For ServiceNow Developers and Product Owners

The age of AI is no longer on the horizon; it’s here, and it’s rapidly transforming how we work. Within the ServiceNow ecosystem, Artificial Intelligence, especially Generative AI, offers unprecedented opportunities to enhance efficiency, automate complex processes, and deliver superior user experiences. But there’s a critical prerequisite to unlocking this “AI goldmine”: your data.

Many organizations find that their journey to AI heaven often goes through “data hell.” Indeed, industry analysts like Gartner have pointed out that by 2026, 60% of AI projects will fail because the data is not AI-ready (https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk). As ServiceNow Developers and Product Owners, it’s our responsibility to ensure our ServiceNow data isn’t a roadblock but a rocket booster for our AI ambitions. This means moving away from siloed, inconsistent data to a well-prepared, governed, and high-quality data foundation.

Why Your ServiceNow Data is the Linchpin for AI Success

Think of AI models, especially Generative AI, as incredibly powerful engines. Just like any high-performance engine, they require clean, high-quality fuel to operate effectively. In the context of AI, your ServiceNow data is that fuel. Poor data quality directly leads to inaccurate AI predictions, biased outcomes, and ultimately, a lack of trust in the AI-driven solutions you build.

Conversely, well-prepared and governed data offers:

• Improved Accuracy: Clean, consistent, and relevant data leads to more precise AI models, resulting in better decision-making and fewer errors.
• Enhanced Efficiency: When AI can trust the data, it can more effectively automate tasks, streamline workflows, and free up valuable human resources.
• Personalized Experiences: Quality data allows Generative AI to tailor user experiences within the ServiceNow platform, making interactions more relevant and intuitive.
• Faster Time to Value: With AI-ready data, you can deploy and leverage AI capabilities much quicker, accelerating your return on investment.

Strategies for Transforming ServiceNow Data into an AI Asset

So, how do we turn our existing ServiceNow data, which may currently reside in various silos or suffer from inconsistencies, into a valuable asset for AI?

1. Prioritize Data Quality: The GIGO Principle Magnified

The “Garbage In, Garbage Out” (GIGO) principle is amplified with AI. Here’s how to focus on data quality:

• Cleanse and Standardize: Regularly perform data cleansing exercises to identify and rectify inconsistencies, errors, and duplicates within your ServiceNow instance. Ensure that data formats are standardized across different tables and applications for seamless integration and processing by AI models. This includes dates, categorizations, choice list values, and reference fields.
• Ensure Completeness: AI, particularly machine learning models, thrives on comprehensive datasets. Identify and address missing information within your existing data. For instance, ensure incidents have proper categorization, resolution notes are detailed, and CIs have complete and accurate attributes.
• Focus on Relevance: Not all data is equally important for every AI use case. Identify the specific datasets that are most critical for the AI functionalities you plan to implement. For example, if you’re building an AI model for incident categorization, focus on the quality and completeness of historical incident data, including short descriptions, descriptions, CIs, and resolution codes.
• Leverage ServiceNow’s Capabilities: ServiceNow itself offers tools and is continually improving its platform to assist with data quality. For example, their RaptorDB is optimized for analytics, and AI agents integrated with Now Assist can guide users in improving data quality.

2. Implement Robust Data Governance: The Rulebook for Your Data

Data governance isn’t just about control; it’s about enabling trust and ensuring responsible AI.

• Establish a Data Governance Framework: This is not a one-time fix but an ongoing process. Define clear policies, roles, and responsibilities for data management within your ServiceNow environment. This framework should cover data accuracy, consistency, security, and privacy.
• Break Down Data Silos: AI needs access to relevant data. Work to integrate data from various parts of the ServiceNow platform (and potentially external trusted sources) to provide a holistic view for your AI models. ServiceNow’s Workflow Data Fabric and the new Workflow Data Network aim to unify and activate data from various sources, facilitating this.
• Metadata Management and Data Catalogs: Understanding your data is key. Tools like data catalogs provide a unified view of your data assets, including their lineage, where they are stored, and how they are used. ServiceNow’s acquisition of Data.world underscores the importance of metadata collection and data catalog tools to provide richer context for AI.
• Security and Compliance: Ensure your AI initiatives align with security, privacy, and regulatory policies. ServiceNow offers built-in governance tools like Now Assist Guardian, Now Assist Data Kit, and Now Assist Analytics to help manage compliance risks and provide visibility and control over AI deployments, ServiceNow Docs on Guardian, ServiceNow Docs on Analytics).

3. Prepare Data Specifically for AI Models: The Technical Groundwork

Once you have a foundation of quality and governed data, some specific preparation steps are often needed:

• Feature Engineering: This involves selecting, transforming, and creating the right input variables (features) from your raw ServiceNow data that your AI models will use for learning and prediction. For developers, this might involve scripting or using platform capabilities to derive new meaningful fields.
• Historical Data for Training: AI models, especially Predictive Intelligence in ServiceNow, learn from historical data. Ensure you have a sufficient volume of clean, accurate historical data relevant to the AI use case. For example, for predictive incident categorization, years of well-categorized incidents are invaluable.
• Data Transformation: Sometimes data needs to be transformed into a more AI-friendly format. ServiceNow’s move towards columnar stores with options like a premium version of RaptorDB indicates a recognition of this need for certain AI and analytical workloads.
• Iterative Approach and Monitoring: Start with specific use cases and iterate. Continuously monitor the performance of your AI models and the quality of the data feeding into them. ServiceNow AI continuously learns from historical data, which can improve accuracy over time, but this requires ongoing attention.

The Role of Developers and Product Owners

• Developers: You are at the forefront of implementing these data strategies. This includes writing scripts for data cleansing, configuring integrations, developing custom AI models where necessary, and ensuring data is structured optimally for AI consumption. Understanding the data model deeply is crucial.
• Product Owners: You play a vital role in defining the AI use cases and, therefore, the critical data elements. You need to champion data quality and governance initiatives, secure buy-in from stakeholders, and ensure that the AI solutions being built are delivering real business value based on trusted data. Your understanding of business processes will help identify where data improvements can have the most impact.

The Future is AI-Powered, and It Runs on Good Data

ServiceNow is heavily investing in making its platform AI-centric, with tools and capabilities designed to leverage your enterprise data. The acquisition of companies like Data.world and the rollout of features like the Workflow Data Network and advanced AI governance capabilities are clear indicators of this direction.

By focusing on data quality, implementing robust governance, and thoughtfully preparing your ServiceNow data, you can transform your data silos into the goldmines that will power the next generation of intelligent applications on the Now Platform. It’s a journey that requires diligence and collaboration, but the rewards – in terms of efficiency, innovation, and user satisfaction – are immense.