ServiceNow AI Agents Implementations_ Enhancing Automation Across the Enterprise

1. ServiceNow AI Agents Implementations: Enhancing Automation Across the Enterprise Introduction to ServiceNow AI Agents Enterprise operations are growing more complex, interconnected, and fast-moving than ever. Traditional automation tools can only handle predefined steps, fixed rules, or repetitive tasks. To support modern business models, organizations now need systems that can learn, adapt, reason, and work across departments. ServiceNow AI Agents were introduced to answer this challenge. Built on a foundation of large language models (LLMs) and designed to operate inside the Now Platform, these AI Agents are not simple chatbots—they are autonomous, goal-oriented digitalworkers. According to ServiceNow, AI Agents can gather information, break down tasks, take action, and learn from ongoing feedback. They operate across IT, HR, Customer Service, CRM, telecom operations, procurement workflows, and more.

2. With the 2025 platform releases such as the Yokohama release, ServiceNow expanded AI Agent availability across multiple workflows. This enables enterprises to deploy out-of-the-box agentic automation, reduce manual workloads, and support business teams at scale. How ServiceNow AI Agents Work Core Capabilities of AI Agents ServiceNow AI Agents work through four key capabilities, as defined by the company: 1.Understand context: Using LLMs plus platform data models, agents interpret requests, workflows, policies, and organizational context. 2.Break tasks into steps: Agents autonomously plan what to do—identifying required data, systems, approvals, and actions. 3.Act across systems: Through tool-calling and integration, AI Agents connect with APIs, databases, IT systems, and third-party apps. 4.Learn and improve continuously: They refine actions based on outcomes, performance data, and user feedback. Because ServiceNow operates on a single platform architecture, AI Agents can work across traditionally siloed departments. They share context and data through the unified ServiceNow data model. This reduces fragmentation and supports true cross-workflow automation. Key ServiceNow Modules Powered by AI Agents AI Agents are being integrated into several major ServiceNow modules, including: ● IT Service Management (ITSM): Automated ticket triage, software provisioning, incident resolution, and knowledge surfacing. ● Customer Service Management (CSM): Faster case handling, customer recommendations, and automated support responses. ● Human Resources Service Delivery (HRSD): Automated employee queries, onboarding workflows, and case routing. ● CRM and Field Service: Ticket pattern recognition, customer issue analysis, and automated solutions.

3. ● Telecommunications Service Management: Detection of network issues, automated repairs, and lifecycle monitoring using industry-specialized agents. These integrations allow AI Agents to participate actively in end-to-end processes, rather than simply responding to queries or providing information. Challenges to Consider Before Deploying AI Agents While ServiceNow AI Agents offer strong benefits, certain considerations must be addressed for successful adoption. Data Accuracy and Privacy Considerations AI Agents rely heavily on clean, structured data. ServiceNow documentation and practitioner insights consistently highlight: ● poor CMDB accuracy, ● inconsistent knowledge bases, ● fragmented data sources as risks that limit agent reliability. Since agents perform autonomous actions, data governance and privacy policies must be clearly defined to prevent compliance risks or unintended changes. Change Management and User Adoption Industry feedback shows that while organizations want AI automation, many are not fully ready. Change management becomes critical because: ● workflows may need redesign, ● employees must understand AI oversight responsibilities, ● process owners must gain trust in autonomous actions. ServiceNow emphasizes governance controls as a necessary layer before scaling agentic workforces. Continuous Optimization Requirements

4. Even with advanced agentic capabilities, AI Agents need: ● continuous monitoring, ● updates to workflows, ● feedback loops, ● regular data cleansing, ● performance tuning. ServiceNow highlights that agents are meant to improve over time—not operate on “set and forget” logic. Use Cases of ServiceNow AI Agents Across the Enterprise IT Service Management (ITSM) Automation AI Agents are increasingly used inside ITSM environments to automate: ● ticket categorization and assignment ● incident root-cause analysis ● automated knowledge recommendations ● software provisioning ● change request preparation ServiceNow shared that its internal IT organization automates 97% of software provisioning using AI Agents. Additionally, autonomous actions solve 85% of internal IT help requests, significantly reducing manual load. Customer Support and Virtual Assistance AI Agents help support teams handle complex case workloads by: ● reviewing customer messages ● identifying solutions ● gathering context from previous interactions ● drafting or automating responses ● escalating cases that require human judgment

5. ServiceNow reported 50% faster resolution on instance administration and maintenance cases when assisted by AI Agents. HR Case Management & Self-Service In HR operations, AI Agents help with: ● employee information requests ● onboarding workflows ● policy guidance ● document retrieval ● routing sensitive cases to HR specialists This improves employee experience while reducing repetitive manual tasks for HR teams. Incident, Request, and Change Automation Across governance and operations functions, AI Agents: ● identify incident patterns ● automate low-risk change processes ● gather dependency data ● prepare standardized remediation tasks ● coordinate approvals This supports ITOM, SecOps, and enterprise risk management workflows. Best Practices for a Successful AI Agent Implementation Assessing Current Workflow Maturity Enterprise workflows must be: ● well-defined, ● consistent, ● documented, ● governed across departments.

