Product Manager

About Keyloop

Keyloop is the largest global automotive technology company, trusted by over 20,000 retailer sites and 80+ OEMs across more than 90 countries. Its platform strategy — Fusion — connects Keyloop’s products through a shared data layer (the Active Data Core), an open Domain Service ecosystem, and a growing portfolio of applications. AI is a central pillar of this strategy: the acquisition of Motortech.ai and its flagship product AIME embeds AI-powered customer communications across the platform, while VEGA and VEGA.ai — Keyloop’s new intelligence suite — bring predictive analytics, natural-language reporting, and automated workflows directly into the hands of dealership teams. Keyloop is not adding AI as an afterthought; it is reshaping how products are built, delivered, and evolved around it.

Purpose of the Role

You will work across data and intelligence layer — defining priorities, grooming the backlog rigorously alongside the team, shaping roadmap direction, engaging directly with Keyloop colleagues across multiple functions, and influencing programme-level decisions. This is not a role where you wait for someone to hand you a backlog. You will identify the right problems, frame trade-offs clearly, and drive decisions that matter to Keyloop’s customers and business.

This role requires an AI-first mindset from day one. You will be expected to use AI as your default tool for how you work — from research, analysis, and stakeholder communication to backlog refinement, specification writing, and decision support. We are not looking for someone who is curious about AI; we are looking for someone who already works this way and can demonstrate it.

Key Responsibilities

AI-First Product Thinking

• Use of AI in your own product management practice. Use LLM-based assistants, copilots, and automated analysis tools daily — for competitive research, requirements synthesis, data analysis, stakeholder communication drafting, and backlog refinement. If a task can be done faster or better with AI, you should already be doing it that way.
• Challenge the status quo on every reporting and insight workflow you encounter. Ask “why is this still a manual export or a static dashboard?” and “what would this look like if we built it AI-first?” — whether it is liveboard design, natural-language querying, anomaly detection, or cross-hub data reconciliation.
• Stay deeply current with AI developments in enterprise SaaS, analytics, and business intelligence. Bring informed, opinionated perspectives to the team on where AI creates genuine advantage and where it introduces risk — including accuracy thresholds for automated insight, data trust, and explainability of AI-generated answers.
• Champion responsible AI adoption across the product organisation, ensuring AI-driven features meet GDPR, accuracy, and transparency standards — while pushing back on the instinct to over-govern innovation before it has been tested.