Episode 18 - Where Does AI Earn Its Place

(The Second Episode in Our 3-Part AI Series) 

In Episode 17, we answered the question: "Is AI even worth it for a business my size?" Now it's time for the next question: Where does AI actually belong in a small business? The answer may surprise you.
It usually isn't your biggest marketing campaign or your most creative project.
It's the repetitive, tedious work you do over and over again.

In this episode, we explore where AI creates real value for food and beverage manufacturers, consumer packaged goods (CPG) businesses, and coffee shops, and how to test it safely before making a bigger commitment.

✔️  A Few AI Tools to Look Into (organized by what they're for):

Start with the free version of one, point it at a single task, and keep a human check on anything that goes out the door.

1. Turning one product into many listings
•General assistants (ChatGPT, Claude, Google Gemini) - the flexible workhorses; paste your product facts once and generate the line sheet, DTC copy, marketplace listing, and shelf card.
•Hypotenuse AI - writes product descriptions in bulk from a CSV or spreadsheet, useful for large or seasonal catalogs; from around $19/month. US Chamber of Commerce
•Copy.ai, Writesonic, Rytr, Jasper - marketing-copy tools built for product pages and ad copy. Copy.ai's free tier covers about 2,000 words a month - enough to test on a small catalog; Writesonic starts around $19/month. ExpansedigitalContentsaurus
•Canva - for the shelf card and social visuals; its AI writing and design features are built in.

2. Demand & inventory - seeing patterns in your own sales
•Skip the big enterprise forecasting platforms - they're built for companies a hundred times your size. For a business like yours, start with the AI and reporting features already inside the system you use (your e-commerce platform, POS, or inventory tool like Shopify, Square, QuickBooks, or inFlow), or export your sales history and ask a general assistant to find the patterns.
•The one caveat from Episode 17: this only works if your sales data is clean and in one place. Messy data in, confident nonsense out.

3. Knowledge capture - turning what's in someone's head into a written procedure
•Transcription tools - Fathom (generous free tier - unlimited recording and summaries), Otter.ai (free tier around 300 minutes/month), Fireflies, or Notta. Record the walk-through, get a transcript. alfred_Ticnote
•Then a general assistant turns that transcript into a clean first-draft SOP. (Many assistants now take the audio directly, too.) You review and fix - you're the expert.

4. Supplier & customer communication - drafting only
•General assistants for the first draft of a quote request, a supplier-pricing comparison, or a wholesale reply. You read, you decide, you send - never auto-send. And as we'll cover next episode, be careful what confidential information you paste in.

For the coffee shops
•Scheduling: 7shifts, Sling - line up labor against your foot-traffic patterns.
•Inventory & food cost: Square, Toast, or tools like MarginEdge / xtraCHEF - right-size your milk and pastry orders and cut waste.
•Reviews & training docs: a general assistant to draft review responses and get new hires up to speed fast.

A few notes
•Start free - most of these have a free tier, prove it helps before you pay.
•One job at a time - take the boring problem from this episode and try it there first.
•Keep a human check on anything generated - AI drafts, you decide.
•Don't paste anything confidential - recipes, customer lists, pricing - into a free tool. That's exactly what Episode 19 is about.

(Tools and pricing current as of mid-2026)

✔️  Next Episode - Episode 19: How to Use AI Without Getting Burned
We'll cover the practical safeguards every small business should have before using AI with customer information, pricing, recipes, financial data, and other confidential business information, and how to avoid the mistakes that can damage trust or expose sensitive data.

📩 Got a Question?

Have a resilience, supplier, operations, or sustainability question you want covered on the podcast? Send it in - your question could become a future episode and help other small business owners facing the same challenge.

🔗 Resources & Contact

🌐 www.3pimpactconsulting.com

📩 anca@3pimpactconsulting.com 

Transcript

Season 2 – Episode 18
Where AI Earns Its Place in a Food, Beverage, CPG, or Coffee - Business

