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Numbers on a website are one thing. But what really happens when a bakery switches to AI-based sales forecasts? How quickly do results become apparent? What challenges arise at the beginning? And where is the greatest impact?
This article summarizes what we at GoNina have learned GoNina working with over 50 locations—honestly, covering both the positive effects and the things you should realistically expect.
The First Few Days: What Happens When You Start?
Implementing AI forecasting begins with integrating it with the point-of-sale system. With GoNina , this usually GoNina just a few days—the direct interface with systems such as HS Soft, ProtecData, or Lightspeed makes the process straightforward. Sales data is imported automatically, so the team doesn’t have to enter any data manually.
As soon as the historical data is available, the AI generates an initial forecast. This moment comes as a surprise to many businesses: for the first time, they see in black and white exactly how much of each product is expected to be sold at each location.
But—and this is important—the initial forecast isn't perfect. The AI needs a few weeks to fully learn the specifics of a store. A new point-of-sale system, a recently opened branch, or an unusual product assortment require a little more time to get up to speed.
What Typically Happens During the First Few Weeks
Weeks 1–2: Building trust. Most businesses start by running the forecasts alongside their existing plans. They compare the results: Would the AI have been more accurate than my gut feeling? In most cases, it quickly becomes clear that the AI is more accurate, especially during sudden changes in weather and holiday seasons.
Weeks 2–4: Gradual implementation. After the initial comparisons, most companies begin to actively use the forecasts—first for individual product groups or stores, then on a broader scale. Some companies adopt the recommendations fully right away, while others proceed more cautiously. Both approaches work.
After 4–8 weeks: Results become measurable. From this point on, the effects are reflected in the numbers: fewer returns, more stable inventory levels, and less time spent on daily planning.
A common mistake in the early stages: The forecast calls for lower production than the plant is used to—and the team doesn’t dare to actually reduce the volume. Those who give the suggestions a chance and evaluate the results after a few days usually quickly realize that lower production doesn’t mean lower revenue.
Concrete results from over 50 locations
Based on real-world data from companies that GoNina , we see the following results:
Up to 52% less excess inventory. That’s the most noticeable effect. Stores that previously struggled with high return rates often see a significant reduction within just a few weeks. The effect is most pronounced for products with high volatility—pastries, snacks, seasonal items—and at locations where customer foot traffic is difficult to predict.
Up to 6% more sales. That sounds paradoxical—produce less and still sell more? The mechanism is simple: When forecasts are more accurate, the right products are available more often. Customers find what they’re looking for instead of settling for whatever’s left or leaving the store without making a purchase. The improved availability of in-demand products drives sales up.
Save up to 12 hours per month. The daily analysis of sales figures, the creation of production lists, and coordination between headquarters and stores—all of this is significantly faster thanks to automatic order suggestions. Instead of an hour a day, the planning review often takes only 10 to 15 minutes.
These figures are averages based on many companies. Individual results depend on the starting point: Those who already plan very well will see less improvement. Those who have relied heavily on gut instinct up to this point often see significant results quickly.
What Works Surprisingly Well
Some effects are regularly highlighted by companies as positive, even though they were not anticipated beforehand:
Fewer discussions. The question “How much should we bake tomorrow?” leads to daily debates in many businesses between production, store management, and executive management. With a data-driven recommendation, the discussion becomes more objective. There is a common ground instead of three different opinions.
Vacations and Absences. When the production manager goes on vacation for two weeks, planning continues as usual. The AI generates suggestions regardless of whether individual staff members are present. This significantly reduces the stress associated with finding replacements.
Store Comparison. The forecast data reveals differences between stores that were previously lost in the day-to-day operations. Why does Store A sell 30% more croissants than Store B, even though they’re in similar locations? Questions like these are only possible thanks to the data.
Assortment decisions. Which products are actually selling well, and which ones are carried out of habit? AI data provides a factual basis that helps with assortment decisions—without anyone having to spend weeks analyzing spreadsheets.
What You Should Realistically Expect
AI predictions aren't a magic wand. A few honest observations:
AI doesn't replace craftsmanship. It tells us how much to produce. How good the bread turns out is still up to the baker. AI optimizes the planning, not the product.
Not every day is predicted perfectly. On most days, the AI performs better than a manual estimate. But there are exceptions—an unexpected event, road construction in front of the store, or a breakdown of the store’s air conditioning system. The AI cannot anticipate such special situations. However, it can learn from them quickly.
The effect builds over time. The greatest improvements become apparent after two to three months, once the AI has a good understanding of operations and the team trusts the forecasts. Anyone who gives up after a week misses out on the real benefit.
Human input remains important. The best results are achieved when a business uses AI suggestions as a starting point and supplements them with its own knowledge. An experienced baker who knows that a regular customer will order 20 loaves tomorrow for a company party can adjust the suggestion accordingly. The combination of data and experience outperforms either one on its own.
Frequently Asked Questions
How quickly does the investment pay for itself?For most businesses, within the first few months. The savings from fewer returns and the increase in revenue from improved availability GoNina far exceed the cost of GoNina . The exact payback period depends on the size of the business and its initial situation.
What do employees say?Experience shows that there is initial skepticism, especially among long-time employees who know “their” company well. As soon as they see that the forecasts work in everyday situations and lighten their workload, their attitude usually changes quickly.
Can I cancel GoNina ?Yes. GoNina a 4-week trial period with a money-back guarantee. Even after that, there are no long-term commitments that make it difficult to cancel.
Conclusion
AI-based sales forecasts are no longer just a theory in the baking industry. More than 50 locations are now using GoNina seeing measurable results: less excess inventory, higher sales, and less planning effort.
Getting started is easy, results are visible after just a few weeks, and the combination of AI and the baker’s experience delivers better results than either one alone.
You can find a comprehensive overview of AI sales forecasts in our complete guide for bakeries.
