AI Innovation Manager

Private Destinations•Seattle, WA

19h•$89,600 - $121,000•Onsite

About The Position

The Manager, AI Innovation is responsible for advancing Holland America Line and Seabourn's AI innovation capability through hands-on solution development, strategic portfolio support, enterprise enablement, and scalable operating practices. This role leads a team of Analysts through complex AI discovery and prototyping efforts, translates ambiguous business problems into actionable AI use cases, and builds practical tools, workflows, prompt assets, automations, and decision-support solutions that help teams improve productivity, decision quality, guest experience, and commercial outcomes. In addition to developing AI-enabled solutions, the Manager serves as a force multiplier for the Director, AI Innovation by helping shape the AI roadmap, preparing leadership-ready recommendations, structuring the use case portfolio, researching emerging AI trends, and converting strategy into repeatable processes, standards, content, and ways of working. The role partners closely with IT, data, security, privacy, architecture, business operations, and functional leaders to define how AI ideas move from intake and experimentation through review, production evaluation, adoption, and ongoing support. The position requires strong business judgment, technical fluency, executive-level communication, and the ability to influence without direct authority across a matrixed organization.

Requirements

Bachelor's Degree in Business, Data Science, Computer Science, Information Systems, Engineering, Analytics, or a related field; or equivalent combination of education and experience.
Strong working knowledge of generative AI, analytics, automation, prompt engineering, workflow design, AI-enabled business tools, and emerging AI trends.
Demonstrated ability to translate ambiguous business needs into structured use cases, requirements, solution concepts, prototypes, documentation, adoption content, and leadership recommendations.
Proven ability to build or configure practical tools using platforms such as Microsoft Copilot, Power Platform, low-code/no-code tools, analytics platforms, AI assistants, workflow automation tools, or similar technologies.
Ability to evaluate AI opportunities for business value, feasibility, risk, user readiness, scalability, and alignment to broader priorities.
Strong written communication skills with the ability to create polished playbooks, training materials, prompt guides, executive materials, business cases, and stakeholder communications.
Strong facilitation and stakeholder-management skills, with the ability to lead discovery sessions, demos, workshops, prioritization discussions, and cross-functional working sessions.
Ability to partner effectively with IT, data, security, privacy, architecture, business operations, and functional teams to support responsible experimentation and scalable delivery.
Ability to work independently, manage competing priorities, create structure in ambiguous environments, and drive work from idea through prototype, recommendation, adoption plan, or production handoff.
5+ years of progressive experience in analytics, data science, AI/ML, automation, product management, business transformation, process improvement, consulting, innovation, or a related field.
Experience leading complex discovery efforts, defining use cases, gathering requirements, sizing business value, and developing practical solutions or prototypes for non-technical users.
Experience supporting strategy, roadmap, portfolio management, executive reporting, or leadership decision-making in technology, analytics, automation, transformation, or innovation environment.
Experience creating scalable operating processes, playbooks, documentation standards, templates, or governance routines that improve consistency across a team or organization.
Experience creating enablement content, training materials, communications, demos, or knowledge assets that help business users understand and adopt new tools or ways of working.
Experience partnering with technology teams to clarify requirements, document assumptions, manage risks, align on production-readiness expectations, and support handoff from experimentation to implementation evaluation.
Experience presenting recommendations, demos, trend insights, business cases, or strategy materials to leaders and cross-functional stakeholders.
Experience in travel, hospitality, cruise, commercial operations, marketing, revenue, contact center, guest experience, onboard, or corporate operations preferred.

Responsibilities

Lead discovery for complex AI opportunities across multiple business areas, including facilitation of stakeholder interviews, workflow mapping, pain point analysis, success metric definition, and readiness assessment.
Translate ambiguous business challenges into clear AI use cases, requirements, user stories, acceptance criteria, risks, assumptions, dependencies, and business value hypotheses.
Build functional AI-enabled prototypes and tools, including agents, copilots, prompt libraries, workflow automations, dashboards, knowledge assistants, decision-support tools, and lightweight applications that business users can test and evaluate.
Design prototypes with scalability and production evaluation in mind by documenting data needs, process impacts, user roles, controls, technical assumptions, limitations, and support requirements.
Lead user testing and iteration cycles, synthesize feedback, evaluate whether solutions meet business objectives, and make recommendations on whether to scale, refine, pause, or retire use cases.
Continuously monitor emerging AI capabilities, vendor tools, generative AI patterns, agentic workflows, automation trends, responsible AI practices, and relevant developments in cruise, travel, hospitality, marketing, revenue, contact center, guest experience, onboard, and corporate operations.
Translate external AI trends into practical internal implications by identifying what is actionable now, what should be piloted, what should be monitored, and what should be avoided due to risk, immaturity, cost, or limited business value.
Develop guidance, examples, demos, playbooks, use case libraries, maturity models, evaluation rubrics, prompt frameworks, and decision aids that help leaders and teams understand how to apply AI responsibly.
Act as an internal advisor to business stakeholders by helping them distinguish between automation, analytics, generative AI, workflow redesign, and process improvement opportunities.
Build organizational AI fluency by making complex technical concepts understandable, actionable, and relevant to day-to-day business work.
Partner with IT, data, security, privacy, architecture, product, and Technical Automation teams to define the end-to-end operating model for AI experimentation, review, handoff, production evaluation, deployment readiness, and support.
Partner with PMO to support repeatable processes for AI intake, use case qualification, risk review, data assessment, solution documentation, testing, user acceptance, escalation, and transition to technical delivery teams.
Translate business needs into technical context for IT partners and translate technical, security, privacy, and architecture constraints into clear business guidance and decisions.
Identify reusable technical and operating patterns, including approved tools, common data requirements, prompt and knowledge-management practices, control points, documentation standards, and production-readiness checklists.
Help reduce friction between business experimentation and enterprise technology delivery by ensuring prototypes are documented, tested, governed, and positioned for scalable implementation where appropriate.
Own the substance of AI enablement content by defining what information, examples, use cases, guidance, recommendations, and messages should be shared with business audiences to drive understanding and adoption.
Partner with L&D to create clear, polished content such as training materials, FAQs, playbooks, demo scripts, prompt guides, responsible-use guidance, adoption toolkits, leadership updates, and audience-specific communications.
Partner with business operations, communications, change management, and functional leaders on distribution and activation plans while ensuring the underlying AI content is accurate, practical, timely, and aligned to strategy.
Facilitate workshops, demos, office hours, discovery sessions, and working sessions that help teams identify opportunities, understand AI capabilities and limitations, and adopt new tools or processes.
Gather adoption feedback and business questions, identify recurring education needs, and convert insights into improved content, tools, processes, and enablement priorities.
Help mature the AI Innovation team's operating model by creating templates, knowledge repositories, intake forms, evaluation criteria, prototype documentation standards, reporting routines, and metrics for tracking value and adoption.
Provide expert guidance and quality review for use case framing, prototype design, requirements documentation, stakeholder materials, business cases, and adoption content.
Identify opportunities to improve how the team works, including prioritization, collaboration routines, status reporting, tool selection, handoff practices, and knowledge-sharing mechanisms.
Model senior individual contributor leadership by proactively identifying gaps, solving operational problems, coaching others informally, and raising the quality and consistency of AI Innovation deliverables.