Aircall is a unicorn, AI-powered customer communications platform used by 22,000+ companies worldwide to drive revenue, resolve issues faster, and scale customer-facing teams. We’re redefining customer communications by bringing voice, SMS, WhatsApp, and AI together into one seamless workspace. Our momentum comes from a simple idea: help teams work smarter, not harder. Aircall’s AI Voice Agent automates routine calls, AI Assist streamlines post-call work, and AI Assist Pro delivers real-time guidance so people can do their best work. The result is higher revenue, faster resolutions, and teams that scale with confidence. Aircall is headquartered in Paris, our European HQ, with a strong North American presence anchored in Seattle, our North American HQ, and teams across Madrid, London, Berlin, San Francisco, New York City, Sydney, and Mexico City. We’ve built a product customers love and a business that’s scaling quickly, backed by world-class investors and driven by rapid AI innovation across multiple product lines. At Aircall, you’ll join a company in motion. We’re ambitious, product-driven, and execution-focused, with visible impact, fast decisions, and real growth. How we work at Aircall: We’re customer-obsessed, data-driven, and focused on delivering meaningful outcomes. We value ownership, continuous learning, and thoughtful speed. If you thrive in a collaborative, fast-moving environment where trust and impact matter, you’ll feel at home here. This is an enablement role first — and an AI role because of it. You'll be joining Aircall's GTM Enablement team at a moment when the function is being rebuilt for an AI-native world. Day to day, you'll be doing what great enablement practitioners do: producing content that changes field behaviour, expanding and managing the Aircall Learning Lab, owning the Highspot-to-Confluence content ecosystem, and making sure our GTM teams have sharp, current product knowledge at their fingertips. The AI piece is real — and genuinely unusual — but it serves the enablement mission. We're piloting a structured AI Operating Model to make the function faster, more consistent, and more scalable. Your job is to help prove it works by doing excellent enablement work with it. This internship is based in our Aircall Paris office . Duration will be 6 months. ~60% Enablement Delivery Content creation, Highspot & Confluence management, Aircall Learning Lab, customer adoption programmes, and GTM product knowledge management. This is the foundation ~40% AI OS Pilot Operate and extend a production AI Operating Model built on Claude Code — because it makes the enablement work better, faster, and more consistent. Key Enablement Responsibilities (~60%) : Content Design & Creation This is where you'll spend most of your time. You'll produce assets that AEs, AMs, SEs/CEs, SDRs, Customers, and Partners actually open and use — not documentation that collects dust in a folder. Design and produce enablement content across formats: playbooks, one-pagers, quick-reference guides, training decks, demo scripts, and certification materials Write for diverse GTM audiences adapting tone, depth, and format to each persona Develop visual assets and content layouts that follow Aircall brand guidelines and information design principles Create micro-learning content: short-form guides, checklists, and job aids that reps can use in the flow of work Highspot & Confluence Content Management Aircall's content ecosystem is mid-migration from Highspot to Confluence. You'll own meaningful parts of that transition — not just moving files, but improving them and giving the field a better experience on the other side. Audit existing Highspot content: assess quality, accuracy, and usage data to decide what gets migrated, updated, archived, or retired Restructure and improve content as it moves into Confluence — tighter copy, cleaner information hierarchy, better discoverability Build and maintain Confluence spaces and pages that GTM teams actually navigate (not a graveyard of outdated docs) Track content performance post-migration and flag gaps or stale material proactively Establish and maintain tagging and taxonomy conventions that make search and surfacing reliable Aircall Learning Lab — Expansion & Adoption The Aircall Learning Lab is the Customer and Partner Academy — the place where customers get onboarded, upskilled, and certified on Aircall products. You'll help grow it and drive engagement with it. Design and build new learning paths and course content for customers and partners, focused on Aircall core product, AI features, and integrations Develop certification programmes that give customers and partners a meaningful credential and an incentive to engage Create session outlines, facilitator guides, and resource packs for live and async learning formats Track learner engagement, completion rates, and certification uptake — and use that data to improve content and structure Collaborate with CS and Partner teams to understand where customers get stuck and build content that addresses those gaps Support scaled engagement campaigns that drive customers into learning paths at key moments in the lifecycle (onboarding, feature launch, QBR prep) Driving Customer Adoption Through Scaled Engagement Enablement doesn't stop at the internal field team. You'll help design the content and programmes that get customers using Aircall more deeply — especially around AI features and new releases. Produce customer-facing enablement content tied to product releases: feature guides, use-case walkthroughs, adoption playbooks Work with CS and PMM to turn launch communications into structured learning moments — not just release notes Help design and populate customer-facing resources that CS teams can use to drive adoption conversations at scale Support road show and customer event preparation with content that lands the product story clearly and concisely GTM Product Knowledge Management A GTM org is only as effective as its product knowledge is current. You'll be a key part of keeping the internal knowledge base accurate, accessible, and actually used. Maintain and improve the GTM Knowledge Base structure, navigation, and search experience in Confluence Partner with Product and PMM to translate new releases into crisp, field-ready content — FAQs, battlecards, one-pagers, talk tracks Own a content lifecycle process: identify stale content, flag gaps, coordinate updates with SMEs, and publish on time Support the GTM KB redesign initiative, contributing