Target ranked store chores by forecast. It saved 2m staff hours a year

Target's myDay app orders shelf, backroom and pickup work by forecast. Target says it saves 2m+ staff hours a year and a $25m backroom call.

Target says its store operations app, myDay, now saves its store teams more than 2 million hours of work a year by ranking each person's tasks against live inventory, incoming trailers, staffing and forecast sales. In the same 14 October 2026 newsroom post, Target says a feature inside the app that tells staff whether a scanned item goes to the shelf or the backroom has helped create 25 million dollars in value. More than 350,000 team members use the app.

The most useful thing about this deployment is what it is not. There is no chatbot in the headline numbers. The result Target put in writing comes from a forecast deciding the order of the day's chores.

What Target actually built

myDay pulls the store's work into one list. A team member opens the app and sees tasks ranked by urgency and value, with similar jobs grouped and a suggested route through the building. The ranking, Target says, draws on real-time inventory, incoming trailers, who is on shift, where items sit in the store and forecast sales.

Store leaders get a second screen, Leader View. Each leader pins the measures that matter for their role, such as shelf replenishment, Drive Up and order-pickup status, or a needs-attention flag for a lagging department. Every leader in the building sees the same numbers from the same source.

The third piece, Scan to Divert, is the one with a dollar figure. When a carton arrives from a distribution centre and is scanned, the app recommends sending it to the sales floor or holding it in the backroom. It weighs how fast the item sells, what is already on the shelf and in the back, what is in transit, the demand forecast and how much room the shelf has.

Target says the redesign started with input from more than 50 store leaders and team members.

The payoff Target put a number on is a forecast deciding what gets done first, not a model answering questions.

Where AI fits, and where Target has not said

Target's post uses the word AI only for what comes next: AI-enabled insights for leaders, alongside task assignment and handoff notes. The ranking and the shelf-or-backroom call are described as data and forecasting.

That matters for anyone copying the idea. Target's generative AI tool for staff, Store Companion, is a separate product. Target's June 2024 release said it was built from real store questions and process documents, piloted in about 400 stores and went from first test to a planned chainwide rollout in six months. Target has not published a result figure for it. In an October 2025 Q&A, chief information and product officer Prat Vemana said Target uses AI to spot low stock earlier than before, again without a number.

So the figures Target has signed are for the forecast-driven work list, not for the assistant.

What do the 2 million hours and 25 million dollars measure?

Target has not said. The 2 million hours are described as the sum of "these efforts" across stores, which reads as the whole myDay programme, not one feature. Target does not give a baseline, a measurement method or a time window for the 25 million dollars, and calls it value that can be reinvested in the guest experience and store operations, which is not the same as cash saved.

The scale helps put both numbers in proportion. By our arithmetic, 2 million hours across roughly 350,000 users is about 5.7 hours per person per year. Across nearly 2,000 stores it is about 1,000 hours per store per year, or around half of one full-time role. The 25 million dollars works out to about 12,500 dollars per store. These are modest per-store gains multiplied by a very large chain, and that is the honest way to read them.

Target's chief operating officer pointed to better in-stock levels on the August 2026 earnings call, as reported by Retail Dive. Target has not tied that improvement to the app.

What did it cost?

Target has not disclosed the cost. myDay was built by Target's own technology teams, a company with its own product managers, designers and engineers. A mid-sized retailer would be buying this capability, not building it, and the price would look nothing like Target's.

Does this apply to a 200-person retailer?

Yes, the idea does, the scale does not. The shelf-or-backroom decision is a rule you can run from data most retailers already hold: sales per day, shelf capacity, stock on hand and stock in transit. You do not need a large AI model to make it. You need clean inventory counts and a way to show the answer at the moment a carton is scanned.

Take a 10-store chain with 200 store staff. If it matched Target's per-person figure, it would get back roughly 1,100 hours a year, a bit over half of one full-time role. That is our illustration, not a forecast. The real prize at that size is usually fewer cartons opened twice and fewer empty shelves while stock sits in the back.

What to do on Monday

Measure the backroom first. For two weeks, count how many cartons each store opens, takes to the floor, finds no room for and carries back. That number is your before-figure; without it, no vendor's savings claim can be checked.

Check whether your inventory system already knows shelf capacity per item. If it does not, the forecast has nothing to weigh against, and that is the first fix.

Ask your store-systems vendor two questions: does the handheld already rank tasks, and can it show a shelf-or-backroom call at receiving?

Run it in two or three stores with a control store alongside, and report hours and empty-shelf counts, not adoption.

What it does not mean

It does not mean a chatbot will run your store floor. Target's own signed numbers come from scheduling and forecasting, and its generative AI tools still have no published result. It does not mean 25 million dollars of savings either: Target calls it value, and has not said how it was counted.