Let me tell you about Marcus. Marcus makes veggie chips - small-batch, beet, kale and sweet potato, more than a dozen flavors and varieties. He sells them direct from his website, at farmers markets on weekends, wholesale to a handful of regional grocers, and on a couple of online marketplaces. Business is good. Growing, even.
Last month, Marcus launched a new flavor. And in his head, the exciting part was the marketing ad - maybe that was where AI would come in.
But here’s what in fact was eating his week. To launch that one new flavor, he had to write the product description not once, but like fifteen times. The version for the wholesale line sheet. The longer version for his website. The shorter version for the marketplace listing. The allergen and ingredient statement. The little shelf card for the farmers market table. Same product. Same handful of facts. Written over and over, slightly different every time, for two solid days.
That, the boring, repetitive, invisible part, is where AI would change Marcus’s week. Not the shiny campaign he was dreaming about. The tedious thing he wasn’t even counting as work.
And that’s the theme of today. When it comes to AI, the shiny thing usually isn’t the opportunity. The boring thing is.
Welcome to The Green Ledger - Tips for a Sustainable Small Business. I’m Anca, founder of 3P Impact Consulting. This season, we’re answering your questions about building resilient, responsible businesses. Whether you’re running a CPG brand, a food or beverage manufacturer, or a coffee shop, each episode tackles one question with practical, actionable answers you can use right away. Let’s dive in.
If you were with me last episode, you know we’re in the middle of a three-part series on AI. Part one was about deciding – if it’s even worth it for a business your size, and whether you’re ready. If you missed it, go back; it’s the foundation for this one.
Today is part two: where is AI important. We’ll walk through the areas it pays off, how to test it without risking the business, and how to tell when it’s feeding you garbage. 
Let’s start with the single most important idea in this episode.
When you think about bringing in AI, you are probably thinking in relationship to your most exciting problem. The rebrand. The big campaign. The thing that feels like it deserves something impressive. And in my opinion that’s the wrong place to start.
Start where it’s boring. Your first pilot should be the most repetitive, tedious, do-it-over-and-over task you’ve got - where a mistake is cheap and easy to spot. Why? Because that’s where AI is strong, and where the stakes are low enough that you can learn without getting burned. Marcus’s fifteen product descriptions? Perfect. It’s repetitive, it’s eating real hours, and if the AI gets a word wrong, he catches it in a five-second read before it goes anywhere.
Exciting problems are usually high-stakes and one-offs, exactly where you don’t want a brand-new tool learning on the job. Boring problems are where the wins are hiding.
So where can you use AI? Let me give you four places, in rough order of how easy they are to start.
The first is the writing the same thing over and over. This is Marcus’s problem - taking one thing and translating it into many. One product becomes a line sheet, a website page, a marketplace listing, an allergen statement, a shelf card. One announcement becomes an email, three social posts, and a note to your wholesale accounts. It’s high-volume, it’s repetitive, and - very important - it’s low-risk, as long as a human reads it before it goes. If you do nothing else with AI, start here. This one category gives you back a few good hours a week.
The second is demand and inventory. Back in Season 1, Episode 9, we talked about waste - how the money draining out of your business is often sitting in inventory. AI can look at your own sales history and help you see patterns you’re too close to notice: which flavors move in which season, when to make more, when to hold back. For Marcus, that’s the difference between a pallet of beet chips going stale in the warehouse past their best-by, and running out of his bestseller right at the back-to-school rush, when parents are stocking up on lunchbox snacks… But - I said this last episode, and I’ll say it again - this one only works if your sales data is clean and in one place. Give AI bad input and you get bad output. So, it’s a great second or third pilot, once you’ve built some trust.
The third is knowledge capture. Think about how much of how your business runs exists only in one person’s head. Your production lead knows the exact way to switch the line over between flavors. Your longtime employee knows which grocer wants their order how. If that person is out sick - or leaves - that knowledge walks out the door with them. In Season 1, Episode 7, we talked about how much your people matter. Here’s a way to protect what they know: have them talk through a process out loud - a twenty-minute voice memo - and let AI turn that ramble into a clean first draft of a written procedure. You review it, fix what’s wrong, and now it’s documented. You just turned knowledge trapped in someone’s head into something the business owns.
The fourth is supplier and customer communication – this comes with a warning. AI is genuinely useful for drafting the tedious back-and-forth: a request for a quote, a reply to a wholesale inquiry, a comparison of two suppliers’ pricing (though anything with confidential numbers in it needs special care, we'll cover next episode). The key word here is drafting. AI writes the first version; you read it, you decide, you send. Never allow it to send on its own. A draft you approve saves time. An email that goes out without you ever seeing it is a liability.
