# How Asana Scales One Idea Into a Full Content Engine With AI

> See how Asana's content team built an AI workflow to turn one idea into videos, social posts, and written content across every channel.

Source: https://asana.com/resources/build-content-engine-ai-workflow

## How Asana scales one idea into a full content engine with AI

Stephanie Bui, content marketing strategy lead at Asana, used to think about her role in straightforward terms: managing content calendars, overseeing assets, and running a team. That framing worked when each idea lived in one or two places, like a gated asset or a blog post. The work started with one strong idea and ended when it shipped.

AI changed all of that. A single idea ending at one asset was no longer enough.

"AI raised expectations," says Steph. "Once people saw what was possible, the appetite for content just grew. Now the job is getting one story in front of your audience across every channel, every format, and at the same time."

As demand grew, so did the coordination required to move a single story across formats and channels. Without a system to support it, that pressure fell on the people doing the work.

We didn't have a system to absorb that pressure. It just landed on the team.

## **What it takes to scale content across channels**

For Steph, scaling content meant rethinking how she thought about her role. "My job isn't really about content," she says. "It's about building the engine that makes content work."

In practice, that meant rethinking how ideas move from pitch through publication, and building a system that keeps work moving across teams at the same time.

"The goal is to create a strong content engine meant to be visible and used by all of our cross-functional teams," says Steph.

## **How the content engine works in practice**

The system runs across four stages, with AI woven throughout.

### Step 1: Intake

Every idea and request comes into one place. A standardized intake process captures the essential information up front, including who the story features, what the focus is, and the target audience or function. 

From there, the team decides what's worth developing into a full story and, most importantly, how it can scale across formats and channels.

AI supports the triage. It reviews incoming ideas, helps prioritize based on business goals and audience signals, sets due dates, and fills in key fields. The admin work that used to slow down the start of each project now happens automatically.

### Step 2: Story planning

Every story that moves forward has a single owner. Based on the intake information, the system assigns accountability upfront.

Planning happens early, before execution begins, so teams align on a direction before anything is created. The single owner defines the core message and ensures it stays consistent as the work expands. 

### Step 3: Parallel execution across formats

Once a story is approved, AI multi-homes the work into each relevant team’s project. Product marketing, comms, digital, and engineering teams all play a role in the content process, and within the content team, there are dedicated leads for social, video, and written content. Everyone works from the same source, in their own space. From there, outputs like social posts, videos, and written content are developed in parallel, with different teams owning different formats.

AI supports execution as well. It helps identify the right formats, speeds up first drafts, and shortens review cycles.

### Step 4: Distribution and performance tracking

Once content goes live, performance data flows back into the same system. 

The team doesn’t evaluate pieces in isolation. AI helps surface what’s working across channels and uses this information to guide what the team will create next.

Steph’s team is also developing an [AI teammate](https://asana.com/product/ai/ai-teammates) that scans the content pipeline, identifies gaps, and flags where additional content could extend a story further.

By having this content engine approach, we've tripled our output and really expanded our reach.With this system in place, the team can scale output without adding strain. Where one idea once produced one or two assets, it now becomes a full story across video, written content, and social.

## **3 lessons for building a content engine that scales**

For for teams looking to build their own content engine, Steph recommends:
- **Start by asking how far an idea can travel.**Before creating anything, consider whether it can extend across formats, channels, and stages of the funnel.
- **Assign one clear owner per story.**A single point of accountability keeps messaging consistent as work expands.
- **Treat content scale as an operational problem, not just a creative one.** Strong ideas matter, but systems determine whether teams can keep up.

## **From individual outputs to a connected story**

Building a content engine has changed how Steph’s team approaches every idea. Work moves faster, reaches more channels, and stays connected from planning through performance. The system handles the coordination that used to slow things down, so the team can focus on shaping the work itself.

#### Supercharge your teams with AI that gets work done

Give your teams AI that understands their work, keeps projects moving, and gets better the more your teams use it.
- [セールスに問い合わせる](http://asana.com/ai-teammates-sales?utm_medium=website&utm_source=blog&utm_campaign=aitga-sl27)
- [セールスに問い合わせる](http://asana.com/ai-teammates-sales?utm_medium=website&utm_source=blog&utm_campaign=aitga-sl27)

AI の現状

- [AI 活用とは？ ビジネスでの活用事例・活用法から導入のステップまで徹底解説](/ja/resources/ai-utilization)

AI の現状

#### コンテンツライター

人手不足や働き方改革への対応が急務となる中、多くの企業が AI 活用を経営課題の解決策として注目しています。生成 AI や AI チャットボットといった AI ツールの進化により、これまで専門部署だけのものだった AI 技術は、現場の業務効率化や意思決定の支援にも活用できる段階に入りました。しかし「AI にできること」や「自社でどのように活用すればよいのか ...

- [AI 導入で変わる意思決定プロセス: 経営戦略と現場を同期させる次世代のガバナンス](/ja/resources/ai-decision-making-process)

AI の現状

#### コンテンツライター

AI による意思決定とはAI による意思決定とは、人工知能が膨大なデータから最適解を導き出し、人間の判断力を高度に拡張するプロセスです。その根幹にあるのは、経験や主観ではなく客観的な数値を拠り所とするデータ駆動型 (データドリブン) 経営です。変化の激しいビジネス環境において、経営層は市場動向や財務データを可視化することでリスク管理に基づいた迅速な判断を行 ...

- [自治体 AI 導入完全ガイド 2026: 官公庁の最新活用事例と行政 DX を成功させる 5 つのステップ](/ja/resources/public-sector-ai-adoption)

AI

#### コンテンツライター

自治体、行政における AI 導入の現状と背景現在、日本の多くの自治体の現場では、多様化する住民ニーズへの対応や深刻な労働力不足という大きな課題に直面しています。この課題解決の切り札として、人工知能、とりわけ生成 AI の活用が急速に広がっています。総務省の調査や各地の報告によれば、自治体における生成 AI の利用は、もはや一部の先進的な取り組みにとどまらず ...

- [中小企業のための AI 活用例 18 選｜補助金や事例、アイディア](/ja/resources/smes-ai-applications)

AI の現状

#### コンテンツライター

中小企業こそ AI を戦略的に導入することで、競争力の強化と持続的な成長を実現できます。本記事では、中小企業がどのように AI の活用を進め、DX (デジタルトランスフォーメーション) を実現できるのか、具体的な活用事例も交えながら解説します。中小企業にとって AI が不可欠な理由2030 年問題や 2050 年問題で代表されるように、深刻な人手不足の時代 ...

- [How Asana scales one idea into a full content engine with AI](/ja/resources/build-content-engine-ai-workflow)

AI の現状

AI の現状

- [コンテンツライター](/author/whitney-vige)

Stephanie Bui, content marketing strategy lead at Asana, used to think about her role in straightforward terms: managing content calendars, overseeing assets, and running a team. ...