r/AIforOPS

For those who’ve been through an AI rollout at work: was the resistance about the tool itself, or about trust/job security/how it was communicated? Trying to understand if ‘AI change management’ is a real gap or just consultant-speak.

Hi guys, I wanted to know what you are using AI for in your freelance work or company.

Let me start.

We have 40 subscriptions for premium seats of Claude Code, and everyone is using it. Developers for writing code, QA for writing test cases, and marketing for content and research.

But everything we are using is for the work we need to get done, with no automation.

I would like to know if you guys are using any practices to make your work at least 60-70 percent automated.

What's the thing you keep working around instead of fixing?

I've got a 22-hour build window this week and four themes to pick from. Rather than invent a problem, I'd rather build something an actual practitioner is annoyed by. The themes:

• UX in AI — how people interact with, trust, correct, or supervise AI systems
• Data for AI — pipelines, quality, lineage, labelling, retrieval
• Security & governance at scale — access, audit, policy enforcement across many systems or agents
• Physical AI — robotics, simulation, digital twins

What I'm asking: in whichever of those you actually work in, what's the recurring annoyance you've built a hacky workaround for and never properly solved? The thing that costs you 20 minutes a week, or that you've explained to three different new hires.

Not looking for startup ideas or moonshots. Looking for small, specific, real. Bonus if you've already tried something and it didn't work — I'd like to know why.

I'll post back with what I build and whether it worked, including if it didn't.

Curious to hear from others who’ve started leaning on AI tools for parts of the job:

1. What tasks have you actually offloaded to AI?

2. Where does it get annoying or breaks down? Juggling multiple chats, losing context between conversations, having to re-explain the AI every time, something else?

A couple of years ago, AI felt like a futuristic tool. Now I see people using it for coding, writing emails, troubleshooting, research, presentations, and learning new skills.

I'm curious where everyone is today.

- What do you use AI for most often?

- Has it actually made you more productive?

- What's one thing AI still does poorly in your experience?

I'm interested in real-world experiences, not just the marketing claims.

Lot of talk about how founders are trying to delegate, to people and to AI. I see 3 buckets:

1. Founders who hire their way out. Adding new staff without any documented systems. The chaos just gets more expensive and still routes through them.
2. Founders who try to AI their way out. Buy every tool, open every tab, end up with a graveyard of subscriptions and a business that still lives in their head.
3. Founders who know they're the bottleneck and want to hand work to both people and AI, but have no clue what goes to a human vs a machine.

Most of us started as bucket 1, then sprinted to bucket 2 the second AI got good.

Heres what I do:

I stopped treating people and AI as two different delegation problems and started running one system for all 21 of them.

If a task isnt defined somewhere, it doesnt get delegated. To anyone. Human or agent.

So delegation isnt me re-explaining what I want every time. Its handing off to something that already knows how the work runs.

How it actually works:

1. Every "employee" gets the same 4 things, human or AI

Doesnt matter if it's a person or an agent. Both need:

- A role (what they own)

- Context (what they need to know to make the call I'd make)

- An SOP (how the work runs when I'm not in the room)

- An accountability loop (how I know it actually happened)

Every time I've been burned it's because I skipped the SOP and assumed someone could read my mind.

2. One filter decides who runs it: complexity vs reproducibility

High complexity, low reproducibility goes to a human. Strategy, relationships, judgment calls, the stuff that changes every time.

Low complexity, high reproducibility goes to an agent. Data entry, research, reporting, scheduling, the stuff that looks the same every Tuesday.

For people, I dont manage the person, I manage the system they operate inside. I spend 5 minutes documenting a process, sometimes a quick screen recording, then hand it off and confirm they actually got it. Full accountability. No mystery Drive folder I'm not even sure anyone opened.

For AI, an agent is just an employee you onboard with a document instead of a conversation. It gets context, a clear job, files it can pull from, and specific skills I call on when I need them. I tag it like I'd tag a teammate in Slack, it loads the right context and does the thing. On demand or on a schedule.

