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Solution · Other solutionsEvery answer carries the page it came from, and every unanswered question finds a page owner
One place to ask, an answer with its source, gaps reported
Employees ask in Microsoft Teams, the agent answers only from indexed sources and cites the page and its date, and every question it cannot answer becomes a task for the owner of that page.
Executive summary
The same questions are asked in channels and tickets because nobody can find the current answer.
The agent is the smaller half of this solution; the loop that repairs the documentation is the half that makes it hold.
Everyone receives the same sourced text, so the answer stops depending on who replies first.
SharePoint and Confluence Cloud pages updated by their owners; ServiceNow tickets for what the sources cannot answer; Microsoft Planner task queues
Business problem
Knowledge
Enterprise knowledge is rarely missing. It is scattered, unversioned and unranked. Policies live on SharePoint, the engineering truth in Confluence, the service desk keeps its own articles, and every team has a thread where the real answer was given once, to one person. Search returns everything and confirms nothing, so people ask a colleague, which costs two people's time, or raise a ticket, which costs a queue.
When a rule changes, the old page stays live. The new rule travels in an email, a slide and a meeting, while the page everyone finds first still says the old thing. Owners never learn which pages are wrong, because nothing connects the failed question to the page that should have answered it.
The people who pay are the ones you can least afford to interrupt. Twelve service desk agents answer questions alongside real requests and report the lot as workload, so every capacity plan is wrong by the share that was never a request. New joiners learn to trust whoever replies fastest rather than the documented rule, which is how a stale expense limit becomes a rejected claim.
How it works today
The route is the same in most organisations, and nothing it touches improves for the next person.
- PersonAn employee searches SharePoint, gets forty results and cannot tell which page is current
- PersonThe question goes into a Teams channel; two colleagues answer differently, one from a page superseded last spring
- WaitingThe employee waits, then raises a ticket because nobody in the thread sounded certain
- PersonAn agent hunts for the knowledge article, answers from memory or asks a specialist, and closes the ticket with free text
- Risk of errorThe resolution text holds the right answer and stays inside the ticket, where nobody will search for it
- WaitingThe same question arrives next week from somebody else, and the page that should have answered it is never touched
Why the current process costs more than it appears
Nobody planned this work; it accumulated.
- Each repeated question is billed three times: the asker's search, the colleague's interruption and, often, a ticket the service desk counts as demand.
- Inconsistency is the expensive part. A wrong answer about a security rule or an expense limit becomes an incident or a rejected claim, never traced back to the page that caused it.
- Specialists become the knowledge base in person, so the organisation runs on memory and holiday calendars.
- Nobody can size the gap, because unanswered questions leave no trace, so the clean-up has no order and never starts.
- Reporting mixes questions with requests, so the capacity plan is built on a number that includes work no request system should have received.
Cost of inaction
Headcount, tools and reorganisations all add questions, and none of them adds documentation. The channels absorb the extra silently, the service desk keeps a fixed share of tickets that were never requests, and the capacity plan stays wrong by that share. Documentation decays at the speed policies change, and the decay stays invisible until an audit finds the live page two versions behind the rule people were following.
The part these rows cannot price is the answer that was simply wrong. A superseded expense limit produces a rejected claim, a superseded security rule produces an incident, and neither is traced back to the page. Employees who expect an assistant will meanwhile find an ungoverned one on their own phone, a governance problem arriving from a direction nobody budgeted for.
A plausible organisation with realistic proportions. The figures are there to be recalculated on your data; they are not a client result.
An engineering consultancy with 4,200 employees in three countries; policies on SharePoint (about eleven thousand pages), engineering standards in Confluence Cloud, service desk articles in ServiceNow, Microsoft 365 and Microsoft Teams in daily use.
About 1,900 questions a month of the "how do I", "where is" and "what is the rule" kind reach the IT and HR service desks and the main Teams channels; twelve agents answer them alongside real requests, and a knowledge manager keeps a list of pages she suspects are stale.
People search, then ask in a channel, then raise a ticket. The answer comes from whoever replies first or from an agent's memory, and the resolution text stays inside the ticket, where the next asker will not find it.
Eleven minutes of answering effort per question by an agent or a colleague, plus about six minutes the asker spends searching and waiting, and no signal from the failed question back to the page that should have answered it.
One place to ask in Microsoft Teams, answering only from indexed sources the asker may open and citing the page with its last-changed date; anything not covered becomes a ServiceNow ticket and a logged gap, and each week's gaps reach page owners as Microsoft Planner tasks with a draft update.
Questions the documents already answer are settled in seconds at any hour, the service desk separates questions from requests for the first time, and the twenty pages that would remove the most questions are named and repaired inside a quarter. Every quantity here is a model, built on stated assumptions.
Proposed solution
The agent is the smaller half of this solution; the loop that repairs the documentation is the half that makes it hold, and we treat that loop as the product. One place to ask is a UiPath conversational agent published to Microsoft Teams as a Teams app, answering from UiPath Context Grounding indexes over your SharePoint sites and Confluence Cloud spaces. Those indexes inherit the permissions of their sources, so the agent cites only what the asker could have opened.
