5-Pillar Framework to Scaling Now Assist Adoption

nehaagrawal · 3 weeks ago

5-Pillar Framework to Scaling Now Assist Adoption

Grounded in direct customer conversations about real AI journeys

By Neha Agrawal, Product Manager – AI Platform

Introduction

AI has already reshaped how enterprises operate, and the challenge today isn't adoption, but scaling AI responsibly and effectively. Two years after the launch of ServiceNow's Now Assist, organizations are moving beyond experimentation toward orchestrating AI-driven workflows across the enterprise. As an AI Product Manager working to drive Now Assist adoption, I'm seeing firsthand that success depends on more than just technology; it requires a blend of readiness, governance, and continuous learning.

ServiceNow's latest Enterprise AI Maturity Index 2025 reinforces this view: among nearly 4,500 organizations surveyed, the average AI maturity score dropped nine points year-over-year (from 44 to 35 on a 100-point scale). This gap underscores a core truth: while AI investment and enthusiasm are at an all-time high, many enterprises are still learning how to operationalize AI at scale and convert potential into measurable value.

For platform teams working with Now Assist on the Now Platform, the opportunity is clear, from enabling smarter self-service to driving agentic automation across ITSM, CSM, HR & beyond. (Refer to our latest Zurich Now Assist documentation for details.) Yet many organizations struggle with moving from pilots and sparks of efficiency to enterprise-wide adoption that delivers measurable value, mitigates risk, and evolves with changing demands.

That's why this article presents a five-step adoption framework designed specifically for enterprise AI leaders, platform owners, and service management teams to scale Now Assist adoption responsibly and effectively. Whether you're just launching your first pilot or preparing to roll out across multiple service domains, this framework draws on credible research and aligns with the Zurich-release capabilities of Now Assist.

📖 Getting the Most Out of This Framework:

This interactive guide contains 5 pillars with expandable content. Here's how to navigate:

Horizontal scrolling: Only 3-4 pillars may be visible at once—scroll left/right to see all 5
Expandable sections: Click on any topic title (e.g., "Secure Executive Ownership...") to reveal detailed guidance, resources, and links
Don't miss the footer: It's packed with resources!
No fixed order: These pillars are designed to work in parallel, not sequentially. Start where your organization needs it most and activate multiple pillars simultaneously.

Pillar 1: Leadership

Balance Vision with Reality: Role of Leaders

Executive Sponsorship

Secure Executive Ownership While Promoting AI Literacy

CEO/C-suite must own AI strategy, governing and guiding AI CoE at the highest level
Leaders set vision, allocate resources, and remove barriers
Champion Persona-Based AI Literacy programs with tailored training for different roles

Emphasize AI as Enabler, Not Silver Bullet

Set realistic expectations from the start
AI is a powerful tool requiring iterative improvement and continuous learning, not an overnight transformation

Build Flexible Strategies that Evolve

Avoid over-reliance on single LLM models; diversify the AI approach
Leverage Model Provider Flexibility to optimize every AI use case

Pillar 2: Awareness

Assess AI Readiness

Foundation for Success

Conduct Multi-Dimensional AI Readiness Assessment

Conduct a structured readiness assessment across three domains: Organization Strategy, Technology & Data, and Operational & Cultural
For the Technology & Data domain, leverage the NA Readiness Evaluation App to baseline the current state and identify skill gaps before deployment
Follow Now Assist Readiness best practices to build a solid foundation

Start with Quick-Win Use Cases

Begin with high-impact turn-on fulfiller skills such as incident summarization, resolution notes generation, etc., to quickly demonstrate value
This is essential for rapid Stage 1 efficiencies while continuing to build foundational readiness for advanced features

Establish Clear Baseline Metrics

Document pre-AI performance indicators such as ticket resolution times, employee satisfaction scores, and operational efficiency rates
Use these baselines to measure post-implementation impact effectively

Pillar 3: Agility

Build & Execute on AI Roadmap: The Agile Way

Iterative Implementation

Focus on Finding the Right AI Use Case Before Adopting the Crawl-Walk-Run-Fly Methodology

Identify your north star, find the high-value business use cases, leveraging frameworks such as GAF, and Crawl-Walk-Run-Fly towards your business outcomes
Be intentional about evaluating When to Use AI Agents: Rationalizing Use Cases for Workflows, GenAI Skills & AI Agents
Leverage ServiceNow Impact to build your AI maturity model
Embed Organizational Change Management (OCM) at each phase to drive user readiness, minimize resistance, and accelerate adoption

Implement Continuous User Research & Feedback

Respond quickly to usage insights (Satisfaction, Frustration, Confusion, etc.), leveraging Conversational Insights
Invest in user research, gather feedback continuously, and iterate to ensure quality and a positive user experience
Look beyond just the Deflection and MTTR metrics

Create Cross-Functional AI Teams

Form a cross-functional AI delivery pod, platform engineers, product owners, UX, data/AI stewards, business SMEs, and OCM leads—to move use cases from prototype to scalable production
Define "exit criteria" for every iteration (adoption, quality signals, policy alignment) to avoid pilot-purgatory
The AI world is moving fast; embed shared ownership so the team continuously evaluates model behavior, risks, and operational readiness

Pillar 4: Integrity

Adopt AI Risk & Security Governance Frameworks

Responsible AI Implementation

Promote Responsible AI Principles from Day One

Embed responsible AI principles (fairness, transparency, accountability) from day one, aligned with regulatory requirements like NIST AI RMF and EU AI Act
Learn how ServiceNow is committed to responsible AI
Leverage Now Assist Guardian and AI Control Tower to monitor compliance, bias, governance, and security across your AI lifecycle

Build a Central AI CoE

Bring together Legal, Security, Compliance, IT, and Business leaders to ensure AI use cases are approved at idea stage before build cycles with AI delivery pods—avoiding wasted effort
No single team owns AI governance; collaboration ensures systems are secure, ethical, and aligned with regulatory expectations

Implement Continuous AI Monitoring & Auditing

AI Control Tower provides a central space to manage, monitor, measure, and oversee all AI activities
Schedule regular audits and establish incident response protocols for AI-related issues
Learn more about how to govern and scale AI effectively

Pillar 5: Impact

Measure What Matters

Quantifying Business Impact

Adopt Multi-Dimensional Measurement Framework

Go beyond traditional ROI
Track Efficiency Metrics (time saved, tasks automated)
Track Quality Metrics (error reduction, accuracy, customer satisfaction)
Track Revenue Impact (pipeline generation)
Track Risk Mitigation (fraud prevention, compliance)

Keep an Open Mind Towards Trending for Long-Term Gains

AI value unfolds over time
Track early signals (e.g., adoption rate, user satisfaction, engagement growth)
Align them with longer-term realization (e.g., sustained cost reduction, revenue uplift, competitive advantage)
Many AI initiatives stall because they only measure early signals or immediate metrics rather than durable outcomes

Capture Value Beyond Tangible Metrics

Document enhanced decision-making speed, improved stakeholder communication, innovation enablement, and strategic advantages
These benefits may not show immediate revenue but drive long-term competitive positioning

Key ServiceNow AI Products (Zurich Release)

Additional Resources

✔Comprehensive Resource List for Now Assist Implementations

Success Indicators based on our Enterprise AI Maturity Index 2025

✔67% report AI increased gross margins by avg. 11%
✔83% of Pacesetters report increased margins vs. 64% others
✔66% of AI Pacesetters employ a platform approach
✔63% of Pacesetters have AI data governance policies
✔55% have rolled out 100+ AI use cases
✔56% improved experiences through agentic AI
✔Only 1/3 piloting Agentic AI—opportunity gap exists

Acknowledgment: Special thanks to the ServiceNow colleagues whose insights and expertise shaped this framework: Diana David, Iryna Shyshkova, TJ Lincoln, Kate Udilina, Rob Ballin, Sonya Smith, Paul van Nistelrooij, Luci Locsin, Ashley Snyder, and Ritesh Shah and more.

𝘗𝘚: 𝘝𝘪𝘦𝘸𝘴 𝘢𝘳𝘦 𝘮𝘺 𝘰𝘸𝘯, 𝘢𝘯𝘥 𝘥𝘰 𝘯𝘰𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘮𝘺 𝘵𝘦𝘢𝘮, 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳, 𝘱𝘢𝘳𝘵𝘯𝘦𝘳𝘴, 𝘰𝘳 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴.