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Proof D Live confirmed

Atlassian

internal genAI agents for account targeting and marketing content production

IndustryTech & SaaSLeverAcquisitionFamilyGenerationImplementationMartech platformStageconsideration
Pattern proven in 8 industries still untouched in Media & entertainment, Travel & hospitality, Food & beverage +4 See the pattern map
Gain d'efficacite
Account preparation and ABM targeting, gain claimed by the team (unquantified)
"a huge efficiency win for us" S1

Atlassian runs its ABM marketing on its own Rovo: a Customer 360 Agent aggregates the CRM to prioritize target accounts and agents write the content, with Rovo counting millions of monthly active users.

Objective

Run its own marketing on its own product. Atlassian's marketing teams use Rovo to spot priority accounts, prepare ABM campaigns, and produce content, as public dogfooding of the technology Atlassian sells.

The deployment

Atlassian uses Rovo, its genAI agent layer, internally in marketing. The account-based marketing team built a Customer 360 Agent that aggregates CRM data (renewal dates, key contacts, product usage, opportunities) to identify high-potential accounts and prepare targeted campaigns. Other agents, fed by Confluence pages and internal metrics, draft blog posts, campaign copy, and social content. The use is documented by Atlassian marketers themselves. The evidence of scale comes from Rovo's adoption, with millions of monthly active users claimed by Atlassian.

Results Proof D

Gain d'efficacite
Account preparation and ABM targeting, gain claimed by the team (unquantified)
"a huge efficiency win for us" S1
Millions d'actifs
Rovo monthly active users (evidence of scale)
"millions of monthly active users who rely on Rovo" S3

The internal marketing use is documented by Atlassian marketers in an official blog, but without a conversion or ROI metric. The evidence of scale rests on Rovo's adoption (millions of monthly active users) communicated in the shareholder letter. Internal, self-reported, honestly graded.

How it works

Documented architecture
comptes prioritaires et brouillonsvalidation et lancement CRM Atlassian Pages Confluence etmetriques internes Agents Rovo (Customer360, agents de contenu) Atlassian Rovo Equipe account-basedmarketing Campagnes ABM et contenu

The stack in detail

  • plateforme Atlassian Rovo Atlassian's genAI agent layer, which hosts the Customer 360 Agent and the content agents used by internal marketing.
  • plateforme Atlassian Intelligence AI features embedded in Atlassian products, foundation of the generative capabilities; the underlying LLMs are not named in the sources.
  • outil Confluence Internal knowledge base whose pages feed the writing agents (blog, copy, social) through RAG.
  • outil CRM Atlassian (connecteur Rovo) Source of renewal dates, contacts, product usage, and opportunities aggregated by the Customer 360 Agent; the CRM vendor is not named in the sources.

How it runs, concretely

For ops teams
CadencePer campaign for ABM targeting, continuous for content production
Operated byAtlassian's account-based marketing team, on Rovo connected to Confluence and the CRM
  1. 1
    Account data aggregation AI

    The Customer 360 Agent consolidates renewal dates, contacts, product usage, and opportunities from the CRM.

  2. 2
    Prioritization and white space AI and marketing

    The agent identifies high-potential accounts and the areas to cover for campaigns.

  3. 3
    Content production AI

    Content agents draft blog, copy, and social from Confluence.

  4. 4
    Human validation Marketing

    Marketers review, adjust, and launch the campaigns.

The signal that drives it

The CRM data and the internal Confluence pages. Without a clean CRM and an up-to-date knowledge base, the Customer 360 Agent returns poorly prioritized accounts and off-topic content.

How your customers perceive this type of use

Sourced studies

Un ecart net separe les annonceurs des consommateurs : 77% des annonceurs voient l'IA positivement contre 38% des consommateurs (Yahoo/Publicis, 2024). Les mesures implicites confirment le rejet declare : en EEG, les pubs generees par IA produisent une activation memorielle plus faible que les pubs traditionnelles et sont decrites comme agacantes, ennuyeuses et confuses (NIQ, 2024). La disclosure a un effet ambivalent : elle augmente fortement la confiance quand elle est remarquee (Yahoo/Publicis), mais 27% des jeunes consommateurs disent faire moins confiance a une entreprise dont la pub est creee par IA (IAB, 2024).

77% vs 38%
Annonceurs qui percoivent l'IA positivement, contre 38% des consommateurs (2024)
72%
Consommateurs qui estiment que l'IA rend difficile de savoir quel contenu est authentique (2024)
+96%
Lift de confiance globale envers l'entreprise quand la mention IA d'une pub est remarquee (avec +47% d'attrait de la pub et +73% de credibilite de la pub) (2024)

Acceptance conditions

  • Une disclosure visible : quand la mention IA est remarquee, la confiance globale envers l'entreprise augmente de 96% (Yahoo/Publicis 2024)
  • Une qualite visuelle suffisante : les visuels IA de basse qualite augmentent l'effort cognitif et distraient du message (NIQ 2024)

Red lines

  • Le contenu IA non declare puis identifie : 72% des consommateurs disent que l'IA rend l'authenticite difficile a etablir (Yahoo/Publicis 2024) et les marques utilisant des pubs IA sont plus souvent jugees inauthentiques ou non ethiques par les consommateurs que par les dirigeants (IAB 2024)
  • Les mannequins et personnes generes par IA : 46% des consommateurs n'en veulent pas dans la publicite, l'inquietude premiere etant les standards de beaute irrealistes (Attest 2025)

Sources: Yahoo / Publicis Media (terrain Ebco) 2024 · IAB (avec Attest) 2024 · NIQ (NielsenIQ) 2024 · Attest 2025

See full acceptance: by country, by use, by generation

How to replicate

Inference, not sourced

Data prerequisites

  • clean, usable CRM
  • internal knowledge base (wiki, docs)
  • definition of the target accounts

Org prerequisites

  • marketing team able to build and supervise agents
  • governance of access to internal data

Possible stack

  • genAI agent platform (Rovo or equivalent)
  • CRM connector
  • RAG engine on the internal wiki
Team to operate1 marketing ops person who builds and maintains the agents + 1 platform admin for the connectors and access; no dedicated data team.

The plan, step by step

  1. Step 1
    Inventory the sources (CRM, internal wiki) and set the access governance: which data each agent is allowed to read.Deliverable: Authorized data scope, documented per agent.
  2. Step 2
    Build a first Customer 360 agent that consolidates account sheets (renewals, contacts, usage, opportunities) and test it on 10-20 accounts.Deliverable: Agent in test with generated, verifiable account sheets.
  3. Step 3
    Have marketers check the reliability of the prioritizations, tune prompts and connectors until the error rate is acceptable.Deliverable: Agent validated and adopted by the ABM team.
  4. Step 4
    Add content agents connected to the wiki, with systematic human review before publication.Deliverable: Generated blog and copy drafts, validation workflow in place.
  5. Step 5
    Measure the preparation time for an account and a campaign before/after to quantify the gain.Deliverable: Quantified efficiency assessment internally.

First step: Connect an agent to the CRM and the internal wiki to automate account-sheet preparation and ABM prioritization.

Sources

  1. S1 AI and Rovo Use Cases for Marketers at Atlassian (Part 1) Interested party community.atlassian.com · accessed 2026-07-11 archive pending
  2. S2 How Atlassian Marketers use AI & Rovo (Part 2): Agents and Rovo Interested party community.atlassian.com · accessed 2026-07-11 archive pending
  3. S3 Our Q3 FY26 letter to shareholders Primary atlassian.com · 2026-04-30 · accessed 2026-07-11 archive pending