Latest Articles

: Stop Confusing Users with Technical Jargon — Design for What They Want, Not How IT Works The Service Catalog Design Trap One of the most common challenges with Service Catalog adoption is that catalog items are often designed around internal team structures instead of user outcomes. Over time, this leads to confusion, misrouted requests, and a return to email or free-text tickets . Where things usually go wrong: Separate items for each support team or technology Technical language that makes sense to IT, but not to users Too many mandatory fields up front "just in case" Routing logic embedded in user choices rather than automation As a result, users struggle to select the "right" option, and fulfillment teams spend time correcting submissions instead of delivering value . A More Effective Design Mindset Start with what the user wants to achieve, not how IT fulfills it. Instead of This (Internal-Focused) Design This (Outcome-Focused) "Active Directory Group Request" "Request access to a system" "Application Incident – Tier 2" "Report an issue with an application" Separate laptop, desktop, and peripheral items "Request new equipment" The internal complexity should live behind the scenes, handled by workflows, flows, and assignment rules . Key Design Principles One Request, One Outcome Users should not need to understand internal ownership or team structures. Each catalog item should represent a single, clear outcome the user wants to achieve. Progressive Disclosure Ask for only what's needed at submission; gather the rest later if required . Users confronted with 15 mandatory fields abandon the request or guess, leading to misrouted tickets. Automated Routing Use category, service, CI, or logic to route — not user guesswork. Don't make users choose which team should handle their request . Consistent Language Use business terms users recognize, not platform or team names. "Order a laptop" is clearer than "Hardware Asset Request - North America Region." Benefits of Outcome-Based Design Benefit Impact Higher portal adoption Users find what they need quickly Fewer misrouted requests Automation handles routing correctly Faster fulfillment times Requests arrive at the right team with complete information Cleaner reporting Consistent categorization enables accurate analysis Easier maintenance Change automation, not dozens of catalog items Real-World Impact As one ServiceNow community expert noted: "A good catalog hides complexity instead of exposing it. When catalog items are designed around outcomes, users succeed faster and IT spends less time correcting mistakes" . Action Items for Your Organization Review your catalog items — do they use technical jargon? Test with real users — where do they get confused? Replace "team-based" items with "outcome-based" items Move routing logic from users to automation Apply progressive disclosure to reduce form abandonment  

Your Service Catalog Should Work Like the Starbucks App — Not a Government Form The Service Catalog Problem One of the most common challenges with service catalogs is the tendency to build "Swiss Army knife" service requests—over-engineered, over-scripted, and overstuffed with UI policies. The result? Users face forms with 15 mandatory fields, leading to guessing, giving up, or opening an incident instead. The Case for Bite-Sized Requests A ServiceNow community expert makes the case: "Bite-sized beats out catalog items that are over engineered, over scripted, and overstuffed with UI policies. Stop building 'Swiss Army knife' service requests and build the kind of experience you would get ordering a coffee from the Starbucks app" . The Problem with Consolidation When organizations try to consolidate too much into a single catalog item: Problem Impact Too many fields Users get overwhelmed and abandon Complex UI policies The form feels unpredictable Too many use cases The item tries to do everything for everyone Difficult to maintain Changes require extensive testing Hard to report One category covers too many request types Why Bite-Sized Works Simpler Reporting When each catalog item is specific, reporting is straightforward. You know exactly what was requested. Reduced Change Risk Smaller items are easier to update without breaking everything else. Higher Maintainability Changes to one item don't cascade through the entire catalog. Modularity Smaller items can be combined for more complex requests. Ease of Access = Frequency of Use Users are more likely to use a catalog item that's simple and clear. The User Experience Principle Design for outcomes, not internal processes . Instead of designing catalog items around internal team structures, design them around user outcomes. Ask "What does the user want to accomplish?", not "What is the technical process behind this?" Progressive Disclosure One of the most effective catalog design principles is progressive disclosure: ask for only what's needed at submission; gather the rest later if required. The wrong way: 15 mandatory fields 5 UI policies that change based on earlier selections Technical jargon users don't understand The right way: 3-5 fields at most Clear, plain language Additional information gathered during the fulfillment process What the Starbucks App Teaches Us The Starbucks app doesn't ask you 15 questions before you can order coffee. It offers a simple, streamlined experience. Your service catalog should work the same way: Simple interface: Clear options, not cluttered forms Predictable flow: Users know what to expect Minimal friction: Fewer steps to complete the request Quick feedback: Users know their request was received Principles for Better Catalog Design Principle Application One request, one outcome Each catalog item does one thing well Outcome-focused Ask what the user wants, not how IT works Progressive disclosure Only ask for what's needed upfront Consistent language Use terms users understand Automated routing Don't make users choose where to route Test with real users Observe how users actually interact Conclusion: Simplify, Simplify, Simplify A service catalog shouldn't feel like a government form. It should feel like ordering from the Starbucks app—simple, clear, and frictionless. Bite-sized beats Swiss Army knife every time. Action Items for Your Organization Review your most complex catalog items—are they over-engineered? Test with real users—where do they get stuck or abandon? Split complex items into multiple smaller ones Apply progressive disclosure principles Use plain language, not technical jargon Measure abandonment rates  

