Sr. AI Product Adoption Specialist

Pura•Pleasant Grove, UT

About The Position

Pura has been revolutionizing the smart home experience for the past several years. We obsess over providing world-class experiences for our customers, partners, and vendors. We pride ourselves on maintaining a high standard of quality and innovation with our products, and continuous growth and development for our people. Pura is looking for an AI Product Adoption Specialist to join our User Data & AI Innovation team and become the company's dedicated product leader for AI enablement across the entire organization. This role sits at the intersection of business operations, product strategy, and applied AI. Your job is to work with every department at Pura to identify their highest-value opportunities for AI-powered tools, automation, and process improvement, then drive those from concept through delivery and sustained adoption. This is not a traditional PM role focused on a single product surface. You'll be an internal AI consultant, builder, and evangelist, partnering with Customer Care, Lifecycle Marketing, Fragrance, Project Management, Finance, Procurement, Legal, Revenue, Supply Chain, and Operations to understand how teams actually work, identify where AI can create leverage, and collaborate with our AI engineering team to ship solutions that people genuinely use. Your success will be measured not just by what gets built, but by how confident and capable the people around you become. AI is only as valuable as its adoption, and you're the person who makes that adoption real.

Requirements

Process Intelligence and Systems Thinking
5+ years in product management, business process analysis, solutions consulting, internal tools PM, or operations leadership, with at least 2 years focused on AI/ML-powered products, internal tools, or applied automation.
Demonstrated ability to map end-to-end business processes across multiple departments and identify the difference between a stated workflow and what people actually do: the workarounds, the tribal knowledge, the spreadsheet that one person maintains that the whole department depends on.
Experience running structured discovery processes. You know how to go from "we have a problem" to "here's the AI-powered solution and here's how we'll measure it."
AI Fluency and Applied Knowledge
Strong working knowledge of modern AI/ML concepts: LLMs, NLP, intent classification, retrieval-augmented generation, automation pipelines, prompt engineering patterns (system prompts, few-shot examples, structured outputs, chain-of-thought), and evaluation methods for measuring output quality.
Hands-on experience using AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, or similar) to rapidly prototype and build functional applications.
Cross-Functional Communication and Change Management
Proven ability to work cross-functionally across multiple departments. You're comfortable in a room with engineers and equally effective with operations leaders who've never written a prompt.
Track record of rolling out new tools or workflows to non-technical teams and achieving sustained adoption 90+ days post-launch.
Excellent communication skills. You can translate technical AI capabilities into business value (time saved, error reduction, cost impact) for non-technical stakeholders and frame engineering constraints in terms a business lead can understand.
Comfortable presenting to leadership and building the narrative around AI ROI.
Data-Driven Mindset
Experience defining and tracking success metrics. You know the difference between "we launched it" and "people actually use it," and you've built the dashboards or reports to prove it.

Nice To Haves

Experience with customer support AI platforms (chatbots, intent routing, deflection optimization).
Familiarity with project management tools and PMO operations.
Background in e-commerce, DTC, or consumer products.
Experience with data infrastructure and customer engagement platforms such as Segment (CDP), Braze (lifecycle messaging), or similar, particularly understanding how data flows between systems and where AI can enhance personalization, segmentation, or automation.
Experience building or managing internal knowledge bases and documentation systems.
Familiarity with workflow automation platforms (Zapier, Make, n8n) and how they integrate with AI services.
Familiarity with building internal tools using platforms like Retool, Replit, or custom lightweight web apps, with an understanding of how to connect them to APIs, databases, and existing systems.
Experience building AI governance frameworks or working in regulated environments.
Prior experience working embedded within or alongside an AI/ML engineering team.

Responsibilities

Cross-Departmental AI Enablement and Discovery
Serve as the single point of contact for all departments seeking AI-powered tools and process automation.
Run a structured intake and discovery process: embed with teams, interview stakeholders, map real workflows (including the workarounds and tribal knowledge), and score opportunities for AI/automation feasibility and business impact.
Own and maintain a prioritized AI opportunity backlog across the company, moving beyond ad hoc requests to a systematic pipeline from idea to tool to adoption.
Hold recurring meetings with stakeholders across Customer Care, Lifecycle Marketing, Fragrance, Project Management, Finance, Procurement, Legal, Revenue, and Operations to surface pain points and identify where AI can amplify what people are already doing well.
Produce clear, structured process documentation that serves as a foundation for tool design and organizational knowledge.
Custom AI Tool Development and Product Management
Lead product management for internal AI tools, from scoping and requirements through design, build, launch, and iteration.
Work closely with engineers and product leadership on the User Data & AI Innovation team to translate business needs into technical requirements, user stories, and success metrics.
Define measurable outcomes for each tool (time savings, accuracy, adoption, error reduction, cost impact) and run post-launch impact analysis to ensure AI initiatives are actually moving the needle.
Prioritize opportunities based on impact, feasibility, and alignment with company goals, starting with high-volume, repeatable tasks where AI can give people their time back.
Cultural Transformation and Adoption
Be the company's AI evangelist. Make AI feel approachable, practical, and genuinely useful for every team, whether someone has never written a prompt or is already a power user.
Become the internal knowledge expert on all existing tools and platforms across the organization. Teach teams how to get more value from what they already have before introducing anything new.
Drive company-wide AI tool adoption toward 75% daily usage across teams, with a focus on genuine integration into everyday workflows, not just launches, but sustained use.
Build a tiered training program: foundational AI literacy for all staff, hands-on tool training for regular users, and advanced prompt engineering and workflow design for technical contributors.
Run internal innovation initiatives like hackathons, idea submissions, and pilot programs that give every team a stake in how AI gets applied to their work.
AI Strategy, Roadmap, and Governance
Own the company-wide AI enablement roadmap, aligning it with Pura's strategic priorities.
Present regular updates to leadership on AI initiative progress, ROI, and emerging opportunities.
Develop and maintain AI usage policies, acceptable use guidelines, and risk frameworks across the organization.
Own prompt injection detection, data privacy, and compliance considerations for all AI-powered features, both internal and customer-facing.
Monitor evolving AI regulations and ensure Pura stays ahead of compliance requirements.
Stay current on AI/ML advancements (LLMs, automation frameworks, agent architectures, emerging tools) and evaluate how new capabilities can be applied at Pura.
AI Team Partnership
Participate in AI team stand-ups and planning sessions as the product voice, ensuring engineering priorities reflect the highest-impact business needs.
Synthesize feedback from departments back into the AI roadmap, closing the loop between what teams need and what gets built.
Help build the case for AI team resourcing as demand scales.