How ServiceNow mapped 4,000+ process variants in 9 days

Overview:

ServiceNow is a leading enterprise software company founded in 2003 and headquartered in Santa Clara, California. Its AI Platform for Business Reinvention, connects AI, data, and workflows in a single system of action to automate and manage business workflows across IT, HR, operations, and customer service for the majority of the Fortune 500.

ServiceNow's growth strategy is built on a vibrant partner ecosystem that spans the world's leading consulting firms, managed services providers, resellers, hyperscalers, and technology companies. The Global Partnerships and Channels (GPC) organization manages five distinct routes to market across every major region, activating an ecosystem that is central to how ServiceNow delivers value to customers at scale.

As ServiceNow accelerates its push to become an AI-native organization, its leadership needed to understand exactly where AI could make the biggest difference, starting inside its own partner operations. 

The challenge

Becoming an AI-native organization requires knowing what to automate. Before ServiceNow could identify which workflows to hand to AI agents, the GPC organization needed to understand how those workflows actually worked, spanning both documented and undocumented processes across a complex global go-to-market engine..

The gap between what leadership believed was happening and what was actually happening had widened over time. Processes had drifted. Workarounds had been created. Informal handoffs and unwritten rules governed how work actually moved. Capturing that current state through traditional methods such as manual interviews, workshops, or institutional knowledge hunts would have taken months.

The solution

ServiceNow’s GPC organization turned to Within to map its operations and surface the highest-value opportunities for AI agent deployment.

The engagement began with document ingestion. The ServiceNow team uploaded policy documents, playbooks, org charts, and operational artifacts into Within’s platform,, giving the AI engine a foundation of documented knowledge to analyze and map. Phase 1 captured approximately 2,000 processes.

In Phase 2, the team deployed Within’s Companion feature to observe how work moved across platforms and systems in practice, doubling the process count. here observation alone couldn’t surface the full picture such as the informal handoffs, the exceptions, and the unwritten rules, Klarity’s AI Interviews filled the gaps through conversational AI sessions conducted across the team.

The output was three context graphs that gave ServiceNow a structured, multi-dimensional view of its operations:

1. Operational context graph: a view of how the GPC organization runs tactically
2. Customer-facing context graph: how GPC sells, supports, and retains customers through partner motions
3. Strategic context graph: how leaders make strategic decisions for the organization

The total: over 4,000 documented process variants across approximately 70 genuinely distinct workflows, with individual, team-level, regional, and partner-specific variants making up the full count—87% faster than traditional methods. That complexity—invisible to traditional mapping approaches—is exactly what Within was designed to reveal.

The impact

Within gave ServiceNow the intelligence to act. More than half of all process improvement recommendations have been addressed.

Two automation initiatives illustrate the impact:

Partner planning automation: Within identified that early-stage planning between partner managers and partners was largely manual and ripe for efficiency gains. ServiceNow is now building a dedicated automated planning tool to streamline that workflow end to end.

AI-powered pipeline visibility: Partner managers across the global organization were navigating multiple dashboards to piece together a complete picture of their pipeline. Within surfaced the pattern and ServiceNow built an AI agent in response. Team members can now query pipeline status, deal details, and performance metrics through natural language, without switching between systems.

The common thread: Within identified where automation could create the most impact. ServiceNow took ownership of the build, on its own platform, using its own AI capabilities