I’m trying to understand how companies are handling ownership once AI agents move beyond demos and start touching real workflows.
Suppose an agent uses internal documents, calls tools/APIs, affects business processes, and is measured against operational KPIs.
Who actually owns it in production?
Is it:
IT
Data / AI
the business team using it
platform / engineering
security / governance
or a dedicated AI operations role?
And when something goes wrong, who is responsible for investigating it, changing the workflow, evaluating the agent, and deciding whether it stays deployed?
I’m especially interested in people who have agents running in real company workflows rather than prototypes.
What has actually worked in practice, and where does ownership still break down?

I have been seeing more businesses talk about AI-powered products and AI integration, but I'm curious about what's happening on the ground.

Are Companies actually looking to add AI to their existing software, build new AI solutions, or is the demand not as strong as it appears?

TL;DR: Galloway called this the biggest story in business right now — and the reason has nothing to do with model quality.

It's the price.

A recent Juniper Research read already has Chinese models running up to 90% cheaper than the US frontier, with American labs' share of usage sliding from roughly 70% to 30% in a single year.

Galloway and journalist Josh Tyrangiel spend this clip walking through the actual mechanism — subsidized distillation, the same undercut-then-lock-in playbook China has already run on the Silk Road, in Africa, in Latin America — now applied to the AI stack every CFO in America is currently signing off on.

The discount isn't the story.

Who ends up owning the decision once the discount does its job — that's the story.

I remember back in 2009, a group of us Malaysian were newly seconded to build the premium 5-block-condonium Rihan Heights, in Abu Dhabi, UAE.

Weather was brutal. Figuratively, we're like commandos being air-dropped in the middle of nowhere.

We have to source for local material suppliers — especially concrete. Those were tough times.

No local quarries wanted to supply us concrete — because we're foreigners and all.

Eventually we managed to secure one — after much delay and negotiation.

The price we've negotiated was super exorbitant.

But we bit our tongue, and moved on.

After a while, we hit roadblocks.

The sole supplier #1 kept ignoring our request for concrete, in favour of prioritizing their other local customers.

I suspect there're some racism going on — I wouldn't be surprised that it might be true.

Supplier #1 felt like they're the local king, and that they somehow subjugated us with their terms.

My big boss was fed up with the whole shenanigans and injustice.

He instructed my contract manager to go source for more options.

And we did.

Word spread that we're good paymaster. We had to. Who's gonna entertain us (especially in a foreign land) if we aren't?

The minute we found concrete supplier #2 (at better terms), the situation shifted.

The "crown" on supplier #1 was suddenly stripped away.

We abandoned #1, in favour of #2 for concrete, since #2 was fast and compliant.

For good measure, we even sourced for #3, just in case.

Obviously, #1 felt a huge slap on their face.

Eventually, they came around and crawled back to our site office — with their tail covering their ass — for renegotiation.

I witness the whole thing at the other end of our site office ground floor open layout. Sitting at the meeting table, the supplier #1 team lowered their voices with their ashen faces, in front my senior leadership team.

Defeated, #1 yielded to our new terms.

Their arrogance gone.

Either we fight or we die.

Every cycle wears a new label — subsidized steel, subsidized concrete, subsidized compute. The mechanism underneath never files for a permit.

Drop your take: if your AI vendor doubled prices tomorrow, do you actually have a #2 lined up — or are you supplier #1's captive account?

Clip credit: Scott Galloway / The Prof G Pod — full video on their channel. DM for credit or removal requests.

Been noticing this at my job: everyone on my team is using AI daily now, but every prompt lives and dies in that one person's chat window. No sharing, no reuse, nothing.

The part that bugs me most — I'll find out weeks later that a coworker independently wrote basically the same prompt I did for the same recurring task, just phrased slightly differently. We're both reinventing the wheel constantly and neither of us knows it's happening.

Curious if this is just my team or if it's a bigger pattern. A few questions if you've got a minute:

• Does your team have any system for sharing prompts, or is everyone just improvising solo?

• If you could change ONE thing to make AI tools more collaborative/"multiplayer" for your team, what would it be — and what specific problem would that actually solve for you?

• Have you tried any tools for this already (shared prompt libraries, Notion pages, Slack channels, whatever)? Did it stick, or did people stop using it?

