In short6 min read
A team reviewed 120 published AI cases from consumer goods companies and found the clearest payoffs in casks, changeovers, and forecasts, not chatbots. Here's what that means for consultants and side hustlers who sell automation.
Most people trying to make money with AI are fighting over the same crowded corner: AI ads, AI content, and customer service chatbots. A recent analysis of 120 published AI cases from consumer goods companies suggests the real, measurable money is somewhere far less glamorous. By the end of this article, you’ll know which operational problems produced the clearest results, why mid-sized companies are an underserved market, and a step-by-step plan to start offering “boring” AI automation as a service.
The timing matters. According to the person who shared the research, giants with in-house AI teams dominate the published results, while mid-sized companies have barely published anything measured. That gap is the opening.
In this article
- What the 120 Cases Actually Showed
- Why Boring Automation Wins
- The Open Question: Does This Work for Smaller Companies?
- Where the Money Comes From: 3 Service Models
- Monetization Difficulty: Medium
- Limitations and Caveats
- Replicating This Today: Realistic Adjustments
- How to Do It: Your Step-by-Step Checklist
What the 120 Cases Actually Showed
A Reddit user in r/automation posted on 10/29/2026 that their team at work had compiled a report on AI cases where consumer goods companies published actual results. The author says they expected mostly AI ads and chatbots. Instead, 54 of the 120 cases were outside marketing, in planning, factories, sales, and back office.
More importantly, the author notes that this non-marketing work is “where the clearest money is.” Here are the three examples they highlighted:
| Company | Problem area | What AI was used for | Reported result |
|---|---|---|---|
| Diageo | Inventory / supply | Tracking 10 million casks of maturing spirit across 300 sites | $100 million in extra sales in a year |
| Hindustan Unilever (Tinsukia factory) | Factory operations | Production changeovers and frozen production planning | Changeover time cut by 85%; frozen production plan went from 14 days to 1 |
| Kao | Demand planning | Forecasting demand for cosmetics in Japan | Write-off losses fell 38% |
As the author puts it, “none of these would get a conference keynote. it’s casks, changeovers and forecasts.”
Why Boring Automation Wins
There’s a simple reason these unglamorous projects show up with hard numbers: they touch things that already have a dollar value attached. A cask of aging spirit is inventory. A changeover is lost production time. A write-off is product thrown away. When AI improves any of these, finance teams can measure it.
Marketing AI, by contrast, often produces results that are harder to isolate. Did sales go up because of the AI-generated ad, the season, or the discount? Operations results are cleaner, which makes them easier to sell, easier to prove, and easier to renew.
For anyone building an AI service business, that’s the lesson: pick problems where the before-and-after is already being counted.
The Open Question: Does This Work for Smaller Companies?
The author raises a fair point. Almost all of the published cases come from giants with their own AI teams. Mid-sized companies have barely published anything measured yet. The author asks whether that’s because the approach doesn’t work at a smaller scale, or because smaller companies simply don’t write it up.
Nobody knows for sure from this data, and you should be honest with clients about that. But both possibilities point toward opportunity:
- If smaller companies are doing it but not documenting it, there’s demand for people who can measure and write up results.
- If smaller companies aren’t doing it because they lack AI teams, there’s demand for outside help that brings the capability without a full-time hire.
Either way, the mid-market is under-served compared to the giants that already have internal resources.
Where the Money Comes From: 3 Service Models
Here’s how a consultant, freelancer, or small agency could translate these findings into paid work. None of these come with guaranteed outcomes; results depend on your skills, your client’s data, and your execution.
1. Demand Forecasting Audits for Small and Mid-Sized Brands
Who buys: Consumer brands selling food, beverages, cosmetics, or household goods that regularly throw away expired or unsold stock.
What you sell: A review of their sales history and current forecasting method (often a spreadsheet), followed by an improved forecast model and a simple reorder dashboard. Kao’s reported 38% drop in write-off losses is the kind of outcome to point to as an example of what’s possible at scale, not a promise.
How you get paid: A fixed-fee audit, then a monthly retainer to maintain and update the model.
