AI-Powered Enterprise Search: Why Your Company Intranet Is Already Obsolete

Every company has the same problem. The information exists — in SharePoint, Google Drive, Slack threads, email chains, Notion pages, Confluence wikis, CRM notes, and that one spreadsheet Dave from accounting keeps on his desktop. The information exists, but nobody can find it.

Your company intranet was supposed to fix this. It didn't. It became a graveyard of outdated policies, broken links, and pages last updated in 2019. Employees learned to ignore it and ask Steve instead, because Steve has been here twelve years and knows where everything is.

Steve is now a single point of failure for your entire organisation's institutional knowledge. And Steve is thinking about retiring.

AI-powered enterprise search fixes this — not by building a better intranet, but by making the concept of "where is the information stored" irrelevant.

Why Traditional Enterprise Search Fails

You've tried search before. SharePoint has search. Google Workspace has search. Confluence has search. They all technically work — and they all practically fail. Here's why:

Keyword matching isn't understanding. Search for "maternity leave policy" and you'll find the policy document — if someone titled it exactly that. Search for "how long can I take off when I have a baby" and you'll get nothing. Traditional search matches words, not meaning.

Information silos. Each platform searches its own content. Your HR policy is in SharePoint. The clarification email from HR is in Outlook. The Slack discussion about how it actually works in practice is in Slack. No single search covers all three.

No synthesis. Even when traditional search finds relevant documents, you get a list of links. You still have to open each one, read through it, and piece together the answer yourself. For a simple question like "what's our returns policy for trade customers?" you might need to read three documents and cross-reference them.

Stale results. Search engines index periodically. The document was updated yesterday, but search still shows the old version. Or worse, search returns both versions and you don't know which is current.

No context. "Revenue" means something different to the sales team, the finance team, and the marketing team. Traditional search doesn't understand who's asking or what they need.

What AI Enterprise Search Actually Does

AI-powered enterprise search uses large language models combined with retrieval-augmented generation (RAG) to fundamentally change how employees find information. Instead of returning a list of documents, it returns answers.

Natural Language Questions, Direct Answers

Before (traditional search): Employee types: "sick leave policy" Result: 14 documents, 3 of which are relevant, 1 is current

After (AI search): Employee types: "If I'm off sick for more than a week, what do I need to provide and who do I tell?" Result: "For absences longer than 7 calendar days, you need a fit note from your GP. Notify your line manager on the first day of absence and HR within 3 days. Self-certification covers the first 7 days. Source: HR Policy v4.2 (updated January 2026), Section 3.1."

The AI doesn't just find the document — it reads it, understands the question, extracts the specific answer, and cites the source so you can verify.

Cross-System Knowledge

AI enterprise search connects to your existing tools and treats all of them as a unified knowledge base:

• Documents: SharePoint, Google Drive, Dropbox, OneDrive, Notion, Confluence
• Communication: Slack, Teams, email archives (with appropriate privacy controls)
• Structured data: CRM records, project management tools, HR systems
• External sources: Industry regulations, supplier documentation, standards

When an employee asks a question, the AI searches across all connected systems simultaneously. It doesn't matter where the information lives — only that it exists somewhere.

Understanding, Not Just Matching

The difference between keyword search and AI search becomes dramatic with real-world questions:

"What did we agree with Acme Ltd about payment terms?" AI search finds the contract in SharePoint, the amendment discussed in email, and the Slack message where your account manager confirmed the change. It synthesises all three into a single coherent answer.

"Has anyone in the company worked with Kubernetes before?" AI search finds CVs in HR systems, project documentation mentioning Kubernetes deployments, Slack messages discussing it, and training certificates — producing a list of people with relevant experience.

"What's our standard approach to GDPR data subject access requests?" AI search finds the formal policy, the step-by-step process document your DPO wrote, the template response letter, and the recent update about new ICO guidance — all in one answer.

How It Works Under the Hood

Understanding the architecture helps you evaluate solutions and set realistic expectations:

1. Connectors and Indexing

The system connects to each of your data sources through APIs or connectors. It ingests documents, messages, and records, then processes them into a searchable format.

