ServiceNow AI Implementation (SNAII) Training

Module 1: Introduction to Generative AI

• Lab Work: Prepare for a Now Assist implementation
• Objectives: This module introduces the fundamentals of AI and its impact on the enterprise. You’ll explore the evolution from rule-based systems to Generative AI, learn key concepts such as models, prompts, training, and inference, and examine the risks and responsibilities of GenAI. The module also highlights how the Now Platform enables AI-powered business transformation across workflows.

Module 2: Now Assist Data Readiness

• Lab Work: Perform Now Assist Readiness Evaluation
• Objectives: In this module, you’ll learn how to assess data and application readiness and estimate the effort required to implement Now Assist and Agentic Workflows in your platform. You’ll use guided checklists and practical evaluation criteria to verify that key components—such as the Knowledge Base, AI Search, Service Catalog, and Virtual Agent—are correctly configured and identify and address any gaps that could impact AI effectiveness.

Module 3: Introduction to Now Assist

• Lab Work: Configure Now Assist in Virtual Agent, Leverage Knowledge Graph in Search
• Objectives: Define Now Assist, its key components, and how it supports enterprise users. You’ll explore the Now Assist foundation and core platform capabilities that power generative AI experiences, understand how Enterprise AI Search
• enhances discovery, answers, and self-service across ServiceNow workflows, and discover how Now Assist Skills extend the platform with custom generative AI behaviors tailored to your organisation. You’ll also see how Now Assist enhances Virtual Agent with conversational intelligence and natural language interactions.

Module 4 Model Provider Strategy

• Lab Work: Explore Model Selection in the AI Control Tower and the Now Assist Admin Console
• Objectives: Explore the ServiceNow model provider strategy and how it supports enterprise AI. Review model deployment options, understand how Now LLMs and integrated providers are used across workflows, and examine how model selection and governance are managed through the AI Control Tower and Now Assist Admin Console. Also discussed are the ServiceNow AI infrastructure and generative AI data flows.

Module 5: Now Assist Administration

• Lab Work: Now Assist Admin Console Walkthrough, Now Assist Skill Activation, Now Assist Context Menu Configuration
• Objectives: Manage Now Assist solutions across your platform using the Now Assist Admin Console. Configure Now Assist Skills, set up experiences, enable data sharing and language support, explore dashboards to measure usage, adoption, performance, and track subscription management analytics.

Module 6: AI Governance and Guardrails

• Lab Work: Configure Content Moderation and Privacy Rules
• Objectives: Configure Now Assist Guardian guardrails for content moderation, understand how content moderation and data masking work together, and set up Now Assist privacy rules in the Admin Console.

Module 7: Extending ServiceNow with the Now Assist Skill Kit

• Lab Work: Build, test, evaluate, and deploy custom Now Assist skills in the Now Assist Skill Kit
• Objectives: Create and manage skills using the Now Assist Skill Kit, craft effective prompts to drive AI interactions, augment skills with integrated tools, evaluate skill performance for accuracy and reliability, and publish and activate skills to augment existing and create new workflows leveraging Gen AI functionality.

Module 8: Now Assist Agents

• Lab Work: Design and build Agentic Workflows and AI Agents
• Objectives: Explore the core building blocks of agentic workflows, AI agents, and the tools that power them. Build AI agents that perform reasoning-based tasks within agentic workflows. Navigate the AI Agent Studio to design, configure, and manage agents and workflows, and track agentic usage, outcomes, and overall value.

Module 9: Evaluating Agentic Workflows

• Lab Work: Assess Agentic Workflow effectiveness
• Objectives: Select evaluation metrics, such as task completion, performance, and tool execution. Configure evaluation runs to assess the agentic workflow’s quality, performance, and reliability.

Module 10: AI Agentic Fabric and MCP

• Lab Work: Allow your AI Agent to “talk” to external systems using an MCP tool
• Objectives: Define AI Agent Fabric and its benefits, discuss common protocols to enable agentic interoperability, configure ServiceNow AI Agents using the MCP Client Tool, and explore use cases for augmenting AI agents using MCP and A2A.

Module 11: AI Control Tower

• Lab Work: Perform a guided tour exploring dashboards and administrative interfaces
• Objectives: Get introduced to the ServiceNow AI Control Tower, a centralised hub for managing AI transformation initiatives across your organisation. Learn how to track AI value, adoption, risks, and compliance in alignment with your overall AI strategy. Conclude with a practical use case showcasing collaboration among an AI Steward, Product Owner, and Risk and Compliance Manager to drive coordinated, effective AI adoption.