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AI-Augmented Real-Time Learning
AI-Augmented Real-Time Learning (ARTL) — Why I Can Learn Anything with AI
What if I told you I can learn almost any well-documented skill in real time, as I’m working on the task itself?
That’s not an exaggeration — it’s how I work today, thanks to what I call AI-Augmented Real-Time Learning (ARTL).
In a fast-moving world where tools, processes, and expectations are constantly evolving, the ability to learn on demand is more valuable than ever. ARTL is a method I’ve developed (and refined daily) to acquire new skills and solve problems in real time using AI tools like ChatGPT, Microsoft Copilot, and others — without waiting for formal training.
What is AI-Augmented Real-Time Learning?
AI-Augmented Real-Time Learning means learning as you work, guided by AI. Unlike traditional learning — which often happens before or outside of tasks — ARTL is about gaining knowledge while solving the actual problem at hand.
This is powered by Interactive AI Learning (IAL) — a conversational approach where I collaborate with AI to break down complex tasks, ask targeted questions, and iterate toward solutions. AI isn’t just giving me information — it’s acting like a real-time coach, adapting its guidance based on my questions and feedback.
“What about accuracy? Give me a standard operating procedure or a well-documented reference to analyze, and its accuracy is exact.”
So, what’s the secret to accuracy in this learning system? It’s all about building context. I make sure to let AI know the scope of what I’m doing. If it’s a task or tool that has been used successfully before, I’ll have AI pull from well-documented, world-standard examples as a guide. The best guidance comes from concrete, established sources known to be effective and trusted. That’s how I maintain accuracy — because AI’s output is only as good as its training data and the context I provide.
AI’s responses improve with every iteration as I layer in more context, helping it give relevant, targeted answers that align with my unique learning needs. This makes AI a true learning partner, not just a search tool.
Why This Matters to Employers
Most people struggle when facing new systems or unfamiliar tasks — but with AI, I don’t.
I’ve trained myself to use AI as a learning partner, meaning I can take on new tools, processes, and challenges with minimal ramp-up time.
“You won’t have to wait for me to take a course — I’ll learn it while doing the work.”
For employers, this means hiring someone who is:
- Self-sufficient and fast to adapt.
- Able to solve problems on the fly without waiting for support.
- Constantly improving, upskilling every day through real tasks.
- Ready to document what I learn for team-wide use — creating guides and training materials from my AI-driven discoveries.
To support this, I use Scrivener, a long-form writing tool that allows me to curate material, take notes, and organize AI-generated insights. Scrivener helps me build structured, living knowledge bases from what I learn, making my AI-augmented learning process organized, repeatable, and easily shareable with a team. To add flexibility for teams, I am comfortable with using MS OneNote for this purpose, as well.
“AI has taught me more than any single course or book — because it teaches me exactly what I need to know, exactly when I need it.”
Conclusion: What I Bring to the Table
In today’s world, you don’t just need people who know things — you need people who can learn anything fast and apply it right away.
That’s what I bring.
If you’re looking for someone who adapts in real time, solves problems independently, and leverages AI to get results, let’s talk.
AI-Powered Workflows
Turning Everyday Workflows into AI-Powered Processes
Before AI Can Help You, It Needs to Know How You Work
Did you know?
“61% of organizations manage half or more of their content outside of official systems, leaving critical processes undocumented and informal.” (AIIM white paper pdf)
You have a company SOP, but ask five people how they actually complete the task — and you’ll get five different answers. Somehow, it works… until it doesn’t. Maybe someone leaves, and their “unofficial” method leaves with them. Or leadership wants to add AI to the process, only to discover no one is following the SOP to begin with.
The truth is, SOPs describe what should happen, but individual workflows reflect how work actually gets done. And as AI tools like ChatGPT, Microsoft Copilot, and RPA bots become part of everyday work, companies are learning that AI can’t improve or automate what it doesn’t understand.
