Our team recently migrated from a self-hosted, legacy semantic search platform (built on Elasticsearch) to Humata for our internal technical documentation and research paper repository. The primary drivers were reducing operational overhead and leveraging more advanced AI-powered query understanding.
From a technical and cost perspective, the migration was successful. Setup was straightforward, and our monthly spend is predictable and slightly lower than maintaining the previous infrastructure. However, our user adoption metrics are concerning. Weekly active users have dropped by approximately 40% compared to the same period pre-migration, and average session duration is down significantly.
We followed a standard change management process:
* Announced the migration with feature highlights.
* Provided training sessions and new documentation.
* Created a feedback channel.
Despite this, the feedback we're gathering points to a few key issues:
* Users report that while Humata's answers are sometimes more conversational, they miss the precise "snippet" extraction our old system provided, which was crucial for quick citations.
* The interface, while cleaner, has fewer advanced filtering options (e.g., by date, specific document type) that power users relied on.
* There's a perceived "black box" feeling; users are unsure which documents were used to generate the answer, whereas the old system displayed explicit ranked results.
Has anyone else faced a similar adoption slump after moving to a more AI-centric platform? I'm particularly interested in:
* Strategies you used to bridge the workflow gap for power users.
* Any configuration within Humata that might expose more "traditional" search results alongside the AI summary.
* How you measured the *quality* of answers versus just usage metrics to determine if this is a training issue or a platform-fit issue.
Our FinOps side is happy, but the value isn't realized if the tool isn't being used. Looking for practical insights.
—A
Every dollar counts.
Totally get the adoption slump - feels like you've hit that classic gap between a vendor's marketing promises and the actual daily workflow friction. We saw something similar when switching our sales team's CRM.
Your feedback about users missing precise "snippet" extraction is key. The AI-powered, conversational answers are great for discovery, but they often fail at the final step: providing a directly usable, citable result. It creates extra work, like users having to click through and scan the source doc anyway. Could you run a quick poll asking what percentage of searches are for "finding a known fact to use" vs. "exploring an unknown topic"? That might pinpoint if the core use case has shifted.
Beyond the interface having fewer advanced filters (which is a huge pain), I'm curious about query patterns. Are users trying the same searches they used in the old system and getting less useful results, so they're just giving up? Sometimes the AI glosses over the exact match a legacy system would nail. A temp fix might be to add a clear "Show exact matches" toggle next to the AI summary, if Humata's API allows it.
Test, measure, repeat