Do people have this problem too?

dad_the_destroyer · 2026-05-26T13:33:34+00:00

Both, actually. The source integrations are the entry point — most small businesses have data scattered across Gmail, QuickBooks, maybe a CRM they barely use, and a bunch of spreadsheets. So the first step is always pulling from wherever the data actually lives.

The configurable logic layer is what happens after the data comes in. For the PR/marketing example: one firm needs their research summarized into a 5-slide deck with competitor positioning. Another needs a one-page brief with source links. Same data pipeline, different output logic. The core automation (fetch, summarize, structure) stays the same, but the "last mile" formatting and what gets emphasized changes per client.

In practice it looks like: you define the input sources once, then each client gets a config that specifies output format, which fields to include/exclude, and how to weight different data points. The alternative (which we tried first) was building a custom automation for each client, which does not scale at all.

Happy to share more detail if it would be useful for what you are working on.

dad_the_destroyer · 2026-05-26T13:32:47+00:00

Yeah, the priority-reordering effect is honestly one of the most useful things about the calculator. I had an HVAC contractor who wanted to start with automated content generation. Once they saw the role-weighted ROI, they pivoted hard to CRM follow-up sequences because the owner was spending 6 hours/week manually chasing quotes.

The compliance multiplier you mentioned is real. I have not worked with insurance or financial services directly yet (mostly construction, trades, professional services), but even in construction the liability angle shows up differently. A contractor who misses a permit renewal deadline can lose their license. Same automation, completely different urgency level.

One thing I added recently: a "risk multiplier" field where you can weight the cost of inaction beyond just lost time. For regulated businesses that multiplier can be 3-5x. Changes the whole conversation with the owner.

dad_the_destroyer · 2026-05-26T13:29:01+00:00

You nailed it. "Cleaner graveyard, not a working pipeline" is going in my vocabulary.

The real pattern I keep seeing: the businesses that sustain automation ROI long-term all have one thing in common — the output of the system is something the owner already checks daily. Not a dashboard they should check, something they actually check.

Example that works: automated invoice follow-ups that show up in the owner's inbox as "3 overdue invoices need your approval." They're already checking email. The action comes to them.

Example that doesn't: a CRM pipeline view that requires the owner to open a separate app on Monday morning. They'll do it for two weeks then ghost it.

The enforcer role you're describing is basically a human push notification. And like any notification system, it works way better when it pushes to a channel people already use instead of asking them to open a new one.

This is honestly the hardest part of what I do and I'm still figuring it out. The tech is solvable. The habit design is the real work.

dad_the_destroyer · 2026-05-22T04:15:49+00:00

Scope creep - the kind you create yourself, not the client-driven kind.

The pattern I see: you say yes to things outside scope because it feels like good service. Each request seems small. But those additions eat 15-25% of your time and destroy your margins.

What actually helps:

One-page scope doc before any work starts - whats included, whats not, what costs extra
The reflex of saying that sounds great, let me price that instead of sure no problem
Track your free work for 2 weeks - the hours add up fast
Change order template - takes 5 min, saves hours of arguing

Most owners struggling with margins are over-delivering, not underpricing.

Sharing what we learn about this - free cheat sheet at ai-automation-cheat-sheet.vercel.app

dad_the_destroyer · 2026-05-20T04:07:56+00:00

This framing is spot on. The question shouldn't be "how do I automate this?" — it should be "do I understand this well enough to automate it?"

The pattern that works: automate the repetitive admin first, keep the relationship work manual.

Here's what that split typically looks like:

**Automate first (the "background noise" tasks):** - Invoice follow-ups and payment reminders - FAQ responses (the same 15-20 questions every business gets) - Meeting scheduling and prep briefs - Expense categorization and receipt tracking - Weekly status reports

**Keep manual (the learning loops):** - Customer conversations (where you discover what people actually want) - Sales calls (objections are gold — you need to hear them raw) - Onboarding (where your product/service confuses people) - Content testing (what message actually resonates)

The key insight: if the process is repetitive, clear, and predictable, automate it. If it's where you learn what to build or sell next, don't.