3. The system compounds

Every process I write once works forever, for whoever runs it next.

New hire? They read the doc. New agent? It reads the same doc. Six months from now the system knows more about how we operate than it does today, and I didnt have to re-explain a thing.

Why am i telling you this?

Because most founders think the answer is one more hire or one more AI tool.

The ones actually pulling ahead didnt find a better tool. They wrote down how the business runs, then let people AND AI operate inside it.

You have to organize the business first. You cant delegate chaos, not to a person and not to a robot. AI doesnt fix a mess, it just automates it faster.

But once it's built, you stop being the human who explains context fifty times a day. The work finally leaves your desk.

We didnt start with 14 agents. We didnt start with any employees either. I started with one document. The document is the system. The person or the agent is just what reads it.

For the last six months I have run a one-person company almost entirely on AI agents, out of a single git repo. Not "AI writes my emails." The actual operations. Marketing, sales, CRM, content, outreach, all of it.

I did not set out to build a framework. I set out to stop doing admin. But after enough things quietly worked and roughly the same number blew up in my face, a rough framework fell out of it. 10 parts. Each one below has what held up and where it broke, because the where-it-broke half is the part I would actually want to read. I am posting it to get holes poked in it, so if you are building the same thing, tell me where I am wrong.

1. Put the whole company where the AI can read it (context as code)

Stop wiring the AI into ten SaaS tabs. It is bad at clicking buttons and good at reading and writing files, so move the company to where it already works well, which is plain files in one repo. Every department is a folder. What held up. The AI went from useless across ten browser tabs to genuinely running things the day it could read and write the whole business as text. Where it broke. The folder gets fat and recall rots (there is a name for it now, context rot). You load context on demand. You do not dump the whole company into the window and pray.

2. A routing brain: one root file, departments as folders with playbooks

Each folder holds a plain-English playbook (a CLAUDE.md) with who you sell to, your voice, the rules, the tools it may touch. A root file routes the work:

TASK: "find leads and email them"
|
root CLAUDE.md (the router)
|
opens the playbooks that own the task
v
sales/CLAUDE.md + crm/CLAUDE.md (plain-English rules)
|
agent becomes that department head
|
does the work
|
writes the result back into the repo
|
next task starts with more context, not zero

What held up. One generalist agent plus good playbooks beats a fleet of brittle specialised bots for most work, and cross-department tasks route themselves. Where it broke. A single generalist still drowns on genuinely complex parallel multi-step work. That is the only place I reach for subagents, because a multi-agent run costs roughly 15x the tokens, so it had better be worth it.

3. Own the tools that touch your core workflow, and treat every platform as hostile

I rebuilt the internal SaaS I was paying for as small apps, each reading one database and one brand kit. A LinkedIn client that drives a real browser session. Its own CLI for Instagram. Google Workspace from the terminal, so an agent can book a meeting or send an email inside a workflow.

The platform-facing ones taught me the most, the hard way. Early on an agent fired actions on a social platform in fast batches and the account got suspended. Fully deserved. So the clients now have hard daily caps in code (20 connects, 40 DMs, 80 profile views), run human-paced, and verify every send by counting the message elements before and after, because the compose flow silently changed twice and cheerfully reported success while nothing actually sent. What held up. Own the workflow tools (a session each, zero integration tax), rent the plumbing (database, email, payments, hosting, lead data). Caps in code, not in the prompt. And never believe a platform's own "success", check the DOM changed before you claim you did anything. Where it broke. Trusting the platform's word and moving fast. Both get you blocked or lied to.

