GTM Enablement Intern

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%)

 

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: