ServiceNow: AI, Data & Analytics

ServiceNow has made its position very clear: it wants to be the AI platform for business transformation. Not just an IT service management tool, not just a workflow engine, but the single platform through which enterprises run their operations, powered by artificial intelligence. It's an ambitious bet - and in 2025 and into 2026, the company has been making the investments to back it up.

But here's the thing about AI ambitions in the enterprise: they live or die on data. And ServiceNow knows it. At its Knowledge 2025 conference, Gaurav Rewari, SVP and GM of Data and Analytics, delivered what amounted to a frank warning to the market: "Here's the uncomfortable truth. AI agents like the ones you just saw are only as powerful as your data." In a world where Gartner predicts 60% of AI projects will be abandoned by 2026 due to lack of AI-ready data, ServiceNow isn't just selling AI - it's building the data and analytics infrastructure that makes AI actually work.

This blog explores what ServiceNow is doing with its AI strategy, why the data and analytics layer is so central to everything, and what enterprise technology buyers and vendors alike should understand about the direction of travel.

From Workflow Tool to AI Platform

ServiceNow's origins are in IT service management - ticketing, incident resolution, change management. For years it was the tool that enterprises used to manage their IT operations, and it was very good at it. But that story has fundamentally changed.

The company's current positioning is as an AI platform - a single, unified system where enterprise workflows across IT, HR, finance, customer service, procurement, and more can be orchestrated, automated, and increasingly run by AI agents with minimal human intervention. CEO Bill McDermott has been unequivocal about this, stating after the company significantly beat Q4 2025 expectations: "With our consistent Rule of 55+ profile, there is no AI company in the enterprise better positioned for sustainable profitable revenue growth than ServiceNow."

That's a strong claim. But the financial results are backing it up. In Q4 FY2025, ServiceNow recorded 244 transactions worth more than $1 million in net new annual contract value, a 98% renewal rate, and accelerating adoption of AI products - particularly Now Assist and AI Control Tower. CRM appeared in 16 of the top 20 Q4 deals, signalling that ServiceNow is successfully expanding beyond its ITSM heartland. Monthly active users of its AI features grew 25% quarter-on-quarter.

The shift from helpdesk platform to enterprise AI operating system is real, and it's happening faster than many observers expected.

The ServiceNow AI Stack

Now Assist

Now Assist is ServiceNow's generative AI layer - the interface through which most users will first encounter AI capabilities within the platform. It provides pre-built AI skills that surface intelligence directly within existing workflows: case summarisation, automatic knowledge article creation, code generation, virtual agent conversations, and more.

For enterprise technology buyers, Now Assist is significant because it removes the complexity of deploying AI separately. Rather than procuring a standalone AI tool, integrating it with your ITSM platform, and training staff on yet another interface, Now Assist brings AI into the flow of work that employees are already using every day. The attach rates are growing rapidly, and the ROI case is getting clearer: Lloyds Bank, for example, used Now Assist and GenAI virtual agents to automatically deflect up to 90% of HR-related cases, saving over 4,000 workdays.

Agentic AI

If Now Assist represents AI-as-assistant, then ServiceNow's agentic AI capabilities represent a more fundamental transformation. Agentic AI refers to autonomous AI agents that can plan, reason, and execute multi-step tasks without waiting for human instructions at each stage.

ServiceNow has been investing heavily here. Its AI Agent Orchestrator acts as a coordination layer — a "super AI agent" that directs individual specialist agents to collaborate on complex enterprise workflows. Think of an incident that crosses IT, security, and HR: traditionally, resolving this would require multiple handoffs between human teams. With AI Agent Orchestrator, specialist agents for each domain can collaborate autonomously, with a human reviewing the outcome rather than managing every step.

"AI agents are designed to carry out assigned tasks. Each AI agent has a single purpose and is really good at what it does. But they need help to work together." - ServiceNow AI Platform documentation

The practical applications are compelling. In ITSM, agents can monitor system health, predict outages, and trigger remediation before users are impacted. In HR, they can manage onboarding workflows end-to-end. In customer service, they can handle dispute resolution, order management, and fulfilment - not just the intake, but the entire resolution chain.

