So AI can apparently read your mind now. Meanwhile, we’re still struggling to:

• turn messy inputs into useful AI outputs
• know what AI should do vs what we should keep
• turn AI use into better workflows, not just faster tasks

If your job involves Slack, docs, meetings, approvals, or some cursed combination of all four, I’d be surprised if you don’t recognise at least one of the five frustrations below.

This week I looked at how other professionals are dealing with them: what they built, what actually helped, and what you can apply to your own work.

WINDOW INTO THE FUTURE OF WORK

Last week was about finding the right task for AI. This week is about turning that idea into something that works. We’ve started calling that the execution gap.

And in real work, that shows up as a bunch of very normal frustrations. So I pulled out five that felt especially familiar, plus the workflows and fixes people are using.

1. I keep having useful conversations that go nowhere.

Ryan Carr shared a tiny workflow this week that I loved because the frustration behind it is something we all experience: useful discussions happen in Slack but we keep losing them. So they created a system where whenever someone reacts with this ✏️ emoji in Slack, it triggers a Zapier workflow where Slack grabs the entire thread → Claude turns it into a ~300-word draft → the draft gets sent back to Slack, Google Docs or Gmail

Ryan says the draft usually gets them about 70% there, with roughly 15 mins of editing left. That’s the bit I like. Nobody on the team has to remember a new workflow. They keep talking in Slack like they already do and AI comes to the work instead of making everyone move their work to the AI.

They already knew the problem: we have useful thinking but we keep losing it. AI just gets one very specific job in the middle.

2. I have a lot of information. Turning it into something useful takes forever.

Nick Baumann from OpenAI shared a workflow recently that made me jealous. He records 50–60 messy video takes and dumps the whole batch in ChatGPT Work. Inside it, he adds his own “UGC Video” plugin he’s built from the instructions and corrections he’d accumulated while making previous videos.

From there, ChatGPT Work transcribes every clip, looks through the footage, uses cues like “bad take”/ “good take,” finds the strongest takes and pieces the story together. In the example below, 27 takes became a 43-second finished vertical video. It even checked for things like sensitive information and blurred them where needed.

I think this is where AI feels magical. Not when it invents more stuff but when it takes the messy material you already have and turns it into something you can actually use.

3. I get plenty of good answers from AI. I still have to decide what’s good.

I loved this interview with Hayashi Aki this week. She’s an ex-journalist who now works with around 15 companies. Her workflow is basically interview → transcript → give AI direction → decent draft → she edits the life back into it.

She says that AI workflow has taken her from drafting roughly one article a night to as many as ten. But the more interesting bit is what happened to her job after letting AI create her first drafts. Clients started asking for more strategy. She spends more time asking uncomfortable questions, drawing the actual story out of CEOs, spotting what they didn’t say, editing, managing risk, making the final call.

Her idea is AI can give you infinite plausible options but a human still has to choose. This reminded me of something I changed my mind about earlier this year. I used to like the rule: AI does the boring work. Humans do the thinking. Now I think it’s better to ask: which thinking should I keep?

I’m increasingly excited about that question and also slightly terrified by it.

4. I made the process faster but it’s still a terrible process

I watched Tomás Dostal Freire describe what happens when companies integrate AI into a broken workflow. He said it’s like putting a Formula 1 engine into a family car.

He calls it individual productivity vs company velocity: making one person faster vs actually removing steps, handoffs and bottlenecks from the work. We saw the opposite in one enterprise team we spoke to. A recruiting workflow with roughly 20 steps across two systems was redesigned down to about 8 using AI, without replacing the underlying software.

That’s what I meant when I wrote about “swapping the motor without redesigning the factory” back in March. I think I understand that line better now. Saving 20 minutes on a task is useful. Removing 12 unnecessary steps is something else.

5. I have the tools and training but my work still hasn’t really changed.

This one makes me a bit mad. One team recently told us they were struggling to get traction with their AI Academy despite a massive comms effort.

They’re not the only ones. Gallup’s workplace data shows how much the boring stuff around the AI matters. When people strongly agree their manager actively supports AI use, 79% are frequent users vs 46% when they don’t. And when AI actually integrates with the systems people work in: 86% vs 52%.

Which makes me increasingly suspicious of “we need more AI training” being the go-to solution for a lot of teams. Sometimes, yes. Sometimes people already know enough. They just don’t have the time, access, permission or manager support to actually change the work.

That’s not a skills problem. That’s the environment around the skill.

So what does good execution actually look like?

Nobody starts with “I would like an agent please.” They start with the thing that sucks, then get specific about what needs to change. That’s probably the execution gap: getting from “this sucks” to “this now works better.”

Still figuring it out, but I’m pretty convinced that’s where the interesting work is moving.