A recent McKinsey study put a number on something most people in retail already feel: merchandisers spend up to 40% of their time on tasks that could run without them... Pulling reports. Reconciling data across systems. Manually checking competitor prices. Maintaining spreadsheets that were outdated by the time they were finished.
That number matters less as a productivity statistic and more as a signal about where retail decision-making actually stands today.
Category managers who spend the first half of their week on data prep reach strategic decisions late, and with less time to act on them. A slow-moving product erodes margin for weeks before it surfaces in a quarterly review. Like: A trend builds momentum in search and social for months before it shows up in sales data. The window for a good decision in retail is often shorter than the time it takes to prepare for one. And that gap between when something becomes visible and when it becomes actionable is where margin leaks.
A planning rhythm built for a different era
Retail traditionally used to move in seasons (and for many retailers still does). Planning in Q1, implementation in Q2, measurement in Q3. That cadence made sense when competition was stable, price changes were infrequent, and the data available to category teams changed slowly. But the internet has moved on, and so has the market (& competitors).
→ Amazon reprices millions of SKUs daily
→ Platforms like Temu compress the cycle from trend signal to production to shelf to weeks
→ Consumer preferences shift faster than quarterly reporting can track them.
The step from data to decision requires a human to do it manually, and that step is where the 40% goes.
On top of that: The volume problem
A category manager responsible for 3,000 SKUs can give each product only a few minutes of attention per quarter, and realistically most of that time goes to the top performers who show up in dashboards and leadership decks. The long tail gets ignored. With this, problems compound: a price sitting 8% below competitive benchmark, a SKU quietly cannibalizing a higher-margin alternative, three products splitting demand that one could own. All of it entirely solvable if someone had the time & passion to look into it.
What changes when the monitoring runs continuously?
AI agents change the ratio between what gets reviewed and what gets decided on. Every product in the assortment can be checked continuously against margin targets, competitor pricing, and sales velocity. The ones that drift outside acceptable parameters surface immediately, ranked by commercial impact, with the context needed to act. With that running in the background, category managers spend less time preparing data and more time evaluating what it means, turning assortment reviews from a data collection exercise into actual decision-making. Walmart's Q4 FY26 results give a sense of what this looks like at scale. Operating income grew 10.8% against revenue growth of 5.6%, with margin expanding faster than sales. The company credited inventory control and AI-driven assortment decisions as key contributors, and has committed over $1 billion to AI integration across merchandising and supply chain operations. (Source: Walmart Q4 FY2026 earnings release)
The McKinsey's study makes one point
The McKinsey study is clear on one thing: deploying the technology is the straightforward part. The harder work lies in changing how teams operate — what category managers are accountable for, how recommendations get reviewed, and how disagreements between data and experience get resolved. Deploying a system that monitors 3,000 SKUs continuously is achievable. Getting a buying team to work with its output, to treat a data-driven recommendation as the starting point for a conversation rather than a challenge to their expertise, requires a different kind of change management. The retailers making the most of this shift have one thing in common: the category team drives adoption. When merchants own the tools they use, the tools get used properly. When tools arrive as IT projects, they get worked around.
Summarizing this: The 40% statistic of merchant time wasted describes the symptom. The underlying question it raises is what retail organizations are actually optimizing for, and whether the answer still matches the speed at which the market moves.