Hello! We are a group of dedicated AI Strategists and Architects committed to advancing the implementation and adoption of AI solutions for our customers. Through countless advisory and hands-on engagements, we’ve gathered valuable insights and practical guidance that we’re excited to share with the broader ServiceNow community.
This article aims to demonstrate why effective governance is the cornerstone of successful GenAI adoption.
Introduction
With AI Agent built into the ServiceNow platform, organizations have new ways to create value, engage with their customers and streamline operations. Yet, despite enthusiasm, some enterprises experience slowing momentum in adopting AI Agent. Key reasons include concerns around regulatory compliance, adoption and ROI (value adoption & roadmap).
We will highlight why effective governance is key to GenAI adoption and outlines key challenges and strategies for two groups:
- Emerging Adopters: organizations at the initial stages of exploring and experimenting with GenAI. They typically grapple with basic questions of organizational readiness and effective use-case identification. Their primary concerns revolve around foundational governance and laying a robust groundwork to ensure safe and responsible experimentation.
- AI Pacesetters: they move beyond the emerging adopters phase and starting to integrate AI Agents within their operational ecosystems. Their concerns focus on sustaining AI accuracy, compliance with regulatory frameworks such as GDPR and the EU AI Act and ensuring continuous value realization through robust model management and monitoring practices.
The governance areas for each group will be outlined, with practical examples and leading practices provided, concluding with recommendations to advance your organization's Agent AI implementation.
Effective governance creates the clarity, accountability and strategic alignment needed to accelerate AI Agent adoption. Without governance, organizations risk misaligned initiatives, operational inefficiencies, data privacy violations and a deficit in stakeholder trust.
According to ServiceNow Enterprise AI Maturity Index about 40% of executives are considering adding AI Agent tools to their tech stack within the next 12 months, but most feel they lack the proper governance guardrails to do so successfully.
We will introduce two types of AI Agent Adopters (Emerging Adopters and AI Pacesetters). For each type, we will outline the necessary government instruments for successful adoption.
Ad 1. Governance Focus for Emerging Adopters
Emerging adopters should prioritize three key governance topics:
- Define Roles and Responsibilities and AI Ownership
Clear accountability is critical. Organizations must explicitly define who is responsible for AI initiatives, which means roles such as a Chief AI Officer, AI Steward, AI Practitioner and AI Admin. Secondly establish a steering committee to provide strategic oversight of your initiatives.
- The Chief AI Officer oversees enterprise AI and IT strategy. Responsibility is to drive innovation and prepares for the future of work and IT, aligns AI projects with business objectives, reports AI investment performance to executives and ensures compliance and security policies are followed
- AI Stewards are responsible for setting up AI app approval and review processes based on internal and external AI governance standards. The responsibility is to understand and manages the lifecycle of AI assets and governance controls, monitors and reports on AI asset status and health and responds to internal and external audits. An ITOM Analyst could be a good candidate for this role.
- AI Practitioner(s) implement AI Agents on the ServiceNow platform and are responsible for maintaining, supporting and monitoring the status (including performance) of individual AI apps on the platform. This role is within the ServiveNow team and can be done by someone with a good understanding of AI Agent.
- AI Admins oversee the platform health with expertise in the AI apps and are responsible for managing overall AI app and plugin usage, customization and coordinating collaboration efforts on AI projects. Usually, this person is part of the ServiceNow platform team.
Practice: start with a small steering committee and gradually expand your efforts. In the early stages of implementing the ServiceNow capabilities, this could be the project’s steering committee. Eventually, it will be integrated into your existing operational steering committee.
