Key Responsibilities and Required Skills for Interaction Planner

🎯 Role Definition

An Interaction Planner designs, maps, and operationalizes interaction flows and conversation models for products that use conversational AI, voice interfaces, chat, or multimodal experiences. This role sits at the intersection of UX, product, and engineering: planning dialogue strategy, defining intents and states, prototyping interactions, measuring conversational performance, and partnering with ML/NLP and engineering teams to ship reliable, delightful user experiences. The Interaction Planner is responsible for end-to-end interaction definition — from concept and script through testing, localization and iterative optimization — and for translating business outcomes into measurable interaction design work.

📈 Career Progression

Typical Career Path

Entry Point From:

Interaction Designer, Conversation Designer, or Voice UX Designer with 1–3 years of hands-on conversational design experience.
Product Designer or UX Researcher experienced in user journeys and prototyping for digital products.
Junior Product Manager or NLP Engineer who has worked on dialogue systems or chat assistant features.

Advancement To:

Senior Interaction Planner / Lead Conversation Designer
Product Owner / Product Manager for Conversational Experiences
Head of Conversational UX / Director of Voice & Multimodal Experience

Lateral Moves:

UX Research Lead (specializing in voice & conversational studies)
NLP/ML Product Manager or Applied AI Designer

Core Responsibilities

Primary Functions

Lead end-to-end interaction planning and conversation architecture for voice assistants, chatbots, and multimodal experiences by mapping user journeys, defining intents, slots/entities, system states, edge cases, and fallback strategies aligned to product KPIs and user needs.
Create detailed, production-ready conversation scripts, decision trees, and flow diagrams that communicate expected system and user behavior to engineering, NLP, and QA teams to ensure consistent implementation.
Translate business objectives into measurable interaction design requirements, success metrics (e.g., completion rate, containment, escalation rate), and experiment hypotheses to drive iterative improvement.
Design and implement persona-driven dialogue strategies including tone of voice, persona guidelines, and response timing rules to ensure consistent, on-brand conversational experiences across channels and locales.
Collaborate closely with NLP/ML engineers to define training data needs, intent taxonomy, utterance coverage, slot/entity schemas and conversation state management approaches for robust NLU / dialogue management.
Rapidly prototype interactions using industry tooling (e.g., Dialogflow, Rasa, Voiceflow, Botmock) and design systems (Figma, Sketch) and validate concepts through user testing, remote moderated sessions, or internal playtests.
Drive experimental design, run A/B and multivariate tests for conversation variants, analyze quantitative and qualitative data (chat logs, funnel metrics, recordings) and propose prioritized fixes based on impact and effort.
Own backlog prioritization for interaction improvements, manage technical dependencies with engineering, and scope deliverables for cross-functional sprints to ensure on-time delivery and high quality.
Define and document fallback, handoff and escalation patterns (e.g., to live agent or fallback flows) and ensure safe, clear behavior for failure scenarios, sensitive content, and data privacy constraints.
Build and maintain a reusable library of conversation patterns, canned responses, templated prompts, and content components that scale across products and accelerate new feature development.
Partner with product managers and business stakeholders to align on use cases, success criteria, and deployment timelines, acting as the subject-matter expert for conversational experience trade-offs.
Conduct and synthesize user research and usability tests (moderated or unmoderated) to surface friction points, contextual misunderstandings, or cultural nuances that affect conversational performance.
Review and QA conversation implementations across channels (mobile app chat, website chat, IVR, smart speaker) to verify wire-to-production fidelity, voice UX best practices and accessibility compliance.
Develop prompt engineering patterns and guardrails for Large Language Models (LLMs) and hybrid systems, including prompt templates, context selection rules, hallucination mitigation strategies and safety/content filters.
Partner with Legal, Trust & Safety, and Localization teams to ensure regulatory compliance, content moderation, and culturally appropriate conversation variations during launch and internationalization.
Create and maintain thorough documentation for conversation flows, version history, acceptance criteria, and release notes to enable smooth handoffs and post-launch troubleshooting.
Mentor and upskill product, design and content team members on conversational design principles, interaction heuristics, and tooling best practices to raise cross-functional capabilities.
Analyze chat transcripts and conversation logs to identify root causes of failures, map error types, and generate prioritized remediation plans (intent re-labeling, data augmentation, UX fixes).
Plan and execute launch readiness activities: monitoring dashboards, rollback criteria, incident response playbooks, and runbooks for operations teams supporting live conversational systems.
Coordinate localization of interaction content, determining translatability constraints and collaborating with translators and in-market reviewers to preserve intent, tone and usability.
Work with data engineering to instrument events, define analytics schemas, and validate telemetry to ensure accurate measurement of conversational flows and key metrics.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Deep expertise in conversation design and interaction planning for chatbots, voice assistants, IVR, or multimodal systems, including experience building dialogue trees and state machines.
Practical knowledge of conversational platforms and tools (Dialogflow, Rasa, Microsoft Bot Framework, Voiceflow, Lex) and prototyping tools (Figma, Adobe XD, Sketch).
Familiarity with LLMs and prompt engineering (GPT, Anthropic, Llama, etc.), including techniques for context management, few-shot prompting and hallucination control.
Understanding of NLU/NLP fundamentals: intent classification, entity extraction, dialogue state tracking, slot-filling and evaluation metrics.
Experience authoring training data and working with annotation teams, intent taxonomies and utterance augmentation strategies.
Proficiency analyzing conversational analytics and logs using SQL, Looker, BigQuery, or analytics tools to extract insights and define performance improvements.
Ability to write clear acceptance criteria, test scripts and QA checklists for conversation flows and to participate in deployment testing.
Familiarity with localization and internationalization constraints for conversational UIs and experience working with translation workflows.
Knowledge of UX writing and content design best practices for short-form, context-aware messages and microcopy.
Experience with A/B testing frameworks, experiment design and statistical interpretation related to conversational product changes.
Basic familiarity with web / mobile / voice integration patterns, REST APIs for bot backends, and telemetry instrumentation.