2. Production Scheduling and Changeover Analysis
Who buys: Small manufacturers and co-packers that switch between product lines on the same equipment.
What you sell: An analysis of changeover logs and production plans to find wasted time, plus an automated planning tool that cuts the hours spent building schedules. The Hindustan Unilever case, where a production plan went from 14 days to 1, shows the type of planning bottleneck worth targeting.
How you get paid: Project fee for the build, optionally tied to a measured improvement agreed upon before you start.
3. Inventory Tracking and Visibility Automation
Who buys: Businesses with stock spread across multiple warehouses, retail locations, or 3PLs.
What you sell: A unified inventory view that pulls data from existing systems and flags slow-moving or at-risk stock. Diageo’s cask tracking operated at a massive scale; your version will be smaller, but the principle of knowing exactly what you have and where is the same.
How you get paid: Setup fee plus a monthly fee for monitoring and alerts.
Bonus: Measured Case Study Writing
Because mid-sized companies rarely publish measured AI results, there may be demand for writers and analysts who can document before-and-after numbers for vendors, consultancies, or the companies themselves. This works best if you pair it with one of the services above.
Monetization Difficulty: Medium
This isn’t a weekend side hustle. You need to be comfortable with messy business data, basic forecasting or scheduling logic, and conversations with operations managers rather than marketers.
The upside is that competition is likely lighter than in AI content or chatbot services, and the value is easier to prove. The downside is longer sales cycles and the need for real domain understanding.
Limitations and Caveats
Before you pitch any of this, keep these realities in mind:
- The headline numbers come from giants. Diageo, Hindustan Unilever, and Kao have scale, data, and internal teams that small clients don’t. Don’t quote their results as what a client should expect.
- Data quality decides everything. A forecast is only as good as the sales history behind it. Many smaller businesses have incomplete or inconsistent records.
- The source is one team’s report, shared on Reddit. It’s a useful signal, not a definitive industry study. The author mentions the full report is available on their company’s AI in consumer goods page.
- Measurement is part of the job. If you can’t show a before-and-after, you lose the main advantage of operations work.
Replicating This Today: Realistic Adjustments
If you’re going after mid-sized clients, scale everything down. Instead of tracking millions of assets across hundreds of sites, you might track a few hundred SKUs across a handful of locations. Instead of re-engineering a factory, you might automate one weekly planning spreadsheet that eats a manager’s entire Monday.
Start with one narrow, measurable problem. Agree on the baseline metric with the client before you build anything. Small, provable wins build the trust you need for larger engagements and give you your own case study to show the next prospect.
How to Do It: Your Step-by-Step Checklist
- Pick one niche. Choose a single segment, such as small food brands, indie cosmetics companies, or regional beverage producers. Narrow focus makes outreach and learning faster.
- Pick one problem type. Choose forecasting, changeover/scheduling, or inventory visibility. Don’t offer all three at first.
- Learn the workflow. Interview two or three operations or supply chain people in your niche. Ask how they plan production, forecast demand, and handle unsold stock today.
- Identify the existing metric. Find what they already count: write-offs, planning hours, stockouts, or changeover time. This becomes your baseline.
- Build a small demo. Use sample or anonymized data to create a basic forecast, schedule, or inventory dashboard you can show in a call.
- Offer a paid pilot. Propose a short, fixed-scope project on one product line or one location, with the success metric written down upfront.
- Measure and document. Record the before-and-after honestly, even if the improvement is modest. Get permission to share it.
- Publish your own case study. Since mid-sized companies rarely publish measured results, a credible write-up can set you apart from AI generalists.
- Convert to a retainer. Offer ongoing maintenance, model updates, and monitoring once the pilot proves its value.
- Expand carefully. Add a second problem type only after you’ve delivered a measurable result with the first.
Want to sell the boring AI work that actually pays?
Today, pick one consumer goods niche and list five mid-sized companies in it that likely struggle with forecasting, scheduling, or inventory. Reach out to one operations manager and ask how they plan production right now; that single conversation will tell you where your first paid pilot is hiding.
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