Designing Intelligence: Automating Research Workflows with AI

We spend a great deal of time talking about AI as a content generator. That is just one interesting use case.

The more transformative application is infrastructure, and building workflows that help automate content generation. These tools allows us to design systems that continuously collect, structure, and surface information without daily manual intervention. Instead of reacting to information, we build pipelines that monitor it.

The idea is simple. Identify something worth tracking. That might be AI-related news coverage, communication job postings, federal policy announcements, research publications, or industry reports. Most of these already exist in structured formats such as RSS feeds, newsletters, or automated alerts. The opportunity is not to create new information. It is to organize what already exists.

In this instance what if your AI tool could help build a workflow, and the workflow follows a clear logic:

Source → Ingestion → Parsing → Structuring → Visualization

First, define the source. That may be an RSS feed, a filtered Gmail label, or a public data stream. In my instance I have a Google Alert set to deliver headlines about AI and Communication. Those emails are autofiltered into a folder taking them out.

Second, ingest that stream into a central environment such as Google Sheets. I chose this option simply due to App Script functionality. I also wanted a simple place to curate the data. Third, use a lightweight script to parse the content. Parsing simply means extracting what matters: title, source, publication date, link, and any relevant category tags. The trick here is to ensure your column headlines match your script. Your bot can help to refine the script and with a little back and fourth, you can test your workflow iteratively. Fourth, store that data in consistent columns so it becomes analyzable. Finally, connect the dataset to a visualization layer that updates automatically.

AI plays a practical role at every stage.

It can draft and refine the script. It can suggest parsing logic. I recreated my script several times to ensure the final output in Google Sheets was a clean and clear. It can generate Boolean classifiers to tag content by topic. It can even help design the dashboard that makes patterns visible.

Below is a simplified example of a script that pulls structured items from an RSS feed and appends them to a sheet: (Feel free to use this)

/** * Generic RSS Importer * Sheet: "Automated Log" */const CONFIG = {  sheetName: "Automated Log",  rssUrl: "https://example.com/rss"};function importFromRSS() {  const ss = SpreadsheetApp.getActiveSpreadsheet();  const sheet = ss.getSheetByName(CONFIG.sheetName);  const response = UrlFetchApp.fetch(CONFIG.rssUrl);  const xml = XmlService.parse(response.getContentText());  const items = xml.getRootElement()                   .getChild("channel")                   .getChildren("item");  const rows = [];  items.forEach(item => {    rows.push([      new Date(item.getChildText("pubDate")),      item.getChildText("title"),      item.getChildText("link")    ]);  });  if (rows.length > 0) {    sheet.getRange(sheet.getLastRow() + 1, 1, rows.length, 3)         .setValues(rows);  }}

Once scheduled with a daily trigger, this script becomes a living system. It accumulates data quietly. It reduces friction. It creates a structured archive.

The real shift is methodological. AI is not only a research subject. It is a research assistant and systems designer. By using AI to construct automated pipelines, we move from episodic observation to continuous monitoring. Students gain datasets instead of screenshots. Faculty gain trend lines instead of anecdotes. Public audiences gain transparency instead of speculation.

The dashboard is visible. The infrastructure is the innovation.

Automation does not replace analysis. It creates the conditions for better analysis.

Readers can see a live implementation of this workflow on the Media + AI Initiative website. The AI media tracking and automation projects are publicly accessible through the Initiative’s projects page at https://www.mediaandai.org/projects. These dashboards demonstrate how structured data pipelines can move from concept to execution, transforming everyday information streams into research-ready datasets and public-facing visualizations.

If you are interested in building intelligent workflows like this, experimenting with automation, or contributing to ongoing research on AI and communication systems, we invite you to connect with the Media + AI Initiative. Our projects explore how AI can support research, teaching, industry practice, and public understanding. Whether you are a student, faculty member, or industry partner, there are opportunities to collaborate, test new ideas, and help shape the next generation of AI literacy and infrastructure design. Explore current projects and get involved at :https://www.mediaandai.org/projects

By: Joshua J. Smith, PhD

Co-Founder, Chair

The Media + AI Initiative

References:

Arora, S. (2024, January 31). Google Sheets Apps Script hack: How to automate your spreadsheets. MakeUseOf. https://www.makeuseof.com/google-sheets-apps-script-hack/

Google. (n.d.). Google Apps Script guides: Sheets. Google Developers. https://developers.google.com/apps-script/guides/sheets

Google. (2025). Getting scripts to work with the Google Sheets app [Google Support thread]. https://support.google.com/docs/thread/203532617/getting-scripts-to-work-with-the-google-sheets-app?hl=en

AI Disclosure: Portions of this blog post were reviewed with the assistance of Google Gemini for clarity and style. The script was shortened by 50% to show an example. Image AI generated, too.

Google Gemini (2026). Gemini 3; Gemini.google,.com