Stop Demanding 15 Fields Up Front — Progressive Disclosure Transforms Service Request Adoption The Form Abandonment Problem One of the most effective catalog design principles is progressive disclosure: ask for only what's needed at submission; gather the rest later if required . When users are confronted with long forms, they either abandon the request or guess at the right options — neither outcome is good. Why Progressive Disclosure Works The Problem with Collecting Everything Upfront Users get frustrated and abandon: A form with 15 mandatory fields feels like a burden, not a help Users guess, leading to misrouted requests: When fields aren't clear, users guess — and get it wrong Support teams spend time correcting, not fulfilling: Every incorrect field must be fixed before work begins The Progressive Disclosure Solution Traditional Approach Progressive Disclosure 15 mandatory fields upfront 3-5 essential fields upfront Technical jargon users don't understand Plain language, clear labels One-size-fits-all fields Conditional fields based on previous answers Information gathered before it's needed Additional information gathered during fulfillment How Progressive Disclosure Works in Practice Example 1: Hardware Request Traditional form: Full name Employee ID Department Cost center Manager approval Hardware type Brand preference Model preference Memory specification Storage specification Screen size preference Operating system preference Delivery address Delivery date preference Progressive disclosure approach: What do you need? (laptop, desktop, monitor, accessory) Who is this for? (myself, new hire, replacement) Any special requirements? (brief description) Additional details — specifications, delivery preferences — are gathered during fulfillment, not at submission. Example 2: Software Request Progressive disclosure flow: Select the software from a searchable list (3 fields) Who needs it? (user lookup) — one field appears based on software selection Why is it needed? (business justification) — appears only if required The "Three-Question Rule" A practical rule of thumb: limit your intake form to three questions on the first screen. Additional information should be conditional or gathered later. The Three Questions: What do you need? (select from a clear list) Who needs it? (user lookup) Why is it needed? (brief justification) Benefits of Progressive Disclosure Benefit Description Higher completion rates Users are more likely to finish a short form Fewer abandoned requests Less friction means more adoption Better data quality Users provide accurate information when it's clear what's needed Faster fulfillment Fewer corrections needed Better user experience Forms feel helpful, not burdensome Conclusion Progressive disclosure transforms service request forms from a barrier to a gateway. By asking for only what's needed upfront and gathering the rest during fulfillment, organizations can dramatically improve adoption, accuracy, and user satisfaction. Action Items for Your Organization Review your most complex request forms — how many fields? Identify which fields are truly needed at submission Move optional fields to conditional questions or fulfillment Test with real users — observe where they get stuck Measure completion rates and abandonment  

From PTO to Parental Leave — How AI-Powered HR Service Requests Are Transforming Employee Support The HR Service Challenge HR teams face high volumes of service requests with unique challenges: sensitive data, complex regulations, and multi-step processes. Traditional HR help desk software often cannot scale or support complex requests, leaving teams in reactive mode rather than strategically solving problems . How AI Is Transforming HR Service Delivery IBM's AskHR: A Case Study IBM's AskHR virtual agent demonstrates the power of AI in HR service delivery: Handles over 2.1 million employee conversations annually Achieves a 94% containment rate of common questions Led to a 75% reduction in support tickets since 2016 Contributed to a 40% reduction in HR operational costs over four years Created more than 11.5 million employee interactions in 2024 alone  AskHR currently operates on a two-tier support model: AI handles routine inquiries while human advisors manage more complex needs, driving both efficiency and personalized service . Common HR Service Request Types Request Type AI Capability Human Touch Required? Payroll inquiries Instant answers from integrated systems Complex cases Leave requests Automated submission and approval Exception handling Benefits questions Personalized answers based on employee data Appeals and disputes Onboarding Guided experience across departments New hire exceptions Policy questions Immediate access to current policies Interpretation The ESM Approach to HR SAP SuccessFactors Enterprise Service Management provides : Agentic AI (Joule): Employees get instant answers and guidance Preconfigured HR scenarios: Templates for leave, grievance, time correction Smart case management: AI-guided workflows, classification, and next-step recommendations Native integration: Real-time access to reliable employee information Cross-functional orchestration: Seamless coordination with IT, finance, and other departments Why HR Service Management Matters The Employee Experience Impact When HR services are slow, confusing, or fragmented, it directly impacts employee satisfaction and productivity. As one industry observer noted: "Service management success can be directly linked to how people feel about their interactions with a business (both internally and externally)" . The Efficiency Gain AI-powered HR service management frees HR professionals from routine inquiries, allowing them to focus on strategic work. IBM documented significant productivity gains in domain-specific tasks between 2022 and 2024, with some areas improving by as much as 75% through AI-powered automation . The Future of HR Service Management Key Trends: Conversational AI: Employees interact with AI in natural language Proactive service: AI anticipates needs before employees ask Unified experience: One interface for all HR, IT, and facilities requests Agentic automation: AI takes multi-step actions to resolve requests end to end Cross-functional orchestration: Seamless coordination across departments Conclusion HR service management is a frontier for AI-powered enterprise service management. Organizations that apply ITSM principles to HR — with service catalogs, workflows, SLAs, and AI automation — will deliver faster, more consistent HR service while freeing HR professionals for strategic work. Action Items for Your Organization Assess current HR service delivery — what's fragmented or manual? Identify the most common HR requests Evaluate AI-powered HR service management solutions Build service catalogs for HR requests Integrate HR systems with service management platform Measure containment rate and employee satisfaction  

