This role sits after the sale, focused on whether enterprise customers actually use the AI agents and get real value from them. A strong candidate has a couple of years in product, implementation, customer success or solutions work at a B2B software company, can read usage data for patterns, and works comfortably with engineers. Engineers own most product decisions, so the job is to bring clear evidence and customer context rather than manage backlogs or write long specs.
How it fits the AI era
AI agents behave differently in every customer setup, so adoption, automation rates and time to value need close tracking after launch. AI tools can help with analyzing usage data, summarizing customer feedback and keeping a knowledge base current. Judgment about why a customer is stuck, trust with enterprise teams, and deciding which learnings are worth turning into product changes stay human.
How it builds AI experience
The role offers hands‑on exposure to AI agents running real procurement work inside large companies, including where they succeed and where they fall short. Candidates can build relevant experience by studying how LLM‑based products handle vendor research, negotiation and delivery tracking, and by practicing with AI tools to analyze data and write playbooks. The knowledge base work also includes internal AI knowledge systems, which is a useful area to learn.
Worth showing
- Working closely with engineers
- Understanding LLM strengths and limits
- Turning feedback into recommendations
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About Lio
AI agents that run enterprise procurement end to end, researching vendors, negotiating terms and tracking deliveries instead of routing purchase requests through a manual back office.
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Product Manager in München Arbeitgeber: present of AI
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