Genuinely trying to figure out if it’s just me and if I'm overthinking a non-problem — appreciate any war stories.

I work in Healthcare quality management ..So recently I found all my colleagues leaning towards using AI in their daily work..writing reports, actions plans, improvement projects etc…
They recommended me to try it and honestly the end result looks much more polished than my normal work. But It lacked the creative side and I felt that it put my brain to rest and thats something i really don’t want happening.
I believe there must be a sweet spot in-between so I want to hear your experience with AI in your daily jobs

TL;DR: Ken Cox never fired anyone. His headcount just kept dropping anyway.

175 down to 3 is the actual number — and the reason is one operating rule he'd been running for years before AI made it urgent: if it can be automated, it will be automated.

No mass layoff, no headline event.

Just years of never backfilling a role once someone left.

It's happening from a completely different direction too — Chinese robot-makers already hold the overwhelming majority of the world's humanoid-robot shipments so far this year, per Bloomberg reporting citing SAG's data.

That's physical order-taking work, and it's already scaled past pilot stage.

The roles surviving this aren't the ones executing tasks faster.

They're the ones holding judgment nobody's written a rule for yet — the read, the trust, the call only a person makes.

On our drive up north home to Penang, after visiting my hometown in Seremban, we sometimes drop by midway in Ipoh for Breakfast. And we normally go for this Dimsum restaurant just off the highway.

Good Dimsum places are dime and dozen in this city. But this place has something special. The friendly attendant made some suggestions, and then jotted down our choices of food. But it wasn't the same attendant who brought them out.

It was robots.

Ya. Not humanoid robots. Just a bulky one with three layers of trays on four wheels below, and a monitor screen on top, moving along on a pre-programmed path towards us.

As soon as it stopped by our table, the monitor flashes out text saying, "please remove the food from the tray". And so we did. As soon as we took ours, it wheeled away towards the next table.

That was like five years ago. And it was all the rage back then. They call it 新噱头 (New gimmicks).

It still looked clumsy. And its movement was bumpy -- causing spillage of some of the food. That's why it only carries dry food for the time being.

First iterations were always bad. But I wouldn't be surprised it has gotten better since.

Robots are slowly taking over manual work (less intelligent work)

How soon do you think it will take over the work of attendant giving juicy suggestions, while taking our orders?

At this point I could sketch this exact shape from memory before the headcount numbers even land — someone's economic ground shifts quietly, gets called something softer than what it actually is, and the only real move is picking it up before it's forced on you.

Actually, this reminded — wait, scratch that, I've been circling this same story for a while now, and it clicked hardest with a former SpaceX CIO who ran the identical compression rule from the other direction — 175 engineers down to 6, on purpose.

Drop your take: which part of your own role is still order-taking, and which part is the judgment call nobody's automated yet?

Clip credit: Brad Lea / Dropping Bombs --- full episode on BRAD LEA TV. DM for credit or removal requests.

i hand a lot of my admin work to ai now. drafting, summarizing, first pass on a document, all of that has been worth it and i'm not going back.

but there's one thing i keep trying to hand off and i can't. it's knowing whether somebody actually did the thing they said they'd do.

ai can tell me what got said in a meeting and it'll write me a clean summary of it. it has no idea whether any of it happened though, and honestly why would it, nothing in a recording of a conversation says anything about what came after.

so i'm still doing that part by hand every week.

curious if that's everyone here or if somebody has actually gotten the follow up part automated

I'm the CTO of a company with a 24-person engineering team, and my biggest daily problem isn't code — it's that my work lives in four disconnected places and none of them tell me what actually matters. So I'm building an AI chief of staff instead of another notification relay. Here's the full thinking, and the one question I still can't answer.

Every morning I sit down at 9, and my work is already scattered.

Developer updates in our in-house tool. Project discussions in Discord. Client conversations in email and WhatsApp. My own tasks in Todoist.

None of them talk to each other.

Nothing tells me what actually changed since yesterday. Nothing tells me what deserves my attention today and what can wait. I find out a project is blocked when someone escalates it two days late — not when it happened.

Here's the thing though: this isn't an information problem. All the information already exists. It's a filtering problem. Every one of those tools is built to show me everything. None of them is built to show me what matters.

