Diagnosed why adoption varied across an enterprise product a year after launch
Overview
An enterprise product had been deployed for roughly a year, but the organization needed to understand how it was actually being used, where it was delivering value, and why adoption varied. I led an evaluative research effort focused on real-world workflow fit, user expectations, usability, integration, and the conditions required for sustained adoption. Unlike the other case studies here, this engagement was scoped as research and strategy. I didn't own the resulting design work, which is why there isn't a UI design phase in this one.
The challenge
My approach
I defined the study around actual usage and adoption: how people worked today, what they expected, which parts of the product helped, and where the experience failed to deliver enough value.
I developed the research plan and discussion guide, identified target users, and supported recruitment planning. The interview set spanned project, digital support, database, data science, flow assurance, and production engineering perspectives.
I consolidated the recurring barriers into one cross-product view: manual and unscalable work, unclear product value, integration and workflow gaps, UX friction, insufficient system feedback, and limited usage analytics.
I translated the findings into recommendations focused on workflow automation, actionable insight, closed-loop integration, simplified summaries and dashboards, segmentation, and stronger usage analytics.
Key deliverables
Selected artifacts
The screens and diagrams below are redrawn with neutral labels and synthetic data to protect confidential specifics. They're recreations, not real screenshots — the structure and logic shown are accurately represented.
Outcome
The research produced a prioritized set of adoption barriers and strategic recommendations for stakeholders. Manual effort came out on top, followed by integration, value clarity, and UX gaps, prioritized by how consistently and strongly each theme surfaced across the interviews, not a statistical score.
What this demonstrates
What I'd validate next
I'd want to close the loop on which recommendations actually got implemented, and re-run a lighter version of this study afterward to see whether the same six barriers shrank or just moved.
Have a similar problem?
If a product's structure has stopped matching how people actually use it, I'd genuinely love to hear about it.
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