- Embed Ethical Guidelines
As organizations embrace AI to streamline operations—from HR service delivery to IT support—concerns around inappropriate outputs, security vulnerabilities and sensitive topics are growing. It is important that from the start you acknowledge this and have guardrails in place for- Offensive behavior
Detect and monitor real time offensive content in text-based generative AI outputs for Now Assist skills. When this happens, you should have the ability to log or block output by activating guardrails at the workflow level. This will assure a safe, human-centered AI experience for the employees. Offensive content can includes: bias, racism, misogyny, prejudice, illegal acts, homophobia, stereotypes and derogatory language including profanities. - Security behavior
Employees might use prompt injection to influence the LLM to disclose the system prompt. This type of behavior should be detected and mitigated actions need to be implemented automatically to prevent this. ServiceNow is using DoomArena as a framework to test AI Agents against security threats. Our customers should follow their own security policies. As an example some of our customers using the OWASP top 10 Risk Mitigations for LLMs and GenAI apps and/ or the AI Risk Management Framework from the National Institute of Standards and Technology. - Sensitivity behavior
Via different channels, employees can discuss sensitive topics with the service desk (especially with HR and Security). It is important for you to decide upfront how you want to respond to this. For example: should the Virtual Agent handle a question when an employee asks a very personal question or should the Virtual Agent hand over the chat to a live agent? For example: when an employee has a conflict with his manager and mention this in the virtual chat. At that moment the Virtual Agent should hand over the chat to a live agent, instead of answering it and propose (for example) a catalogue item.
Practice: Start using ServiceNow Guardian, a built-in component with the GenAI Controller. It ensures Secure and Responsible AI by assessing real-time risks, undesired behaviors and dangerous platform usage such as offensiveness and prompt injection. For more information see our FAQ.
- Offensive behavior
- Organization Change Management (OCM)
In many GenAI use cases, employees have the option to follow existing processes. For instance, when Now Assist for summarization is enabled, agents may choose to leverage it, but usage is not mandatory. As a result, driving adoption is essential. Without targeted adoption efforts, new technologies risk underutilization, which limit the organization’s ability to realize their intended value.
OCM operates on three levels:
- Individual: guiding employees through personal change.
- Project: supporting adoption of new tools and processes.
- Enterprise: making change capabilities a core competency.
Without a clear adoption strategy, organizations risk poor engagement and weak ROI. Proactive OCM planning can boost user adoption from 10% to over 80%. OCM is especially vital for AI Agent rollouts, where behavioral shifts are significant. An OCM plan is key to decreasing resistance and increasing acceleration.
Practice: The table below provides guidance for drafting your own OCM plan.
|
# |
Step |
Action |
Outcome |
|
1 |
Prepare OCM |
1. What is your current OCM strategy? 2. Write out/validate your AI vision for your company and your department |
1. What’s your current OCM maturity and AI readiness? What’s worked before? 2. Craft & validate your AI vision: Make it simple, compelling and tied to business goals. Show how AI augments people, not replaces them. Be explicit: “AI will help us automate X, freeing you to focus on Y.”. Link your message to the skills you will implement |
|
2 |
Identify user groups |
1. Identify individuals in the champions, early adopters, and majority wave of the rollout. 2. Map them to the NA skills you implement 3. Which message do you want to bring 4. Grant access for only this group to the AI Agent |
1.Are champions part of the POC? Are early Adopters in the second and Majority in the third wave (are 3 waves are required) 2.Who uses what? Who benefits how? 3.Customize messaging for Champions: “Lead the change”, Early adopters: “Shape the future” and Majority: “Here’s how it will help you today” 4.AI Agent available for target audiance |
|
3 |
Create awareness |
1. Launch internal campaign 2. Highlight success stories 3. Leverage champions to spread awareness 4. Address the psychological aspect of change |
1. Internal comms (videos, newsletters, intranet stories). Team briefings & roadshows 2. Share relatable success stories from early adopters. Leverage storytelling, not just stats. 3. Address resistance early: Run “AI Mythbusters” open forums to discuss fear, uncertainty, job impact. |
|
4 |
Training & Support |
1. Hands on training (online, in-person) 2. Office hours |
1.Multimodal training: Online modules for self-paced learning. Hands-on, scenario-based workshops (e.g., “A day in your role using AI”) 2.Accessible support: Office hours, Q&A sessions Peer-to-peer learning groups or Teams channels |
|
5 |
Monitor Usage |
1. Which datapoint will you use from the NA Analytics dashboard 2. Feedback to development team |
1. Usage rates from NA Analytics. Task time saved and sentiment scores. 2. Establish a feedback loop: check-ins with users and share feedback with developers |
|
6 |
Embed behavior |
1. Share success stories 2. Address proactively negative feedback |
1. Celebrate teams/ users who automate work, innovate or collaborate better. 2. Acknowledge challenges openly. Make it part of identity: “We are a learning, AI-augmented organization.” |
Ad 2. Governance Focus for AI Pacesetters
The AI Pacesetters need a more sophisticated governance structure. Their focus should be on model validation, roadmap, and value framework to effectively manage business risks. It is preferred to do this is in small, manageable steps to avoid an overwhelm for the organization.