Okay. You’ve picked your boring problem. How do you test AI on it without it turning into a mess? Here’s a simple pilot you can run in two weeks.
Step one: before you start, decide what a real result looks like, and make it a number. Not “see if it’s helpful.” A measure you can check. How many hours did this used to take me? What’s my error rate? In Season 1, Episode 10, we talked about measuring what matters - same discipline here. If you can’t name the number you’re trying to move, you won’t know if the pilot worked.
Step two: run it in parallel. Don’t rip out your old way and bet the business on a tool you’ve used for three days. Do it both ways for a couple of weeks. Let AI draft Marcus’s product descriptions, but also Marcus should create the descriptions as he usually does.
Step three: keep a human check on anything that is generated. This is very important: AI drafts, humans decide. Especially anything that touches your customer or your brand.
Step four: at the end of two weeks, look at your number and make a call. Keep it, kill it, or expand it. If it gave you back six hours a week with no drop in quality, keep it, and go find the next boring problem. If it created more cleanup than it saved, kill it, no harm done.
Now, how do you know when the AI is wrong?
Because it will be, sometimes. And you probably know this by now, it’s wrong in a confident voice. It hands you a made-up number or a slightly-off fact with the exact same certainty as a true one. So you need to check. You stay the expert in the room.
Three simple gut checks. First, does it pass the sniff test? You know your business. If the AI tells you your best-selling flavor is your worst performer, you’ll feel that it’s off - trust that. Second, spot-check it against something you know is true. Pull three of its numbers and check them against your records. If those three hold up, you’ve earned some confidence; if one’s wrong, slow down. And third: never release anything you couldn’t put your own name on. If you wouldn’t stand behind it, it doesn’t go out.
That’s enough to keep a pilot safe. There’s a more complex version of “getting it wrong”, but we’ll cover that in the next episode.
Alright. If you’re running a café, you might be thinking none of this applies to you. It does. Same logic, smaller surface.
Let me tell you about Priya. Priya runs a small coffee shop - one location, a tight team, thin margins, like most cafés. She doesn’t have a dozen SKUs or a wholesale line sheet. But she absolutely has boring, repetitive, expensive problems.
Take scheduling. Every week she stares at her foot-traffic patterns and tries to line up staff hours against them - too many people on a slow Tuesday morning burns money, too few on a Saturday rush burns customers. That’s a pattern problem, and it’s exactly what AI can help her think through. Or take waste - same Episode 9 idea - how much milk and how many pastries to order so she’s not throwing product out on Sunday night. Or the review responses she dreads writing. Or the training documents for a team that turns over more than she’d like - remember, Episode 7, people are everything, and getting a new hire up to speed fast is its own kind of resilience.
None of that is glamorous. All of it is boring, repetitive, and costing her money. Which is exactly the point. Whether you’ve got fifteen SKUs or one espresso machine, the move is the same: find the boring thing and let AI take the first pass.
Ok, here are your to do tasks for this week.
1. Pick your one boring problem. The most repetitive, tedious, do-it-again-and-again task on your plate. Don’t overthink it - the obvious annoying one is probably right.
2. Before you touch a tool, write down the number you want improved. Hours saved, errors reduced, dollars - whatever “a real result” means for that task. One number.
3. Start the two-week pilot. Do it in parallel, keep a human check on anything generated, and at the end, look at your number and decide: keep, kill, or expand.
If you want a few specific tools to look into for these use cases, I’ve put a list in the show notes - organized by the job you’re trying to do.
Remember Marcus, dreaming about the flashy campaign while hours of his week disappeared into rewriting the same paragraph fifteen times? Once he pointed AI at the boring thing instead of the shiny one, he got those hours back, and he still made every final call on what went out the door. One boring problem, solved.
Next time, part three, we close the series with the one that keeps us up at night: the AI mistake that could leak your recipe, your customer list, or your reputation, the slip that doesn’t come with a do-over. And the one-page fix, sized for a business like yours, that keeps it from happening. Don’t miss it.
Thanks for listening to The Green Ledger. If today’s episode sparked a question for you, something you’re dealing with in your own business, send it to me. My email is in the show notes. Your question could become the next episode and help dozens of other business owners navigating the same challenge. If you want more tools and resources for building business resilience, sign up for my email list on my website, link in the show notes. And if you found this episode helpful, share it with another small business owner who needs to hear it. Until next time, remember - small steps lead to big impact, and resilience isn’t just about surviving, it’s about thriving.