to information architecture and design system implementation Serve as the connective tissue between Product launches and field readiness — so no AE is caught off guard by a feature they've never heard of AI Operating Model Pilot (~40%) This is the part that makes the role unique — but being a unicorn only makes sense on top of excellent enablement fundamentals. You'll be embedded in a live experiment: a production AI OS built on Claude Code that gives the enablement function persistent operational memory, deterministic workflows, and a tool ecosystem (Slack, Gmail, Google Calendar, Google Docs, Jira). The experiment only matters if the enablement output improves. That's the test. Months 1–2: Learn the System Get onboarded onto the existing AI OS instance and shadow core workflows (/scope, /think, /roadmap, /eow) Document what works, what's confusing, and what breaks — from a genuinely fresh perspective Start using Claude Code to accelerate your own enablement tasks: content drafting, KB audits, learning path outlines Months 2–4: Operate and Extend Build your own skills for recurring enablement tasks (e.g. "generate a one-pager from a product brief," "audit a KB article against the style guide," "draft a learning path outline from CS call notes") Write context documents that feed the system's operational memory Test skill portability — can a workflow you build be used by a teammate with minimal modification? Begin documenting what a team-wide AI OS looks like vs. a single-operator one Months 4–6: Design for Scale Tackle the hard multi-user problems: skill governance, shared vs. personal context, onboarding playbooks, permission models Define the measurement framework: how do you prove this produces better enablement outcomes, not just faster ones? Produce the primary deliverable: GTM Enablement AI OS Team Deployment Guide, tested with at least one other team member Draft the cross-departmental scaling proposal if successful (what changes going from 6 people to 60?) Qualifications : Required Strong writing and content design instincts. You take complex information and make it clear, scannable, and useful for a busy field audience. Documentation, tutorials, course content, structured blogs — all counts. Currently pursuing a degree in Computer Science, Data Science, Information Systems, Web Development, Digital Communications, or a related field blending technical and communication skills. Comfortable in a terminal. You don't need to be a senior dev, but you shouldn't be afraid of the command line. Basic familiarity with git and version control. Branching, committing, PRs — you'll work with markdown files in repos. Strong written English. The team operates in English; French is a plus for Paris office life. Genuine curiosity about AI as a productivity tool, not just a novelty — you should have opinions about how LLMs should and shouldn't be used. Structured thinking. You can break an ambiguous problem into steps and document your reasoning. Strongly Preferred Experience creating instructional or educational content — training materials, e-learning modules, how-to guides, technical documentation Eye for information design — you notice when a document is hard to scan, when a slide buries the point, when a help article doesn't answer the question Familiarity with Highspot, Confluence, Notion, or similar content management or knowledge management platforms Experience with markdown as a working format for real documentation and knowledge management (not just README files) Exposure to AI tools (Claude, ChatGPT, Copilot) used seriously for work, not just trivia Basic understanding of APIs and integrations — "Claude talks to Slack via an API" should make intuitive sense Interest in enablement, education design, or knowledge management — the AI OS is a means; better GTM outcomes are the end Any experience with SaaS, B2B, or tech companies, even as a user or in a previous internship Would be a plus Familiarity with LMS platforms or learning experience design (xAPI, SCORM, or equivalent) Google Apps Script, Confluence administration, or Jira configuration experience YAML familiarity and prompt engineering concepts Data analysis skills (Python, SQL, or advanced spreadsheet work) — for measuring content performance or AI OS impact Video editing or multimedia content creation experience What Success Looks Like: By Month 3 ✓ 2+ enablement assets shipped and actively used by the field ✓ Highspot content audit complete — migration plan in motion ✓ At least one new Learning Lab module live and tracking engagement ✓ Go-to person for at least one content domain ✓ Independently using the AI OS for your enablement work ✓ 3+ original skills built and usable by teammates ✓Highspot-to-Confluence migration complete — content restructured, not just moved By Month 6 ✓ Learning Lab expanded with measurable uptick in customer engagement and certifications ✓ GTM KB is current, well-structured, and actively used by field teams ✓ Customer adoption content in place for at least 2 major product areas ✓ AI OS Team Deployment Guide exists and has been tested ✓ Before/after impact documented on 2+ enablement workflows What You'll Learn Enablement craft. How to design content and programmes that actually change field and customer behaviour — not just materials that tick a box. Content ecosystem management. How to run a Highspot-to-Confluence migration without losing institutional knowledge or momentum. Learning programme design. How to build, launch, and iterate on a Customer and Partner Academy that drives measurable product adoption. Knowledge management at scale. Information architecture, content lifecycle, multi-audience content strategy. AI agent system design. How to deploy and govern AI workflows for real business use cases — knowledge that barely exists in the market right now. Cross-functional collaboration. Working across Sales, CS, Product, PMM, and Engineering teams Why this matters : Most companies are giving employees ChatGPT access and calling it "AI transformation." That's not transformation — that's a search engine with better prose. The thesis behind this role is that AI becomes dramatically more useful when you give it structured operational memory, deterministic workflows, a tool ecosystem, and the ability to improve its own processes over time. One person has proven this works at Aircall. The question is whether it scales — and whether it produces better enablement outcomes, not just faster ones. That's what you're here to answer.
