How this content team’s AI workflow delivers content as fast as the forecast

When your mission is to help people make informed decisions in the face of ever-changing weather, stories can’t afford to get stuck in the production process. 

That's the challenge facing The Weather Company, a leading weather forecasting company trusted to deliver accurate, real-time weather insights by the hundreds of millions of people using The Weather Channel app and weather.com. Their content team produces everything from live weather alerts to trend stories, each with its own format, tone, and production path.

Before Asana, content production was managed manually across spreadsheets, slowing work down and making it harder to stay aligned when priorities shifted. That changed when the team adopted Asana to centralize their editorial planning, and leveled up with AI Studio to automate key workflows across pitch intake, task setup, and content production. 

“Our content production process is complex, with different outlets, franchises, and so much nuance,” said Brandon Burton, senior assignment editor at The Weather Company. “With traditional rules, it was almost impossible to account for everything. Adding AI Studio on top of Asana made things so much smoother. It's a power team.”

Now, whether they’re reporting on live weather or building out a feature story, the team moves from idea to execution with more clarity and less rework.

“The Weather Company has been using AI and machine learning to advance weather forecasting and comprehension for decades,” said Brandon. “AI Studio was a natural fit as we continue to leverage AI to help people and businesses make more informed decisions based on weather.”

Building an AI-powered content pipeline

To simplify planning and accelerate the editorial process, the team rebuilt their content production workflow in Asana. AI Studio powers key parts of that process, including reviewing pitch language to determine audience personas, filling in creative and production requirements for each story, and creating tasks for each role based on the story’s staffing plan.

“Even in the first week, we saw a big shift,” said Brandon. “Before, it was hard to see what was going on. Now, we’ve got one centralized home base for each story, and that’s made a big difference.”

Here’s how it works.

Step 1: Teams work within a centralized content production planner

Teams manage upcoming content work inside a shared Asana project called the content production planner. This project acts as the central hub for all stories, whether they begin as early-stage ideas or arrive fully scoped. It’s where editors review new stories and move them through production. 

The project is organized into four sections that reflect the most common type of content the team creates, including:

• Live weather

• Quick snippets

• Feature stories

• Third-party stories 

In addition to content type, the team uses custom fields to map every story to a content franchise, an internal framework that defines the content’s narrative angle, production requirements, and staffing needs. Franchises include:

• Breaking news

• Forecast watch

• Daily weather

• Weather 101

This setup helps the team move quickly while maintaining consistency, even as content types and priorities shift.

Step 2: Content pitches enter through structured entry points 

Stories enter the content production planner in one of two ways, depending on how developed they are.

Early pitches are submitted through a form and tagged by AI

Story ideas in their earliest stages come through the story pitches project. Teammates submit pitches via a standardized form that captures required fields like title and concept summary, so editors can review and refine ideas before they’re scoped.

These tasks land in the new pitches section of the project for review. From there, an AI Studio rule kicks in to analyze the task and align it to the right audience personas by referencing a detailed internal document outlining that outlines 20 target audience segments, including:

• Environmentally conscious readers

• Tech-savvy professionals

• Parents and caregivers

• Wellness-focused individuals

• Outdoor adventurers

• Mindful consumers

To automatically tag the pitch with the correct personas, the rule:

• Scans the pitch content, including the title, summary, and any form data provided to better understand the core idea and intended audience.

• Compares the pitch language against the uploaded persona guide to identify which audience segments the pitch aligns with.

• Updates the “Personas” multi-select custom field with all applicable personas that match the language and themes in the pitch.

• Leaves a comment on the task explaining why each persona was selected, referencing specific language from the pitch.

Once an editor reviews and approves a pitch in the story pitches project, they update the task status to “Move to planner.” This action triggers an automatic rule that:

• Removes the task from the story pitches project.

• Adds the task to the new stories section of the content production planner.

• Preserves all context from the original pitch, including attached files, persona tags, and comments, so the next stage of content production can kick off with all the needed information.

Scoped or time-sensitive stories are added directly to the production project

Not every idea goes through the formal pitch process. Stories that are already scoped or need to move quickly, like breaking news or timely features, are added directly to the new stories section of the content production planner using a standardized task template. The template captures required custom fields up front, so the task is ready for franchise tagging and production planning right away.

Step 3: AI populates the creative and production needs for each story 

Once a story lands in the new stories section of the content production planner—whether through the pitch project or directly—editors select a content franchise to guide its narrative arc and production requirements. Each franchise comes with its own set of style, tone, staffing, and distribution standards.