If AI is going to help — instead of create chaos — companies must first document the real workflows people use, then align and evolve their SOPs. AI-ready businesses start by understanding how their people truly work.
What Is an Individual Workflow — and Why It’s the Missing Link
An individual workflow is the personal, step-by-step process an employee follows to get a task done — the real way work happens, not just what’s written in the manual. These workflows often include shortcuts, workarounds, and adjustments employees make to keep things moving.
For example, entering expense reports might involve downloading files, renaming them for easier tracking, and manually copying numbers into a system — but AI could automate steps like data extraction if it knew this process existed. Or think about responding to customer emails — employees might reference templates or past emails. AI could draft replies, but only if it understands the flow.
While SOPs say “what should happen,” individual workflows reveal what does happen — and what AI needs to learn to be useful. Ignoring these workflows leads to inefficiency, mistakes, and missed opportunities for AI to improve the process.
Why AI Needs to Understand Individual Workflows to Be Useful
AI tools are only as good as the workflows they’re built to support. Without a clear understanding of how work actually happens, AI risks making processes more complicated, not less.
We’ve all seen AI miss the mark — suggesting steps that don’t align with how teams really operate, or automating the wrong part of a process because no one mapped the workflow in the first place. Imagine AI drafting reports that don’t match your company’s format, or flagging data that employees never review.
To avoid this, companies need to understand real workflows — every step, decision point, and tool involved. Only then can AI be trained (even for internal use) to augment human effort instead of creating extra work. AI doesn’t replace human knowledge — it depends on it. And without that insight, AI can’t help you the way you expect.
How to Capture and Analyze Individual Workflows (with AI Augmentation in Mind)
To bring AI into your processes effectively, you first need to know what’s really happening on the ground — step by step. Here’s a simple framework for capturing individual workflows and spotting where AI can add value:
- Identify key tasks where AI could assist — like reports, communications, or data entry.
- Interview and observe employees doing those tasks. Focus on the real details, not just the big-picture steps.
- Document the entire workflow, including decision points, workarounds, and tools used — even the sticky notes on someone’s monitor!
- Analyze for inefficiencies and AI opportunities:
- What can be automated?
- What needs human judgment?
- Where can AI assist or accelerate decisions?
- Compare these workflows to existing SOPs to spot gaps or conflicts.
💡 Example: “If you find that employees spend hours manually formatting spreadsheets for reports, Copilot in Excel could handle that. But until you map that step, AI won’t know it’s needed.”
Mapping workflows this way makes AI a partner, not a guessing game.
Building AI-Augmented SOPs: Combining Company Standards with Real Workflows
“According to McKinsey, nearly 30% of sales-related activities can be automated, freeing up valuable time for human interaction and higher-value work.” (McKinsey & Company white paper pdf)
Once individual workflows are documented and analyzed, it’s time to bring them back into the official playbook — but better. Standard Operating Procedures (SOPs) shouldn’t be static documents that gather dust. Instead, they should become AI-augmented blueprints that reflect how work truly gets done, with AI built in.
What does an AI-augmented SOP include?
- Standard steps everyone should follow.
- Known workarounds that employees use to handle exceptions.
- AI-assisted actions, like “Use Copilot to generate a first draft summary.”
- Human checkpoints to review AI outputs (ex: “AI drafts email, human reviews before sending.”)
Why is this important? Because AI can’t replace human context, but it can amplify human effort when workflows are clear and aligned. Plus, as AI tools evolve, SOPs that already map where AI fits in are easier to update, keeping processes efficient and future-proof.
Real Benefits of Mapping Individual Workflows for AI Augmentation
“74% of companies prioritize AI, but few align it to real workflows.” (PwC Global AI Study white paper pdf)
When companies take the time to document how work actually happens — and build AI into those workflows — the benefits are immediate and long-term.
First, productivity skyrockets. AI takes over repetitive, time-consuming steps, freeing employees to focus on higher-value work.
