Product Manager - Generative AI

As Product Manager - Generative AI (GenAI), you will define, deliver, and commercialize client-facing GenAI capabilities across IDeaS products, with an initial focus on capabilities IDeaS groups under 'IDeaS Intelligence'. You will turn complex revenue, commercial, and operational data into trusted, intuitive experiences that help hospitality professionals understand performance, explore causes, summarize insights, and take informed action.

You will work at the intersection of customer problems, product strategy, user experience, data, AI engineering, analytics, and responsible AI. The role owns outcomes rather than models alone: selecting the right problems, defining the experience, establishing quality and safety standards, validating value with customers, and driving adoption at enterprise scale.

This role requires strong product judgment, practical fluency in GenAI product patterns, and the ability to translate emerging technology into dependable SaaS capabilities. Success depends on balancing speed of learning with accuracy, security, privacy, explainability, cost, latency, and customer trust.

What you'll be doing...

GenAI Product Vision and Strategy

• Define the product vision, strategy, and roadmap for GenAI-powered experiences within IDeaS Intelligence and the broader IDeaS product portfolio.
• Identify high-value client problems where conversational interfaces, summarization, explanation, natural-language exploration, content generation, or agent-assisted workflows can improve commercial decision-making.
• Distinguish where GenAI is the right solution from use cases better served by deterministic analytics, optimization, workflow automation, or conventional user experience patterns.
• Develop investment hypotheses and business cases grounded in customer value, strategic differentiation, adoption potential, implementation effort, operating cost, and commercial opportunity.
• Monitor relevant AI market, technology, regulatory, and hospitality trends and translate them into practical product choices.

Customer Discovery and Experience Definition

• Lead discovery with revenue leaders, analysts, property teams, multi-property operators, and enterprise customers to understand decisions, workflows, trust requirements, and adoption barriers.
• Define end-to-end GenAI experiences, including entry points, conversation flows, follow-up interactions, source grounding, citations, user controls, escalation paths, and human review.
• Translate customer and business needs into product requirements, product playbooks for AI execution, user stories, acceptance criteria, prototypes, and measurable experience-quality targets.
• Design for different levels of AI confidence and user expertise, ensuring outputs are clear about evidence, assumptions, uncertainty, recommended next steps, and limits.
• Run concept tests, design-partner programs, alpha and beta releases, and structured feedback loops with strategic customers.

AI Product Development and Evaluation

• Partner with AI/ML Engineering, Data Science, Architecture, Design, Analytics, Security, Privacy, Legal, Quality Assurance, and domain experts to deliver production-grade capabilities.
• Define and prioritize product patterns such as retrieval-augmented generation, tool use, structured outputs, in-context assistance, multi-turn conversation, summarization, and agent-assisted workflows.
• Create evaluation frameworks that combine offline test sets, expert review, automated evaluation, red-team scenarios, and production telemetry.
• Establish release criteria for groundedness, factual accuracy, relevance, completeness, usability, latency, reliability, safety, and cost per interaction.
• Own the product response to failure modes such as hallucination, unsupported recommendations, prompt injection, data leakage, stale context, inconsistent answers, and overreliance by users.
• Ensure feedback, corrections, model and prompt changes, evaluation results, and production incidents contribute to a disciplined continuous-improvement cycle.

Responsible AI, Trust, and Enterprise Readiness

• Embed responsible AI principles into product requirements and lifecycle decisions, including fairness, transparency, accountability, privacy, security, accessibility, and appropriate human oversight.
• Define how experiences communicate source provenance, limitations, confidence, and the distinction between generated explanation and authoritative IDeaS calculations or recommendations.
• Partner with Security, Privacy, Legal, and Architecture to define data boundaries, retention expectations, access controls, auditability, vendor and model governance, and acceptable-use safeguards.
• Ensure all defined and agreed boundaries and controls are implemented prior to release.
• Maintain clear product documentation for intended use, known limitations, evaluation evidence, and operational ownership.

Delivery, Commercialization, and Adoption

• Own prioritization and product delivery across discovery, experimentation, production hardening, launch, and scaled adoption.
• Partner with Product Marketing and Commercial teams on positioning, packaging, monetization, competitive differentiation, demonstrations, and sales enablement.
• Work with Solution Success, Consulting, Support, and customer-facing teams to define onboarding, education, adoption, feedback, and support models.
• Develop launch and release-readiness plans that include quality evidence, operational monitoring, support playbooks, customer communications, and rollback or feature-control options.
• Communicate decisions, trade-offs, progress, dependencies, and risks clearly to executives, delivery teams, and customer stakeholders.

Measurement and Product Growth

• Define a balanced scorecard covering customer outcomes, adoption, engagement, answer quality, trust, task completion, time saved, retention, revenue impact, operational reliability, latency, and unit economics.
• Instrument user journeys to understand which questio