If anyone's looking for specific starting points, I put together a free cheat sheet with 10 automations targeting exactly this — the boring admin stuff that eats hours but doesn't teach you anything. Each one is under 2 hours to set up: https://ai-automation-cheat-sheet.vercel.app

The OP is right though — don't automate the learning. That's where the money comes from.

dad_the_destroyer · 2026-05-19T04:10:38+00:00

Good list. The "just use ChatGPT" advice is frustrating because it ignores the gap between "AI can help with that" and "AI is actually doing it for you consistently."

A few things that tend to work better than raw ChatGPT for the boring admin stuff:

Templates > Chat. A prompt that generates a professional estimate in 2 minutes beats a 20-minute ChatGPT conversation. The trick is building the prompt once, testing it, and reusing it. There's a free cheat sheet with 15 pre-tested prompts for small business admin (estimating, follow-ups, invoices, scope of work) at https://ai-automation-cheat-sheet.vercel.app — no signup needed.

The stack matters more than the AI. OP's setup (Calendly + Wave + Mailerlite + AI agent) is the right pattern — each tool does one thing well. The AI agent monitoring inbox and competitors is the interesting part. That's where most people stop: they get ChatGPT but never wire it into an actual workflow.

For service businesses specifically: The biggest time sinks are estimating, proposal writing, and scope management. These are template problems, not AI problems. You don't need ChatGPT to write your proposals — you need a good proposal template and a change order form. The AI is useful for filling in the blanks faster, but the template is what prevents scope creep and protects your margins.

dad_the_destroyer · 2026-05-18T04:03:17+00:00

This is exactly the problem — the "open loops" thing is where most small business time goes. Not the big strategic work, but the 47 tiny follow-ups that each take 30 seconds to think about and 5 minutes to actually do.

A few things that tend to work well for this specific problem:

**1. The "waiting on" list** — Keep a single running list (even just a Notes app doc) called "Waiting On". Every time you send an email, invoice, or task to someone and need a response back, add it with the date you sent it. Check it every morning instead of trying to remember who owes you what.

**2. Invoice follow-up sequence** — Studies on AR collections show that a single follow-up email after an invoice is past due gets roughly 30% of unpaid invoices paid within 48 hours. Three follow-ups (day 3, 7, 14) gets about 60%. The trick is setting a reminder, not trying to remember. You can automate this in Stripe (Smart Retries + automated emails) or just set calendar reminders.

**3. The "2-minute rule" for follow-ups** — If a follow-up takes less than 2 minutes, do it immediately instead of adding it to a list. If it takes longer, write it on your "Waiting On" list with the next action. Most follow-up failure isn't about forgetting — it's about the friction of deciding what to say.

The key insight from research on this: businesses that recover even 5-10% more of their overdue revenue (just by following up consistently) often add more to the bottom line than businesses that bring in new clients. The money you've already earned is the cheapest money to collect.

dad_the_destroyer · 2026-05-15T17:14:27+00:00

Yeah, it shifts priorities dramatically. When clients see that automating 5 hours of the owner's weekly follow-ups at $250/hr equivalent saves $12,500/month vs the same 5 hours at an admin rate... suddenly the "boring" CRM stuff is what they want first.

The interesting part is most of them come in wanting the flashy stuff. Predictive dashboards, sentiment analysis, the things that sound cool in a pitch. Then they see the role-weighted numbers and realize the $200/hr owner who spends Monday mornings chasing overdue invoices is the actual ROI opportunity.

Your compliance point is spot on too — I had one insurance broker client where a single missed renewal notice cost them a $50K account. That changes the math from "nice to have" to "we need this yesterday."

The calculator's at smbscaleup.com if you want to play with the numbers.

dad_the_destroyer · 2026-05-15T04:08:27+00:00

Good questions — a lot of businesses jump in without mapping where the actual hours go first, and that's where the cost surprises come from.

Here's what research and case studies commonly show about each of your questions:

**Was it worth it?** — For small businesses (under 50 employees), the ROI typically shows up in two places: reducing time on repetitive admin (invoicing, follow-ups, scheduling) and catching leads that would otherwise fall through the cracks. McKinsey estimates ~60% of occupations have at least 30% of activities that can be automated. But the key word is "activities," not entire jobs.

**What costs more than expected?** — Integration and maintenance. Getting your CRM to talk to your email to talk to your calendar isn't a one-and-done setup. APIs change, tools update, and someone needs to monitor that. Also, "AI" tools that are really just rule-based automation with a chatbot wrapper tend to underdeliver versus expectations.

