You need the Q3 pricing deck. The meeting starts in nine minutes. You search Drive, Slack, and the project folder, and somehow find six files named some version of “pricing_final_final_v3.” So you message Maya, because Maya always knows where things live.
That is the real problem AI workplace search is trying to solve: not “can a chatbot answer questions?” but “can your company stop depending on one helpful coworker as its memory system?”
The Problem Is Company Memory, Not Search
Traditional workplace search is fine when you know the exact filename. It falls apart when you ask the question the way a person would: “What pricing did we approve for healthcare customers?”
The answer might be in a deck, a Slack thread, a Salesforce note, a Jira ticket, a PDF, or a Confluence page nobody has touched since March. AI workplace search tries to pull those pieces together while respecting what you’re allowed to see.
That permission-aware search part matters. A good enterprise AI search tool should never show you a document you couldn’t open yesterday. If it does, you don’t have a productivity feature. You have a security incident with a nicer interface.
Glean, Slack AI, and Rovo: Same Category, Different Strengths
Think of the current tools in three shapes.
Glean AI is the company-wide layer. Glean describes its Work AI platform as enterprise search, assistants, agents, connectors, and an enterprise graph that maps relationships between people, content, and activity. Translation: it wants to sit across Drive, Slack, Salesforce, PDFs, ticketing systems, and internal docs so you can ask from one front door.
That’s powerful if your knowledge is scattered everywhere. The tradeoff is setup. Connectors, permissions, source ranking, and governance matter more than the chat box.
Slack AI is the conversation layer. Slack says it can summarize channels and threads, create daily recaps, search conversations and connected files, translate conversations, and take meeting notes. It also says customer data is not used to train large language models, and LLM providers do not have access to customer data.
Slack AI is strongest when the real answer lives in the thread, not the official document. If your team makes decisions in Slack and updates the wiki later — or never — that’s useful. Slack reported an internal pilot analysis where users saved an average of 97 minutes per week through recaps, summaries, and AI search answers. Nice claim, but the real win is fewer “does anyone remember why we delayed this?” messages.
Atlassian Rovo is the Atlassian layer. Atlassian positions Rovo around Search, Chat, Studio, and Agents for teams using eligible Jira, Confluence, Jira Service Management, or Teamwork Collection cloud plans. If your projects already live in Jira and Confluence, Rovo is the natural place to ask, “What changed, who approved it, and which ticket caused the delay?” Atlassian also says Rovo has completed SOC 2 and ISO 27001 external assessments, which is helpful for security review.
The Five Questions I’d Use Before Buying
Do not judge these tools by the homepage demo. Demos are house-trained. Test them on your messiest shared drive, noisiest channel, and least maintained wiki.
Use five real questions:
1. Where does the current travel policy live, and which source proves it is the version people should follow today? 2. What decision did we make about the delayed launch, who made it, and what document or conversation supports that answer? 3. Who owns the customer escalation workflow now? 4. What changed in this account last month across notes, tickets, messages, and the customer record? 5. What is the latest pricing guidance for this segment?
A good answer should give you the conclusion, cite its sources, and flag when sources disagree or look stale. If an AI search tool answers without sources, treat it like office gossip: maybe useful, absolutely not something you forward to leadership.
Permissions and Stale Docs Are the Real Product
The scary part is not that AI search finds things. It’s that it finds the access problems you forgot existed.
Before rollout, audit permissions. Contractor accounts, abandoned folders, old HR docs, and “temporary” shared links have a way of becoming visible the moment search gets good. Glean says it enforces strict permissions, runs in a single-tenant cloud, safeguards sensitive content, and validates agent actions before they happen. Those claims are worth checking against your own security requirements.
Stale knowledge is the second trap. An AI can beautifully summarize an old policy that nobody follows anymore. That’s worse than no answer, because it sounds official.
The fix is boring hygiene: mark official sources, archive dead pages, assign owners to key documents, and make source freshness part of the answer. AI search rewards teams that keep shelves labeled.
Start Read-Only, Then Expand
Agents sound exciting, but reading and summarizing is one risk level. Updating records, creating tickets, or triggering workflows is another.
Start read-only. Let the tool find, summarize, and cite. Once people trust the answers, add approval gates for actions. Human review may feel slower, but it beats an automated update based on a misunderstood document.
For rollout, skip the giant announcement. Pick one team with real pain: sales enablement, customer support, product operations, or HR policy. Give them two weeks and a shared scorecard. Track wrong answers, missing sources, time saved, and questions the tool could not answer.
The practical verdict: choose based on your center of gravity. Company knowledge everywhere? Test Glean. Mostly Slack conversations? Start with Slack AI. Atlassian-heavy projects? Rovo deserves a serious look.
This week, pick one recurring “where is that?” question. Ask your current tools, then ask one AI workplace search product. Compare not just the answer, but the sources.
The win is simple: fewer scavenger hunts, fewer interruptions, and a company memory that does not depend on asking Maya again.
