Hugo R.

I support SMEs and startups in identifying, prioritizing, and implementing high-ROI AI initiatives.

My interventions notably cover:

• AI audit and opportunity mapping

• Business process automation

• Identification of low-value-added tasks

• Transformation of AI opportunities into concrete projects

• Support for leaders in their transformation initiatives

Objective: reduce operational costs, improve team productivity, and accelerate the adoption of impactful AI solutions.

Product Owner of a critical application used daily by over 3,000 users within DSM-Firmenich.

End-to-end product lifecycle management: scoping, prioritization, delivery, deployment, and adoption monitoring.

Structuring and prioritizing the backlog by aligning business needs, technical constraints, and business objectives.

Coordinating a team of 8 developers and 2 support profiles to secure feature delivery and ensure the quality of production releases.

Strengthening alignment between business, UX, IT, and data teams to streamline decision-making and accelerate delivery.

Contribution to the integration of distributed services (Kafka, APIs, MongoDB) in a high-volume environment.

Leading the deployment of AVA, an AI-based recommendation feature that automatically identifies the most relevant products based on project characteristics.

Implementing product KPI tracking and post-launch analysis: adoption reached 93% of the target audience, continuous increase in usage, and progressive improvement in recommendation relevance.

Assignment within a critical CRM & Data environment, with a strong focus on the quality, consistency, and international use of customer data.

Ensuring CRM data reliability through flow analysis, inconsistency detection, and Salesforce automation controls.

Optimizing support processes and structuring the management of business requests, simultaneously managing a portfolio of up to 60 active tickets.

Improving processing times by prioritizing requests and implementing more efficient workflows.

Deploying BI and data initiatives to identify data anomalies and improve visibility for business teams.

Strengthening collaboration between CRM, support, and data teams to enhance data quality and operational performance.

Assisting users in adopting tools and new processes.