Latest Articles

VRM Governance That Scales — The Hub and Spoke Model The Governance Challenge The complexity of organizational structures and the multiple stakeholders involved in the management of third party risk remains a key challenge to management teams . Inefficiencies in TPRM programs can expose organizations to reputational risk . The Hub and Spoke Model To respond to an increasingly complex risk environment, firms should utilize a multidisciplinary approach to TPRM by adopting a hub and spoke model . The Hub The TPRM function would function as a hub with a central leadership team responsible for : Setting policies and standards Defining reporting requirements Establishing risk appetite of its operation Overseeing the TPRM program The Spokes The central hub would be supported by subject matter experts ("spokes") from relevant risk domains : Privacy Cyber security Business Continuity Disaster Recovery Legal Compliance IT Security Procurement Lines of Defense The hub and spoke model enables setting up a Lines of Defense model : First Line (Business Owners) : Manage day-to-day vendor relationships and operational risks. Second Line (Risk and Compliance) : Establish policies, standards, and risk appetite. Provide oversight and challenge. Third Line (Internal Audit) : Provide independent assurance on the effectiveness of VRM. Benefits of the Hub and Spoke Model Benefit Description Comprehensive risk identification Multiple risk domains are considered  Holistic risk mitigation Risks are addressed from multiple angles  Consistent practices Consistency in risk management and compliance practices  Flexibility Flexibility to address specific business needs  Clear accountability Roles and responsibilities are clearly defined Cross-Functional Collaboration Collaborating with the other business functions adds value as they understand the full scope of the potential geopolitical risks and their impact considering their expertise on international law, sanctions and local regulations that may impact vendor relationships . Governance Reporting As part of the governance process, organizations should adopt and establish stringent process controls, which need to be validated per timelines mutually agreed upon with vendor organizations . Areas of focus for the governance report : Documented evidence of vendors' security policies/procedures Contracts documenting the vendor's commitment Periodic management review reports Intervention based on internal and external audits findings Data privacy input based on scope of services Application security (secure development life cycle, vulnerability and penetration test reports) Governance reporting mechanism for key risk areas and action plans Conclusion The hub and spoke model provides a scalable governance framework for VRM. By establishing a central leadership hub supported by subject matter experts, organizations can achieve comprehensive risk identification and mitigation while maintaining consistency and flexibility . Action Items for Your Organization Establish a central TPRM leadership hub Identify subject matter experts for each spoke Define Lines of Defense Create governance reporting mechanisms Establish cross-functional collaboration  

Your Employees Don't Want to Fill Out Forms — They Want Results. AI Agents Are the Answer. The End of the Service Request Form as We Know It Employees don't want to navigate complex portals, decipher technical jargon, or wait days for approval. They want outcomes: access, equipment, answers. And increasingly, they're getting them instantly from AI. Service request management is at a tipping point. Research shows that 87% of organizations are already deploying AI in ITSM or expect to do so within 24 months, and 97% say AI capabilities would influence their next platform decision . The question is no longer whether to deploy AI for service requests, but how to do it effectively and at scale. The Shift from Chatbots to Autonomous Agents Early AI implementations focused on simple chatbots that redirected users to knowledge articles. The new generation of AI agents is fundamentally different. They don't just suggest answers; they take action. AI agents bring context, decisioning, and autonomy to service request management across internal and external journeys . They intelligently orchestrate, prioritize, communicate, and resolve. From triaging requests to guiding resolution paths, these agents transform service delivery into a seamless experience for employees, customers, vendors, and partners alike. ServiceNow describes this evolution as the shift from "AI-assisted" to "AI-led" service delivery. In the AI-led model, agents coordinate workflows across platforms autonomously while maintaining human oversight — a critical distinction from simple chatbots that merely redirect. Why This Matters for Your Organization The shift to AI-powered service requests directly impacts key business metrics: Metric Impact Employee Productivity Employees spend less time waiting for access or approvals Service Desk Efficiency Agents spend less time on repetitive requests Cost Reduction Automation reduces the cost per request Employee Experience Instant resolution creates a consumer-grade support experience Scalability AI