My Mid-May 2026 AI Insight: Intelligent Automation Rocks

Hey everyone, Ryan here from agntwork.com. Hope you’re all having a productive week. As I’m writing this, it’s mid-May 2026, and honestly, the pace of AI evolution still blows my mind. Just yesterday, I was wrestling with a particularly stubborn data migration for a client – the kind of task that used to eat up an entire afternoon, leaving me feeling like I’d just run a marathon without actually moving from my desk.

But that’s where the beauty of intelligent automation truly shines. We’re past the hype cycle, past the “AI will take all our jobs” fear-mongering (mostly). What we’re seeing now are practical, tangible applications that don’t just save time, but fundamentally change how we interact with our digital lives. Today, I want to talk about something specific, something that’s become a cornerstone of my own workflow and frankly, an obsession: Automating the Unstructured: Taming Your Inbox and Meeting Notes with LLMs.

We all deal with it: the endless stream of emails, the rambling meeting minutes, the barrage of Slack messages. It’s the digital equivalent of trying to drink from a firehose. And while there are plenty of tools out there to help manage these, I’ve found that traditional methods often fall short when dealing with truly unstructured text. You know, the email from a client that’s half project update, half personal anecdote, and buried in there is a crucial action item. Or the hour-long meeting where three key decisions were made, but they’re scattered across 15 pages of notes.

This is where Large Language Models (LLMs) come in, not as a replacement for human judgment, but as an incredibly powerful assistant. They can read, understand, and summarize text in a way that rule-based automation simply can’t. And trust me, once you start leveraging this, you’ll wonder how you ever managed without it.

The Inbox Avalanche: From Chaos to Clarity

My inbox used to be a graveyard of good intentions. I’d mark emails unread, flag them for follow-up, and then promptly forget why I flagged them. The sheer volume was overwhelming. My goal wasn’t to achieve “inbox zero” – that feels like a myth in my line of work – but to achieve “inbox clarity.” I wanted to quickly identify what needed my immediate attention, what could wait, and what could be archived without a second thought.

Here’s how I started to tackle it using an LLM-powered workflow. The core idea is to route emails through a system that can understand their content and extract key information, then present it to me in a digestible format or even kick off further automation.

Step 1: The Filtering Layer (Existing Tools + LLM)

First, I still use my email client’s basic filtering rules for obvious stuff – newsletters go into a “Reads” folder, internal team updates into another. But for everything else, especially client communications, sales inquiries, or partnership opportunities, that’s where the LLM magic happens.

I route these emails to a custom webhook that triggers a serverless function (I use AWS Lambda, but Google Cloud Functions or Azure Functions work just as well). This function takes the email body and subject line as input. The crucial part is the prompt I give to the LLM.

Prompt to LLM:
"You are an expert email triage assistant. Analyze the following email and extract the sender's name, their primary intent (e.g., 'Project Update', 'Sales Inquiry', 'Support Request', 'Collaboration Proposal', 'Informational'), any explicit action items for me, and a concise one-sentence summary. If there are no clear action items, state 'None'.

Email Subject: [email_subject]
Email Body:
[email_body]

Output in JSON format:
{
 "sender": "Sender Name",
 "intent": "Intent Category",
 "action_items": ["Action Item 1", "Action Item 2"],
 "summary": "Concise one-sentence summary of the email's core message."
}
"

This little snippet, paired with a good LLM (I’ve had great success with OpenAI’s latest models or even some of the fine-tuned open-source ones hosted privately for cost-effectiveness), does wonders. It turns a wall of text into structured data.

Step 2: Intelligent Routing and Prioritization

Once I get that JSON output, the serverless function then takes over. Based on the `intent` field, it can do several things:

• “Sales Inquiry” or “Collaboration Proposal”: Automatically creates a new lead in my CRM (I use a simple Airtable base for this) and notifies me via Slack with the sender’s name and summary.
• “Project Update”: Appends the summary to a running project log in Notion, so I have a quick overview of recent developments without digging through old emails. If there are action items, it creates tasks in my To-Do app (Todoist) with a link back to the original email.
• “Support Request”: Forwards the summary and action items to my support team’s dedicated channel, again, with a direct link to the original email.
• “Informational”: Archives the email after appending the summary to a daily digest I receive. These are emails I might want to skim later, but don’t require immediate action.

The beauty here is that I’m not just summarizing; I’m acting on the summary. My inbox now feels like a well-oiled machine, pushing relevant information and tasks to the right places, allowing me to focus on what truly matters. I no longer dread opening it.

Meeting Minutes: From Rambling Records to Actionable Insights

Ah, meetings. The necessary evil of modern work. And the notes? Often a haphazard collection of bullet points, half-formed thoughts, and a few scribbled action items that are easily lost. I used to spend an hour after a significant meeting trying to distill the core decisions and tasks. Not anymore.

My approach here is similar to the email triage, but with a slight twist because meeting notes tend to be even more free-form and conversational.

Step 1: Capturing the Raw Notes

Whether I’m typing notes directly during a meeting, recording the audio and transcribing it (using a service like Otter.ai or even some of the built-in transcription features in meeting platforms), or receiving notes from a colleague, the first step is to get that raw text. For live meetings, I often just type furiously into a simple text editor or a dedicated note-taking app.

Step 2: The LLM as Your Super-Secretary

Once I have the raw text, I feed it to the LLM. The prompt here needs to be a bit more robust, guiding the model to extract specific types of information.

Prompt to LLM:
"You are an expert meeting minute summarizer and action item extractor. Analyze the following meeting transcript/notes and identify:
1. Key Decisions Made: A concise list of all final decisions reached during the meeting.
2. Action Items: A clear list of tasks assigned, including who is responsible (if mentioned) and any deadlines (if mentioned).
3. Discussion Points: A brief summary of the main topics discussed, distinct from decisions.
4. Open Questions/Follow-ups: Any questions that were raised but not answered, or topics that require further discussion.

Meeting Notes/Transcript:
[meeting_transcript_text]

Output in Markdown format:
## Meeting Summary

### Key Decisions Made:
- Decision 1
- Decision 2

### Action Items:
- [Person Name]: Task description (Deadline: YYYY-MM-DD)
- [Person Name]: Task description

### Discussion Points:
- Brief point 1
- Brief point 2

### Open Questions/Follow-ups:
- Question 1
- Topic for follow-up
"

I choose Markdown here because it’s easily readable and can be directly pasted into Notion, Confluence, or even a simple text file. The LLM excels at taking a conversational transcript and boiling it down to these structured points. I still give it a quick read-through, of course, but it saves me 80% of the manual effort.

Step 3: Dissemination and Task Creation

Similar to the email workflow, the output from the LLM isn’t just for my eyes.

• I often paste the Markdown summary directly into our project management tool (again, Notion is my go-to for many clients, but Jira or Asana would work).
• The “Action Items” are parsed (often using a simple Python script looking for the `[Person Name]:` pattern) and automatically created as tasks in Todoist or assigned directly within the project management tool, tagging the responsible person.
• The full summary is then shared with all meeting attendees, ensuring everyone is on the same page and has clear visibility into decisions and next steps.

This process means that within minutes of a meeting ending (or as soon as I get the transcript), I have a crystal-clear summary and all action items are already in their respective queues. No more “Who was supposed to do what?” debates a week later.

Beyond the Basics: My Thoughts on Building These Workflows

When I started playing with these ideas, I worried it would be overly complex or require deep coding knowledge. The truth is, it’s far more accessible than you might think. Here are a few things I’ve learned:

1. Start Small, Iterate Fast

Don’t try to automate your entire life at once. Pick one pain point – maybe it’s just summarizing a specific type of email. Get that working, then expand. My email triage started with just extracting intent, and I gradually added action items, summaries, and then the routing logic.

2. The Prompt is Everything

Seriously, spend time refining your prompts. Be explicit about the desired output format (JSON, Markdown, plain text), the role of the LLM (“You are an expert email triage assistant”), and what you want extracted. Experiment with different phrasing. A well-crafted prompt makes all the difference in the quality of the output.

3. No-Code/Low-Code Tools are Your Friends

You don’t need to be a full-stack developer to build these. Tools like Zapier, Make (formerly Integromat), and even Microsoft Power Automate can connect your email client, LLM APIs, and other apps with minimal code. For the more complex routing I described, serverless functions (like AWS Lambda) are fantastic because you only pay for what you use, and they integrate well with webhooks.

For example, a simple Make.com scenario could look like this:

1. Webhook Trigger: Catches incoming data from your email client (e.g., via a forwarding rule to Make’s email parser).
2. OpenAI Module: Takes the email body, applies your prompt, and gets the JSON output.
3. Router Module: Based on the ‘intent’ field from the OpenAI output, routes to different paths.
4. Action Modules:
   • If ‘Sales Inquiry’: Create a new row in Google Sheets (CRM) + Send Slack message.
   • If ‘Project Update’: Add a page to Notion + Create a task in Todoist.

This kind of visual builder makes it incredibly easy to string together powerful automations without writing a single line of Python or JavaScript (though those skills certainly help for more bespoke integrations).

4. Embrace the “Human in the Loop”

AI isn’t perfect. There will be times it misinterprets an email or misses an action item. My workflows are designed to reduce my manual effort, not eliminate my oversight. I still review the summaries and action items, especially for critical communications. Think of the LLM as a highly efficient first pass, not an infallible oracle.

Actionable Takeaways

If you’re feeling overwhelmed by unstructured text, here’s how you can start to tame it using LLMs:

1. Identify Your Biggest Text-Based Pain Point: Is it email triage, summarizing meetings, sifting through support tickets, or something else? Start there.
2. Choose Your Tools: Decide on your LLM provider (OpenAI, Anthropic, or a local open-source model), and your automation platform (Zapier, Make, Power Automate, or serverless functions if you’re comfortable with a bit of code).
3. Craft Your First Prompt: Be specific. What information do you want to extract? In what format? What role should the LLM play?
4. Build a Simple Automation: Connect your source (email, notes) to the LLM, and then connect the LLM’s output to your desired destination (CRM, project manager, notification system).
5. Test and Refine: Don’t expect perfection on day one. Send test emails, run test notes through the system, and adjust your prompts and automation logic until you’re getting consistent, useful results.

The ability of LLMs to understand and process natural language is a superpower. By integrating them thoughtfully into your daily workflows, you can transform what used to be tedious, time-consuming tasks into streamlined, efficient processes. It’s not about replacing you; it’s about empowering you to do more meaningful work. Give it a shot – you might just find yourself reclaiming hours you didn’t even know you were losing.

Until next time, keep automating, keep creating!

🕒 Published: May 17, 2026