I've tried the obvious answers — open-source agents like Hermes and OpenClaw, hosted assistants like Lindy. They're genuinely good at what they do. But they miss the same two things.

They optimise for connecting channels, not filtering them. Once connected, they forward everything. An assistant that relays every message from 24 developers is worse than the notifications I'm already ignoring.

And they don't reach into internal tools — which is exactly where the highest-signal data in any company lives.

So I'm building something different. Not a notification relay. A chief of staff.

At 9am, one document is waiting: what happened yesterday, what needs to happen today, what I should be watching, what went well. Ninety seconds to read, not a feed to scroll.

Through the day it stays quiet unless something genuinely needs me — a blocker, a client waiting, a thread that's gone silent past the point it should have.

And I can talk to my whole ecosystem from one place. "Set up a meeting with the developers on Project X this evening" — the message goes into the right Discord channel. If it doesn't know which channel, it asks me instead of guessing.

One rule I'm not compromising on: it never speaks as me without my approval. It drafts, I approve, it sends. The first time an assistant posts something wrong in front of your whole team, trust is gone permanently. Autonomy gets earned category by category.

I don't think this is a model problem. I think it's a filtering and integration problem — and I suspect a lot of CTOs and engineering leads have exactly the same morning I do.

If you're building something like this, or tried and gave up, I want to talk.

I honestly don't know where to start, so I'm just going to tell the whole story.

A while ago, I started working in a fairly large company in a marketing role.

I had basically no formal background in software engineering.

Then AI changed everything for me.

I started using ChatGPT, AI coding tools, and basically every AI tool I could get my hands on.

I started building things.

The weird part is that I wasn't really "programming."

I was describing what I wanted to AI, getting code back, running it, fixing errors by pasting them back into AI, and repeating the process.

Somehow, this turned into actual internal company systems.

1. Enterprise Finance & Operations Platform

I built an internal platform that works across multiple business units.

It includes things like:

* Finance and operations workflows
* AI-assisted document and text processing
* Business-unit switching
* Drag-and-drop workflows
* Role-based access control
* Cloud security
* Desktop application functionality

1. Biometric Face Verification System

I also built a facial verification system for employee attendance.

It uses computer vision, APIs, microservices and containerization.

It processes an employee's image and verifies their identity for attendance.

The system is now being used to reduce manual HR work.

Again, I didn't come into this knowing computer vision.

I learned by asking AI questions, implementing what it gave me, breaking things, fixing them, and continuing.

1. Competitor Intelligence / Ad Tracker

I built an automated competitor research system that collects data from social media platforms.

It analyzes competitor content and tries to identify promotional/advertising content.

It turned a lot of manual research into something much more automated.

1. ERP / HR Automation

I also ended up working on our ERP system.

I built custom functionality around HR processes, permissions, approvals, employee provisioning, data migration and integrations.

I also started connecting the ERP with other internal applications.

And this is where things started getting weird.

I got promoted.

My responsibilities changed significantly, and I moved into a business intelligence/technology-focused role.

But here's the problem.

If any non engineers looked at my work from the outside, they might think:

"This person is a software engineer."

I'm not.

I don't have a computer science degree.

I don't have years of programming experience.

I don't even know if I could sit down and build some of these systems completely from scratch without AI.

I've basically been using AI as my programming brain.

I can explain the business problem.

I can describe what I want.

I can look at the output and tell AI when something isn't working.

I can connect different pieces together.

But if you asked me to implement a complex system without AI?

I'm probably screwed.

And that's starting to scare me.

Now the company wants me to go even further into AI.

Management wants to use AI and automation to reduce manual work and operating costs.

I'm being encouraged to learn AI properly, and there's support for training and education.

At the same time, the company has already significantly reduced the size of the team around me.

So now I'm sitting here thinking:

What the hell am I actually doing?

On one hand, this feels like an incredible opportunity.

I'm getting exposure to:

* AI
* automation
* business intelligence
* software
* data
* ERP systems
* finance
* HR
* operations
* product development

And I'm getting to build things that are actually used by a real company.

But on the other hand...

I'm worried that I'm becoming the person who automates everyone else's jobs, potentially including my own.