handles volume increases without proportional team growth How AI Agents Transform Service Request Management According to industry research, Agentic AI introduces three defining capabilities that fundamentally change service delivery : Contextual awareness to interpret user intent Dynamic reasoning to plan actions and resolve dependencies Multi-agent coordination to execute complex workflows across systems When John, an employee, requests a business trip, an AI agent detects the intent from a Teams conversation, extracts details like destination and duration, and automatically creates a travel request. His manager receives an approval prompt within the same conversation. Upon approval, multiple AI agents spring into action — one validates eligibility against HR data, another checks budget limits, a third verifies device encryption status for remote access . This is the fundamental difference between automation and Agentic AI: Automation Frameworks Agentic AI-Driven Service Delivery Process-driven: executes preconfigured workflows based on static rules Intent-driven: interprets user intent and autonomously fulfills requests Siloed data and fragmented systems Connected intelligence across IT, HR, and Finance One-size-fits-all delivery Personalized service that adapts to each user Reactive: workflows trigger only after users submit a request Proactive: agents anticipate needs and resolve issues before tickets are created Real-World Impact Leading organizations are already achieving dramatic results with AI-powered service request management: BDO Canada achieved an 84% auto-resolution rate across service requests  Organizations report 50% fewer call volumes to service desks and 80%+ auto-resolution rates Time-to-value with first AI agents can be as little as eight weeks The Governance Imperative However, the promise of AI agents comes with a critical caveat: governance. As AI agents gain the ability to take autonomous action, organizations must ensure they operate within defined boundaries. Three critical success factors stand out: the readiness to adopt agentic AI, the quality of knowledge sources to avoid AI hallucinations, and understanding the governance and regulatory landscape . Conclusion: The Autonomous Service Desk Is Here The AI service request agent is not a future concept. It's a proven solution already delivering results at scale. Organizations that embrace Agentic AI will move beyond incremental efficiency gains to build a service ecosystem that is anticipatory, resilient, and personalized at scale . Action Items for Your Organization Assess your current service request volume and identify patterns Evaluate AI agent platforms for service request management Start with a pilot focused on a high-volume, low-complexity request type Establish governance frameworks for AI agent autonomy Measure auto-resolution rates, deflection, and service desk capacity impact  

Some Problems Can't Be Predicted — But Most Can Be Prevented The Two Sub-Processes Problem Management is broken into two distinct sub-processes : Reactive Problem Management: Identifying the root cause, or providing suitable workarounds, of known incidents Proactive Problem Management: Identifying and eliminating the root cause of incidents, or providing suitable workarounds, in order to prevent their recurrence Both are essential for effective problem management. Reactive Problem Management Definition: Reactive Problem Management is triggered by incidents and aims to identify root causes and provide suitable workarounds . When it's triggered: Major incidents Recurring incidents Patterns of incidents Events from monitoring Key activities: Root cause analysis of incidents Workaround development Known error documentation Change requests Outcome: Permanent resolution of issues that have already occurred. Proactive Problem Management Definition: Proactive Problem Management identifies and eliminates root causes before incidents occur . When it's used: Trend analysis Monitoring data review Continuous improvement initiatives Preventive maintenance Key activities: Trend analysis Pattern detection Predictive analytics Preventive actions Outcome: Prevention of incidents before they occur. Balancing Reactive and Proactive Dimension Reactive Proactive Trigger Incidents Data analysis Focus Past Future Timeframe Immediate Strategic Resource investment High initially Ongoing The 80/20 Rule A significant portion of incidents come from a small number of underlying problems. By identifying and fixing these root causes, organizations can dramatically reduce incident volume. Proactive Problem Management in Practice 1. Trend Analysis Review incident data over time to identify patterns: Which incident types are increasing? What systems have the most incidents? What times of day/week have the most incidents? 2. Predictive Analytics Use historical data to predict future incidents: Which patterns preceded past incidents? What thresholds indicate risk? What systems are at risk? 3. Preventive Action Take action based on analysis: Apply patches before vulnerabilities are exploited Scale resources before capacity is exceeded Update procedures before they cause errors The Business Case for Proactive Investment Return Trend analysis Fewer incidents Predictive analytics Reduced downtime Preventive maintenance Lower incident volume Root cause elimination Permanent fixes Conclusion Reactive and proactive problem management are not competing approaches—they're complementary. Reactive problem management addresses issues after they occur; proactive problem management prevents them from occurring in the first place. Action Items for Your Organization Assess your current balance between reactive and proactive problem management Allocate dedicated time for proactive analysis Start trend analysis on incident data Identify high-volume incident patterns Implement preventive actions for top patterns

