

Discover the critical information architecture foundations that make AI effective, learn how to finally resolve the folders vs. metadata debate with hybrid approaches that work, and get practical strategies from real client implementations. 

From marketing teams finding campaign templates in seconds to HR departments enabling intelligent policy discovery, you'll hear exactly how proper information architecture transforms both human productivity and AI effectiveness.

Whether you're a SharePoint administrator, IT professional, or business owner responsible for Microsoft 365, this episode provides the roadmap for building SharePoint environments that don't just survive the AI revolution—they thrive in it.

 Plus, learn about the new comprehensive guide that's helping organisations prepare their information architecture for Copilot success.

Key Topics:

• Why most SharePoint environments fail with Microsoft Copilot
• The three pillars of AI-ready information architecture
• Hybrid folder and metadata strategies that actually work
• Real-world implementation examples and quick wins
• How to audit your current SharePoint for AI readiness

Ready to future-proof your SharePoint? This episode shows you exactly how to get started.

Transcript

SPEAKER_01: 0:00

Welcome to Simply SharePoint, the podcast where we cut through the complexity and give you practical strategies for SharePoint success. I'm Liza Tinker, and today we're diving into what might be the most important SharePoint conversation of 2025. Is your organization ready for Microsoft Copilot? Now, I know what you're thinking. Another AI episode? Really? But here's the thing. While everyone's talking about what Copilot can do, almost no one is talking about what you need to do to make Copilot actually work in your environment. And trust me, if you think your current SharePoint setup is ready for AI, you're probably in for a rude awakening. Today, we're going to explore why most SharePoint environments will fail with Copilot. what you can do about it and how to build information architecture that doesn't just survive the AI revolution, but thrives in it. Let's start with a reality check. Microsoft Copilot is rolling out across organizations right now and the marketing promises are incredible. AI that understands your content, surfaces relevant information, automates workflows and basically turns your SharePoint into this intelligent business assistant. But here's what Microsoft isn't telling you in those glossy demos. Copilot is only as good as your information architecture. And if I'm being honest, most SharePoint environments I see are nowhere near ready for AI. Let me paint you a picture. Imagine you're working on a project proposal and you ask Copilot to find similar proposals from the past year. In a well-structured environment, Copilot instantly surfaces three relevant proposals with clear metadata about project types, departments and outcomes. But in real world SharePoint environments, Copilot returns a random mix of documents because it can't tell the difference between a project proposal and a project status report. Why? Because they're both just Word documents sitting in folders with names like project stuff and important documents. I was speaking with another consultant last month who was working with a client which was a mid-sized consulting firm, and they were so excited about Copilot. They'd seen the demos, bought the licenses, and expected immediate productivity gains. Three weeks later, their IT director called them in frustration. Copilot keeps giving us irrelevant results, he said. It's actually making things worse. When they audited their SharePoint environment, the problem was obvious. They had 47 different document libraries across 23 sites with no consistent naming inventions, minimal metadata and folder structures that went eight levels deep. Copilot was trying to make sense of chaos and chaos doesn't make good AI training data. So what does AI ready information architecture actually look like? Let's break this down into the fundamental building blocks that make Copilot effective. First, you need rich, consistent metadata. Think of metadata as the vocabulary that helps AI understand your content. When you upload a document, you're not just storing a file. You're creating a data point that AI can analyze, categorize, and connect to other information. But here's where most organizations go wrong. They either have no metadata strategy, or they have metadata that's inconsistent across different sites and libraries. I see this all the time. HR calls their document type field category, while marketing calls theirs content type, and legal uses document classification. To humans, these might seem similar, but to AI, they're completely different data points. The solution is developing enterprise-wide metadata schemas that work across your entire organization. This means standardizing not just the field names, but the values within those fields. Instead of having some documents tagged as policy, others as policies, and still others as company policy, you need one consistent term that everyone uses. Second, you need logical content relationships. AI excels when it can understand how different pieces of content connect to each other. This goes beyond just organizing documents in folders. It's about explicitly defining relationships between projects, departments, processes, and outcomes. For example, instead of just having a folder called quarter four marketing campaign, you want metadata that connects campaign documents to specific products, target audiences, budget allocations, and performance metrics. This allows Copilot to understand not just what the documents are, but how they fit into your broader business context. Third, you need structured content types. This is where SharePoint's content type functionality becomes crucial for AI readiness. Content types define not just what fields a document has, but what kind of information it contains and how it should be processed. When you create, say, a content type for project proposal, you're telling the system, and by extension, Copilot, that this document contains specific types of information, such as project objectives, timelines, budgets, stakeholders. This structure enables AI to extract and analyze information in meaningful ways. Now, let's address the elephant in the room. The eternal folders versus metadata debate. If you've been in the SharePoint world for more than five minutes, you've heard this argument. Folders are evil. Use metadata for everything. Or the opposite. Users understand folders. Metadata is too complicated. Here's the truth that's going to save you years of implementation headaches. It's not either or. The most successful AI-ready SharePoint environments use hybrid approaches that leverage the strengths of both folders and metadata. Think about it from a user perspective. When someone needs to find a contract, they might naturally think it's probably in the legal folder, under contracts, under 2024. That's intuitive folder navigation. But when Copilot needs to analyze contract data across your entire organization, It needs metadata like contract type, parties involved, expiration dates, and renewal terms. The hybrid approach gives you both. You create a logical folder structure that matches how people think about content organization, but you also implement rich metadata that enables AI analysis and cross-content connections. Now, here's a practical example from a client implementation. Their marketing department has folders organized by campaign type, product launches, brand campaigns, event marketing. Within each folder, they use metadata to tag documents with specific products, target audiences, budget ranges, and performance metrics. This means a marketing manager can browse to product launches and find what they need intuitively. But Copilot can also analyze all marketing content across campaigns to identify successful strategies, budget optimization opportunities, and audience engagement patterns. The key is using folders for user navigation and metadata for AI intelligence. Folders provide the browsing experience that people expect, while metadata provides the structured data that makes AI effective. But here's the critical part. your folder structure needs to support metadata inheritance. When someone saves a document in the, say, the product launches 2024 folder, it should automatically inherit metadata values for campaign type and product. This reduces the metadata burden on users while ensuring consistent data for AI analysis. Now let's get practical. How do you actually implement AI-ready information architecture in the real world? I want to share three implementation strategies that I've seen work across different types of organizations. Strategy one, start with your most critical business processes and don't try to fix everything at once. Identify the business processes where AI could have the biggest impact and focus your information architecture if it's there first. I worked with a small marketing team that was constantly recreating the wheel. Every time they needed to create a new campaign brief, they'd spend ages looking through old folders trying to find a good example to copy. They had folders called things like 2023 campaigns, old projects, and marketing stuff. You know the drill. Finding anything useful was like searching for a needle in a haystack. So we made one simple change. we created a campaign briefs library with just three pieces of information for each brief. The campaign type, like say product launch or brand awareness, the target audience, like small business or enterprise, and whether the campaign was successful or not. That's it, nothing fancy. Now, when they're starting a new product launch campaign for small businesses, they just ask Copilot, Show me successful product launch campaigns for small businesses. Instead of digging through random folders for hours, they get exactly what they need in seconds. The result, they went from spending half a day hunting for examples to having the perfect template in minutes. And because they're building on what actually worked before, their campaigns will be more successful too. Strategy two. Implement metadata inheritance to reduce user burden. The biggest barrier to metadata adoption is user resistance. People don't want to fill out forms every time they save a document. The solution is smart defaults and inheritance patterns. For example, when someone creates a document in a project folder, it automatically inherits the project name, project manager and department. Users only need to add document specific metadata like document type and status. This reduces the metadata burden while ensuring consistent data for AI analysis. Strategy three, design for both current needs and future AI capabilities. This means thinking beyond just organizing content for human consumption You want to structure information in ways that enable AI to identify patterns, make connections, and provide intelligent recommendations. A great example is how one client restructured their HR documentation. Instead of just organizing policies by department, they added metadata for policy types, compliance requirements, effective dates, and related procedures. This enables Copilot to not just find relevant policies, but also identify compliance gaps, flag outdated procedures, and suggest policy updates based on regulatory changes. The key insight here is that AI-ready information architecture isn't just about better organization. It's about creating data relationships that enable intelligent analysis and automated insights.