ServiceNow has also made it easier to build agents through AI Agent Studio, which allows non-technical users to create and deploy agents using a low-code/no-code interface. This is a smart move: it puts AI building tools in the hands of the people who actually understand business processes, rather than requiring expensive technical resources every time a new workflow needs to be automated.

AI Control Tower

As AI agents proliferate, governance becomes critical. ServiceNow's AI Control Tower is its answer to the question every CIO and risk officer is asking: how do I manage, monitor, and govern all the AI running across my enterprise?

AI Control Tower provides a single pane of glass for managing AI performance, tracking agent decisions, monitoring compliance, and ensuring that autonomous activity stays within acceptable boundaries. It connects strategy, governance, management, and performance for all AI across the enterprise.

This matters more than many enterprises realise. According to one industry analysis, 40% of agentic AI ventures are projected to fail by 2027 due specifically to governance and integration failures. ServiceNow's early investment in governance infrastructure is a genuine differentiator - particularly for risk-averse enterprises in regulated industries like financial services, healthcare, and government.

The ServiceNow Data Foundation

You can build the most impressive AI agents in the world, but if the data feeding them is siloed, stale, or incomplete, those agents will make poor decisions at enterprise scale. This is the central challenge facing every organisation trying to deploy AI seriously - and it's where ServiceNow's recent investments are most strategically significant.

Workflow Data Fabric

Launched in October 2024, Workflow Data Fabric is ServiceNow's integrated data layer that unifies business and technology data across the enterprise. The concept is elegant in its ambition: rather than moving data into ServiceNow through traditional ETL processes (extract, transform, load), Workflow Data Fabric uses a Zero Copy architecture that queries data where it lives.

What does that mean in practice? Your Snowflake data warehouse, your Databricks analytics platform, your Google BigQuery datasets, your Amazon Redshift environment — they all stay exactly where they are. ServiceNow's workflows and AI agents can access them in real time, without data duplication, without the latency of traditional integration, and without the governance headaches of maintaining multiple copies of sensitive information.

Zero Copy connector library is extensive, covering major platforms including Amazon Redshift, Databricks, Google Cloud BigQuery, Microsoft SQL Server, Oracle, Snowflake, Cloudera, and Teradata — as well as over 500 pre-built integrations with enterprise applications. The acquisition of Raytion extended this further, adding unstructured data connectivity to systems like SharePoint, Confluence, Box, and Google Drive. For many enterprises, the inability of AI to work with unstructured content (documents, emails, policies) has been a significant limitation. ServiceNow is working to close that gap.

RaptorDB

Underpinning Workflow Data Fabric is RaptorDB Pro, ServiceNow's high-performance columnar database that replaces the MariaDB foundation the platform previously relied on. The performance improvements are striking: early use cases demonstrate up to 53% improvement in overall transaction times, 27x faster report and analytics retrieval, and 3x more transactional throughput.

For enterprise technology buyers evaluating ServiceNow, these numbers matter in a specific way. AI-driven operations require real-time data - not yesterday's data, not data that's ten minutes old, but genuinely live information that reflects what's happening right now. RaptorDB Pro is what makes that possible at enterprise scale. It's the reason executives can view live KPIs and trend analysis rather than relying on overnight batch reports, and it's the reason AI agents can make real-time decisions rather than working from stale snapshots.

Knowledge Graph

Raw data access isn't enough. AI agents don't just need to see data - they need to understand relationships within data. ServiceNow's Knowledge Graph is the semantic layer that provides this contextual intelligence.

Think about what an enterprise actually needs to understand: this customer owns these products, is located in this region, has this support history, is subject to these SLA commitments, and interacts with these internal systems. The Knowledge Graph connects all of these relationships, enabling AI agents to work with context rather than isolated records. The result is AI that can provide genuinely personalised, contextually appropriate responses and actions - rather than generic outputs that ignore the complexity of the enterprise environment.