Headline: AI Agents That Manage Change Themselves — What It Means for Change Practitioners What Is Agentic AI? Agentic AI refers to systems that can independently plan and execute multi-step workflows rather than simply generate outputs in response to prompts . In more advanced cases, agentic AI can take on parts of end-to-end processes with minimal human oversight . The Impact on Change Management The emergence of agentic AI is reshaping change management in several ways: New Skills Required Project and change teams need to develop skills in : Supervising autonomous AI activity Ensuring governance and ethical use Embedding AI into delivery processes Maintaining alignment with organizational intent Evolution from AI Literacy to Agent Supervision The role of change professionals is evolving from "How do I use AI tools?" to "How do I manage AI agents that act autonomously?" The Governance Challenge Agentic AI reshapes workflows, decision rights, team roles, and communication patterns—making structured change management essential . Key governance considerations: Who is accountable for agentic AI decisions? How do we ensure ethical use? What are the boundaries of agentic AI autonomy? How do we maintain human oversight? What happens when agentic AI systems fail? The Current State McKinsey reports that while some organizations are beginning to scale agentic AI within specific functions, most remain in the exploration phase, reinforcing the need for structured capability uplift rather than ad hoc experimentation . Preparing Your Organization 1. Assess Readiness What agentic AI capabilities are relevant? What governance frameworks exist? What skills are needed? 2. Build Capabilities Develop AI literacy across teams Create agent supervision skills Establish governance frameworks 3. Start Small Pilot agentic AI in low-risk areas Learn and iterate Scale gradually Conclusion Agentic AI is not a distant future—it's emerging now. Organizations that prepare for autonomous AI agents with governance, skills, and structured change management will be better positioned to benefit from this technology. Action Items for Your Organization Assess your organization's readiness for agentic AI Develop governance frameworks for autonomous AI Build agent supervision skills Pilot agentic AI in low-risk areas Plan for the evolution from AI literacy to agent supervision

Employees Don't Want to Know Where to Go — They Want One Place for Everything The Fragmentation Problem Employees don't want to navigate multiple tools, remember different URLs, or know which department handles which request. They want a single place to get help. This "single pane of glass" philosophy is driving the evolution of self-service portals. Microsoft's Employee Self-Service Agent was built on the premise that employees should have one place for all support needs — combining IT, HR, and facilities . Why One Portal Matters The Employee Experience Impact As one industry expert noted: "The line between customer support and internal service management has continued to blur. All end users expect consumer-grade experiences all the time. Every service interaction, whether HR, IT, finance, customer support, legal, or facilities contributes to a perception of your organisation's competence" . The Cost of Fragmentation Problem Impact Multiple portals and URLs Employees don't know where to go Department-specific interfaces Inconsistent experience across functions Separate login credentials Friction at every interaction No unified view of requests Employees can't track progress IBM's AskHR: A Case Study in Unified Service IBM's AskHR virtual agent demonstrates the power of a unified approach. The system: Handles over 2.1 million employee conversations annually Achieves a 94% containment rate of common questions Has led to a 75% reduction in support tickets since 2016 Contributed to a 40% reduction in HR operational costs over four years  AskHR operates on a two-tier support model: AI handles routine inquiries while human advisors manage more complex needs. Behind the scenes, complex HR processes are streamlined through deep integration with enterprise systems such as Workday, SAP, and Concur . Key Design Principles for Unified Portals 1. Single Point of Entry All service requests — regardless of department — start in one place. This reduces confusion and ensures all requests are tracked consistently . 