Opening: The Arrogant Intern Arrives You’ve probably heard this one already: “AI can run SharePoint now.”No, it cannot. What it can do is pretend to. The newest act in Microsoft’s circus of automation is the SharePoint Knowledge Agent—a supposedly brilliant little assistant that promises to “organize your content, generate metadata, and answer questions.” The pitch sounds amazing: a tireless, robotic librarian who never stops working. In reality, it behaves more like an overly confident intern who just discovered search filters. This “agent” lives inside SharePoint Premium, powered by Copilot, armed with the optimism of a first-year analyst and the discipline of a toddler with crayons. Microsoft markets it like you can finally fire your SharePoint admin and let AI do the filing. Users cheer, “Finally! Freedom from metadata hell!”And then—spoiler—it reorganizes your compliance folder alphabetically by emoji usage. Let’s be clear: it’s powerful, yes. But autonomous? Hardly. It’s less pilot, more co-pilot, which is a polite way of saying it still needs an adult in the room. In fact, it doesn’t remove your metadata duties; it triples them. Every document becomes a theological debate about column naming conventions. By the end of this, you’ll know what it really does, where it fumbles, and why governance officers are quietly sweating behind the scenes.So. Let’s start with what this digital intern swears it can do. Section 1: The Sales Pitch vs. Reality — “It Just Organizes Everything!” According to Microsoft’s marketing and a few overly enthusiastic YouTubers, the Knowledge Agent “organizes everything for you.” Those four words should come with an asterisk the size of a data center. What it really does is: generate metadata columns, create automated rules, build filtered views, and answer questions across sites. In other words, it’s not reorganizing SharePoint—it’s just giving your documents more personality disorders. Think of it like hiring an intern who insists they’ll “clean your desk.” You return two hours later to find your tax receipts sorted by paper thickness. It’s tidy, sure, but good luck filing your return. Before this thing even works, you must appease several bureaucratic gods: * A paid Microsoft 365 Copilot license, * An admin who opts you into SharePoint Premium, * And, ideally, someone patient enough to explain to your boss why half the columns now repeat the same data differently capitalized. Once summoned, the agent introduces three main tricks: Organize this library, Set up rules, and Ask a question. This triumvirate of convenience is Microsoft’s long bet—that Copilot-trained metadata will fuel better Q&A experiences across all 365 apps. Essentially, you teach SharePoint to understand your files today so Copilot can answer questions about them tomorrow. Admirable. Slightly terrifying. Now for reality: yes, it can automatically suggest metadata, yes, it can classify documents, but no, it cannot distinguish “Policy Owner” from “Owner Policy Copy2.” Every ounce of automation depends entirely on how clean your existing data is. Garbage in, labeled garbage out. And every fix requires—you guessed it—a human. The seductive part is the illusion of autonomy. You grant it permission, step away, and when you come back your library gleams with new columns and color-coded cheerfulness. Except behind that cheerful façade is quiet chaos—redundant fields, inconsistent tags, half-applied views. Automation doesn’t eliminate disorder; it simply buries it under polish. That’s the real magic trick: making disarray look smooth. So what happens when you let the intern loose on your document library for real? When you say, “Go ahead, organize this for me”?That’s when the comedy starts. Section 2: Auto‑Tagging — The Genius That Forgets Its Homework Here’s where our talented intern rolls up its digital sleeves and promises to “organize this library.” The phrase drips with confidence, like it’s about to alphabetize the universe. You press the button, expecting harmony. What you get looks more like abstract art produced by a neural network that just discovered Excel. The “organize this library” function sounds deceptively simple: it scans your documents, then suggests new columns of metadata—maybe things like review date, department owner, or document type. Except sometimes it decides your library needs a column called “ImportantNumberTwo” because it found the number two inside a filename. Yes, really. It’s like watching a gifted student ace calculus and then forget how to spell their own name. The first time you run it, you’re tempted to panic. The suggestions look random, the preview window glows with meaningless fields, and nothing seems coherent. That’s because it isn’t ready yet. The engine quietly does background indexing and content analysis, a process that can take hours. Until then, it’s