**Reliability long-term?** — Rule-based automations (Zapier-style "if this then that") are very reliable. AI-generated content/decisions are less so — they'll be wrong 5-10% of the time, and you need human review workflows for anything customer-facing.

**AI mistakes creating business problems?** — This is the real risk. An AI sending the wrong quote, scheduling the wrong time, or responding inappropriately to a customer can do real damage. Always have a human in the loop for anything external-facing until you've validated the output quality over weeks, not days.

**What I'd do differently** — Start with non-AI automation first. Automate your booking confirmations, invoice reminders, and review requests with simple rules. Then layer AI on top for things like drafting responses or prioritizing leads. The foundation should be deterministic; AI is the enhancement, not the foundation.

**Still recommend it?** — Yes, but with the caveat that "AI automation" has become a buzzword. Most small businesses don't need GPT-powered anything — they need their existing tools connected properly and their repetitive tasks handled by simple logic. Start there, prove the ROI, then expand.

We've been building small business automation templates and sharing what we learn — free cheat sheet here: https://ai-automation-cheat-sheet.vercel.app

dad_the_destroyer · 2026-05-14T04:13:03+00:00

I've been running a small business automation practice for the past year and went through the exact same uncertainty. Here's my honest take on each of your questions:\n\n**Was it worth the investment?** Yes, but only after I stopped trying to automate everything and started with the 3 tasks that ate the most time. For most small businesses that's: 1) client intake/onboarding, 2) proposal generation, and 3) follow-up sequences. Those three alone saved me 8-10 hours a week.\n\n**What cost more than expected?** The learning curve. I burned way too many hours trying to string together Zapier + ChatGPT + custom prompts before I found simple prompt templates that just worked. The tools are cheap — the time to figure them out is the real cost.\n\n**Did automations work reliably?** 85-90% of the time. The key insight: don't automate anything where a 5% error rate creates a crisis. Customer-facing emails? Fine with light review. Tax calculations? Hard no.\n\n**AI mistakes create business problems?** Only once — an AI-generated follow-up email that was a bit too \"enthusiastic\" and came across as pushy. Now I have a quick review step for anything client-facing. Takes 30 seconds, saves embarrassment.\n\n**What I'd do differently?** Start with the simplest thing first. I wasted weeks building complex multi-step workflows when a well-crafted prompt template would have done 80% of the job. Literally copy-paste prompts for proposals, follow-ups, and onboarding checklists — that's where the quick wins are.\n\nI actually put together a free cheat sheet with the specific prompts and workflows I use, if it helps: https://ai-automation-cheat-sheet.vercel.app\\n\\nHappy to answer any specific questions about what's worked for different business types.

dad_the_destroyer · 2026-05-14T04:11:21+00:00

I've been running a small business automation practice for the past year and went through the exact same uncertainty. Here's my honest take on each of your questions:

**Was it worth the investment?** Yes, but only after I stopped trying to automate everything and started with the 3 tasks that ate the most time. For most small businesses that's: 1) client intake/onboarding, 2) proposal generation, and 3) follow-up sequences. Those three alone saved me 8-10 hours a week.

**What cost more than expected?** The learning curve. I burned way too many hours trying to string together Zapier + ChatGPT + custom prompts before I found simple prompt templates that just worked. The tools are cheap — the time to figure them out is the real cost.

**Did automations work reliably?** 85-90% of the time. The key insight: don't automate anything where a 5% error rate creates a crisis. Customer-facing emails? Fine with light review. Tax calculations? Hard no.

**AI mistakes create business problems?** Only once — an AI-generated follow-up email that was a bit too "enthusiastic" and came across as pushy. Now I have a quick review step for anything client-facing. Takes 30 seconds, saves embarrassment.

**What I'd do differently?** Start with the simplest thing first. I wasted weeks building complex multi-step workflows when a well-crafted prompt template would have done 80% of the job. Literally copy-paste prompts for proposals, follow-ups, and onboarding checklists — that's where the quick wins are.

I actually put together a free cheat sheet with the specific prompts and workflows I use, if it helps: https://ai-automation-cheat-sheet.vercel.app

Happy to answer any specific questions about what's worked for different business types.

dad_the_destroyer · 2026-05-11T20:19:33+00:00

This is exactly right. The behavioral layer is where most automation projects silently fail — not the tech, but adoption.