AI Risk Is No Longer Theoretical — Use AI Agents to Scale Your TPRM Program The AI Opportunity in VRM AI risk is no longer theoretical—it's an immediate, critical organizational problem . As much as AI is the source of this challenge, it is also the solution . AI and automation represent transformative tools that optimize efficiency and visibility in risk processes . What AI Can Do in VRM Capability Description Automated vendor discovery Identify vendors across the organization without manual effort Evidence analysis Analyze SOC reports, penetration tests, audit certifications, and public pages  Risk scoring Assess vendors against industry-specific risks  Sanctions monitoring Real-time identification of sanctioned individuals or entities  Contract analysis Extract clauses and flag deviations faster than manual methods  Continuous monitoring Real-time alerts for control gaps and breaches  The "Heavy Lift" Approach The most practical AI guidance is simple: use AI where it accelerates analysis, consistency, and scale—but don't outsource the actual risk decision . AI's role in VRM: Automate the "heavy lift" work of gathering and analyzing data Compare all findings to a baseline of controls Ensure all vendors are assessed in the same terms Scale coverage without scaling headcount  Human's role in VRM: Keep the judgment and accountability Make accept/avoid/mitigate decisions Apply governance and oversight  AI Governance in VRM If you use AI, you need monitoring for drift, hallucinations, and traceable evidence . AI does tend to hallucinate and it will make things up . Governance requirements: Dig into citations and sources Spot check AI outputs "Babysit" models Provide provenance and controls around the AI workflow  As one practitioner noted: "Use AI for sure but please provide governance and oversight. Don't trust this thing to tell you what's going on in your organization, specifically your risk and your mission statement" . The Agentic AI Opportunity Agentic AI can be deployed throughout the vendor lifecycle to automate time-consuming manual processes . Specific use cases include: Identifying duplicate vendors Segmenting third-party criticality Automating approvals and rejections Evaluating SLAs Reducing false positives from inbound adverse news  The Vendor AI Risk Challenge AI systems are inherently different from traditional technology. They are dynamic, adaptive, and opaque, introducing new risks like model bias, data governance gaps, and compliance failures . The far wider and faster-moving threat is in the supply chain. It seems like every vendor, from HR platforms to code repositories, is using AI. And that extends risk far beyond traditional attack surfaces . The advice: "Every vendor is now an AI vendor, knowingly or not. Visibility is the first defense. Map AI across your ecosystem, verify vendor claims with evidence, and apply governance proportional to the risk" . Conclusion AI is transforming VRM from a manual, resource-intensive process into a scalable, automated capability. Organizations that use AI for the "heavy lift" while keeping human judgment and accountability will achieve greater coverage and efficiency . Action Items for Your Organization Identify VRM processes that can be automated with AI Implement AI-powered vendor discovery and monitoring Use AI to analyze vendor evidence and documentation Establish governance for AI-driven VRM processes Monitor AI outputs for drift and hallucinations  

You Can't Improve What You Don't Track — Why Service Request Documentation Matters Why Documentation Matters Documenting all service requests — current and closed — prevents work from falling through the cracks and enables continuous improvement . Key information to document includes: Type of request Completion timeline Assignee Requester Action taken SLAs  What to Document Field Why It Matters Request type Identifies patterns and trends Requestor Enables follow-up and satisfaction tracking Assignee Shows who handled the request Action taken Documents what was done Timeline Tracks completion time and SLA compliance Status Shows current and historical states Using Documentation for Improvement Documentation is valuable when improving processes, as teams can easily see data like how long requests typically take to complete or how many stakeholders are involved . Key analyses: Cycle time: How long does each request type take? Volume trends: Which request types are increasing? SLA compliance: Which requests miss targets? Satisfaction patterns: Which request types have low satisfaction? Documentation Tools Tool Type Benefits ITSM platform Structured data, built-in reporting Templates Consistent documentation Automation Automatic capture of timelines and actions Conclusion Documentation isn't just about record-keeping — it's about improvement. Organizations that document well can analyze, optimize, and continuously improve their service request management. Action Items for Your Organization Review what data you currently capture Identify gaps in documentation Standardize documentation fields Use documentation for reporting and analysis Create dashboards for key metrics