Second, errors decrease, and consistency improves. When AI is guided by real workflows, it follows the same steps every time, reducing mistakes caused by manual processes.
Third, onboarding becomes faster and smoother. New hires don’t have to guess how to get things done — they follow a clear, AI-augmented process.
Finally, mapping workflows creates a culture of innovation. Employees see how AI can make their work easier and start to suggest more ways to improve processes, fueling continuous improvement.
Conclusion: Why Individual Workflows Are the Starting Point for AI-Ready Businesses
If there’s one takeaway, it’s this: before AI can help your business, it needs to know how you work.
Too often, companies try to layer AI on top of broken or undocumented processes — and then wonder why the results fall short. But when you start by understanding and mapping individual workflows, AI has a clear path to follow. It knows where to help, where to step back, and where human input is essential.
By capturing the real way work happens and evolving your SOPs to include AI, you’re not just adding a tool — you’re creating a smarter, more resilient way of working.
The companies that thrive in an AI-powered future will be the ones that respect how people really get work done — and give AI a role that truly fits.
So, start by asking one simple question — one that might change everything: “How do you really do this task?”
This Tool Is Next-Level
How I Use Plaud Note AI Voice Recorder for SME Interviews, Employer Interviews, and Process Improvement
Capturing detailed conversations accurately and efficiently is essential for the kind of work I do. Whether I’m interviewing subject matter experts (SMEs) to analyze workflows or participating in job interviews, I need a way to stay focused on the conversation while making sure I don’t miss important details.
To support this, I use a compact AI voice recorder called Plaud Note AI. It has become a valuable part of my workflow for both professional interviews and process analysis.
How I Use Plaud Note AI in Different Scenarios
1. Preparing for Conversations
Before any conversation—whether with an SME or a hiring manager—I prepare a list of topics and questions I want to cover. A simple outline is usually enough. Knowing that I won’t need to take detailed notes during the conversation allows me to focus on listening and asking thoughtful follow-ups.
2. Recording During the Interview
Plaud Note makes it easy to record with a single tap, and its small size makes it easy to carry and use. During SME interviews, I capture explanations of workflows, tools, and pain points. In job interviews, I focus on recording details about the role, expectations, and company culture—things that are easy to miss when you’re trying to stay present in the conversation or formulate meaningful responses.
3. Reviewing Output
After recording, Plaud Note’s AI generates transcripts, summaries, and mind maps, which I review to pull out key points. These different forms of output help me:
- Clarify process steps and pain points when analyzing workflows.
- Import the transcript into my favorite LLM for deep analysis, understanding, and advisement.
- Review and reflect on job interviews, especially when preparing for follow-up conversations.
- Keep accurate records without relying on memory or rushed handwritten notes.
- Offer multiple ways to share the interview with the other party if needed.
4. Using Plaud Output in My Workflows
For SME interviews, I can use transcripts to create process maps, identify areas for improvement, and prepare documentation. For employer interviews, I refer back to transcripts to write follow-up emails, evaluate how the role aligns with my goals, and prepare for future interview rounds—without missing a single detail.
Why It Works for Me
- Portable and easy to use: I can bring it to meetings or interviews without adding bulk or fuss.
- Accurate transcripts: Saves me from having to replay audio repeatedly or take excessive notes during the conversation.
- Helps me stay present: I can focus on listening and asking better questions, knowing I can review everything later.
- LLM integration: I’m able to share the conversation with my favorite LLMs (like ChatGPT), ask it questions about the conversation, and gain valuable insights I might not have thought of otherwise.
Final Thoughts
Plaud Note has become a useful tool for capturing and working with complex conversations. Whether I’m analyzing workflows to recommend process improvements or preparing for career opportunities, it helps me focus on people and ideas, not on note-taking.
For anyone who works with detailed conversations—whether for interviews, documentation, or analysis—having a way to reliably capture discussions and analyze them with an LLM is a game-changer.
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