This isn't simple copying. The system:

• Extracts text from PDFs, Word documents, spreadsheets, and presentations
• Processes images and diagrams where possible (OCR and visual understanding)
• Respects access permissions — employees only see results from systems they have access to
• Maintains freshness — re-indexing on schedules or via webhooks when content changes

2. Embedding and Vector Storage

Each piece of content is converted into a mathematical representation (a vector embedding) that captures its meaning, not just its words. These embeddings are stored in a vector database.

When someone searches, their question is also converted into an embedding. The system finds content whose meaning is closest to the question's meaning — regardless of whether the exact words match.

This is why "how long can I take off when I have a baby" successfully matches a document titled "Maternity and Paternity Leave Policy" even though few words overlap.

3. Retrieval and Generation

When a question comes in:

1. The system finds the most relevant chunks of content from across all connected sources
2. These chunks are passed to a large language model along with the question
3. The model generates a natural language answer, synthesising information from multiple sources
4. Source citations are attached so the answer can be verified

4. Access Control

This is critical and often underappreciated. The AI must respect your existing permissions. If a document is restricted to the HR team, it shouldn't appear in answers to non-HR employees.

Good enterprise search systems inherit permissions from the source systems. If you can't access a file in SharePoint, you won't see it in AI search results either.

Practical Benefits for UK Businesses

Time Savings That Actually Add Up

Research consistently shows knowledge workers spend 20-30% of their time searching for information. For a company with 100 employees at an average fully-loaded cost of £45,000, that's £900,000 to £1.35 million per year spent looking for things.

AI enterprise search doesn't eliminate all of that — some searching is genuinely complex research. But cutting information retrieval time by 50-70% is realistic for routine questions. That's a significant productivity gain without hiring anyone or changing processes.

Onboarding Acceleration

New employees typically take 3-6 months to become fully productive, largely because they don't know where to find information or how things work. AI search lets new hires ask questions and get instant, accurate answers drawn from the organisation's collective knowledge.

Instead of waiting for someone to be available to explain the expenses process, the new hire asks the AI search system. It finds the policy, the step-by-step guide, and the link to the expenses form — in seconds.

Knowledge Preservation

When experienced employees leave, their knowledge walks out the door. If they've been answering questions in Slack, writing process notes in Confluence, and sending explanatory emails for years, that knowledge is scattered across systems. AI search makes it accessible regardless of where it was originally captured.

This is particularly valuable for UK SMEs where a single departure can create significant knowledge gaps. The information doesn't disappear — it just needs to be findable.

Compliance and Consistency

When employees can easily find the correct, current policy, they follow it. When they can't, they guess — or ask a colleague who might be wrong.

AI search surfaces the authoritative answer and cites the source. For regulated industries (financial services, healthcare, legal), this consistency is directly relevant to compliance.

Implementation: What Actually Works

Start Small and Specific

Don't try to index everything on day one. Start with the systems that generate the most "where do I find..." questions:

Phase 1 (Week 1-2): Connect HR policies, company handbook, and IT documentation. These are the highest-volume routine queries in most businesses.

Phase 2 (Month 1-2): Add project documentation, client files, and CRM data. This addresses the "what did we agree with..." and "have we done this before..." questions.

Phase 3 (Month 3+): Add communication archives (Slack, Teams, email) with appropriate privacy controls. This captures the informal knowledge that never makes it into formal documents.

Choose the Right Tool for Your Size

Small businesses (under 50 employees): Dedicated AI search tools like Glean, Dashworks, or Guru can be set up in days, not weeks. They offer pre-built connectors to common tools and handle the infrastructure. Costs typically start at £10-20 per user per month.

Mid-market (50-250 employees): The same tools work but become more cost-effective with annual contracts. You might also consider Microsoft Copilot if you're already embedded in the Microsoft ecosystem, or Google's AI search features for Google Workspace shops.

Enterprise (250+ employees): You'll likely need a more customisable solution. Open-source options like Danswer (now Onyx) let you self-host, controlling data residency and customisation. Enterprise solutions from vendors like Coveo or Elastic offer advanced features like analytics on what people search for (revealing knowledge gaps).

Getting Permissions Right

This is the most important technical decision. Get it wrong and you either expose confidential information or make the system so restrictive it's useless.

Mirror existing permissions: The AI search system should respect the same access controls as the source systems. If someone can't access a file in SharePoint, they shouldn't see its content in search results.