4. Give it senses: a nightly Scout, intelligence digesters, inbound monitors, signal farming

This is the part people skip, and it is most of the magic. The company perceives the world through a few standing flows:

inboxes ----\
CRM --------\
rankings ----> SCOUT (nightly) --> one brief: what moved, what needs you
competitors-/
feeds ------/

HN / Instagram / X / a FB community --> digesters --> scored signal + ideas
LinkedIn + FB inbox --> hourly monitors --> new reply? --> queue + phone ping
buyer-relevant posts --> signal farming (read + like only, 3x/day) --> lead pool

all of it --> STRATEGIST --> the day's few highest-leverage moves

A Scout surveys everything overnight and writes one brief (it only does reversible CRM syncs, it never sends). Digesters mine Hacker News, Instagram reels, X and a Facebook community for signal I would never scroll for. Hourly monitors listen to my LinkedIn and Facebook inboxes and push a new reply straight to my phone. A signal-farming loop likes and reads buyer-relevant posts three times a day and pools the people who engage. What held up. Nothing happens in the dark. I wake up to a briefed world, not a blank feed. Where it broke. The signal-farming ceiling, and this one stung. Public engagement on business content self-selects for sellers, not buyers. A clean pipeline still returned close to zero actual buyers, because the pool was other people selling the same thing I was. Read the pool, do not trust the lead count.

5. Copilot, not autopilot: one approval queue, a fleet of proposers behind it

Nothing an agent produces goes out on its own. A fleet of proposers (outreach, nurture, backlinks, SEO, content repurposing, community replies) drafts into one queue. I review on desktop or phone. Only an explicit apply step sends.

proposers (outreach / nurture / backlinks / SEO / repurpose / community ...)
| draft, never send
v
APPROVAL QUEUE (one Postgres table)
|
cockpit on desktop + your phone
| approve / edit / reject
v
apply step --> actually sends / posts / commits
|
writes the event back to the CRM (full attribution)

What held up. This is the single highest-leverage piece. Agents do the volume, I do the judgment, approving is a five-second tap, and every applied action logs itself so nothing is a dark touch. Where it broke. I underbuilt it at first and let a few actions bypass the queue. Every single one became a leak, which is conveniently the next two points.

6. A draft is not a touch, and every queue needs a live consumer

A warm prospect said yes. The system drafted a genuinely good reply in 35 minutes, then it sat in Gmail drafts for three days, because nothing in the pipeline reads Gmail drafts. Separately, a second internal queue (the reverse one, where I hand tasks to the agents) quietly collected 31 approved tasks that nothing ever ran, for a week. What held up. Route every outbound through the one queue, and ship every queue with its consumer, a way to see its depth, and a backlog alarm, in the same change. Where it broke. "Drafted" and "routed somewhere else" both read as "done" on every dashboard. A queue with no running consumer is worse than no queue, because it looks like it is working.

7. Run it on a schedule you can watch: the runner loop

Autonomy is just a scheduler with good manners. One local loop wakes up every few minutes, fires the proposers that are due, drains the queues, stamps a heartbeat.

tick --> fire the due proposers --> drain the queues --> stamp a heartbeat
^ |
|_________________ job ledger + health surface ___________|

What held up. A heartbeat file, a per-job ledger, and a health check that goes red when the loop is down or a job keeps failing. When a proposer goes dark, I check the runner first. Where it broke. Every failure here was silent, which is the worst kind. A dead runner was invisible for days. A weekly job that failed deterministically retried every single tick and burned a hundred-plus agent sessions a day with no alert, because nothing wrote a failure marker or backed off. A guard you have never watched fire is a guess, not a guard.

8. Reversibility discipline: gate irreversible actions, and kill one-way ratchets

Two faceplants, same root cause. First, a bot working on a stale checkout of the repo hit a conflict and force-pushed the deploy branch backwards. Live pricing reverted and checkout broke on the main funnel, 37 minutes after the correct fix had already taken a real payment. Second, an auto-follow that scored content quality instead of whether the person was my customer ran for months, followed around 481 accounts (roughly 330 of them not my customer at all), and quietly turned my feed into 0 of 8 relevant posts. What held up. Agents propose, deterministic gates decide, nothing irreversible ships without a human tap. Bots pull before they work and never force-push main. Anything that auto-adds (follow, subscribe, enrol, tag) needs a quality gate, a periodic prune, and a blacklist so the prune cannot silently undo itself. Where it broke. The danger was never bad code. It was an agent acting on a stale view of the world, and an add-only automation with no prune. A rejected push means you are behind, not that you should shove harder.