Previously, this meant manually updating a long list of custom fields for each story, increasing the chance of inconsistent or missing information and requiring time-consuming changes if the direction shifted mid-project. Now, an AI Studio rule handles it all. 

When an editor sets (or changes) the content franchise custom field for a task in the new stories section, the rule:

• Detects that the franchise field has been changed. 

• Matches the franchise to the standardized task template.

• References a shared internal doc that outlines standardized requirements for each content franchise.

• Auto-fills all related fields so the task is production-ready.

These fields include:

• Distribution channels

• Style and tone 

• Production requirements 

• Staffing needs

• Duration, shot density, and estimated effort 

If the franchise changes mid-production, the same AI rule re-runs automatically, updating the fields to match the new creative direction. 

“AI is helping us fill in the gaps and streamline content production. Before, we had to build each task from scratch,” said Brandon. “Now, everything is built out up front. AI helps steer us in the right direction from the start, while still leaving room for manual adjustments when needed.”

Step 4: A human review ensures accuracy before work begins

Once AI has populated the task with franchise-specific fields, a rule automatically generates an approval subtask, prompting a content team member to review and approve the pitch and details before it moves into production.

During this review, the content team member:

• Confirms the franchise and distribution plan.

• Reviews AI-generated staffing assignments and production requirements.

• Verifies tone, duration, and estimated effort fields for consistency.

Once the team member approves the story, they mark the approval subtask complete and update the production status to “Assigned.”

Step 5: AI generates and assigns production subtasks

With the production phase in motion, an AI Studio rule scans the parent task’s staffing and distribution details, then automatically generates a full set of production subtasks.

To do that, AI Studio:

• References the content franchise and staffing fields on the parent task.

• Creates execution subtasks for each production role involved.

• Names each subtask with a clear title that reflects the work to be done.

• Assigns owners based on staffing roles and due dates to keep work on track.

For example, if a story requires both platform video and social clips, and the staffing includes an editor, scriptwriter, and voiceover artist, the AI might create and assign subtasks for:

• Scriptwriting

• Platform video creation

• Social cut video creation

• Video editing

• Voiceover recording

• Final review

If any staffing details are missing or incomplete, an AI rule flags the issue with a comment on the parent task, prompting the team to fill in the missing fields before production begins.

“Consistency has been a huge improvement,” said Brandon. “Things were pretty inconsistent before, so having that standardization has been great. Whenever there’s a chance for AI to help us with analyzing a submission or a story, it gives us that extra boost in brainpower.”

Step 6: Production progresses with structured ownership and visibility

Once the production subtasks are created and assigned, the content owner moves the story from the new stories section into the appropriate swimline within the content production planner. Subtasks keep execution clear and accountable, while the parent task remains the single source of truth.

As work moves forward, each subtask tracks a specific production responsibility, like scripting, editing, or voiceover, so it’s clear who owns what, what’s been completed, and what’s still in progress. 

“The AI elements have really helped us flesh out our ideas and build a clearer picture of what we’re doing before work begins,” said Brandon. “That’s made a big difference. There’s less backtracking, and we’re not left wondering who’s doing what. It’s brought everything into one place now.”

The impact so far: consistency and a clearer path from pitch to publish

Even in its early stages, the workflow is making a noticeable difference across the content production process.

• One shared view of every story: With everything in one place, editors, contributors, and producers can easily see where a story stands and what needs to happen next.

• Less rework: With AI auto-filling key production fields and franchise details, the team spends less time correcting or updating tasks when editorial direction shifts.

• Smoother handoffs: AI-generated subtasks and staffing assignments ensure that editors, producers, and contributors know what’s expected.

• Built-in consistency: By standardizing franchise-specific requirements through AI rules, the team ensures every story reflects the right tone, format, and production flow.

• Less manual work: With AI handling task setup and creation, the team has more time for creative and strategic work.

“As a team, we feel really well positioned to succeed with our new content approach because of how we were able to add AI into the process,” said Brandon.

What’s next: Expanding AI workflows across planning, publishing, and beyond

With a strong AI-powered content production workflow in place, the team is planning to expand AI Studio across the full content lifecycle. Brandon says they see “endless possibilities” to use the technology beyond content production, including to enable their engagement editors, who oversee content promotion and distribution after stories go live, and to streamline workflows with third-party content partners.

Beyond content, team members are exploring new use cases like automated invoice submission and AI-supported vendor tracking. And interest is growing across the organization, with other departments like marketing starting to explore what’s possible.

Weather moves fast—and with help from Asana and AI Studio, the team behind the forecast does, too. With AI-powered workflows in place, the team is ready to adapt quickly, without sacrificing accuracy.