How a global software provider streamlines web requests with AI Studio

At Intapp, a global software provider serving more than 2,700 firms, web requests ranging from page updates to legal policy changes could carry real weight. Without a reliable way to capture and triage them, even simple requests turned into bottlenecks. Missing information, time zones, and slow triage often stretched quick updates into multi-day delays.

“When people submitted a web request they were often missing a piece of information,” said Gabe Shaughnessy, Intapp’s Director of Marketing Innovation and Web Services. “Often it would be a whole day before they got their ticket triaged, and then found out they were missing something important. We’d lose two or three days on ticket intake.”

Building a smarter intake workflow with AI Studio

As an Asana ambassador, Gabe was already familiar with AI Studio. When Intapp launched a company-wide AI initiative, he saw web requests as the perfect pilot.

With AI Studio, the team piloted a new process that cut delays, removed the burden of chasing down details, and made intake more consistent from the start. Now, submissions are checked automatically, missing details are flagged right away, and tasks are quickly set up for work to begin. Humans remain in the loop at every step, easily able to check in on progress as tasks are triaged. The end result is a process that feels faster, lighter, and more consistent for everyone involved.

Here’s a simplified example of how the workflow operates in practice:

Step 1: Submitters request a web update through multiple entry points

Every incoming request, whether it’s a policy update, new event page, or navigation change, lands in the “New requests” section of the Web Requests project. To make intake simple for everyone, including teammates without an Asana license, requests can come into the project through multiple channels:

• A structured Asana form that captures required fields like request type, urgency, description, publication date, and supporting files.

• A Microsoft Teams integration that converts comments directly into tasks.

• An email import rule that turns forwarded emails into requests.

• A connected creative requests board, which feeds work directly into the web pipeline. 

No matter how a request comes in, it’s centralized in one project, giving AI Studio a consistent starting point to review and route the work.

Step 2: AI reviews each request for missing information

Once a request is submitted, it’s automatically routed to the “New requests” section of the team’s intake project. From here, Intapp’s AI Studio workflow takes over, running a QA check to see if the submission has enough information to move forward.

The required details vary by request type, so the workflow references a dedicated Web Request Types and QA Framework project, which defines:

• Request types, such as legal document updates, new blog articles, page development, navigation changes, and security issues.

• Definitions of each request type, so AI understands what to expect.

• Required details that each request type needs to move forward, such as the description of the update, the URL of the page to be changed, or confirmation of stakeholder sign-off.

• Optional details that aren’t required but are helpful, such as the target publication date or associated campaign.

Once the scan is finished, AI Studio moves the task forward in one of two ways:

• Complete requests: Tasks with all the required details are moved automatically to the “Prep for work” section.

• Incomplete requests: Tasks with missing information are sent to the “Needs more info” section, it’s followed up on.

This ensures that only requests with the right information progress, while incomplete submissions are caught immediately instead of days later.

Step 3: AI follows up on incomplete requests with targeted questions

When a request task is flagged as incomplete, a follow-up workflow in AI Studio automatically steps in to close the gaps. This step is designed to:

• Pinpoint the exact details missing from the submission.

• Draft targeted follow-up questions in task comments.

• Tag the original requestor so the questions are seen and answered quickly.

To do this, AI Studio references the QA Framework project and attached SharePoint documents for the context it needs to identify what’s missing and generate follow-up questions. For example, if a request to publish a new event page is missing a URL, AI Studio prompts the requestor for that specific detail. The workflow also includes guidance on what not to ask, like avoiding design preferences, so the questions stay relevant to what’s needed to move work forward.

By tagging the requestor directly in the task comments, the workflow cuts down on delays that come from manual back-and-forth. Instead of days waiting to find out that a request was incomplete, requestors receive immediate, tailored guidance to complete their requests and keep work moving.

“We saw actual improvements in our metrics for the time it took to bring in new tasks and how long it took us to do the work after the task was assigned, which is an indicator that there wasn’t a lot of back and forth,” said Gabe.

Step 4: Complete requests are prepped for work to begin

When the requestor responds with the missing information, AI Studio re-runs the QA check to confirm that the request is complete. The task then task moves into the “Prep for work” section, where AI preps the task for execution by:

• Assigning the task to the right teammate based on request type.

• Setting a due date according to SLA baselines, adjusted for urgency if flagged.

• Updating custom fields like campaign ID, URL, and urgency level.

This step standardizes responsibilities and timelines across the team, without the need for manual triage.

Step 5: Tasks move smoothly into development

After assigning an owner, due date, and key fields, AI Studio moves the request into the “Approved” section of the project, where it continues through the team’s standard process for execution, review, and completion.

Early benefits: faster intake, less stress, and better follow-up

Instead of bottlenecks and delays, every web request now follows a clear, AI-powered process that saves time, ensures consistency, and helps the team deliver updates faster. Since launching the workflow, the team has seen improvements like:

• Quicker turnaround: AI checks for missing details right away, so tasks move to “approved” faster with fewer back-and-forth delays.

• Less manual triage: Instead of chasing down information across time zones, AI handles the first review and flags what’s missing automatically.

• Better experience for requestors: Requestors get faster responses and clearer guidance, which builds muscle memory for including the right details next time.

• Built-in consistency: Every request goes through the same intake framework, ensuring standards are consistently met.

Not all the benefits show up in metrics. Beyond faster turnaround times, the workflow eased the stress of back-and-forth communication. Instead of teammates worrying about how to phrase follow-up questions, or feeling anxious about asking for missing details, AI Studio handles it consistently and clearly. 

“It lifted a burden off my shoulders,” said Gabe. “The AI agent does the work for me. I don’t have to stress over how to communicate or the questions to ask.”

What’s next for AI Studio at Intapp

With Intapp’s AI workflow running smoothly, the team is looking to scale the same approach across marketing operations. The goal is to create a single intake system where every request moves through the same AI-powered QA and routing process. 

Beyond intake, the team is already piloting new use cases, including: 

• Weekly KPI reporting: AI pulls data from HubSpot emails, populates KPIs in Asana, and drafts a summary for the team’s Friday readout, turning a manual prep task into an automated update.

• AI tool library: A searchable database helps the team quickly evaluate tools and surfaces the right fit for each use case.

• UTM tracking: AI generates standardized tracking links from templates, reducing errors and keeping campaign reporting clean.

Each experiment builds on the same foundation: offloading repetitive, manual tasks to AI, so people can focus on higher-value work.

“If we could roll this process out across all of our creative requests, and even see a portion of the improvements we’ve seen with web requests, it would be a pretty effective way to reduce the stress on people,” said Gabe.

Intapp’s results are just the beginning. Explore how AI Studio can help your team streamline requests, reduce bottlenecks, and focus on the work that matters most.