Viva Engage Question to Expert Routing

Overview

This flow connects Viva Engage community questions to subject-matter experts. On an hourly schedule it scans a configured Viva Engage community, identifies posts that look like questions and have gone unanswered past a threshold, matches each question to an expert from a Dataverse roster (by topic), notifies the matched expert in a Microsoft Teams channel, and records the routing in a Dataverse ledger for idempotency and audit.

Why it matters: Unanswered questions erode community value. Automatically routing them to the right people lifts response rates, surfaces organizational expertise, and prevents questions from slipping through the cracks.

Status: Built as an unmanaged solution; ships Off. Going live requires only connection authorization and environment-variable configuration.

Use Case

A knowledge-sharing Viva Engage community wants questions that nobody has engaged with to be automatically routed to the appropriate subject-matter expert. Rather than relying on people to monitor the feed, the flow watches for unanswered questions, matches them by topic against a maintainable expert roster, and posts a prompt to a Teams routing channel. Every routing is logged so the same question is never routed twice.

Flow Architecture

Environment Variables

Connectors & Connections

Connector	API name	Actions used
Viva Engage	shared_yammer	GetMessagesInGroupV3
Microsoft Teams	shared_teams	PostMessageToConversation
Microsoft Dataverse	shared_commondataserviceforapps	ListRecords CreateRecord

Note — All connections are referenced as solution connection references; the flow is portable between environments as long as a connection is mapped at import time.

Customization Guide

Almost every realistic variant of this flow can be implemented by changing environment variable values. A few cases require small edits inside the flow definition — those are called out explicitly below.

Maintain the expert roster

Add rows to Engage Expert with a single keyword in flowlibs_topicarea (matched against the question text) and flowlibs_isactive = Yes. Use flowlibs_topickeywords for documentation/future multi-keyword matching.

Tune the threshold

Change flowlibs_QuestionUnansweredHours to make routing more or less eager; adjust the Recurrence frequency to match.

Smarter topic matching

Replace the single-keyword contains match with an Azure OpenAI classification step that maps the question to a topic, then match on that.

Thank responders (phase 2)

Add a companion flow that re-reads the community, detects when a routed question has gained a reply/reaction (via liked_by), and posts a thank-you.

Resolve author names

The flow stores the Viva Engage sender_id; add a GetUserDetailsById call to display the asker's name.

Key Expressions

The flow is intentionally light on Power Fx / WDL gymnastics — the heaviest expressions are the branch-name concatenation and the approval outcome check. They are listed below in the order they appear in the flow.

@and(equals(coalesce(item()?['system_message'],false),false), contains(coalesce(item()?['body']?['plain'],''),'?'), equals(coalesce(item()?['liked_by']?['count'],0),0))

@div(sub(ticks(utcNow()), ticks(formatDateTime(coalesce(items('Foreach_Question')?['created_at'], utcNow()),'yyyy-MM-ddTHH:mm:ssZ'))), 36000000000)

@contains(toLower(coalesce(items('Foreach_Question')?['body']?['plain'],'')), toLower(coalesce(item()?['flowlibs_topicarea'],'~~nomatch~~')))

@if(greater(length(body('Filter_Matching_Experts')),0), coalesce(first(body('Filter_Matching_Experts'))?['flowlibs_expertemail'], variables('varDefaultExpert')), variables('varDefaultExpert'))

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