Product Manager

About Lio Build AI that runs inside the world's largest enterprises. Everyone is building AI. Very few are making it work at enterprise scale. At Lio, we're building the AI workforce for procurement. Our AI agents automate complex, mission-critical workflows across procurement, logistics, and finance for some of the world's largest companies. Backed by leading investors including a16z and Y Combinator, we're growing fast and tackling some of the most complex challenges in enterprise AI. We're looking for people who want to take ownership, solve hard problems, and build technology that delivers real business impact. The Role We're looking for a Product Manager – Post-Sales & Customer Value who wants to make enterprise AI genuinely useful at scale. Getting AI agents into production is only the beginning. What matters is what happens next: Are customers actually using them? Are workflows becoming more efficient? Are we delivering measurable business value? Your mission is to connect what happens in real customer environments with how we continuously improve our products. You'll work closely with Implementation Managers, Forward Deployed Engineers, Customer Success, and our engineering teams to identify adoption barriers, improve existing agents, and turn individual customer learnings into scalable product improvements. Product Management at Lio works differently. Our engineers own their features and make most product decisions. You won't spend your days managing backlogs, writing lengthy specifications, or assigning tickets. Instead, you'll bring customer insights, usage data, and commercial context to the table, enabling engineers and implementation teams to make better decisions. Your success is measured by the value our customers actually receive. What You'll Do Drive customer adoption: Work with Implementation and Customer Success teams to ensure customers successfully adopt our AI agents and integrate them into everyday workflows. Turn deployments into insights: Identify recurring implementation challenges, configuration issues, and product gaps across enterprise customers. Understand product performance: Analyze usage, adoption, automation rates, workflow completion, and time-to-value to identify opportunities for improvement. Connect customers with engineering: Establish regular feedback loops with users, implementation teams, and engineers to understand what works and what needs to change. Influence product decisions: Translate customer challenges into clear, evidence-based recommendations for improving existing agents and developing new capabilities. Build scalable knowledge: Own and continuously improve our product and implementation knowledge base, including deployment playbooks, best practices, and internal AI knowledge systems. Identify expansion opportunities: Recognize where additional capabilities can increase customer value, retention, and account expansion. Measure impact: Validate whether product improvements lead to better ad