9. Close the taste loop: the part that actually makes it grow itself

Two rules on every task. Document as you go (if a task builds, changes or breaks something, update the playbook that owns it before it is done). Capture every decline (when I reject or edit a draft, write the reason back into the playbook that produced it).

you reject or edit a draft
|
the reason is written back into the playbook that made it
|
the next draft of that kind starts from your last correction
|
edits-per-draft fall week over week
|
near-zero categories earn more autonomy

What held up. I measure edits-per-draft by category, and it falls week over week. That falling number is the entire difference between "I have automations" and "the company gets a little sharper every week without me." Where it broke. A signal you write but never read does nothing. My commenting agent got four warm replies in a week and proposed zero follow-ups, because the engagement log had no reader. Every signal needs a consumer or it is just dark data with extra steps.

10. The real bottleneck is deciding and shipping, not building

This is the one I am most embarrassed by. The system made building so pleasant that I stopped shipping. At my worst I had 54 drafts and 1 published. Across everything, I had planned 294 content slots and shipped 31. I also built a whole layer to keep my priorities visible, and nine of the tracked goals had never once moved in the system's entire life. What held up. Flip the system into ship-mode when the unshipped pile crosses a line, and denominate the daily loop in the currency that is actually scarce, which is my taps, not my ideas. The Scout and Strategist exist to hand me a short list of decisions, not more to read. Where it broke. Building machinery to make unwanted work louder. That priority layer never moved a goal because the constraint was want, not awareness, so I deleted it. Before you build software to make something visible, check whether it is invisible or just unwanted. Only one of those is a software problem.

Where I actually am, and what I want from you

That is the framework at six months. First paying client closed on exactly this setup. Around ten subscriptions cancelled and rebuilt as tools I own, only the usage-based plumbing left. Every win traces back to point 9, the taste loop. Every faceplant traces back to an action with no shipping path, or an agent acting on a stale view of the world.

A company that grows itself is one where the machine does the volume, you do the taste, and the taste gets written down so the machine needs you a little less each week. A company that just runs is one where you automated the typing, kept every decision and every silent failure, and called it leverage.

The two parts I am least sure about. Whether the single-generalist model (2) holds as the company grows past one person. And whether the taste loop (9) actually converges or just plateaus once the easy corrections are gone.

So poke holes. If you are running agents against a real business, which of these 10 is wrong in your experience, and what is the 11th I am missing?

PS the diagrams are ASCII on purpose. I was not going to make you look at another branded "AI architecture" hairball.

Edit:
A few people asked what the business actually is: it’s the system itself, I sell this as a service to be a growth cofounder to agencies and small startups. It does lead gen / outreach, seo/seo, content, ads, etc I posted a link in the comments if anyone is curious.

PS. On the topic of whether this is AI slop; def apologising for AI responding to some comments, some may find it disrespectful and that’s fair, my intention is for it to deliver value based on insights from the codebase it’s in or docs it has that are all internal and real, but the delivery wasn’t as good maybe due to ambiguity and the copy. I still think the future is more AI is in social media, and I don’t think it’s a bad thing if it delivers value, which it hasn’t for some here. I’ll continue improving the value delivery to be something I can stand by and be proud of.

A bit about myself. I'm a former AI R&D manager at an AI agency. Using, discovering but also creating tools with AI was literally my job.

But all of a sudden I noticed the amount of tools we built. They were everywhere. HR, sales, marketing, Administration..