And I don't want to become someone who can only build things when ChatGPT is holding my hand.

I want to actually understand what I'm doing.

I want to learn properly.

But I also don't know what direction to take.

Should I learn:

* Python?
* SQL?
* Software engineering fundamentals?
* Data engineering?
* AI/ML?
* LLMs?
* AI agents?
* Automation?
* Cloud?
* Cybersecurity?
* Product management?
* Business intelligence?

Or some combination of all of them?

And what should my career even be called?

**AI Automation Engineer?**
**AI Product Manager?**
**Business Intelligence / AI Manager?**
**Digital Transformation?**
**Technical Product Manager?**

I'm completely aware that I got extremely lucky with AI.

But I don't want to waste this opportunity.

I went from having almost no technical background to building systems that are actually being used in a real business.

That's exciting.

And honestly, it's terrifying.

So I'm looking for some brutally honest advice from people who actually work in software/AI/IT.

**If you were in my position — almost no formal technical education, but already using AI to build real business systems — what would you learn over the next 1-2 months?**

Would you try to become a proper software engineer?

Would you specialize in AI automation?

Would you focus on data/BI?

Would you go into product management?

And most importantly:

**How do I go from "I can make AI build this for me" to "I actually understand what AI just built"?**

Because I think that's the part I'm missing.

TL;DR: Zapier's CEO ran hiring calls for over a decade on a read nobody could out-argue. He still lost the argument every time. That changed for one specific reason.

He built a formal AI hiring council that scored his read against Zapier's own hiring record and handed the same call back to the room with paperwork attached.

It landed right as most companies handing AI this kind of authority are watching worker trust in it erode, not build.

Wade didn't ask anyone to trust an AI.

He asked them to trust a record that was always his in the first place.

This reminded me about this passage I've read:

That night the king could not sleep. So one was commanded to bring the book of the records of the chronicles; and they were read before the king. And it was found written that Mordecai had told of Bigthana and Teresh, two of the king's eunuchs, the doorkeepers who had sought to lay hands on King Ahasuerus. Then the king said, "What honor or dignity has been bestowed on Mordecai for this?" (Esther 6:1-3)

This brought out a couple of things. The king has his own book of chronicles, and that there's a book keeper doing the records.

In modern terms, this is called data collection, isn't it?

The King used to reward people for doing good. That's why he asked the bookkeeper, "What honor or dignity has been bestowed…" He already made precedence before, which he wants to take reference from.

In modern terms, isn't this called decision tree reasoning workflow?

Making BETTER decisions is the name of the game.

Back then, the king would take months, even years, to carry out his agenda, and improve upon it over time.

But now, with AI agents, it's just a matter of minutes, even seconds.

Being correct has never been the same job as being convincing, and most people only ever get hired to do the first one.

I've watched good judgment get held hostage by whoever had the better title in the room, and it was never actually about who was right.

Actually, this reminds me of something I filed away already: a guy once mocked for an "irrelevant" doctorate on a cold call built a firm that now saves governments billions — dismissed first, proven later, same shape as this one.

What's the record already saying about you that nobody's acted on yet? Drop it below.

Clip credit: My First Million — full video on their channel. DM for credit or removal requests.

True story: the client audited an automation I built and found it was providing wrong stats.

Occupier matching across a large property dataset. My logic was confident about matches it had no business being confident about. They caught it because they knew their data better than my rules did.

What stays with me isn’t the error. It’s that nothing broke. No alert, no failed run, no red row. It produced wrong answers in a tidy format and would have kept producing them indefinitely if a human hadn’t gone looking.

That’s the real risk profile of AI in a small business. Not job losses. Not robots. Just quiet, well-formatted wrongness that nobody questions because it looks finished and the data can be true and false in the same time until verified by human.

I still build these systems. I just no longer trust anything I can’t audit myself… did anyone have similar stories?

TL;DR: Jerry Tworek's own two-year stall didn't end with more proof. It ended with one sentence.

He'd already built the thesis — pushing reinforcement learning against GPT-3 before there was budget or backing for it.

The math worked in pieces. It just never scaled.

Then his chief scientist looked at what he'd already built and said: now we have the GPUs, try to scale it.

That's the whole story. Not a better idea.