- Model Lifecycle Management
The purpose of lifecycle management is to ensure that AI models, use cases and agentic capabilities are governed, optimized and retired in a structured and value-driven manner throughout their operational lifespan. In the context of AI Agent, lifecycle management supports several key objectives:- Maximizing Business Value Over Time: lifecycle management ensures that each GenAI capability-such as summarization, resolution note generation, or catalog automation-is continuously evaluated for:
- Usage: Is the feature being adopted by users?
- Usefulness: Does it consistently deliver accurate, relevant, and trusted outputs?
- Impact: Is it saving time, reducing cost, or improving service quality?
By assessing these three factors across the lifecycle, organizations can scale high-impact use cases and retire or iterate on those that fall short.
- Driving Continuous Improvement
GenAI lifecycle management plays a vital role in ensuring that AI capabilities remain effective and aligned with evolving user expectations and business needs. It enables continuous monitoring of AI performance, such as summarization accuracy and user acceptance rates.
Through structured feedback loops - gathered via surveys and analysis of interaction outcomes, which are visible in the dashboard on the ServiceNow platform - organizations can gain insights into how the AI is perceived and used in practice. Based on this feedback, prompts, workflows and configurations can be adjusted to improve relevance and usability. In parallel, user training efforts help build confidence and trust in AI-generated actions, driving adoption and responsible use. - Ensuring Responsible Deployment
Lifecycle management for GenAI includes structured checkpoints that safeguard responsible adoption and sustained value delivery. One critical aspect is Change management, which ensures that any updates or expansions of GenAI use cases align with the organization’s readiness—both in terms of capability and culture. This helps prevent resistance, confusion, or underutilization.
Governance is another pillar. It helps prevent the rise of shadow AI by ensuring that all GenAI implementations are properly vetted, documented, and accountable. This maintains control over the use of AI and reinforces alignment with broader IT and compliance standards.
With Transparency and Trust, the AI outcomes will be validated and explainability maintained, particularly in HR or compliance-sensitive scenarios. - Decommissioning Ineffective Use Cases
As newer and more effective GenAI capabilities emerge, lifecycle management plays a key role in decommissioning outdated or underperforming use cases. It enables organizations to retire obsolete functionalities, ensuring that only relevant and impactful AI solutions remain in use. This process also helps eliminate redundant or duplicative agents, streamlining the AI landscape and reducing unnecessary complexity.
By systematically phasing out ineffective use cases, organizations can refocus their resources—both technical and human—on innovations that deliver measurable business outcomes. This disciplined approach ensures that GenAI investments remain aligned with strategic goals and continue to generate meaningful value over time.
- Maximizing Business Value Over Time: lifecycle management ensures that each GenAI capability-such as summarization, resolution note generation, or catalog automation-is continuously evaluated for:
- AI Agent Roadmap
As resources are limited, it is crucial to allocate them wisely. A roadmap enables your teams to concentrate on the platform capabilities that are most important to your organization and to align your business objectives with ServiceNow's capabilities for AI Agent.