Maybe it was a bit much at our office, and in the meantime I have a solution, but I can not imagine there are'nt any other businesses with the same problem. Mostly it's just one (or 2) person(s) in the company who creates all these things.

Are you that person? How do you cope with all these tools, automations and other AI related stuff?

As AI rises day by day, I hear and read everywhere that AI will do this and AI will do that.

But I realized something when I asked my non-technical team to use it, whether through Cowork, Codex or whatever medium was made for them to use with ease.

And guess what they have achieved so far, other than document files and Excel sheets? I think you guessed it right not as much as the giants are marketing.

They need technical people beside them to actually make something they can properly use. For a long time, they have depended on technical people for these automation tasks, and suddenly the world is expecting that they can do it themselves now.

BULLSHIT!!

What are your thoughts on this? Do let me know.

We create complex quoting systems (cpq) at my company. Today I watched a call where someone created a prototype of EXACTLY what they need in ClaudeCode. Knowing from person experience it wasnt trivial but also not impossible.

I could spin up a vercel site to host it, set up the authentication so it works with their CRM and help them get it deployed.

The problems:

-Now if there is an issue we own it and im not tooled for that (we arent a software vendor(

-Future product updates must also be vibe coded by th3 client or we will need to work out a maintenence structure which is going to be nasty even with AI assist.

- I came in via the SaaS provider that is supposed to be providing the tooling to do this job

- I dont have staff that can handle both the "hands off" nature of ai assisted development and also get things nats ass perfect.

Am I overthinking? Should I just do the work and deploy it for them?

One of the easiest mistakes to make with AI is automating too early.

Imagine a clinic has a follow-up problem.

One person follows up the next day.

Another waits three days.

Someone records it in the CRM.

Someone else keeps it in WhatsApp.

And sometimes nobody knows who owns the next action.

Adding AI to this workflow doesn't automatically solve it.

First, I would define:

• What triggers the follow-up?
• Who owns it?
• When should it happen?
• Where is the outcome recorded?
• What happens if there is no response?
• When does a human need to step in?

Now AI has a system to support.

It can help with reminders, summaries, task creation, or repetitive communication.

But without the underlying process, you're not automating a system.

You're automating inconsistency.

Process clarity first. AI second.

What’s your last 3 months with so much happening with agents?

Do you see less demand or actually more demand in your agency work?

Do people actually do more things themselves and just want help with understanding ai?

I’ve started using AI for automated daily job searches. Does this actually work and provide good results based on the filters you set? Has anyone here been doing this for a while and can share whether it’s been effective?

Hey everyone, I'm currently researching how firms encourage employees to use AI at work. I was wondering what is actually the situation in practice.

Do you guys have some explicit incentives to use AI? For example, is some part of your compensation toed to AI use? Do you have any performance metrics? Other examples may be AI innovation prizes or team bonuses for AI impact.

I was also wondering whether AI use is part of your performance evaluation? Do you discuss this with your manager?

I'm interested in all sorts of occupations, so not just programmers or software developers, but also accountants and other white-collar workers.

Looking forward to your responses!

​

I work long shifts for days sometimes, then come home and spend whatever time I have left working on the product.

The internet is slow. The resources aren't always there. And building something like this as a solo developer means constantly having to work around limitations that people in other places might not even have to think about.

But I think the hardest part isn't always the technical side.

It's the mental side of trying to build something ambitious while dealing with all of that at the same time.

There are days when you're tired, stuck, unsure if you're moving fast enough, and you still have to sit down and keep working.

The product itself is an AI infrastructure layer for e-commerce. I'm basically trying to solve a problem where most online stores give pretty much the same experience to everyone, even though different visitors obviously want different things.

What I'm building works across the UI, visuals, and product data. I can't really explain much more without giving too much away 😅. It's a pretty complex system, and I'm building it alone.

I don't plan on stopping. Whatever happens, I'll keep working on it and figuring things out.

But I'd genuinely like to hear from people who've been through something similar.

