4 priorities for implementing AI agents

Enterprise leaders rushed to deploy agentic AI. According to the ServiceNow Enterprise AI Maturity Index, nearly six in 10 organisations (59%) are implementing AI agents, up from a third in 2025.

However, the Index shows that just 5% of them are using AI agents to redesign work. Most are adding agents to existing tasks.

Organisations need a framework that prepares them to move beyond fragmented experimentation and deliver end-to-end impact with agentic AI. Here are four priorities to guide your AI agent implementation strategy.

1. Identifying AI agent use cases

Agentic AI offers the speed and scale needed to redesign work. According to Statista, the most common ways AI agents are used globally are customer support, cited by 78% of organisations, followed by process automation (71%) and predictive analytics (57%).¹

Where AI agents add the most value varies between organisations. Rather than deploying agentic technology in search of a problem, take a demand-driven approach based on a clear business case. Consider these steps to identify the best use cases for agents in your organisation:

Analyse demand: Evaluate service tickets, employee feedback, and customer interactions to highlight bottlenecks ripe for agents to help resolve.

Assess suitability: Target processes requiring a deeper level of contextual reasoning than linear, rules-based automation allows. AI agents excel in scenarios involving unstructured data interpretation and complex decision-making at scale.

Prioritise business impact: Focus on high-value use cases that can help cut costs, improve business decisions, and enhance customer satisfaction.

2. Establishing clear workforce roles

A flawed data layer is a common implementation hurdle. ServiceNow research reveals that seven in 10 executives (71%) cite inadequate data accuracy and access as a blocker to AI adoption. Among employees, 41% rank data silos among their organisation's biggest AI mistakes.

AI agents require context, governance, and strategic direction to deliver business outcomes. Assigning explicit oversight across data and systems helps create the accountability needed to put agentic AI to work safely and at scale.

Appointing dedicated stewards to make data AI-ready helps ensure agents have the context they need to reliably execute tasks. Data stewards define data ownership and enforce governance standards to keep knowledge accurate and ready for AI action.

Organisations also need AI platform owners to manage readiness, security, and performance. This group is responsible for governing agents and orchestrating them to handle multi-step workflows and achieve specific business goals.

Enterprise leaders own the business transformation AI agents help deliver. They set the vision for AI investment decisions and how employees should use their freed-up capacity.

3. Configuring the tools AI agents need

AI agents need tools to autonomously execute tasks inside workflows. These tools provide the architectural foundation that enables agents to act safely, access enterprise context, and collaborate with human teams.

On the ServiceNow AI Platform, there are several important tools to configure before implementing AI agents:

• Flow actions: Predefined tasks that automate routine processes within workflows. Example: resetting a password in an IT Service Management flow.
• Retrieval-augmented generation: A technique agents use to extract and apply real-time enterprise knowledge. Example: suggesting a knowledge base article to help resolve a known IT issue.
• Scripts: Code snippets that allow agents to execute complex tasks. Example: integrating with an external customer relationship management (CRM) system.
• AI skills: Generative capabilities that enable agents to perform specific, repeatable tasks. Example: summarising an IT incident in plain English.
• Subflows: Reusable sequences of steps implemented to complete tasks. Example: verifying user credentials.

Working with IT and your AI platform owner to configure tools ahead of implementation can lay the foundation for an effective autonomous workforce. When agents have the tools needed to navigate business systems and execute multi-step tasks, organisations can start redesigning work end to end.

4. Managing organisational change

Deploying AI agents is as much a cultural shift as a technological one. Without effective change management, implementing agents risks operational friction and unrealised value. To support adoption, organisations can take a people-centric approach before, during, and after deployment.

Start by clearly articulating your vision for agentic AI within the enterprise and developing a plan to support your workforce. Analyse role-specific impacts and tailor how you communicate the proposition to teams accordingly.

During deployment, shift your focus to team enablement. Delivering hands-on upskilling, establishing feedback channels, and tracking activation metrics can help build employee confidence. Teams should feel supported as they start collaborating with agents in daily workflows.

Once agents are deployed in multiple functions, reinforce the transition by recognising early implementation wins, incorporating user feedback, and leaning on internal champions to model best practices. Setting a precedent for continuous optimisation can turn initial momentum into lasting change.

Prepare for the agentic enterprise

These four priorities are how organisations join the 5% redesigning work to deliver end-to-end impact in the agentic enterprise. Leaders can move beyond experimentation by equipping employees with the direction, oversight, tools, and change management protocols they need to thrive alongside agents.

Find out how ServiceNow can help you get started with AI agents.

¹ Statista, Leading functional areas for AI agent deployment worldwide 2025, 26 April 2026