In developing a roadmap, collaboration among various departments and stakeholders is important. Engaging different perspectives provides a more complete understanding of the requirements and challenges, leading to shared solutions. A jointly established roadmap ensures communication and transparency within the organization. Furthermore, the roadmap helps to allocate time for training and development to ensure teams are prepared to use ServiceNow's AI Agent effectively.
Practice: ServiceNow introduces new AI Agent capabilities into the platform each quarter. It is important to review and update your roadmap regularly to accommodate changing needs within your organization and emerging technologies on the platform. The steering committee will initiate and approve this roadmap. - Value Framework
The adoption of AI Agent is reshaping the digital way of working for enterprises around the globe. Besides the enthusiasm for intelligent automation, one foundational question remains under addressed: how do we measure the value in a way that drives strategic outcomes?
A value framework will give you a structured approach to quantify the impact of your AI initiatives. Without such a framework, organizations may risk investing in the wrong use cases, have lower adoption rates and miss opportunities to improve productivity.
The value of AI Agent is not in the technology itself; it’s in the outcome it delivers. A value framework translates AI capabilities into benefits like cost reduction, time saving, operational efficiency and higher CSAT. According to the ServiceNow AI Value framework success is in connecting AI productivity metrics (agent deflection rate, hours saves, etc) to financial and strategic impact.
To setup up an AI value framework for AI Agent involves the following five steps:
- Identify personas and use cases
AI Agent spans multiple roles—so begin by identifying the personas and workflows where AI Agent operates. - Instrument system to capture usage and interaction data
You need to measure actual system behavior and user interactions to determine value. Track for example: number of automated workflows completed, successful resolution via virtual agent, usage volume of summarization and resolution note features, acceptance rate of AI-generated outputs - Calculate time and cost savings using real-world metrics
Translate AI interactions into time saved and then into financial value using defensible assumptions. See following example: - Introduce productivity and efficiency scores
To measure performance, you can create automation scores:- Agent Productivity Score: measures how much work is done jointly by AI agents and human agents.
- Workflow Automation Score: tracks how much of the work within a case or request is completed by AI vs. humans. Useful for monitoring low vs. high complexity task automation.
- Self-Service Efficiency Score: calculates the % of support interactions resolved without human intervention.
- Tie value to OKRs and optimize through governance
Connect AI Agent metrics to business-level OKRs or KPIs such as:- Support cost reduction
- Resolution time reduction
- Increased self-service deflection
- Agent efficiency targets
Then use the data for:
- Executive reporting
- Governance reviews
- Iteration decisions (e.g., what to scale next)
Practice: to track the usage and efficiency an out of the box dashboard is available on your instance. With the AI Agent Analytic Dashboard you will have the ability to monitor the AI Agents that are running on your instance. It will show for example the percent improved of close time for tasks using AI agents compared to tasks that are not using AI agents or the tasks with an associated AI agent execution plan, where we take the average of the time taken to open and close the task.
Conclusion
Strategic governance is the differentiating factor for organizations successfully accelerating their AI Agent adoption journeys. Emerging adopters who invest early in clear accountability, ethical practices and organizational change management will have a strong foundation for future growth. Established adopters who advance in model life cycle management, develop a roadmap and setup a value framework scales their operations, realize continuous value and mitigate significant operational and regulatory risks.
Ultimately, the successful adoption and scaling of AI Agents demands robust, purposeful governance. By clearly differentiating your organization's approach - whether emerging or established - you can align your governance strategy effectively, accelerating your journey to unlock AI Agent’s transformative potential.
If you have questions or thoughts, feel free to drop them in the comments—we’ll respond or update the article as needed. And if you found this helpful, please share your feedback or link to it on your preferred platform. This is just the beginning of our series on AI —stay tuned for more!
For tailored guidance, reach out to your ServiceNow account team.
