ServiceNow
internal agentic AI for lead-to-sale conversion and task automation
ServiceNow uses its own Now Assist internally, with a 16x lead-to-sale conversion improvement and more than 86% deflection of repetitive tasks, figures cited by the CEO on 2025 earnings.
Key points
- Internal deployment of the Now Assist genAI suite across the sales funnel and support.
- ServiceNow Now Assist stack on the Now Platform, with scoring and next-best-action agents.
- 16x lead-to-sale conversion, more than 86% deflection, 2.3 million hours saved in 2025.
- Evidence level A, confirmed status, figures cited by the CEO on 2025 earnings.
Objective
Run ServiceNow on ServiceNow. The company deploys its own Now Assist internally to improve the conversion of its leads into sales and automate its teams' repetitive tasks, as public proof of its product.
The deployment
ServiceNow uses Now Assist, its genAI suite, on its own processes. CEO Bill McDermott reported on earnings a 16x lead-to-sale conversion gain and more than 86% deflection of repetitive tasks thanks to the internal deployment. Beyond the sales funnel, ServiceNow claims 2.3 million hours saved through employee self-service in 2025, a level-1 IT service desk operated by an AI agent described as 99% faster than the previous human agents, and 85% of its service desk staff redeployed to higher-value tasks.
Results Proof A
Figures put forward by CEO Bill McDermott during an earnings call (Q1 2025) and picked up by the business press. Statement in financial results plus aligned press sources.
How it works
Inferred typical approachThe internal detail is not public. Here is a proven approach that leads to the same result, to adapt to your stack.
The stack in detail
- plateforme ServiceNow Now Assist ServiceNow's genAI suite (agents, summarization, generation), deployed here by ServiceNow on its own sales and support processes
- infra Now Platform workflow platform on which the sales funnel, IT service desk, and employee self-service run
- outil Agents de scoring et de next-best-action lead prioritization and next-best-action suggestion; the underlying models are not detailed in the sources
- outil Agent IA de service desk niveau 1 handling of internal IT requests, described as 99% faster than the previous human agents, with deflection to self-service
How it runs, concretely
For ops teams-
1Lead capture and scoring AI
Now Assist prioritizes leads and suggests the next sales action.
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2Automation of repetitive tasks AI
Recurring internal requests are deflected to self-service and agents.
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3Employee self-service AI and employees
Staff resolve requests on their own via the agent, which frees up hours.
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4Human redeployment Operations
Teams freed from routine tasks move to higher-value work.
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5Measurement Revenue operations
Lead-to-sale conversion, deflection, and hours saved are tracked and communicated in results.
Lead data and the history of internal interactions. Without clean, instrumented funnel data, lead-to-sale scoring and deflection lose reliability.
How your customers perceive this type of use
Sourced studiesLe pricing algorithmique est le terrain le plus inflammable : 68% des consommateurs disent se sentir leses quand les marques utilisent le pricing dynamique et 80% jugent plus dignes de confiance les marques aux prix constants (Gartner, 2024). L'equite percue varie selon le secteur : le pricing dynamique n'est juge juste que par 33% a 40% des repondants selon qu'il s'agit de concerts ou de cinemas (YouGov, 17 marches). Le prix personnalise par les donnees individuelles est le plus rejete : 47% des Americains s'y opposent fermement (Consumer Reports, 2024).
Acceptance conditions
- La constance des prix comme signal de confiance : 80% jugent plus fiables les marques aux prix stables (Gartner 2024)
- Le secteur conditionne l'equite percue : le pricing dynamique est mieux tolere pour les cinemas (40% le jugent juste) que pour les concerts (33%) (YouGov 2024)
Red lines
- Le pricing dynamique percu comme abus : 68% se sentent leses (Gartner 2024)
- Le prix individualise a partir des donnees personnelles : 47% d'opposition ferme (Consumer Reports 2024)
- Les frais caches et hausses imprevues, vecus par 79% des consommateurs sur un an et associes a la perte de confiance (Gartner 2024)
Sources: Gartner 2024 · YouGov 2024 · Consumer Reports 2024
How to replicate
Inference, not sourcedData prerequisites
- first-party sales funnel data
- history of internal requests
- conversion instrumentation
Org prerequisites
- a revenue operations team
- a process to redeploy freed-up teams
- governance of internal agents
Possible stack
- a genAI agent platform (Now Assist or equivalent)
- CRM
- workflow engine
The plan, step by step
- Step 1Instrument the funnel: trace each lead-to-sale stage in the CRM and workflows, define the reference conversionDeliverable: Funnel measured end to end with a quantified baseline
- Step 2Connect the scoring and next-best-action agent on a pilot sales segmentDeliverable: Leads prioritized by the AI, conversion tracked against the baseline
- Step 3Map the repetitive internal requests and arm self-service and the level-1 agentDeliverable: Deflection rate measured on the target cases
- Step 4Redeploy the freed-up hours to higher-value tasks, with HR supportDeliverable: Effective redeployment plan and tracking of hours saved
- Step 5Extend to the other segments and set up agent governance (permissions, response review, human escalation)Deliverable: Generalized agents, routine conversion and deflection reporting
First step: Instrument the lead-to-sale funnel, then connect a scoring and next-best-action agent on a pilot segment.
The same pattern, proven in other industries
adidas
AI-driven audience prospecting and bidding (ARO) across the full funnel
Allwyn
AI-driven omnichannel programmatic buying with retail data activation
Nivea
genAI testing and optimization of creative elements + automated localization at scale
Sources
- S1 ServiceNow (NOW) Q1 2025 Earnings Call Transcript Primary archive pending
- S2 ServiceNow sees tariffs as opportunity, not threat, as Q1 earnings exceed expectations Established press archive pending
- S3 ServiceNow Q4 FY 2025 Earnings Highlight AI Platform Momentum Secondary archive pending
An error, newer info, a source?
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