Tips, Wins & Connections

By integrating AI Assistant on Web directly into the search experience across our traditional Digital and HR ServiceNow portals, we're making it easier than ever for employees interact with InfoBot to find answers, access knowledge, and complete support tasks.Since launch, adoption has increased by more than 150%, reaching 5,000+ users per week, while maintaining a 4.0 CSAT score. Just as importantly, interaction data from the search experience is helping us better understand employee needs, identify knowledge gaps, and continuously improve our content and support experience.By bringing InfoBot directly into the flow of work, we're reducing barriers to adoption and making support more accessible, intuitive, and effective for everyone. This marks another important milestone in our ongoing AI and digital transformation journey as we continue to simplify access to information, enhance self-service, and create more connected Digital and HR service experiences.  

I am looking to clean up a handful of older connectors, indexed content, files and such that were set up but are not in use. How do we go about doing that? I seem to only have access to delete http connectors, plugins and things in agent studio but not in Setup.

When an employee is facing a personal crisis like a serious illness, a bereavement, or a family emergency, the last thing they should have to do is navigate a ticketing system to get HR support.One customer built a plugin that detects HR emergency language in employee messages, automatically creates a ServiceNow case, and notifies the HR team without any manual routing. HR can respond quickly, and the employee gets connected to support in seconds rather than hours.What employee situations would benefit most from faster, more automated routing to the right support in your organization?

Hey everyone 👋 I’m new here. Curious, what’s the simplest AI use case you’ve rolled out that actually had the biggest impact at your org?I’m talking small things that made a real difference, maybe something that started as an experiment but ended up saving time, cutting headaches, or just making life easier for your team.Would love to hear your examples!

Hello Community!By default, Moveworks Concierge notifications often pull the specific agent's name into the "Last Activity" section. For many teams, privacy or a unified "Service Desk" brand is a priority. This solution allows you to mask individual agent names—replacing them with a generic "Service Agent" title—while keeping the actual activity log intact. Here’s how to build this -Step-by-Step approach:Part 1: ServiceNow ConfigurationTo start, we need to create a "sanitized" version of your comments within ServiceNow. Installing the update set attached to this post helps you achieve that.What does this Update Set do?Creates a Custom Attribute: Navigate to System Definitions > Dictionary and create a new field (e.g., u_comments_sanitized). Applies a Business Rule: Create a Business Rule that triggers on comment updates. Use a regex pattern to identify agent names, mask them as "Service Agent," and append the cleaned text to your new custom attribute. Part 2: Moveworks SetupNow, we

Whether you just shipped something small or something wild, we want to hear about it. Use this format to keep it quick and useful for everyone: Here's what I built: (1–2 sentences) What problem did you solve or idea did you bring to life? Here's what I learned: (1–2 sentences) What surprised you or stretched your skills? Here's my #1 tip: (1–2 sentences) What advice would you give to the next builder?Here’s an example of a similar share out for your reference! 

It's almost always the output mapper.When you return structured JSON, the reasoning engine parses it — extracts the schema, filters fields, routes to code execution if needed. When you return a flat string via RENDER, it can't do any of that. The whole blob gets dumped into context as-is.The fix: use $MAP() for lists and direct field mapping for single objects. Save RENDER for cases where you genuinely need static text nothing will process downstream.Kevin digs into exactly how the engine handles both paths — including what triggers the ~7K token structured data analysis mode — in this week's newsletter. subscribe here.!-->

Most device health issues don't start as tickets, they start as slow laptops and frustrated employees who can't figure out why their machine is struggling.One customer connected their device monitoring platform to their AI assistant to flip that script. Instead of waiting for a ticket, the AI proactively reaches out when a device is running low on disk space or needs a restart — identifies large unused apps, surfaces recommendations in chat, and lets employees take action with a single click. No IT involvement required."Employees completing device health actions conversationally — no ticket, no IT call, no back-and-forth."These agents are used 1,500+ times per month. The win here isn't just efficiency — it's removing friction before employees even realize there's a problem.We'd love to hear from you: What device management tasks would you most want to handle proactively rather than reactively? Drop your response below.

An incorrect offboarding can lock out an active contractor for hours or even days, disrupting live projects and generating urgent escalations across IT, HR, and management. It's one of those processes that seems simple until it isn't.One customer tackled this head-on by automating contractor expiry and offboarding through their AI assistant. They added real-time compliance alerts to flag potential issues before they snowballed into disruptions.The outcome: contractor lockouts essentially eliminated, and compliance risk reduced without layering on more manual oversight."Contractor lockouts essentially eliminated. Compliance risk reduced. No added manual overhead."The win here wasn't just efficiency. It was proactive risk reduction before issues ever surfaced.We'd love to hear from you: What offboarding processes would you most want to automate? And where does manual handling create the most risk for your team today?

If your plugin starts repeating questions, forgetting what the user said, or hallucinating field values after a few turns, the config probably isn't the problem. The context window is.Every plugin has a context budget: the total information the AI can hold in working memory during a conversation. Slot descriptions, action outputs, system instructions, conversation history. They're all competing for the same finite space. And here's the thing: before your user says a single word, your plugin has already burned thousands of tokens just loading up.Every turn after that is a fight for what's left.This is why plugins that work fine in testing fall apart in real conversations. Short demos never push the budget. Real users do.The good news: most context waste comes from just two places. Overstuffed slot descriptions and action outputs returning way more data than the AI actually needs. Clean those up and your plugin can hold a 20-turn conversation without losing the thread.Want to go deeper?

Most teams launch their AI Assistant, see decent early numbers, then plateau. Sound familiar?We analyzed data across thousands of deployments and found a clear pattern: many organizations achieve good adoption but stall before reaching great adoption, because they treat it as a change management exercise rather than an organizational transformation.The top performers don't just launch and move on. Here's what they do differently.The two metrics that matterMAU% — breadth of adoption (average customer: 22%, top performers: 51%) WAU/MAU — depth of engagement, i.e. how often monthly users return weekly (average: 35%, top performers: 42%)Where you sit on these two dimensions tells you exactly what to focus on next.The three-stage playbookStage 1 — Build Your User Base If you're below 40% MAU, this is your only job. Awareness campaigns alone won't move the needle, you need to optimize the product and run a deliberate change management strategy in parallel. The three biggest levers: enable th

​Objective:  The objective of this design is to enable the consumption of Moveworks Large Language Model (LLM) capabilities from enterprise platforms such as ServiceNow, rather than restricting LLM usage solely to a conversational chatbot interface. This document uses ServiceNow as a reference integration platform to illustrate the approach.  Business Use Case Overview The proposed integration enables multiple high‑value business use cases designed to improve operational efficiency, service quality, and knowledge reuse across IT and business teams.The solution supports, but is not limited to, the following cases like: 1. Incident Summarization and Contextual AnalysisThe integration enables automatic summarization of incidents by consolidating incident descriptions, activity logs, and resolution notes into clear, concise summaries. 2. Intelligent Solution Generation for IncidentsBased on historical incidents, known errors, and existing knowledge articles, the solution can recommend or g

If your Agent Studio plugin used to respond in 2 seconds and now takes 8, you might not have a bug, you might have an architecture problem.When you stack multiple actions back-to-back in a conversation process, the AI has to stop and process everything after each one. More actions, more data, more thinking, and your user is stuck watching a spinner.The fix: either add a natural user interaction between actions (like collecting a required field), or bundle your chained actions into a single compound action that runs behind the scenes.One reasoner turn. Cleaner context. Faster plugin.📬Want more tips like this? Subscribe to the Agent Studio Developer Newsletter in Community or via email.

We recently hosted a session on how to move from AI ambition to real enterprise automation — and what it actually takes to deploy AI agents that work across the business.Here are the key takeaways 👇 🔎 The Core ChallengeEnterprise work rarely lives in one system.Even simple tasks often span:• Multiple applications• Several approvals• Manual handoffs• Status chasing and follow-upsThe result? Operational friction, slower execution, and teams stuck in low-value work. 🤖 What Makes AI Agents Actually ValuableAI agents deliver real impact when they can:• Understand intent• Plan multi-step workflows• Take action across systems• Respect governance and user permissions• Provide a single conversational front doorIn the session, we broke down how the Moveworks reasoning engine works under the hood:It determines who the user is → builds a step-by-step plan → selects the right plugins (actions) → executes → validates the result.Instead of hard-coded workflows, agents dynamically compose “atomic”

Huge thanks to everyone who joined Sarah Chudawala, Senior Customer Success Manager at Moveworks, and Joe Birney, Director of IT Product Enablement at Motorola Solutions, for an honest and insightful look at Motorola’s journey from a homegrown support bot to a scalable, enterprise-ready AI assistant: Echo, powered by Moveworks.Here’s your quick recap with key takeaways 👇 🧩 The “Build vs. Buy” MomentMany organizations have the talent to build internal tools. The bigger question becomes:Is what you’ve built keeping pace with employee expectations and AI innovation?For Motorola Solutions, the turning point was clear:Their existing bot (TechBot) was powerful — but menu-driven and limited to predefined responses Employees increasingly expected natural, conversational interactions Leadership introduced an aggressive timeline to evolve their AI strategy  That’s when the conversation shifted from “Can we build this?” to “Should we?” ✅ When They Chose to Buy, They Chose MoveworksJoe shared th

Big thanks to everyone who joined Esther Dsouza and Will Davis for this technical walkthrough on how ServiceNow teams are deploying agentic AI with Moveworks in live production environments — with real examples of search, actions, approvals, and proactive workflows.Here’s your quick recap with key takeaways 👇 🧠 The Big Idea: ServiceNow + Moveworks = An “Intelligent Front Door” for EmployeesServiceNow is your system of record — Moveworks becomes the conversational layer that helps employees find answers and take action across systems without needing to know where something lives (ServiceNow KB, Confluence, SharePoint, etc.) or how to navigate the workflow.What powers it:Intent + context understanding (not just keyword search) Action execution across systems Permissions-aware results (users only see what they’re allowed to see) 🔎 1) Connect Knowledge + Find Answers AnywhereWill demoed a simple example (“How do I update my email signature?”) to show the foundation: Moveworks connects t

