Senior Product Manager, Emerging Products & AI

Workiva Inc.•Ames, IA

2d•$129,000 - $207,000•Remote

About The Position

Workiva's Growth organization is building the next generation of AI-native products. We are seeking a Senior Product Manager, to transform ambiguous, complex customer challenges into validated, high-trust enterprise product solutions. In this role, you will champion early product spaces from inception to evaluation: uncovering systemic customer challenges, developing rapid technical prototypes, determining where artificial intelligence drives measurable value, and validating product-market fit with clear evidence before scaling. This is a technical, zero-to-one validation role focused on systemic exploration rather than static roadmap maintenance. Your core challenge will be architecting probabilistic AI experiences that deliver high utility while maintaining strict predictability, data security, and user safety within a highly regulated, high-trust enterprise compliance environment.

Requirements

6+ years of experience in product management or a closely related technical product-building role with a Bachelor’s degree; OR 4+ years of experience with an Advanced Degree
Demonstrated track record managing B2B SaaS applications, enterprise workflow automation software, data-intensive products, or corporate business tools
Strong conceptual understanding of the modern applied AI landscape, including Large Language Model (LLM) capabilities, multi-agent orchestration, and human-in-the-loop design patterns. You know what AI can do for a user, and how to design experiences around it
Proven experience translating highly ambiguous, unstructured user pain points into structured technical requirement documentation, validation roadmaps, and testable hypotheses
Exceptional product craft with a deep focus on user psychology, interface transparency, and building high-trust solutions in risk-averse environments

Nice To Haves

Ability to understand how modern software products are structured, including APIs, front-end experiences, back-end services, data flows, AI/ML services, and integrations, and to use modern AI tools to explore, prototype, and work effectively with engineering
Advanced degree
Background working within deeply regulated domains (e.g., corporate accounting, financial auditing, compliance reporting, risk management, legal operations, or ESG/sustainability tracking)

Responsibilities

Lead qualitative user interviews, observational workflow walk-throughs, and technical discovery sessions to analyze how enterprise finance, compliance, and audit teams manage complex operational data. Diagram enterprise dependency structures, human-to-system handoffs, multi-stakeholder approval gates, and historical operational failure points
Synthesize messy, fragmented user signals into structured, reusable problem definitions rather than one-off custom feature requests. Translate field observations into precise product-market hypotheses, defining the explicit customer value, repeatability, and expected business impact of your proposed AI workflow interventions
Collaborate closely with design and engineering partners to develop, script, and launch low- and high-fidelity validation concepts (utilizing tools like SQL, Python, Retool, or Streamlit). Establish clear Minimum Viable Product (MVP) boundaries, explicit non-goals, and concrete validation timelines to decisively recommend whether to scale, pivot, or sunset an early-stage product vector
Translate complex enterprise compliance anxieties into intuitive, high-trust agentic experiences. You will define the interaction paradigms that dictate how autonomous agents collaborate with humans—ensuring the product interface clearly signals when an AI is making a probabilistic recommendation versus when deterministic rules apply. You will champion the design of elegant human-in-the-loop touchpoints that allow users to easily audit, verify, and approve AI-driven outcomes without sacrificing workflow momentum
Formulate, model, and implement digital authority boundaries for autonomous enterprise AI agents. Design enterprise-grade, permission-aware access policies that precisely define what an agent can read, summarize, compile, update, escalate, or execute, ensuring absolute auditability under active corporate compliance guidelines
Act as the central, transparent product leader connecting technical engineering workflows, UX design iterations, data security requirements, and go-to-market (GTM) pilot rollouts. Establish the core quantitative Key Performance Indicators (KPIs) that track early feature adoption, and conduct open, evidence-driven retrospectives on experimental features to constantly iterate on team strategy