Soft Skills

Strong cross-functional communication and stakeholder management — able to translate technical constraints into product trade-offs and vice versa.
Empathy and user-centered thinking: ability to synthesize research and translate user needs into tangible conversational experiences.
Systems thinking: sees how conversation flows interact with product features, backend services, and analytics pipelines.
Analytical mindset and data-informed decision making; comfortable using both quantitative metrics and qualitative feedback to prioritize work.
Excellent writing and documentation skills with attention to tone, clarity and consistency across interaction content.
Problem-solving orientation with the ability to decompose ambiguous problems and scope pragmatic experiments.
Collaboration and facilitation skills for workshops (journey mapping, co-creation) and iterative design reviews.
Time management and prioritization: able to balance exploratory research, iteration and delivery under tight timelines.
Mentorship and teaching: willingness to build team capability in conversational design and tooling.
Adaptability: comfortable operating in evolving technical stacks and changing AI model behaviors.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree or equivalent practical experience in Interaction Design, HCI, Cognitive Science, Linguistics, Computer Science, Human Factors, Communications or related field.

Preferred Education:

Master's degree or advanced coursework in HCI, Cognitive Science, Computational Linguistics, or related design/AI discipline.

Relevant Fields of Study:

Human-Computer Interaction (HCI)
Linguistics / Computational Linguistics
Cognitive Science / Psychology
Computer Science with UX specialization
Communication or Technical Writing

Experience Requirements

Typical Experience Range:

3–7+ years designing conversational experiences, interaction design, or product design with demonstrable projects in chatbots, voice or AI assistants.

Preferred:

5+ years in a product or design role focused on conversational UX with experience shipping production conversational systems, working directly with NLP/ML teams, and running A/B experiments. Prior experience with LLM-based systems and platform integrations is highly desirable.