basically guessing. In other words: if you click “create columns” right away, you get digital gibberish. Give it a night to sleep—apparently, even artificial intelligence needs to dream before it makes sense. When it finally wakes up, something magical happens: the column suggestions actually reflect structure. You might see “Review Date” correctly pulled from the header of dozens of policies. You realize it read the text, detected a pattern, and turned it into metadata. For about ten seconds, you’re impressed. Then you notice it also created “Policy Owner,” “policy owner,” and “POLICY OWNER” as separate fields. SharePoint now speaks three dialects of English. This is the first real lesson: the AI doesn’t create order—it amplifies whatever chaos already exists. Your messy document formatting? Congratulations, it’s now immortalized as structured data. It’s not malicious; it’s just painfully literal. Every inconsistency becomes a column. Every formatting quirk becomes an ontology. The intern has taken notes... on your sins. Now, Microsoft anticipated your existential crisis and thankfully made this process optional. None of these changes apply automatically. You, the alleged human adult, must review every suggestion and explicitly choose which columns to keep. The interface even highlights pending changes with polite blue indicators, whispering, “Are you sure about this?” Copilot isn’t autopilot, it’s manual labor dressed up in predictive text. You approve each change, remove duplicates, rename things, and only then commit the metadata to the view. The irony? It took you longer than just building the columns yourself. Still, when it works, it’s genuinely clever. You can preview values across a sample set—see how “Policy Owner” fills with department names, how “Review Date” populates from document headers. It’s a quick way to audit the mess. Then you apply it across the library and watch the autofill process begin: background workers hum, metadata slowly populates, and you briefly consider sending the AI a thank-you note. Don’t. It’s about to betray you again. Because here comes the lag. Updating and filling metadata is asynchronous; while it churns, the columns display blank values. For minutes—or hours. Users think nothing happened, so they rerun the task, doubling the chaos. Then, minutes later, both sets of updates collide, overwriting partially filled data. It’s not a bug; it’s a test of faith. The agent rewards patience, punishes enthusiasm. Think of it as hiring a genius who works fast only when you stop looking. Versioning adds another comedy layer. Suppose you upload “Policy_v2.docx.” The AI dutifully copies the metadata from “Policy_v1,” including the outdated owner field. Then it takes a breath—sometime later, it realizes the content changed—and kicks off another round of metadata inference. Eventually, it catches up. Eventually. If your workflow relies on instant accuracy, this delay will drive you to drink. Once you understand that timing problem, you start treating it like the quirk it is. You schedule re-indexes overnight, monitor autofill queues, and laugh bitterly at anyone who thought this feature meant less work. That’s the human‑in‑the‑loop model in action: the AI proposes, the human disposes. You curate its guesses. You correct its spelling. You restrain its enthusiasm. The agent doesn’t replace judgment—it demands supervision. On good data sets, the results can be surprisingly useful. Policy libraries gain uniform fields. Audit teams can filter documents by owner. Searching for “review due next quarter” suddenly returns everything tagged correctly. The machine gives structure to your chaos—but only after you rebuilt half of it yourself. The paradox of automation: it scales efficiency and stupidity at the same time. The truth? This tool shines in broad classification. It can tell contracts from templates, policies from forms. But when it comes to compliance tagging—records management, sensitivity labels, retention categories—it’s out of its depth. It reads content, not context. It recognizes words, not accountability. That’s fine for casual queries, disastrous for legal retention. And yet, despite all that, you’ll keep using it. Because even partial organization feels like progress. The intern may forget its homework, but at least it showed up and did something while you were asleep. Just remember to check its math before sending the report upstream. Of course, our intern isn’t satisfied with sorting papers. No, it wants responsibility. It insists it can follow rules too—rules written in plain English. Naturally, we’re about to let it try. Section 3: Natural Language Rules — Governance for Dummies Enter the second act of our drama: rules. The Knowledge Agent now claims it can “set up rules” using natural language—no coding, no Power Automate wizardry, just a friendly c