Every answer carries the page it came from and the date that page was last changed, which is what lets a reader judge it. Where the sources hold no answer, the agent says so, offers a ServiceNow ticket with the conversation attached, and logs the question as a gap. Those refusals are the valuable output: the only honest measurement of what your documentation fails to cover.
Weekly, UiPath Communications Mining clusters two populations by intent: questions the agent could not answer, and tickets that were really questions. Ranked by volume, the list opens with the pages that would remove the most questions rather than the pages someone remembers. A robot turns each cluster into a Microsoft Planner task for the page owner, with the questions attached and, where a closed ticket holds the answer, a draft to accept, edit or reject. Nothing publishes itself.
Where you hold Microsoft 365 Copilot licences, a Microsoft Copilot Studio agent with SharePoint knowledge can carry the same answers, the loop unchanged underneath. Power BI shows the answer rate with a source, the top unanswered intents, the failing pages and the owners who have not acted.
UiPath conversational agents in Microsoft Teams; UiPath Context Grounding indexes over Microsoft SharePoint, OneDrive and Confluence Cloud with inherited permissions; UiPath AI Trust Layer allow-list, PII masking, EU routing and prompt logging; UiPath Communications Mining intent clustering; Microsoft Planner tasks in Teams; Microsoft Purview audit and retention; Power BI; optionally a Microsoft Copilot Studio agent with SharePoint knowledge
The index scope and refresh rules, the agent instructions, citation format and refusal behaviour in each working language, the gap log, the weekly clustering and ranking, the owner-task robot with its drafts and chasing, the service desk escalation and the Power BI dashboard
ServiceNow tickets and knowledge articles through the UiPath Integration Service connector; Confluence Cloud spaces indexed by Context Grounding; Microsoft Planner tasks and page metadata through Microsoft Graph and the Microsoft OneDrive & SharePoint connector
How the automated process works
- PersonAn employee asks in Microsoft Teams, where they would have asked a colleague
- AutomationThe agent retrieves from the Context Grounding indexes, trimmed to what that person may open, and answers with the source page and its date
- AutomationIf the sources carry no answer, the agent says so, offers a ServiceNow ticket with the conversation attached, and logs the question with its intent
- AutomationEach week UiPath Communications Mining clusters the unanswered questions and the tickets that were really questions, and ranks the clusters by volume
- SystemA robot creates a Microsoft Planner task for each affected page's owner, with the questions attached and a draft where a closed ticket holds the answer
- PersonThe owner edits or rejects the draft and publishes the page in SharePoint or Confluence Cloud, where versioning keeps the previous text
- AutomationThe index refreshes, the dashboard records the closed gap, and the next person who asks gets the answer with its new date
Human-in-the-loop model
Automation handles
- Answering from indexed sources with the page and its date, or declining and offering a ticket
- Logging every question with its outcome, intent and confidence
- Clustering unanswered questions and question-tickets weekly and ranking them by demand
- Creating and chasing owner tasks, drafting updates from ticket resolutions, refreshing the index after a change
People decide
- What a page should say: owners accept, edit or reject every draft, and nothing publishes without them
- What the documentation cannot answer, resolved by the service desk as a normal ticket
- Which sources belong in scope and which questions the agent must never attempt, set by the knowledge manager
- Whether an HR or security page is fit to be indexed, decided by the team that owns the policy
Before and after
Systems and integrations
Where a rule suffices we do not use a model. Where judgement is needed, a person decides.
Inputs
- questions asked in Microsoft Teams
- SharePoint policy pages
- Confluence Cloud engineering spaces
- ServiceNow articles and closed question-tickets
- page metadata with owners and dates
Automation layer
- UiPath Orchestrator
- UiPath conversational agents
- UiPath Context Grounding
- UiPath Communications Mining
- UiPath Integration Service
Target systems
- SharePoint and Confluence Cloud pages updated by their owners
- ServiceNow tickets for what the sources cannot answer
- Microsoft Planner task queues
- Power BI
Human touchpoints: the agent conversation in Microsoft Teams; owner tasks in Microsoft Planner; service desk escalations; the knowledge manager's weekly review of the gap list
Technologies used
one place to ask, published to Teams as a Teams app
Apermission-inherited indexes over SharePoint, OneDrive and Confluence Cloud
Aallow-listed model, PII masking on prompts, EU region routing, prompt and answer log
Aweekly intent clustering of unanswered questions and question-tickets
Aowner tasks, drafts, chasing, index refresh; ServiceNow and Microsoft connectors
Athe owner's task queue inside Microsoft Teams, with due dates
Aanswer rate with a source, top unanswered intents, failing pages, owner response times
AIllustrative economic model
Start by questioning the assumptions.
The 55% is the number worth arguing about, so it comes first: once the ranked gaps are closed, the model assumes that share of questions can be answered from documentation, measured by a pilot on one month of your real questions. Two pools sit under it. Answering effort: 1,900 questions a month at 11 minutes by an agent or a colleague, €47 an hour fully loaded, of which 55% is addressable. Asker effort: the same questions at about 6 minutes of searching and waiting, at €54 an hour. Blended, that is roughly 12 minutes per question at €50, and the rounding moves the annual figure by about one and a half percent. Wrong answers, onboarding and Copilot Credit consumption stay outside the model, and nothing here was measured at a client.