As ServiceNow's own documentation puts it, Knowledge Graph provides "a navigation map that guides AI agents to efficiently access and relate data both inside and outside ServiceNow." As more integrations are built, the map expands - creating an increasingly rich picture of the enterprise that AI can navigate intelligently.

Data Catalog and Governance

At Knowledge 2025, ServiceNow announced its intent to acquire data.world, a leading data catalog and governance platform. This acquisition signals something important about ServiceNow's data strategy: it's not just about connecting data, it's about making data trustworthy, discoverable, and governable.

A data catalog solves a fundamental enterprise problem - people don't know what data they have, where it is, or whether they can trust it. Before an AI agent can use data, someone needs to know that the data exists, what it means, and whether it's accurate. data.world's platform addresses exactly this, and its integration into ServiceNow will bolster the Knowledge Graph with reliable business intelligence - canonical definitions for business metrics like mean time to resolve, customer churn risk, and financial KPIs.

The combination matters because AI agents are only as reliable as the data definitions they work from. If "resolved" means different things in different systems, AI agents making decisions based on resolution rates will be working with fundamentally inconsistent information. Data governance — the unglamorous but essential work of agreeing what data means - is what bridges that gap.

Analytics: The Intelligence Layer

Performance Analytics and Predictive Intelligence

ServiceNow's analytics capabilities have been evolving significantly. Performance Analytics provides real-time visibility, forecasting, and trend analysis across any workflow - moving beyond static dashboards to predictive insights that tell decision-makers not just what happened, but what's likely to happen next.

Predictive Intelligence uses machine learning to analyse historical data and surface intelligent suggestions: routing incidents to the right team before a human has to read them, recommending knowledge articles based on the pattern of a reported issue, or flagging anomalies in system behaviour that precede a larger outage. The practical impact is meaningful - enterprises can realistically expect 30%+ reductions in ticket resolution time from these capabilities, based on documented ServiceNow case studies.

The Pyramid Analytics Partnership

In early 2026, ServiceNow announced a partnership with Pyramid Analytics, adding a powerful AI-powered analytics layer that extends the platform's intelligence capabilities to data wherever it lives - not just within ServiceNow's own environment.

Pyramid Analytics brings a semantic layer that creates canonical definitions for business metrics, working alongside the Knowledge Graph to give AI agents more reliable business intelligence foundations. This is particularly significant because, as ServiceNow's data leadership has acknowledged, AI agents need the same quality of trusted insights that human decision-makers require. The partnership addresses the gap between operational data (what ServiceNow manages natively) and the broader analytical data estate that enterprise decisions depend on.

Final Thoughts - ServiceNow AI, Data & Analytics

ServiceNow's AI story is compelling, and the financial momentum behind it is real. But the most important thing to understand about what ServiceNow is doing is this: it has recognised that AI in the enterprise is ultimately a data and analytics problem as much as it is an AI problem.

The investments in Workflow Data Fabric, RaptorDB Pro, Knowledge Graph, data.world, and Pyramid Analytics are not peripheral to the AI strategy - they are the AI strategy. ServiceNow's bet is that the enterprises which succeed with AI will be those that can deploy it on trusted, unified, real-time data. And that if ServiceNow can be the platform that solves the data problem while also providing the AI capabilities, it becomes extraordinarily difficult to displace.

Gaurav Rewari's honest assessment at Knowledge 2025 - that "the journey to agentic AI heaven goes through a data hell" - captures something important. The enterprises that will look back on 2025 and 2026 as a turning point will be the ones that took the data work seriously, not just the AI work. ServiceNow is building a platform for exactly that outcome.

Whether you're buying, building on, or competing with ServiceNow, understanding the data and analytics layer is essential to understanding where this is heading.

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If you are looking at ServiceNow - or indeed a new data or AI platform, here are some other good information sources that might be useful:

• If you are in the read-up and research phase, take a look at our AI or Data Guides.

• If you are looking to buy new technology, our new comprehensive "Enterprise Software Selection Playbook 2026" walks through all the steps to go from an initial market assessment, to vendor selection.