2. Consistent Experience The interface should feel the same whether you're requesting IT support, HR approval, or facilities service. Consistency builds confidence and reduces learning time. 3. Departmental Logic Behind the Scenes Users don't need to know which department handles which request. Automation should route the request to the right team based on the request type . 4. Cross-Department Coordination ESM systems thrive when cases can move smoothly from one team to another. Employees use a single, unified portal to request any internal service and track progress in one place . The Benefits of Unified Service Delivery Benefit Impact Higher adoption One place to go for everything Better tracking All requests in one system Consistent experience Same interface across departments Faster resolution No time wasted finding the right place Better data Complete view of all service activity Conclusion The "single pane of glass" isn't just about convenience — it's about delivering a consistent, frictionless employee experience. Organizations that unify service delivery across departments will see higher satisfaction, faster resolution, and better operational efficiency. Action Items for Your Organization Map all service portals and entry points — where is the fragmentation? Identify the most common request types across departments Design a unified experience — one portal, one login, one interface Build automation to route requests to the right team Measure adoption and satisfaction before and after unification  

Service Request Management Is No Longer Just for IT — Here's Why That Matters The ESM Expansion Service request management is no longer confined to IT. The same principles — service catalogs, workflows, SLAs, and self-service — are being applied across HR, finance, legal, and facilities . Enterprise Service Management (ESM) extends the proven ITSM model beyond IT by adapting the same organized, efficient approach for other business areas . Where ESM Is Being Adopted Department Common Service Requests HR Benefits questions, leave requests, payroll issues, onboarding Finance Expense approvals, procurement requests, budget inquiries Legal Contract reviews, compliance questions, document access Facilities Office space, equipment, maintenance, parking IT Access, hardware, software, troubleshooting Why ESM Matters The Cross-Department Reality HR services rarely operate in isolation. Many requests depend on timely input from other departments, such as IT, finance, payroll, procurement, and compliance. ESM systems thrive when cases can move smoothly from one team to another . The Employee Experience Impact Every service interaction — whether HR, IT, finance, legal, or facilities — contributes to a perception of your organization's competence . Fragmented service experiences across departments create frustration and reduce trust. Research confirms the trend: Market research shows organizations expanding ESM programs are prioritizing common taxonomies, governance alignment, and operating readiness before choosing tools  Platform consolidation and enterprise-wide unified service operation models are the way forward, as companies recognize that only a unified, orchestrated ecosystem can support AI-driven service delivery and reduce complexity  Case Study: HR Service Delivery with ESM SAP SuccessFactors Enterprise Service Management adapts ESM for HR's unique system and service needs, providing : Intuitive self-service: Agentic AI offers employees instant answers and guidance A unified workspace: Central UI with core data from employee records Preconfigured HR service scenarios: Ready-to-use templates for common HR cases Smart case management: AI-guided workflows, case classification, and recommendations Actionable insights: Analytics on case volumes, service performance, and SLA compliance Cross-Department Service Scenarios Onboarding Process Employee onboarding requires coordination across HR, IT, facilities, finance, and security. ESM orchestrates tasks through shared workflows and data, providing employees with a single guided experience while ensuring every team completes their responsibilities on time . Payroll Discrepancy Management Resolving payroll issues requires gathering and validating information across different systems and teams. AI-backed case management systems help service representatives gather correct details, route issues to appropriate experts, and resolve discrepancies quickly . Best Practices for ESM Implementation Start with high-volume, cross-department processes like onboarding or expense approval Build common taxonomies so all departments use the same language Establish cross-department governance to ensure consistency Use a unified platform rather than separate department solutions Measure the end-to-end experience, not just department-specific metrics Conclusion Service request management is evolving beyond IT to become an enterprise-wide capability. Organizations that embrace ESM will deliver a more consistent employee experience, reduce fragmentation, and improve operational efficiency across all service functions. Action Items for Your Organization Map service delivery across all departments — where is it fragmented? Identify cross-department processes that could be unified Define common taxonomies and service categories Evaluate ESM platforms that support multiple departments Start with a pilot in one non-IT department, then expand