Is AI Actually Making You More Productive...?

or is it just keeping you busy?

When artificial intelligence first became a part of my workflow, I genuinely believed it would change everything. I had visions of AI summarizing long reports, drafting emails, updating decks, and giving me back hours in my day. Like many professionals, I had been operating at a constant sprint — and AI felt like the first real chance to slow down without falling behind.

And at first, it did feel magical. I could produce more, respond faster, and check off low-effort tasks with ease. But weeks in, I noticed something odd: I wasn’t actually less busy.

In fact, I was juggling even more tabs, editing even more documents, and feeling just as time-starved as before (and sometimes even more so.) Which led me to ask a question that’s become the foundation of many TechStack conversations:

“Is AI actually making me more productive — or is it just making me feel like I am?”

This question matters. Not because we doubt AI’s capability, but because we need to examine how we’re using it, and why.

The Difference Between Activity and Impact

One of the biggest traps in AI adoption is mistaking speed for progress. When a tool generates a first draft of an email or builds a client deck in seconds, it’s easy to feel a dopamine hit — something is done. Something moved forward.

But speed without clarity doesn’t lead to better results. In many cases, it leads to more noise: outputs that require multiple rounds of revision, tools that automate low-leverage work, and workflows that feel productive but don’t actually change outcomes.

Take, for example, a team using an AI tool to summarize all meetings. Initially, it’s a game-changer: no more manual note-taking. But over time, it becomes clear that while summaries are being generated, decisions still aren’t being made. Action items pile up, but ownership is murky. Context is missing. Workstreams stall.

Why? Because summarizing isn’t the same as understanding. Generating more outputs doesn’t automatically create clarity.

And this is where many professionals get stuck: in the illusion of productivity.

The AI Loop Trap

Here’s what that illusion often looks like in practice:

1. You use AI to generate something quickly — a proposal, a project brief, a response.

2. You realize the tone isn’t quite right, or the output lacks context.

3. You begin editing it manually or bouncing it off another tool.

4. You run it by a colleague to confirm accuracy.

5. You wonder whether it would have taken less time to just do it yourself.

Sound familiar?

I call this the AI Loop Trap : where each layer of automation creates an extra layer of oversight. You’re not reducing the task, you’re reframing it into something more fragmented and often more exhausting.

This isn’t a flaw of the technology — it’s a misalignment between how we work and how we integrate AI into that work.

So How Do You Know If AI Is Actually Helping?

To cut through the noise, we need a different lens. Not just “how fast can I do this,” but:

• What is the total time from start to finished output?

• How much human oversight is required?

• What is the quality delta between an AI-assisted output and a human-only one?

• Is this AI use case aligned with strategic or high-leverage goals?

• Is the tool replacing a task or just reshuffling it?

Below are four ways to perform a deeper audit on whether AI is truly making you more productive:

⏱ Measure Total Workflow Time — Not Just Task Time

It’s easy to say “this took 2 minutes with AI.” But how long did it take to review? To revise? To contextualize for the audience?

The real benchmark is total time from prompt to publish. If AI saves 10 minutes up front but costs 20 minutes in corrections, it’s a net loss.

Before declaring a tool “efficient,” track how long it takes to reach an acceptable output without excessive rework.

✏️Watch for Fragmentation and Redundancy

In an effort to “AI everything,” some teams build overly complex workflows: one tool for note-taking, another for writing, a third for design, a fourth for meeting insights. Suddenly, you’re managing five AI tools just to complete one task — each with its own friction points.

When AI increases the number of steps or tools involved, it often creates cognitive overload. The result? Slower decision-making, tool fatigue, and unclear ownership.

Simplify. Consolidate. Use fewer tools, but use them well.

📑Audit Outputs for Strategic Alignment

Not all tasks deserve automation. If you’re spending 90% of your AI energy on low-leverage work (like summarizing Slack messages or reformatting spreadsheets) you’re improving motion, not impact.

Instead, focus on high-leverage AI use cases:

• Accelerating research workflows

• Prototyping concepts for faster iteration

• Structuring data for strategic insights

• Supporting customer personalization at scale

Ask yourself weekly: Is this AI tool helping us move faster toward a meaningful goal? Or is it just saving us time on work that doesn’t matter?

🧠 Track Decision Confidence and Ownership

Here’s a subtle but important signal: if your team is using AI, but decision-making still feels slow, it may be because no one fully trusts the output.

AI doesn’t replace judgment — it augments it. But if people don’t feel empowered to take ownership over what AI produces, the workflow collapses.

You know AI is working when the review process feels lighter, not heavier — and when teammates feel confident owning AI-generated work without over-explaining how it came to be.

The Real Productivity Metric

In the end, AI should give you back something far more valuable than task speed: Clarity, creativity, and space.

If you’re constantly managing AI outputs, switching between platforms, or second-guessing the results, then it’s worth stepping back to re-architect your approach.

But when it works, when the system is aligned, the tools are clear, and the team is calibrated, you’ll feel it. You’ll feel the reduction in mental load. You’ll feel the extra margin in your calendar. You’ll spend more time thinking, building, iterating, and less time redoing, reviewing, or translating tool-to-tool.

That’s not just productivity. That’s transformation.

Final Thoughts: Don’t Confuse Movement with Momentum

AI isn’t a shortcut. It’s an accelerator — but only if you’re already facing in the right direction.

If you feel like AI has made you busier, not better, it might be time to pause and ask:

• Are my tools actually solving the right problems?

• Am I using AI to extend my thinking — or to avoid it?

• What would it look like to build systems where AI removes friction, not adds to it?

Because in the age of intelligent tools, productivity isn’t about doing more. It’s about doing what matters — with clarity, confidence, and a little less chaos.

Welcome to the new kind of work!