CASE STUDY
Building an Enterprise AI Platform
Led a hybrid product and design engagement for an early-stage AI company, sharpening product direction, strengthening execution discipline, and creating a clearer path from vision to build.
From AI readiness to pilot-ready execution
Clearmatrix was developing an enterprise AI enablement platform for organizations under pressure to act on AI, but without a clear, governed path from opportunity to execution.
The product had strong market potential and a capable MVP. The challenge was focus: product direction, workflows, governance, engineering priorities, and quick wins were all evolving at once.
The engagement aligned product strategy, UX, AI governance, and delivery around a clearer path from enterprise readiness to prioritized pilot opportunities with defined value, timelines, and next steps.
Outcomes
- $350K priority opportunity surfaced
- $1.4M portfolio value modeled
- 6–12 week pilot path defined
We focused on
- Structured the BRD, roadmap inputs, and product documentation to clarify requirements, assumptions, dependencies, risks, and open decisions.
- Connected the Ideal Customer Profile and business priorities to enterprise workflows and experience direction.
- Introduced T-shirt sizing and quick-win prioritization to balance momentum, technical complexity, AI dependencies, and delivery risk.
- Improved Jira structure, epics, and ticketing direction to support clearer engineering execution.
- Designed foundational experiences across authentication, onboarding, navigation, progress visibility, and next-step guidance.
- Defined an embedded AI guidance concept that supported users within the workflow rather than through a disconnected chatbot.
- Established Responsible AI and design-system foundations spanning human review, validation, prompt governance, accessibility, and WCAG 2.2 AA.
What helped
- Direct collaboration with founders, executive leadership, AI architecture, and engineering.
- Daily office hours and short feedback loops that surfaced blockers quickly.
- Rapid prototyping in Figma and Lovable to make future-state direction tangible.
- A hybrid product, design, and product-operations approach that kept strategy connected to build reality.
The work behind the results
These artifacts show how product strategy, workflow design, AI governance, and rapid prototyping shaped the platform direction.
What I learned
This project reinforced that AI maturity is often an organizational maturity problem. The quality of the product depends on the quality of the systems behind it: clear documentation, defined workflows, sound governance, and shared decision-making. When those foundations are weak, AI does not hide the gaps. It exposes them faster.
What I took away was that the real value of hybrid product and design leadership is not any single artifact. It is the ability to connect vision, experience, governance, and engineering into one coherent path forward. When those pieces align, teams move with greater clarity, make better decisions, and turn ambitious ideas into something they can actually build.