6. AI Agents depend on workflow clarity; ambiguity or inconsistent processes reduce performance and accuracy. Training AI Agents with Clean, High-Quality Data Clean data is the foundation of reliable automation. A successful implementation includes: ● updated CMDB ● standardized knowledge articles ● validated case history ● unified data models ● accurate role and access mapping ServiceNow’s platform architecture allows AI Agents to leverage structured data effectively—but only if the data is accurate. Integrating AI Agents with Existing Tools & Systems AI Agents use tool-calling to interact with: ● IT systems ● cloud platforms ● HR apps ● CRM tools ● workflow engines ● external APIs Integration planning ensures that actions taken by agents align with organizational policies and access controls. Real-World Examples of AI Agents in Action Enterprise Support Teams ServiceNow uses its own AI Agents internally for IT support automation. Results published by the company include:

7. ● 40% reduction in IT service desk volume ● 85% of routine IT help requests solved autonomously This demonstrates how AI Agents can scale operational efficiency within large enterprise environments. Employee Self-Service Portals AI-powered self-service portals allow employees to: ● ask questions ● request access ● initiate tickets ● retrieve documents Agents assist by identifying intent, retrieving data from multiple systems, and completing the request without human intervention. Automated Ticketing and Routing AI Agents analyze tickets using: ● past interactions, ● patterns across similar cases, ● knowledge articles, ● organizational hierarchies. This enables accurate routing and faster resolution. For customer service and CRM scenarios, this increases satisfaction scores and reduces backlog. Benefits of Implementing ServiceNow AI Agents Faster Resolution Through Automated Workflows ServiceNow’s reports highlight several measurable gains: ● Up to 97% automation of software provisioning ● 50% faster resolution for customer support maintenance cases ● 85% of routine IT help requests solved autonomously

8. These improvements reduce response times and eliminate human bottlenecks. Improved Employee and Customer Experience Employees and customers receive: ● faster answers ● proactive recommendations ● automated resolutions ● consistent quality of service ServiceNow’s “AI Experience” UI upgrade also adds conversational voice, web, and multimodal interaction—making agent interactions feel more natural. Reduction in Manual Work and Operational Costs AI Agents reduce reliance on repetitive, administrative work. This helps organizations: ● lower operational expenses ● reduce service delivery overhead ● scale without proportional staffing ● improve team focus on strategic work Enterprises report significant productivity increases when combining AI Agent orchestration with unified workflows. How TechWize Helps Enterprises Implement ServiceNow AI Agents End-to-End ServiceNow Consulting and Deployment Implementing AI Agents requires more than enabling a feature—it requires strategic planning. TechWize supports enterprises by: ● assessing workflow maturity ● preparing data for AI readiness ● implementing domain-specific AI Agents ● customizing AI Agent actions using ServiceNow’s low-code tools

9. ● ensuring governance and secure deployment TechWize ensures implementations align with business goals and measurable outcomes. Optimization, Integrations and Ongoing Support To maintain long-term value, AI Agents must adapt as the organization evolves. TechWize provides: ● ongoing tuning and optimization ● integration with legacy and cloud systems ● analytics and performance monitoring ● continuous improvement cycles ● expert support for new ServiceNow AI releases This ensures businesses benefit from AI advancements without operational disruption. Conclusion ServiceNow AI Agents represent a major step toward enterprise-level autonomous automation. They combine LLM intelligence, structured data, platform governance, and cross-system orchestration to solve real business challenges—from IT and customer service to HR and telecom operations. While organizations must address data readiness, governance, and change-management challenges, the benefits are clear: faster resolution times, lower operational costs, and more efficient digital workflows. As enterprises accelerate toward AI-driven service delivery, ServiceNow AI Agents provide a practical, scalable, and future-ready approach. With the right implementation partner—such as TechWize—businesses can build an intelligent automation ecosystem that supports growth, adapts to change, and delivers measurable value across the enterprise.