Define sensitive categories: Some content needs extra protection beyond system permissions — salary information, disciplinary records, board minutes. Define these categories explicitly and ensure the search system handles them appropriately.

Audit regularly: Run test queries as different user roles. Can a junior employee access information they shouldn't? Does a manager see the right level of detail? Monthly audits prevent permission drift.

Managing Quality and Trust

Employees will only use AI search if they trust the answers. Build trust intentionally:

Always show sources. Every answer should cite the specific document, message, or record it drew from. Users can click through to verify.

Flag uncertainty. When the AI isn't confident or when sources conflict, it should say so. "Based on the current policy document, the answer is X — however, an email from June 2025 suggests this may have changed. You may want to check with HR." Honesty builds trust faster than false confidence.

Handle "I don't know" gracefully. When the information doesn't exist in any connected system, the AI should say so — and ideally suggest who might know or where to look. This is infinitely more useful than returning zero results.

Create a feedback mechanism. Let employees flag incorrect answers. Use this feedback to improve the system and identify knowledge gaps (if the same question keeps getting wrong answers, the source content probably needs updating).

Common Objections (and Honest Answers)

"Our data is too sensitive for AI." Understandable concern. Self-hosted solutions keep all data on your infrastructure. Cloud solutions from enterprise vendors offer data processing agreements, no-training guarantees, and UK/EEA data residency. Evaluate the specific solution's data handling — don't dismiss the category.

"People will stop documenting things if AI just finds everything." Actually, the opposite tends to happen. When people see that their documentation is actively used (because the AI surfaces it in answers), they write better documentation. Knowing your process guide actually helps people — rather than sitting unread in a wiki — is motivating.

"What if the AI gives wrong answers?" It will, sometimes. That's why source citations are mandatory, not optional. The question is whether AI search gives wrong answers more or less often than Steve from accounting — who's confident, helpful, and wrong about 15% of the time but nobody checks.

"We don't have enough content to make this worthwhile." If your employees ask each other questions, you have enough content. The threshold is surprisingly low — even a 20-person company with a few hundred documents and active Slack/Teams usage has enough for useful AI search.

"We already have Microsoft Copilot / Google AI." These are improving rapidly and may be sufficient, especially if you're fully committed to one ecosystem. Their limitation is cross-system search — Copilot searches Microsoft tools brilliantly but doesn't index your Slack, custom CRM, or non-Microsoft file stores. Evaluate whether your information lives in one ecosystem or spans several.

Measuring Success

Track these metrics to prove (or disprove) value:

Search-to-answer time: How long from question to answer? Baseline this before implementation.

Repeat question rate: Are the same questions being asked repeatedly? If the AI search handles them, that's measurable time saved.

Helpdesk/IT ticket deflection: How many "how do I..." tickets decrease after implementation?

Adoption rate: What percentage of employees use AI search weekly? Low adoption usually means the answers aren't good enough or the tool isn't accessible enough — both fixable problems.

User satisfaction: Simple monthly survey. "Did you find what you needed?" with thumbs up/down is enough.

What This Means for the Company Intranet

The traditional intranet isn't dead — it just changes role. Instead of being the primary place people go to find information (a job it was always bad at), it becomes a publishing platform. HR publishes policies there. IT publishes guides there. The AI search system indexes it alongside everything else.

The intranet becomes a source, not a destination. Nobody needs to navigate its menu structure, remember which section something is in, or bookmark pages. They just ask their question and get an answer.

That's not the death of the intranet. It's the intranet finally fulfilling its original promise — making company information accessible to everyone who needs it.

Getting Started This Month

1. List your top 20 "where do I find..." questions. Ask your helpdesk, IT team, or just pay attention in Slack for a week.
2. Map where the answers live. Which systems contain the information? This tells you which connectors you need.
3. Trial a tool. Most enterprise AI search tools offer 14-30 day trials. Connect 2-3 sources and test with real questions.
4. Measure before and after. Time how long it takes to answer your top 20 questions today. Measure again with AI search.
5. Roll out to a pilot team. Pick a department that asks a lot of questions (often new hires or customer-facing teams). Get their feedback before company-wide rollout.

---

Want help implementing AI-powered search for your organisation? Get in touch — we help UK businesses unlock the knowledge they already have.