Run the numbers on your data
An illustrative estimate from your own inputs. It models released capacity; it is not a promise of savings.
Business benefits
- Everyone receives the same sourced text, so the answer stops depending on who replies first
- Time to an answer falls from a thread that runs for days to seconds in Teams, with a page and date the asker can check
- Service desk capacity returns as questions stop arriving as tickets, and reporting separates questions from requests
- Documentation improves in the order demand dictates, because the gap list is ranked by how often each answer was needed
- Stale pages are found by the questions they fail, not by an annual review nobody finishes
The management view
- Question volume becomes a measure of where the organisation is confused, worth reading before a policy roll-out or a system change lands
- The top unanswered intents are visible weekly, with the pages and owners behind them, so knowledge work is prioritised like any other backlog
- Every indexed page has a named owner, a task queue and a response time, and service desk demand splits into questions and requests
Board-level KPIs
Security and governance
Where the data sits and who can see it.
- The index inherits the permissions of SharePoint and Confluence Cloud, so a question about a document the asker may not see returns nothing rather than a summary
- Prompts and answers pass through the UiPath AI Trust Layer: an allow-listed model, PII masking, processing kept in the EU region, and a log of every exchange retained under Microsoft Purview with the Teams conversation
- The agent answers only from indexed sources and declines otherwise; HR and security pages enter the index once the team that owns the policy confirms them
- Drafts never publish themselves. A page changes when its owner approves it, where versioning keeps the previous text and the audit trail shows who changed what, and when
Why now
Employees compare an internal tool with the assistant on their own phone. A channel that answers with a source is the expectation, and the alternative is not "no assistant" but an ungoverned one nobody approved
Retrieval over your own documents is now a platform feature rather than a project: UiPath Context Grounding indexes SharePoint and Confluence Cloud with permissions inherited, so the weak point is the documentation and the loop that maintains it
The modelled €19,000 a month is paid a few minutes at a time by people who never report it, and it grows with every tool, policy and reorganisation the year adds
Relevant executive roles
A governed answer channel with sources and an audit log, instead of the ungoverned assistant employees will otherwise use
Fewer interruptions of the people who know, and consistent answers in place before a process change lands
Policy questions answered from the current policy, with a record of what was told to whom and when
Question volume separated from request volume, and a service desk that stops being a search engine
Common questions and objections
For a licensed user it does, and where you hold those licences we can put the same answers there. What it does not do is log the questions it could not answer, cluster them by intent, route them to the page owner and chase the fix. That loop is what we add, on top of Copilot rather than instead of it.
Which is why the agent cites and dates every answer, draws only on sources you approved, and declines when they are silent. A refusal costs one ticket; a confident invention costs trust.
That is the argument for starting, not for waiting. The gap list ranks the clean-up by demand, so the first month names the twenty pages that would remove the most questions.
When this is not the right solution
- Documentation so thin that less than about a fifth of questions could be answered from it; write the pages first, then index them
- No page owners willing to accept tasks: without the loop this is search with citations and the pages stay wrong
- A single-domain need such as HR policy alone, better served by a narrower agent with its own validation rules
A question for the next management meeting
When our last policy change went live, how long did the superseded page keep answering people, and who in this company could have told us that it was?
Implementation approach
We start with one slice of the process and extend only once it is proven.
We deliver
- A classification of one month of your real questions and question-tickets, which sets the answerable share before anything is built
- Index scope, refresh rules and the permission model, agreed with the owner of every source, and the agent itself: instructions, citation format, refusal behaviour, working languages, guardrails and the question bank it is tested against
- The gap loop: logging, weekly clustering in UiPath Communications Mining, ranking, owner tasks in Microsoft Planner and the draft robot
- The escalation into ServiceNow with the conversation attached, so nothing the agent declines is dropped
- The Power BI dashboard, owner training and the weekly review routine with your knowledge manager
We need from you
- Page owners who will accept tasks, and a decision on what happens to pages that have no owner
- One month of questions and tickets, and access to the sources you want indexed with permissions intact
- A rule for retiring superseded pages, because an index of contradictions answers confidently and wrongly
Stages
Sizing
Classify a month of questions and measure what the documentation actually answers
Design
Index scope, agent behaviour, refusal and escalation rules, the owner and task model
Pilot
Two domains, typically IT and HR policy, for one business unit, with the service desk watching the log
Scale and run
The remaining sources and domains, then the weekly gap cycle with the knowledge manager
Departmental. The effort sits in the content rather than the agent: how many sources, how much they contradict each other, how many pages have no owner, and how many languages the answers need.
Three answers in ten minutes, and the page that should have settled it is two years old.
Export one month of "how do I" questions from your service desk and busiest Teams channels. You get back the share your documentation could answer today, the twenty pages that would close the biggest gaps, and a written assessment of what the loop needs from your owners.
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