The ProblemMoveworks has a native ServiceNow approvals flow that includes three plugins (Approval Information, Update Approval Record, and List of Approvals). In a perfect scenario, the user would simply ask to show details on their approvals and ask to approve only one record at a time. The more common scenario, users ask to approve multiple records at once and because the chat bot supports planning it will attempt to complete this task and sometimes it can work, but most of the time it fails. The SolutionWe can disable the native plugins and create a custom plugin that supports bulk actions and recreates the native functionality. The downside to this approach is that the calls would be real time and I suspect the native plugins ingest the approval details which is why it serves the info so quickly. We opted for a hybrid approach where users would opt into the bulk approval plugin (we had some users who had hundreds of approvals to get through) while the majority population would stay

Easy win for the Moveworks Web Assistant. We are a Google shop and maintain a required pinned folder of bookmarks for all employees, giving them quick access to the most important places to find information. We recently added the Web Assistant URL to this bookmark list and have found that it’s helping employees access the assistant more easily and driving adoption up. It’s been a simple, quick win for the team.See Screenshot below.

Device issues are one of IT’s biggest headaches, but many can be proactively prevented. ​@mirel.ivan from Keysight built an Ambient AI agent that is able to listen to Nexthink events and alerts device owners the moment a problem arises. It then evaluates the event details, compares them against available Remote Actions, and automatically runs the right one to fix the issue. This agent can powerfully reduce manual triage and helps keep employees productive, rather than troubleshooting. Check out his video here included in this post.We were able to catch up with Meril after the event and here’s what he had to say:​​What inspired you to build your AI agent?We are very excited at Keysight about the new features offered by Moveworks so I was inspired by the potential to simplify complex workflows and make information instantly accessible. Even though we are quite young with our Moveworks AI Assistant deployment, the idea of creating an agent that could proactively assist users, rather than

For People Lookup, it’s not recognizing our employees named “Wesley” as “Wes”. What are some ways for users to be able to look up Wes and found Wesley? Is it possible to set a conditional rule in “Source-Specific User Attribute Mapping” in Import User advanced setting so that anyone named Wesley can be recognized as Wes as well? Any ideas?

What’s one thing you’re excited to create, improve, or rethink this year, and why? Share in the comments below! 

Huge thanks to everyone who joined our session with Consumers Energy! Their team shared a behind-the-scenes look at how they’re scaling AI across the organization, from field operations to HR, and the lessons they’ve learned along the way.Here’s your quick recap with key takeaways 👇🔧 What Helped Them Scale AI1. Strong process foundationsBuilt a storm-support manual for volunteer responders Created an internal acronym library to reduce confusion Decommissioned legacy phones and streamlined device replacement, saving nearly 2,000 hours per year  2. Clear, consistent communicationA dedicated change enablement team drives adoption Ongoing reminder comms keep users engaged Weekly VP-level updates reinforce leadership support Key stakeholders are always plugged in throughout the journey  ⚡ How CEVA (their AI Assistant) Supports Electric & Gas TeamsSafety first — and the results show it.Field teams now have fast access to critical processes, procedures, and compliance information, helpi

Consumers Energy, a Michigan-based natural gas and electricity provider, has been steadily expanding how they use AI to support both employees and field teams. After modernizing their service desk with Moveworks, they’re now exploring how their AI assistant, CEVA, can improve coordination between people, processes, and day-to-day operations.Tomorrow, Joe Langa, IT Manager at Consumers Energy, will share practical examples of how his team is approaching this work — from connecting field crews with timely information to building internal tools that support safety, productivity, and decision-making.🔍 Topics we’ll cover: How AI can assist field operations by helping route issues quickly and surface safety guidance Approaches to scaling self-service and automation across a large enterprise How Consumers Energy is using Moveworks’ AI Agent Studio to build solutions tailored to their environment This session is a great fit for anyone interested in how large operational teams are applying

Looking to give users more control over proactive Agent Studio notifications? Check out this new post that shares a simple architecture using a Moveworks Notifications Table to manage opt-in/opt-out preferences for specific use cases (like approval reminders or service anniversaries). Tried something similar? Create a post and tell the community about it!

Idea: Turn raw feedback into actionable insights for better customer experience. We’re excited to share an idea to make CSAT feedback more meaningful and actionable. Here’s what we’re planning:Capture Free-Text ResponsesCSAT survey comments are now flowing into the Data API.Link Interactions for ContextIntegrate parent interaction IDs for file ticket link clicks and MW form agent handoffs. This will require significant engineering effort but will provide richer context.Mandate Comments for Low ScoresRebuild the CSAT process in Agent Studio to require comments for lower ratings—acknowledging trade-offs in analytics.Better Feedback AssociationUse user IDs and timestamps to link interactions with specific topics/domains tied to poor scores. Our goal: Deliver these enhancements on time and make CSAT insights more actionable.