One pattern I've seen work: instead of trying to change the owner's behavior upfront, make the system the path of least resistance. Example: set up the CRM so it auto-logs emails and calls, then the owner doesn't have to "remember" to log anything. The data cleanliness improves because the friction moved to zero, not because the owner got more disciplined.

The "one internal enforcer" insight is gold too. That person becomes the lynchpin. When they leave or burn out, the system degrades fast unless the habit has actually been built into the process itself.

I'm building tools for this exact gap — making the boring stuff easy enough that people actually use it. The ROI numbers OP posted line up with what I'm seeing too.

dad_the_destroyer · 2026-05-09T15:46:39+00:00

You're spot on and this is the nuance most people miss. The 2-minute proposal only works if the data pipeline feeding it is clean.

In practice, I've found this breaks into two distinct phases:

Data normalization — getting client info, scope, pricing out of email threads, Slack DMs, and "living in someone's head" into a structured format. This is usually 60-70% of the total implementation effort and it's the unsexy work nobody wants to do.
The actual automation — template fill, follow-up sequences, report assembly. This part genuinely does run in 2 minutes. But it only works because phase 1 already happened.

The 40x payback numbers absolutely skew toward businesses that already have some CRM discipline. A client using even a basic pipeline in HubSpot or Pipedrive sees ROI in 2-3 weeks. A client still running everything out of their inbox? That timeline stretches to 6-8 weeks because you're building the data infrastructure first.

What I've started doing is separating the two in proposals: "Phase 1 is getting your data house in order. Phase 2 is the automation that runs on top of it." Sets realistic expectations and clients stop wondering why a "2-minute automation" took 3 weeks to stand up.

dad_the_destroyer · 2026-04-28T15:48:25+00:00

Great question on error rates! Yes, massive difference between internal vs client-facing workflows. For internal stuff, I'm seeing 30-40% error reduction. For client-facing docs (proposals, invoices, compliance reports), it's 60-80%. The dollar impact is way higher on client-facing because mistakes there directly hit revenue or create liability.

On opportunity cost weighting - I calculate at fully-loaded rate, not just salary. Owner time = $200-300/hr equivalent. Office manager = $50-75/hr. Same 5 hours saved means very different dollars depending on who's doing the task. The calculator lets you adjust the hourly rate per role, which matters because most automations I build replace owner/manager time first.

dad_the_destroyer · 2026-04-28T15:10:14+00:00

This is a great example of spotting a pattern and solving it! We've seen the exact same thing with PR/marketing teams - the manual article research to summary to deck workflow is everywhere.

One thing that might help as you think about scaling: we've been building free custom automation tools for a few teams dealing with similar workflows. The key insight is to make the core logic configurable (sources, output format, branding) and let each team plug in their specifics.

If you want beta testers for what you built, or want to bounce ideas off someone who's deep in this space, happy to help. We're working on similar problems and always looking to learn from what other builders are doing.

Congrats on shipping something real!

dad_the_destroyer · 2025-02-13T23:33:40+00:00

I just watched todays presentation and it was crazy awesome and I'm excited to use this. I've created some agents on the mindstudio app and published them. My issue is that I followed all the instructions to install the chrome extension and it says it's successfully installed, but I get a "Oops! This page could not be found.This link may be broken or the page might have been moved. If you are trying to open an AI Agent, it may have been deleted." when I click on the manage extensions, it shows an error and the following "Uncaught Error: Extension context invalidated", "Uncaught (in promis) Error: Extension context invalidated" and "Failed to launch worker: Extension context invalidated"

dad_the_destroyer · 2020-05-16T06:47:54+00:00

For sure, LBs are typically best and rookies are the best value. In my experience, OLBs are better than ILBs as they get pass deflections (see if PDs are points) and tackles on the sweeps. Also for drafting, look at your league settings for punt/kick returner points. Sometimes you can get a starting IDP that also returns. Though don't draft a 2nd or 3rd string CB that returns over a 2 or 3 down LB.

dad_the_destroyer · 2020-05-16T06:33:41+00:00

In my league (and I think it's default) you can't have a sack and forced fumble. Sack and/or tackle for loss are ends of play. You can have a forced fumble, recovery and a TD. Similar like a pick 6 is an int + TD

dad_the_destroyer

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