I noticed this first in myself so I'm trying to see If you're facing it too...

Back in the day, I had to first plan things, do the

- wireframes, test them, improve

- audit the IA flow (information architecture)

- map the user journey then test again

- real band building

- extract the design system

- plan my component system and make sure it is modular

- build the component system

- build one page at a time, finish then test it

Today? Few prompts. Done.

I feel like I'm getting dumber and lazy overtime.

My muscle memory is no longer the same. I'm at fault cause I could've simply constrain it from the start, however as we're all racing to deliver faster sometimes we just forget and things just start slipping bit by bit.

You assume that it got it right, or you have your system in place then you realize as you're building (vibe-coding) your app, you are not getting your results, and you end burning tokens for no reason and you get nowhere.

The AI slop is slapping you with each output. I have personally degraded in my skills, that is my fault but now I literally do not start any code until all of my laws in place and this has put me back to where I was in terms of production quality.

Just imagine people that have 0 experience in IA, branding, ux, ui, and code?

The results would be terrifying I would assume.

What about you? Faced the same issue?

I work for a US bank and I have observed that my job has shifted more towards creating Agentic workflow (fancy name of using LLM to automate tasks). In the last one year, I haven't touched any ML model. I am curious to know what is the experience of other folks.

I’m building a setup to handle day to day operations in my small business.

The goal is to manage incoming leads, reply to customer messages, schedule jobs, track ongoing work, send invoices, and make sure follow ups don’t get missed.

Right now most of this is manual across email, messages, and spreadsheets, and it’s easy to lose track of what needs to happen next.

I’ve been looking into tools that claim to handle these kinds of workflows end to end, like capturing leads, organizing them, automating replies, and keeping everything in one place without needing a bunch of separate tools.

I’m looking for ai tools that can plug into this kind of workflow and actually help automate or organize parts of it.

Has anyone built something like this or found tools that actually help with these sepcific use cases?

I've run a small consultancy for the past couple of years building AI agents for larger companies, 50-100 employees, 20m+ turnover. Chat and voice, inbound and outbound. Mostly on Voiceflow, Vapi and Retell, because the level of control you get over workflows is hard to beat: you can make an agent way more specific to a business and wire in deeper API integrations, which is what companies at that size actually need. More effective, reliable and cheaper to run than throwing a raw LLM and a knowledge base at everything.

That part was going fine. The problem was how the client themselves interacted with the agent.

Every client wanted the same things once the agent was live: a way to see conversations, jump in and take over when the AI got stuck, route to the right department, monitor their voice calls, some analytics. And I wanted to hand them all of that under my own brand.

The problem was that when I went looking for a layer like this, the only one that actually looked good enough was Intercom, but Intercom would have locked me into a single type of agent (their own, Fin), I couldn't brand it as mine, and I'd have no ownership over hosting. That's not a business, that's an implementer: less money, no leverage, and closer to an internal employee than a partner. I was building a company, so I needed something that could do all of it and still be mine.

There were only two whitelabel options that even came close.

The first, Chatdash, was genuinely nice to look at but had no handover capability. With a client running 40 support staff who needed to take over chats day and night, that ruled it out immediately.

The second, Convocore, could actually do handover and let me put my branding on it, so by default that's who I went with. But the UI wasn't what I wanted, there was no departmental handover, and over the following months it would frequently go down and support were hard to get hold of at the best of times.

When I asked about features like departmental handover or other enhancements, they wouldn't really bite. They'd decided to go into the AI agent market themselves and fancy their chances against companies like Voiceflow... which, fair play, more power to them, but it meant they cared a lot less about the layer I actually depended on. Updates got pushed left, right and centre, and half the time they broke access or functionality for my clients.

So I kept rebuilding the same sub-par layer for every client. At some point it stopped being a layer for my clients and started being a product I was accidentally maintaining.

I knew if I wanted to deliver what was actually in my head which was a dashboard that looked like Intercom, worked at scale, and allowed multiple types of high level agent providers etc.. I had to build it myself.

I'm not going to pretend I knew how. I started with the grand version in Lovable, which got me a nice-looking shell but nothing that would survive a real client. So I moved over to Claude Code and actually set it up properly with the right integrations, a real backend, real infrastructure...and after 8 months of sweaty sessions on claude code... it actually works. Genuinely works, at scale... which is unreal and i know that only a few years ago a company would have spent 100x more money building something like this.

So now there's one dashboard that I BUILT MYSELF and sits on top of the best agents: live view of every conversation, take over text chats mid-conversation, monitor voice calls, department routing, white labelling incase other companies wanted to use it and full analytics etc. WhatsApp integration built in. The agency keeps its own brand on the front... the agency's end client never sees the platform, or me.

