Implementing ServiceNow AI Search in a large enterprise environment is not just a configuration exercise—it’s a strategic data engineering challenge. For architects and implementers, success depends on how well the underlying data is understood, structured, and optimized for intelligent retrieval.
In a recent engagement with a large client, our team applied a structured, three-phase data analysis framework that proved instrumental in delivering a high-performing AI Search solution. The approach was met with enthusiastic feedback from both the client and internal teams preparing for follow-up work. Here’s how we did it—and how you can replicate it.
Phase 1: Data Profiling & Quality Assessment
Before implementing out of box search applications we started with a deep dive into the structured data. This phase focused on surfacing patterns and identifying fields that would meaningfully contribute to search relevance. If you already implemented AI search do not worry you can always go through this exercise and tune you Search Application / Search profiles from the analysis you have discovered.
🔍 Key Activities:
- Data distribution analysis to detect skewed or dominant values.
- This concept helps in understanding what values should be used as search facets because there is not much value in enabling a search facet for Priority when %95 of task records Priority is P5.
- Field usage analysis to isolate high-impact metadata and empty fields.
- Understanding what fields are always populated will enable your organization the ability filter out task records via Search source conditions. This way you are only returning task records of the best quality.
🛠 Tools Used:
- Platform Reporting for scalable, native insights.
✅ Outcomes:
- Search query enhancement using additional fields facilitated through Recommended Actions > Contexts.
- Shortlist of fields that can be used as search facets
- Search source Conditions that remove task records of bad quality data.
This step helped us eliminate noise and prioritize fields that would drive precision in search results.
Phase 2: Unstructured Data Analysis
Unstructured content—knowledge articles, case notes, and descriptions—is where AI Search earns its keep. But without proper analysis, it can introduce bias or dilute relevance.
🔍 Key Activities:
- Quality assessment of unstructured text to flag templated and low-variability data.
- Stop word identification to reduce semantic clutter.
- Synonym mapping to improve lexical matching and recall.
🛠 Tools Used:
- Predictive Intelligence – Clustering
- Client hosted LLM
✅ Outcomes:
- Comprehensive stop word library contextually aligned with underlying data
- Identification and documentation of synonyms relevant to industry as well as organization processes
- Implemented Result Improvement boosting rules that took search queries that contained heavily tasked configuration items and boosted corresponding task records. This enabled the correct records to surface when a Configuration item was used in the Search Query because it would boost the task record that had the corresponding configuration item.
- Bonus: Opportunities for process improvement (incident vs. request)
- This concept would be identified via Predictive Intelligence – Clustering. For example, if you continue to receive incidents with the same short description and they are Business as usual. Meaning the process to closure is the same for every incident and they are only jamming up the incident process these incidents should be reimagined into request.
This phase ensured that the AI model could interpret user intent accurately and return contextually rich results.
Phase 3: Solution Design & Testing
Armed with data insights, we moved into solution design—tailoring the AI Search configuration to real-world use cases and performance benchmarks.
🔍 Key Activities:
- Component design aligned with business workflows.
- Search phrase identification to target high-value queries.
- Alignment on testing approach focused on search experience and relevancy.
🛠 Tools Used:
✅ Outcomes:
- Modular design and draft stories for dev team.
- Baseline search performance measurement to track improvements.
This phase laid the foundation for translating data intelligence into robust search design, ensuring the solution was both technically sound and user-centric.
Takeaways for Architects & Implementers
- Start with data: AI Search performance is only as good as the data it consumes.
- Don’t skip unstructured analysis: It’s often the source of the most valuable insights—and the most hidden problems.
- Design with intent: Use case alignment and performance baselining are critical to long-term success.
- Use our comprehensive AI Search Implementation Guide to make informed implementation decisions
- Detailed walk through of this framework is also available in this SOW Academy recording: Configuring Recommended Actions with AI Search and NowAssist
Share your experiences implementing AI Search. Did you take a similar or a different approach to data analysis? What tools did you use to understand underlying data?