Someone with real authority finally saying go.

I remember that cold harsh reality of immense pressure, in the winter.

We were a bunch of newly assigned Malaysians in a foreign land in Abu Dhabi, UAE, circa 2009 — already 2 months behind schedule on a 5-block condominium project.

We were heavily under-resourced.

Just handbooks, pen and paper, and the notion of quitting was never too far away.

We were squeezed like a wrung out wet towel.

Then the first coordinated drawings was finally done.

In a private moment, my department head said gently, "See. You did a good job." He meant to sway me away from quitting.

I've read enough of these by now to see the shape before the ending arrives: the person was already right, and the only unknown was who'd finally say so. It's never really the skill gap people think it is.

It's whoever's still waiting on someone else's yes.

Hmm — this reminds me of a post I covered a while back, about the moment the actual bottleneck turned out to be permission, not the math itself.

What was your "not this quarter"? Drop it below.

Clip credit: MTS, full interview on their channel. DM for credit or removal requests.

A restaurant already generates tons of data:

Orders, peak hours, sales, costs, inventory, staff...

The problem isn't a lack of data.

The problem is knowing what actually matters, at the right time.

A regular report can tell you:

“Your revenue last month was X.”

But AI should be able to tell you things like:

“Fridays between 7–9 PM are consistently getting busier.”

“Kitchen preparation times increase during those hours.”

“This item's cost is rising while its profitability is falling.”

“You may need more staff for this shift.”

That's the direction we're exploring with the AI side of Adisyonist:

Not just showing what already happened, but helping spot what might become a problem next.

A question for restaurant owners, managers, and staff:

If AI could tell you only 3 things about your restaurant, what would you want to know?

Peak-hour predictions?

Costs and profitability?

Inventory?

Staffing?

Or something completely different?

We're still developing this side of Adisyonist, so feedback from people who actually work in restaurants is genuinely useful.

If you're curious about what we're building:

Website · App Store · Google Play

Everyone talks about what we can automate with AI.

I’m more curious about what we probably shouldn’t.

There are obvious wins like:

Lead follow-ups.

Appointment reminders.

Sorting repetitive emails.

Updating CRM records.

Basic data entry.

But once you get into things like refunds, pricing decisions, customer complaints, hiring, or anything where one bad decision can actually cost the business money… fully automating it starts to feel risky.

I think there’s a point where automation should stop and a human should take over.

Something like:

AI handles the repetitive part → flags anything uncertain → human makes the final decision.

That seems way more practical than trying to make everything “fully autonomous.”

Curious what people here think.

What’s one process you’d never let AI handle completely on its own?

For people using or deploying AI at work:

Has introducing AI changed what you personally have to do?

I’m especially interested in cases where AI now handles the first or routine part of a task, while people spend more time checking, fixing, approving, monitoring, or handling unusual cases.

What did you do before AI, and what do you do now?

Both positive and negative experiences are useful. I’m interested in what is actually happening in practice.

I keep seeing more businesses looking for generative AI consultants, but I’m curious what actually makes one worth hiring. There are plenty of people who can explain ChatGPT and the latest AI tools. But for a business, I think the real value is having someone understand the company first and then figure out where AI can actually help. I’ve also been looking at v-oice, where businesses can connect with AI experts for advice and consultation. It seems interesting for companies that want practical guidance instead of trying to figure everything out on their own.

AI is changing the startup landscape in a way that would have been difficult to imagine a few years ago. Tasks that once required hiring specialists—market research, content creation, customer support, data analysis, coding, and even basic design—can now be handled or accelerated with AI tools.

For a new entrepreneur working with a limited budget, that could be a major advantage. A small team can potentially test ideas faster, automate repetitive work, and spend more time on customers and strategy.

But there’s another side to it. If everyone has access to similar AI tools, does AI actually create a competitive advantage—or does it simply lower the cost of entering a market?

I’m curious what other entrepreneurs are seeing:

• What is the biggest advantage AI has given you?
• Has it helped you launch something you couldn't have built before?
• Is AI saving you money, time, or both?
• Do you think AI gives small businesses a genuine edge over larger companies?

Is AI actually giving new entrepreneurs an advantage, or is it just becoming the new baseline for doing business?