We even got Meta approval, which was its own saga. And having now seen everything else out there properly, I can say fairly confidently that what I've built is better than the alternatives.

Which means the only thing left is getting it in front of people, and now ive suddenly got one more business i need to grow 😂

No ask here, thats the story. It's free while I'm testing it with real agencies, if youre one I'll even help you get your first client set up on it. Not trying to sell anything yet, I mostly just want feedback from other people who do this for a living. DM me etc.

The wave of AI related layoffs in tech and project management isn’t really about AI replacing humans. It’s about organisations realising they can get the same output with fewer people when some of those people are using AI effectively and others aren’t.

This is already happening in software development, where a developer using AI assisted coding tools can review, write and debug code significantly faster than one who isn’t. In project management, where status reports, risk registers and stakeholder communications that used to take hours are being turned around in minutes. In operations, finance, marketing, the pattern is the same everywhere.

The uncomfortable truth is that AI isn’t the threat. The colleague who figured out how to use it properly six months before you did is the threat.

The good news is that the actual skill involved isn’t complicated. It’s not about learning to code or becoming a prompt engineering expert. It’s about understanding how to give AI the right context, push back when the output isn’t good enough and iterate until you get something genuinely useful. Anyone can learn that with a bit of deliberate practice.

The people who will struggle are the ones who decided early on that AI was overhyped and stopped paying attention. That window for catching up is getting smaller.

What’s your experience of this in your industry? Are you seeing colleagues pull ahead because of how they’re using AI?

I've noticed AI has quietly become part of almost every aspect of running a business.

Some of the biggest changes I've seen are:

1. Saving hours on research and brainstorming.
2. Drafting emails, proposals, and marketing content.
3. Automating repetitive admin tasks.
4. Providing faster customer support through chatbots.
5. Analyzing data and spotting trends more quickly.
6. Helping small teams get more done without hiring more people.

That said, I don't think AI replaces good decision-making. It just frees up more time for the work that actually requires experience and creativity.

For those running a business:

• What's the biggest way AI has changed your workflow?
• Has it actually saved you money or just saved you time?
• What's one task you still wouldn't trust AI to handle?

I'd love to hear how others are using it in their day-to-day business.

My company gave me unlimited access to the latest AI models. As a full stack dev, my job's been pretty easier with AI tools recently. With a salary as high as 68lpa, having 16yoe and all the best of tools and free time I'm wondering if I can do something or learn something that helps me start a business or atleast become future proof in my career.

Whenever I think I should build this platform or tool or extension, I either see a million other devs do the same or that's something that won't survive in the market for an hour.

Have you ever been in such a situation where you've all that you've ever dreamt of but can't really do anything about it but just worry thinking about the date that this comfort will kill your job one day.

I am writing this at 1am guys. You can imagine what all fears might be running in my mind.

My org is looking to get Ai tools for org wide & i want to contribute them with proper analytic which tool is best in which area with some case study. i am primarily looking to explore two area .1. Ai agentic IDE ie Antigravity, Cursor 2 agentic alone like cluade,Codex,open coder, my background i do use all except open coder and do vibe coding and all are very satifactory .but i need to find the best out of all these or any other i am missing ,BTW my organization looking to buy aground 50 seats

I’m interested in hearing from people who’ve used multiple LLMs (ChatGPT, Gemini, Claude, Grok, DeepSeek, etc.) extensively over the past few months.

I’m less interested in benchmark scores and more interested in real-world experience. Which models do you trust most for reasoning, coding, writing, research, or brainstorming? What strengths and weaknesses only became obvious after long-term use? Have your preferences changed over time, and if so, why?

I just pitched a custom workflow to a potential client who owns a busy beauty parlor in a hectic Midwestern mall, and this is exactly the kind of small-business ops problem where AI can be useful without becoming ridiculous.

The business is not new. She has regular clients, years of history, and built-in mall foot traffic: people shopping, eating, waiting on kids, walking around, or killing time.

And she still ends up with empty chairs.

That matters because chairs are inventory. If nobody sits in the chair at 2:00, the owner cannot sell that 2:00 tomorrow. That inventory expired.

This is not SaaS, where you can pretend there are unlimited potential users. A brick-and-mortar beauty shop has a local radius, a fixed number of chairs, and only so many realistic customers who can walk through the doors.

So the ops problem is not simply “get more leads.”

The ops problem is: how do we maximize every person already showing interest, reduce schedule leakage, and recover appointment inventory before it expires?

Some of this can be handled with basic automation.

Appointment reminders.

Rebooking links.

Simple “we haven’t seen you in a while” messages.