SPEAKER_00: 12:29

Before

SPEAKER_01: 12:32

we wrap up, let's talk about the common pitfalls I see organizations fall into when preparing for Copilot and how you can avoid them. Pitfall number one, assuming your current SharePoint structure is good enough for AI. I can't tell you how many times I've heard, our SharePoint is pretty well organized. Copilot should work fine. Then they implement Copilot and wonder why the results are inconsistent or irrelevant. The solution is conducting an honest audit of your current information architecture. Look at your metadata consistency, content organization patterns, and user behavior. If humans struggle to find information in your current setup, AI will struggle too. Pitfall number two, over-engineering metadata schemas. Some organizations go to the opposite extreme and create incredibly complex metadata requirements that users simply won't follow. Remember, metadata is only valuable if it's consistently applied. The solution is starting simple and evolving over time. Implement core metadata fields that provide immediate value, then gradually add more sophisticated classification as users become comfortable with the process. Pitfall number three, ignoring change management. Technical implementation is only half the battle. If users don't understand why information architecture matters for AI success, they won't follow the new processes. The solution is connecting information architecture changes to business outcomes. Show people how better organization leads to more effective AI assistance, which leads to increased productivity and better business results. So where do you go from here? If you're feeling overwhelmed by the scope of preparing your SharePoint environment for Copilot, you're not alone. This is complex stuff and the stakes are high. Organizations that get this right will have significant competitive advantages. Those that don't will struggle to realize AI benefits while their competitors pull ahead. The good news is you don't have to figure this out on your own. I've spent the last year documenting everything I've learned about information architecture across my entire, almost 20-year career working with SharePoint. And I've just released the first piece of that journey, a comprehensive guide called Modern Information Architecture in SharePoint and Microsoft 365. This guide is your foundation. It's 109 pages that walk you through all the core components of information architecture. You'll understand libraries, views, folders, metadata, content types, document sets, permissions, and workflows. Most importantly, you'll see real-world examples of how organizations are implementing these components to prepare for AI. And this isn't just theory. It's practical implementation strategies and specific frameworks you can adapt to your organization. you'll learn how to balance user needs with AI requirements, so you're not just building for technology, you're building for people and productivity. Now, this guide is just the beginning of your journey. At the end of this month, I'm releasing a complete toolkit that will take you from understanding information architecture to actually implementing it in your environment. This toolkit will include all the templates, schemas, and step-by-step implementation guides you need to transform your SharePoint. But if you're serious about preparing your SharePoint environment for the AI revolution, you need to start with understanding the fundamentals. This guide provides that foundation. You can find it at simplysharepoint.com. And because you're a podcast listener, I want to make sure you know that this isn't just about organizing content. It's about positioning your organization for competitive advantage in an AI-powered world. The organisations that invest in proper information architecture today will be the ones that dominate their markets tomorrow. The question is, will you be ready? Thanks for listening to Simply SharePoint. Until next time, keep building better SharePoint experiences.