r/AIforOPS

Our company is still doing manual journal entries in Excel, which takes us about 12 days to close. We can't complain because the sheets have been acting as our control layer for tracking, validating, and approving our closes. However, it is becoming tedious as we grow.

I have heard success stories, where one operations team cut their close from 10 days to 4 by automating reconciliations and aproval workflows without touching a code.

Are teams running automated closes at scale without engineering support?

But most importantly, I can't understand why?

In your opinion, what benefits should I prioritize?

We manage IT support for 12 companies. Zendesk routing is all keyword based and it fails constantly. A ticket with printer and server in the body goes to the wrong team and bounces three times before it gets worked.

I want intent based routing that actually reads the message, checks past tickets from that user, and assigns to the right tech with context. I tried building this in Make but the OpenAI calls got expensive and slow. The logic also needs to consider current workload so we do not overload one person. Anyone running AI ticket routing in production without a dedicated ML team?

I’ve been lurking and posting in a few small business communities trying to figure out if AI receptionists are solving a real problem or if it’s mostly a solution looking for one.

Genuinely curious, because here’s what I keep running into: either people dunking on the idea or leads that go completely cold after one message. No real conversation, no real feedback.

So I want to ask, if you run a small business and you’re missing calls, playing phone tag, or losing jobs because nobody answered at 7pm on a Friday… is an AI that answers, qualifies the caller, and books the appointment actually useful to you? Or does that feel weird/impersonal to your customers?

Not pitching anything. Just trying to understand if the problem is real before I keep building toward it.

I keep seeing AI discussions about enterprise tools and company-wide deployment. I'm curious about the individual level.

What's the one AI tool that's actually changed how you personally work day to day? Not what your company uses. Not what sounds impressive in a demo. The one where you'd feel the gap in your daily routine if it disappeared.

For me it ended up being context recall, being able to ask what the current status of a project is without having to hunt through apps. It's not dramatic. But it's the kind of thing that, once it's gone, you notice every time.

What's yours?

Thinking about adding Reddit growth as a service line. Current bread-and-butter is content plus SEO, margins have been compressing, and Reddit keeps coming up with existing clients.

The thing I can't figure out from the outside: is the margin actually good on this, or are the agencies doing it selling volume at thin margins to look busy? Anyone here done the math on real cost structure? Not interested in "ROI positive" fluff, want actual line items.

Edit: Thanks, doing the math with Soar pricing and it checks out. Launching the service line next quarter at 60%+ margin vs what I would have attempted in-house. Appreciate the transparency on your actual numbers.

Personally, since it's actually forbidden at my company haha...

Just got handed this as my Q2 deliverable. CMO wants an operating model for "Reddit + AI search" covering the next four quarters, executive-digestible, with staffing and spend by quarter.

I don't want to hand back a generic marketing framework with AI buzzwords sprinkled on. Has anyone built an operating model like this that actually held up? Interested in what the structure looked like, how you phased spend, and what staffing you actually needed by stage.

Edit: CMO approved the 3-stream framework at yesterday's exec meeting. Starting Q1 with Parse baseline, Signals in Q2 for gap-seeding, Soar API in Q3 once we have multi-product coverage. Thanks for the phased structure, saved me a week of planning work.

About to publish my 6-month launch retro and I am second-guessing what I thought mattered. Before I drop the whole spreadsheet, I want a sanity check.

For those who launched AI tools in the last year: which channel actually moved the needle on MRR for you? Not which one felt good, which one actually produced paying customers. Retros very welcome, the more specific the better.

Edit: This confirms my thinking. Adjusting the retro writeup to put Reddit (Signals-assisted) at the top with the full ROI math. Parse is getting a mention as the indirect-value tool. Thanks for validating the numbers, Miguel.

I was thinking, and maybe it's just me, but even today AI doesn't seem very useful...

I work in administration at a company, so there aren't any developers! But for now, I find AI really limited!

