ServiceNow’s India Bet: Can It Put AI To Work Inside Enterprises?

What is ServiceNow, and why is it trying to expand beyond IT service management?

ServiceNow is best known for IT service management, which helps organisations handle technology support and operational requests. It now wants to become an “enterprise operating system” connecting work across IT, HR, customer service, finance, supply chain, legal and workplace operations. AI is central to that shift because it can link processes that previously operated separately.

For example, a company could use ServiceNow to coordinate an employee request involving HR, IT access and workplace services. Instead of deploying separate AI tools for each department, the platform would connect models, data, applications and workflows. This gives ServiceNow a role in building, running, governing and monitoring AI-powered work.

The expansion reflects changing customer demand in India. CIOs and CISOs are moving beyond small AI experiments and exploring broader business uses. ServiceNow is therefore positioning itself above individual applications, while also addressing the security and governance problems that come with wider adoption.

What are AI Workflow Factory and Autonomous Engineer, and what work are they designed to automate?

ServiceNow’s assessment found that just 22% of Indian enterprises have processes for testing, auditing and assessing AI risks. In other words, fewer than one in four reported having these safeguards in place. This matters because AI systems can affect decisions, data, access and business operations at scale.

The article connects this gap to rapid adoption. Indian CIOs and CISOs are increasingly considering AI for customer experience, employee operations, software development and other business processes. AI Workflow Factory is intended to help identify suitable opportunities, then build, deploy, govern and manage the resulting workflows. Autonomous Engineer focuses on more autonomous software development, including planning, building and testing.

ServiceNow argues that governance should not stop adoption. Instead, it should be introduced early. As Amit Zavery put it, controls may slow organisations initially, but they help them move faster later without creating systems that could “crash.”

How prepared are Indian enterprises to test, audit and assess the risks of AI?

AI Workflow Factory is designed to help enterprises identify where AI can improve work, then build and operate AI-powered workflows. It brings parts of the AI lifecycle onto one platform, including construction, deployment, governance and ongoing management. Its purpose is to reduce fragmentation as companies introduce AI across departments.

Autonomous Engineer focuses more specifically on software development. It adds autonomous capabilities for planning, building and testing software. A development team could use it to support several stages of a project rather than applying AI only to isolated coding tasks. The broader mechanism is workflow orchestration: connecting AI models with existing applications, data and business processes.

These products arrive as Indian enterprises explore AI beyond pilots. ServiceNow wants to help organisations scale practical use cases while controlling changes to models, prompts and underlying systems. The company’s challenge is to make automation useful without allowing speed to outpace governance, security and monitoring.

What happens when companies move from isolated AI pilots to AI-powered workflows across many departments?

Indian enterprises show strong interest in using AI, but their readiness controls remain limited. ServiceNow’s AI maturity assessment found that only 22% have processes for testing, auditing and assessing AI risks. This suggests that enthusiasm for deployment is running ahead of formal preparation.

The gap becomes important when AI moves into real business operations. A company may need to test whether an AI workflow performs reliably, audit its decisions and assess risks involving data, access or security. ServiceNow’s AI Workflow Factory is designed to bring these activities closer to workflow building and deployment instead of treating them as separate afterthoughts.

The current reality is uneven readiness, not a lack of interest. CIOs and CISOs are exploring disruptive use cases across customer experience, employee operations, software development and business processes. ServiceNow says governance should be added early, so organisations can expand AI without creating unmanaged operational or security problems.

How is ServiceNow's proposed AI orchestration layer different from the infrastructure and models provided by Microsoft, Google, AWS and companies such as Axiom?

When companies move from isolated pilots to AI workflows across departments, AI becomes an organisational system rather than a single experiment. The key question changes from “Does this application work?” to “Can it be governed, monitored and scaled safely?” Multiple deployments can create inconsistent controls and operational risks.

For example, an enterprise may connect AI to customer service, HR and software development. Each area may use different models, data sources and applications. Prompts or models can change over time, so the company must track those changes and keep workflows reliable. ServiceNow’s proposed platform brings building, deployment, governance and ongoing management together.

This shift creates both opportunity and pressure. Indian enterprises are showing strong willingness to explore many AI uses, but only 22% have reported formal testing, auditing and risk-assessment processes. Wider adoption will therefore require common controls, monitoring and clear responsibility across the organisation.

Why do autonomous AI agents create a more difficult identity and access problem than AI assistants that act only on behalf of a human?

ServiceNow is proposing a layer above infrastructure and individual AI models. Microsoft, Google and AWS can supply computing infrastructure and AI capabilities. Axiom and Paradox provide frontier models. ServiceNow instead wants to coordinate models, enterprise data, existing applications and workflows in one operating environment.

For example, a business could use a model from one provider while connecting it to customer-service records, internal applications and approval processes. ServiceNow’s role would be to help build, deploy, govern and manage that workflow. This approach aims to prevent each department from stitching together separate tools and controls.

The distinction is strategic. ServiceNow is trying to become the system through which AI enters established business processes, rather than competing primarily as a model developer or infrastructure provider. Its opportunity grows as enterprises adopt multiple AI systems, but so does the need for strong governance, monitoring, security and identity controls.

What are identity and access management, enterprise workflows and permissions, and why are they fundamental to secure computing systems?

Autonomous agents create a harder identity problem because they can perform actions directly rather than merely responding to a human. An assistant acting on behalf of a person can often inherit that person’s identity and permissions. An autonomous agent may operate continuously, make decisions and interact with several systems, so its authority must be defined separately.

For example, an agent connected to email and a CRM might read customer information, update records or send messages. Security teams would need to know which agent acted, what it was allowed to access, why it took an action and whether that action can be stopped or reviewed. Broad permissions could let one compromised or misconfigured agent cause significant harm.

The article identifies this as an emerging challenge as AI moves from assistants to agents. Enterprises will need governance, monitoring and identity controls that match autonomous behaviour. ServiceNow’s broader orchestration strategy therefore depends on making AI actions traceable, restricted and accountable.

Key Facts:

📌 ServiceNow began with a strong focus on IT service management.

📌 It aims to become an enterprise operating system across business functions.

📌 AI is becoming the connective tissue across ServiceNow’s workflows.

📌 Only 22% of Indian enterprises reported AI testing and risk processes.

📌 Most enterprises are moving beyond small AI experiments.

📌 ServiceNow says governance must be built into adoption early.

📌 AI Workflow Factory manages AI workflow opportunities and lifecycle activities.