Basic no-show tracking.

That is all useful, and she could probably get some version of it off the shelf.

But the custom workflow is where it gets interesting.

The first feature I pitched is what I’m calling Last-Minute Chair Fillers.

Not the public name. Just the internal idea.

The idea is simple: what if empty beauty chairs worked more like airline standby seats?

If an airline has an empty seat, they want to fill it before the plane leaves. A mall beauty parlor has the same kind of problem. When a client no-shows, cancels same-day, or calls to say they are running late, that chair becomes expiring inventory in real time.

So instead of only treating that as a lost appointment, the system turns it into a last-minute opportunity.

There are two standby groups.

The first is a flexible deal list. These are budget-conscious clients who live close, have flexible schedules, and are happy to take a haircut, nails, brows, facial, or whatever service they need that day or week if the price is right.

They are not offended by last-minute offers. They want them.

The second is more mall-specific. These are people who are already in or near the mall for a specific block of time. Maybe they are shopping, eating lunch, waiting on their kids, or they just know they will be nearby from 2–4.

They can opt into that block and say, “If something opens while I’m here, text me.”

That is where this stops being generic salon automation.

A regular reminder system says:

Don’t forget your appointment tomorrow.

This system says:

We just had a 2:30 chair open. If you are still near the mall, tap to claim it.

That is a different business move.

It is not discounting for the sake of discounting. It is recovering revenue from inventory that was about to expire anyway.

The admin side has to stay extremely simple.

The front desk should not need to build a campaign, manually text people, or decide in real time who is eligible while the shop is busy.

They press one button:

Open Slot.

That is the whole admin experience.

Someone is ten minutes late? Open Slot.

Someone has a history of no-shows and the owner is ready to move on? Open Slot.

Someone calls and says they are running late, but there is enough time to fit in another client? Open Slot.

Someone cancels same-day? Open Slot.

Behind the scenes, the workflow needs to do a lot.

It needs to identify the service type.

Check how much time is actually available.

Determine whether the gap is fillable.

Match the opening to the right standby group.

Exclude people who should not receive the offer.

Send the alert.

Lock the slot when one person claims it.

Show everyone else that the opening is gone.

Update the admin view.

That is the real ops work.

The front desk gets one button, but the backend needs service matching, eligibility logic, time-block routing, and first-person claim control.

The claim control matters a lot.

No vague “reply if interested” chaos.

The message needs a claim link. First person taps Claim, the slot closes, and everyone else sees:

Sorry, this opening has already been claimed. We’ll text you next time.

That prevents five people from showing up for one chair.

The customer workflow is also intentionally boring.

They get a text.

They tap Claim.

They get the appointment.

Done.

No app download. No 15-step profile setup. No dashboard.

That is usually the difference between software people like in theory and software people actually use on a busy Tuesday.

The second custom feature I pitched is Truly Blind Feedback.

The owner has been expanding and mentioned something very real: she is nervous that her new staff may or may not be awesome yet, and her longtime clients may be too kind to say so directly.

That creates an ops gap.

If the owner is no longer personally touching every service, she needs a feedback loop that does not depend on public reviews, awkward complaints, or vibes.

So after appointments, the client gets a simple text conversation asking about their experience.

Not a giant survey.

Not a public review request.

A bounded chatbot that makes it clear the feedback is anonymous and gives the client a low-pressure way to be honest.

It can ask things like:

Was the shop in a good mood today?

Did the service feel rushed?

Would you book with the same provider again?

Is there anything the owner should know?

The bot is not there to argue, defend, or upsell.

It is there to collect honest feedback, summarize patterns for the owner, and flag issues early.

For a growing service business, that is not drama. That is quality control.

And it connects directly back to the empty chair problem.

Because an empty chair is not always caused by a bad reminder.

Sometimes the client forgot. Sometimes they could not reschedule easily. Sometimes they were price-sensitive and needed a better-timed offer. Sometimes they had a mediocre experience and never said anything. Sometimes they liked the owner but did not click with the new staff member.

Those are different failure modes.

They should not all trigger the same workflow.

That is where AI becomes useful.

Not for the button.

The Open Slot button is regular software.

The claim link is regular software.

The calendar update is regular software.

Basic reminders are regular software.

Where AI becomes useful is in the matching, feedback interpretation, and judgment calls.

A last-minute haircut, nail appointment, brow wax, and facial are not the same kind of inventory. A loyal regular, bargain hunter, mall standby client, repeat no-show, and quietly unhappy client should not all get the same alert.

You can build all of that with hard-coded rules, but the rules get ugly fast.

AI becomes useful because it helps sort messy client history, service type, timing, tone, eligibility, and feedback into one simple action for the front desk:

Open Slot.

