Why Capturing Transactional Data Is as Crucial—If Not More—Than Historical Data for Now Assist

[Miguel Donayre]

Why Capturing Transactional Data Is Just as Important—If Not More—Than Historical Data for ServiceNow Now Assist

One of the most common concerns customers express about ServiceNow’s Now Assist is the quality and volume of their data, particularly historical data. In the past, certain ServiceNow features, such as machine learning models for predictive analytics and task intelligence, required significant amounts of historical data to work effectively. This led to the misconception that organizations must amass large amounts of clean historical data before deploying Gen AI-powered features.

With Now Assist, this is no longer the case. While historical data remains valuable for supporting predictive capabilities, pattern recognition, and features like Task Intelligence, Intelligent Routing, and Anomaly Detection, organizations don’t need massive amounts of pre-existing data to see value. Now Assist operates effectively from day one by leveraging transactional data, which is just as important—if not more—because it provides the immediate, context-rich inputs needed for grounded and actionable recommendations.

By focusing on capturing high-quality transactional data today and maintaining robust data governance practices, organizations can ensure that Now Assist provides value immediately while naturally building high-quality historical data for future use.

What Powers Now Assist: Transactional, Static, and Historical Data

Now Assist leverages three primary types of data:

1. Transactional Data: Real-time inputs from daily operations, such as incident tickets, HR cases, and service task logs.
2. Static Data: Foundational resources like Knowledge Articles, policies, and procedures, which Now Assist also uses for Retrieval-Augmented Generation (RAG) in its AI-powered search.
3. Historical Data: Aggregated records of past operations used for machine learning capabilities, including Task Intelligence, Intelligent Routing, and Anomaly Detection.

Each data type uniquely powers Now Assist’s features, but transactional data provides the foundation for its immediate effectiveness.

Transactional Data: The Foundation for Grounded Outputs

Transactional data is the live, real-time information captured during daily operations.

Examples include:

• Incident Tickets: Descriptions, comments, work notes, and resolution details.
• HR Cases: Employee requests, actions taken, and case outcomes.
• Service Tasks: Updates and logs documenting ongoing processes.

Key Benefits of Transactional Data

1. Immediate Context: Transactional data provides Now Assist with the details needed to deliver accurate, real-time recommendations.
2. Grounding Prompts: High-quality transactional data ensures that prompts sent to Now Assist are contextually rich, reducing the risk of hallucinations and improving output reliability.
3. Knowledge Generation: Transactional data feeds into Knowledge Articles, enriching static data for future use in RAG-based AI search.
4. Building Historical Data: Capturing transactional data today creates reliable historical records over time, enabling machine learning capabilities such as pattern recognition, predictive modeling, and workflow optimization.

Agents directly benefit from these practices by experiencing faster ticket resolution times, clearer task assignments, and access to validated solutions derived from their contributions.

Static Data: Enhancing Now Assist Through RAG

Static data, such as Knowledge Articles, policies, and procedures, play a critical role in Now Assist’s Retrieval-Augmented Generation (RAG) capabilities. By incorporating static data, Now Assist can:

• Provide contextually relevant results in AI search.
• Ground its recommendations and responses in verified organizational knowledge.

By continuously converting transactional data into Knowledge Articles, organizations enhance their static data, which improves Now Assist’s ability to deliver reliable and actionable insights.

Agents benefit as they gain quicker access to validated solutions and enriched knowledge bases, making their work more efficient and impactful.

Why Historical Data Is Still Important—But Not a Barrier

Historical data remains valuable for predictive and pattern-recognition capabilities, enabling organizations to:

• Identify Trends: Recognize recurring issues, such as repeated system outages or bottlenecks in workflows.
• Build Proactive Workflows: Predict when devices need maintenance or when service requests might spike.

However, unlike traditional AI models, Now Assist doesn’t require vast amounts of historical data to function effectively. Organizations don’t need to wait to amass or clean every record before deploying Now Assist. Instead, the system can start delivering value immediately, while transactional data captured today naturally builds high-quality historical records for future use.

Key Points About Historical Data and Now Assist

1. Valuable but Not Mandatory: Historical data enriches Now Assist’s ability to recognize patterns and support advanced features like Task Intelligence, Intelligent Routing, Predictive Analytics, and power AI Agents. However, it is not a prerequisite for success. Organizations can begin using Now Assist without large amounts of historical data.
2. Builds Over Time: The transactional data captured today evolves into high-quality historical data over time. This ensures that historical records grow organically and are aligned with consistent and meaningful data practices.
3. Empowers Predictive Models: As historical data grows, Now Assist becomes more effective at identifying trends, recommending proactive actions, and automating workflows.

By contributing to meaningful historical records, agents enable Now Assist’s advanced features, such as Intelligent Routing and Anomaly Detection, to make smarter decisions, streamlining their workflows and enhancing task alignment.

Best Practices for Ensuring High-Quality Data

To maximize the value of transactional and historical data, organizations should implement robust data governance and user training practices. Importantly, agents need to understand the direct value they gain by capturing correct and meaningful data—both for their immediate work and long-term efficiency.

1. Grounding Prompts with Rich Context

Encouraging agents to enter detailed and accurate information ensures that Now Assist receives contextually rich data, which improves the quality of its recommendations and outputs.

• Example: A well-documented incident ticket enables Now Assist to suggest the exact steps to resolve a similar issue in the future, saving agents significant time and effort.
• Value to the Agent: Agents can resolve tickets faster by relying on actionable suggestions generated from their own well-documented data, reducing repetitive troubleshooting tasks.

2. Training and Governance

Training agents to enter meaningful data into incident tickets, HR cases, and other workflows helps maintain consistency and accuracy. Governance frameworks ensure these standards are upheld.

• Example: Clear work notes like “Rebooted the router and updated configuration settings” provide useful, searchable information for future cases.
• Value to the Agent: Agents benefit from a growing library of high-quality Knowledge Articles created from their input, giving them quick access to validated solutions.

3. Showcasing Agent Value

Agents should see how capturing accurate data directly benefits their work by:

• Reducing workload through more precise automation.
• Enhancing team collaboration by creating more explicit records.
• Elevating their impact as contributors to organizational knowledge.

The Virtuous Loop: Continuous Improvement Through Data

Focusing on transactional data today establishes a virtuous loop:

1. Transactional Data Powers Now Assist: Rich, real-time data enables grounded and accurate recommendations.
2. Knowledge Articles Enhance RAG and Search: Transactional data evolves into static data, enriching AI-powered search and Retrieval-Augmented Generation.
3. Historical Data Supports Machine Learning: Over time, transactional data builds into actionable historical records, enabling features like Task Intelligence, Intelligent Routing, and Anomaly Detection.

This loop ensures continuous improvement without the need for massive pre-existing historical data, allowing organizations to unlock value immediately.

Conclusion

ServiceNow’s Now Assist provides immediate value by leveraging transactional and static data while complementing these with historical records over time. With advanced machine learning features like Task Intelligence, Intelligent Routing, Predictive Analytics, and Anomaly Detection, Now Assist transforms workflows without requiring a massive historical data backlog.

By focusing on capturing high-quality transactional data today, grounding prompts with detailed context, and implementing strong data governance and training, organizations can maximize the power of Now Assist. This approach not only ensures immediate benefits but also builds a foundation for advanced capabilities, creating a continuous cycle of improvement and innovation.