How did you deal with the pressure, the uncertainty, and the feeling that you have to keep moving even when the circumstances around you aren't ideal?

What kept you going?

When I first started this project, I assumed the first AI I needed was a salesperson.

That's what everyone talks about.

AI that closes deals.
AI that sells.
AI that generates revenue.

So I started building one.

After a couple of weeks, I realized I was solving the wrong problem.

The biggest bottleneck wasn't closing deals.

It was everything that happened before anyone was ready to buy.

Missed calls.

People filling out forms at 10:30 at night.

Leads that never got a response.

Someone asking a simple question and never hearing back.

By the time an agent called them the next day, they'd already talked to three other agents.

That's when it hit me.

The first employee most businesses need isn't another salesperson.

It's someone who never misses an opportunity.

So I scrapped my original plan and built an AI receptionist instead.

Not because receptionists are more exciting.

Because they're more important than I realized.

A good receptionist answers every call, greets every visitor, asks the right questions, routes people to the right person, and makes sure nothing falls through the cracks.

If that part breaks, it doesn't matter how good your sales team is.

Interestingly, once the receptionist was working, everything else became easier.

The marketing AI suddenly had better information.

The follow-up AI knew what to say.

The CRM stayed organized automatically.

The sales AI wasn't chasing cold leads anymore.

It made me realize something I hadn't expected.

Maybe building AI isn't about creating the smartest agent.

Maybe it's about building the right team.

That's still the idea I'm exploring with CloseBoss AI.

I'm curious...

If you could automate one employee in your business—not replace them, but give them an AI teammate—which role would you start with?

Mine turned out to be completely different from what I expected.

There’s a lot of discussion right now around AI agents and automation in customer support, but something I keep seeing is teams focusing on the AI before fixing the information the AI has access to because in a lot of cases, the technology isn’t the hardest part. The harder part is that company knowledge is usually scattered everywhere. There’s the help center that hasn’t been updated in months, random Slack conversations where important decisions were made, internal docs that only a few people know exist, and years of “tribal knowledge” sitting with specific team members.

Humans can usually work around this because they know who to ask or they’ve built up context over time but when you introduce AI into that environment, all those gaps become much more obvious. if the information is inconsistent, the answers are probably going to be inconsistent too.

I think AI is forcing companies to confront something that was already a problem: a lot of teams don’t have a single source of truth for customer knowledge and maybe the biggest challenge with AI isn’t making it smarter, maybe it’s making sure the business itself is organized enough for AI to help actually.

Has anyone else experienced this after introducing AI into support and did it solve problems immediately, or did it expose bigger issues with documentation and internal processes?

I run a small software agency called FAARNS.

Most of our work is custom software, AI tools, websites, and internal systems. Every client has a different problem, but I've noticed something interesting over the past few years.

The code usually isn't what compounds.

The data does.

A recent project was an AI estimation platform. We expected the hard part to be the AI. Instead, most of the engineering time went into collecting, validating, structuring, and enriching business information so the AI had useful context.

Then I looked back at previous projects.

One client needed contractor data.

Another needed local business information.

Another needed company enrichment.

Another needed technology stack data.

Every project solved a different problem, but each one quietly added another layer of structured business data.

Now we're sitting on years of internal datasets that were originally built to deliver client work.

We're not interested in selling databases or becoming another lead-list company.

The question we're debating internally is whether proprietary business data eventually becomes the actual competitive advantage.

If you were in this position, would you:

• Keep it as internal infrastructure?
• Build APIs around it?
• Create AI products on top of it?
• Or am I overthinking this, and data is just another operational asset?

I'd love to hear from people who've crossed that line from "client work" to "product." What made you realize the data itself had become valuable?

Hi everyone,

I'm a final-year student planning my project. I'm thinking of building an AI agent that helps businesses by answering customer queries, booking appointments, taking orders, and automating tasks through WhatsApp or a web app.

Do you think this is a good and practical final-year project?

Thanks!