It can also help turn blind feedback into patterns the owner can actually use.

Not “one person complained once.”

More like:

Three clients this month said appointments with this provider felt rushed.

Clients are happy with the service but confused about pricing.

People who book facials are not coming back after the first visit.

Repeat no-shows are concentrated in one appointment type.

Mall standby claims are filling quick services but not longer services.

That is useful ops intelligence.

The point is not “AI replaces the front desk.”

The point is that AI can remove the twenty tiny decisions that would otherwise make the front desk hate the system.

The owner does not need a giant CRM. The client does not need another app. The front desk does not need to become a marketing department.

The system simply protects the calendar, recovers expiring inventory, and helps the owner see what is happening inside the business.

My proposal for this client was $3,500 and three weeks.

A stripped-down version with reminders, rebooking links, basic reactivation, and private feedback would be smaller. Not nearly as fun, though. I really do love when AI gets used creatively instead of just slapped onto a workflow.

But the two custom features change the scope:

Last-Minute Chair Fillers to recover expiring inventory.

Truly Blind Feedback to catch quality issues before they become silent churn.

Now the system needs service matching, time-block alerts, first-person claim logic, an admin workflow, a bounded feedback chatbot, and an owner dashboard that stays simple during a busy day.

That is usually where custom software gets interesting.

The idea sounds simple:

Fill empty chairs.

Making it feel simple is the hard part.

your first 10 clients will all ask for the same thing. "we just need something simple." and you'll build them something simple. and it will break. not because you're bad at your job. because "simple" is a lie they tell themselves about a process that's actually held together with tribal knowledge, exceptions, and one guy named Dave who remembers how everything works.

the job isn't building the automation. the job is figuring out what's actually happening before you touch a single tool.

I used to start building on day one. client describes the process in a kickoff call, sounds straightforward, I go build it. two weeks later I demo it and they go "oh but sometimes the supplier sends a PDF instead of an email" or "oh but when it's a bulk order Alex handles it differently." every single time.

now I spend the first week just watching. I sit with whoever actually does the work. not the founder. not the ops manager. the person who does it every day. and I write down everything. not the process as it should work. the process as it actually works at 9pm on a friday when three things are going wrong simultaneously.

that week of watching has saved me from rebuilding projects more times than I can count.

the other thing. real data is disgusting. your test data is clean because you made it. real invoices are photos taken at an angle in bad lighting. real customer names have weird characters in them. real spreadsheets have merged cells and hidden rows and a column that says "DO NOT DELETE" with no explanation.

build for that. not for your clean CSV.

anyway. been building these systems for a while now. happy to answer questions if anyone's in the weeds on a specific automation problem. no pitch, just been down most of the roads.

The better question:

Which IT workflow is creating the biggest operational bottleneck?

A lot of organizations jump straight to finding an AI tool, but the teams seeing the strongest results take a different approach. They start by identifying where IT operations are losing the most time, creating the most risk, or slowing the business down, then they build AI around those workflows.

Some of the highest-value starting points are:

• Event correlation
• Incident triage
• Ticket enrichment
• Root cause analysis
• Low-risk automated remediation

Deploy AIOps to Improve IT Operations

The biggest mistake is treating AIOps as a technology project instead of an operations strategy. Align AI with business goals first, prioritize a few measurable use cases, define what success looks like, then scale once those workflows are proven.

AI doesn't fix broken IT processes. It amplifies good ones.

Blueprint: Deploy AIOps to Improve IT Operations.

Businesses are told constantly to adapt to AI or die, and it made many founders panic and buy more tools and chase every new model.

I read PwC's 2026 Global AI Jobs Barometer last night, and it kind of reframed everything for me:

AI-exposed junior roles are now 7x more likely to require traditionally senior skills like judgment, leadership, and decision-making.

So the routine work is going to AI. But what's left requires more thinking, not less. And if you never clearly defined what good judgment looks like in your business, if that standard only lives in your head, your team is now moving faster and hitting that undefined wall more often.

What do you think it actually takes for a team to get real value from AI?

I keep seeing people talk about using AI for business operations, but a lot of it still sounds like one off prompts instead of real daily workflow help imo.

For people actually using it, what parts of your operations have been useful to hand off? I’m mainly thinking about daily admins tasks, content drafts and cold outreach/followups as these tasks require at least half of my day.

If you’ve been using any AI tools or system for this for a while, which ones have actually held up in your business and what are you using them for?

Nothing like being told that the work you are passionate about will become obsolete in 10 years or less. AI and ML can do it cheaper and better. lol should have been a fucking general contractor. They might still have jobs in 20 years.