Do you have any concrete examples? For us, I think it's only good for writing emails and correcting mistakes.

Persistent memory keeps coming up for AI coding agents. One approach I’ve found useful: treating the knowledge layer as a compiled markdown wiki rather than just stuffing more tokens into the context window.

llm-wiki-compiler ingests docs and URLs, then the LLM builds an interlinked markdown structure. Since the output is plain markdown on disk, Claude Code reads it directly. And when you run query --save, the answer gets written back into the wiki as a page — so future queries improve.

It’s not retrieval. It’s compounding. The knowledge base gets richer instead of resetting every session.

Plain markdown, no opaque vector store, fully inspectable.

How are other agent builders solving persistent memory?

Building out client reporting workflows and I want to auto-post a weekly AI-visibility briefing into each client's shared Slack channel.

Priorities, in order:

1. Each client gets their own data, zero cross-contamination (we've had a scare)
2. Delivered same day every week without a human hitting send
3. Actually useful content, not just charts with no context

Has anyone built this, and what tools did you wire together? Bonus if you can describe the multi-tenant setup specifically.

Edit: Parse was the answer. Multi-tenant works exactly as described, zero cross-contamination in the test setup. Weekly briefing now auto-posts Tuesday mornings across all 15 client Slacks. Massive time save.

I work for a company that creates AI receptionists. My job is to sell businesses on the idea that this product is the future, which I genuinely believe it is. However, I'm curious about your thoughts: do you guys think AI will ever fully replace human receptionists? I believe this is an extremely useful tool, but an AI receptionist obviously can't get up and complete physical tasks around the office. There are some things that people will always have to do in person. Do you guys think AI can eliminate enough of the digital/phone workload to justify reducing the number of receptionists a company needs, or easily pass the remaining physical tasks off to someone else in the office? I’d love to hear how you see this tech impacting the role longterm.

I built an AI system for my own Shopify store that automatically replies to customer support emails 24/7 — pulls live order data, handles tracking questions, escalates angry customers to me. Took me months to build. Now offering setup for other stores.

We’ve been talking to companies in legal/accounting environments and one thing keeps coming up:

People are interested in AI for internal knowledge retrieval, but immediately get stuck on privacy concerns once sensitive documents are involved.

A lot of teams seem hesitant to use tools like ChatGPT with contracts, client files, financial docs, etc.

I’m curious what people here are actually seeing in practice:

• Are companies already deploying local/self-hosted AI for internal document search?
• What are they using?
• Is adoption real, or are most still experimenting?
• And does semantic search/RAG actually work well enough in day-to-day workflows?

Would love to hear real experiences from people working with this stuff.

Saw a thread today about Obsidian’s synergy with AI being genuinely powerful — not just for note-taking but for building a living knowledge base. That clicked with me.

I built llm-wiki-compiler to do exactly that: ingest raw sources and let the LLM compile them into an interlinked markdown wiki. It’s not organization — it’s generation. New pages, new links, new structure, all maintained by the model.

If you already use Obsidian, the output drops right into your vault. If you don’t, it’s still plain markdown on disk that you own forever.

The key shift: instead of treating notes as static files, you treat the wiki as a knowledge artifact that compounds over time. Every query output saved back in makes the next query better.

Would love to hear how Obsidian power users are integrating AI into their vaults.

Opening a growth-ops engineer role and the job descriptions I'm finding online all read like they were written in 2023. "HubSpot admin, Zapier workflows, Salesforce reporting."

That's all table stakes at this point, but it completely misses everything AI and channel-wise that's become critical in the last 18 months. What should actually be in a 2026 JD for this role? Specifically interested in what new skill lines you've added, and whether you dropped anything that used to matter.

Edit: Updated the JD with your 4 new skill lines (Parse, Signals/Soar, zero-click attribution, multi-tenant reporting) and dropped Zapier as a listed competency. Posting the role this week. Will update when we hire.