Faster, Smarter, Better: AI & Automation’s Impact on ServiceNow ERP & EAM

Enterprise Resource Planning (ERP) systems and Enterprise Asset Management (EAM) are essential pieces in the tech stack of many industries. While these solutions are mission-critical, challenges such as legacy applications and difficult cloud upgrades create headwinds to modernization.

Making the transition to the latest version of technology always brings benefits. Right now the software tech landscape is in a generational leap with the combination of automation powered by agentic AI. This technology is expanding and evolving on (without an ounce of hyperbole) a second-by-second basis.

AI is also on the radar screen of most businesses, in January 2025 ERP Today reported on research from Rootstock that found more than 77% of manufacturers have implemented AI solutions -- up from 70% in 2023 – across applications including production, inventory management, and customer service.

“We like to think of ServiceNow as the ‘platform of platforms’ or the ‘single pane of glass’ for enterprise workloads because there are so many great integration and automation frameworks and enough flexibility to fit any company’s use cases,” said Jay Wigard, Head of Innovation, CoreX.

ServiceNow stands out in AI and automation, not only because of its new AI Agent Orchestrator serving as an agentic “control tower,” and AI Agent Studio platform for creating customized agents, but also because, in ServiceNow, EAM and ERP are combined to manage physical assets and business processes inside one platform. The result is higher data visibility, improved workflows, and a clearer, more complete view of this information, leading to better decision-making.

Let’s take a deeper look into the impact of AI and automation on each system.

Improving Employee Productivity and Efficiency With ServiceNow ERP

AI and automation in ServiceNow’s ERP helps teams achieve higher efficiency by automating repetitive tasks, using data analysis to improve decisions, and bringing in a variety of tools like AI-powered chatbots that handle day-to-day service requests and improved knowledge base search functionality.

What does ServiceNow ERP efficiency over legacy systems look like? Just taking accounts receivable as an example, productivity increased by 70% -- and cycle time was reduced by about two days in one use case.

Adding AI and automation across the ERP means everything from efficiency improvements and reduced costs to better customer experience and higher employee satisfaction. This is achieved through several main functions including:

• Using machine learning to analyze large datasets for patterns and making predictions.
• Implementing natural language processing in chatbots and search.
• Extracting data from documents and imaging through document intelligence.
• Using predictive intelligence for everything from classifying and routing tasks, incidents, and cases to automated task suggestions and major incident detection.

Going back to ServiceNow’s “single pane of glass” framework, Wigard said that value is magnified when adding in Now Assist’s agentic AI and machine learning capabilities to use cases such as facilitating data normalization at scale or using DocIntel to automate procurement.

“There are so many possibilities -- I think one of the main things is to sit down and workshop to figure out what the best entry point use cases are going to be,” explained Wigard. “It's important to take a look at all the different ways we can realize value with ServiceNow’s AI, EAM, and integration capabilities, and prioritize a roadmap.”

Optimizing Employee Value Through ServiceNow EAM

ServiceNow EAM optimizes the employee’s value through streamlining many tasks and processes and automating the entire physical asset lifecycle. Key results include cost control, risk mitigation, and productivity improvement.

Here are a few areas where AI and automation provide additional, tangible value:

• Improved Data Quality: Automated data cleansing identifies and corrects asset data issues.
• Risk Assessment: Asset data is analyzed to find potential risks and prioritize maintenance to mitigate those issues.
• Predictive Maintenance: Maintenance is scheduled proactively based on usage and historical data, improving downtime.
• Optimized Assets: Underutilized assets are uncovered, and inventory levels are optimized, reducing costs.
• Automated Assets Classification: AI categorizes new assets according to attributes and specifications, improving data entry accuracy.
• Automated Work Order Generation: Created based on attributes like asset health, maintenance routines, and sensor readings.
• Improved Reports: Delivered to asset owners accurately, compliant with audit and government regulations.
• Automated Field Service Operations: Field techs are assigned based on location, skills and availability.

“Enterprise asset management is complex, especially when you consider all of the legacy ERP systems and non-normalized data you have to connect to do efficiently,” said Wigard. “The idea of bringing that workload into ServiceNow might seem a little daunting, but ServiceNow is the ideal platform for enterprise integration and AI transformation. It's really just about picking the right entry point or use case to start your journey.”

Looking Ahead

Tomorrow’s technology is no longer a distant vision. AI-powered automation is driving progress now, and those who wait for this technology to plateau may never see that day come.

As Wigard said, “Even if you aren’t ready to jump in headfirst with AI, it’s worth at least starting to map out what we can do today to prepare for where you want to land.”