Target’s Next Chapter: How AI & Supply Chain Reinvention Could Rewrite Its Future
- Hardik Shah
- 2 minutes ago
- 5 min read
Target promoted an insider CEO after a year of sinking financials and a stock down nearly 36 percent, widening the gap with Walmart, which is up about 17 percent. The leadership choice disappointed many investors who wanted an outside reset. Yet beneath the soft quarter lies a deeper and more ambitious attempt to rebuild Target’s operating system through AI-driven merchandising, digital experience, fulfillment redesign, and machine-learning forecasting.
Target’s third quarter numbers left little room for optimism. Sales fell 1.5 percent, comparable sales declined 2.7 percent, operating income dropped nearly 19 percent, and adjusted EPS slipped again. The stock has mirrored the trend, sitting roughly 36 percent lower year to date while Walmart has climbed about 17 percent as investors reward stronger execution and a clearer growth narrative.
Then came the leadership announcement. Instead of recruiting an external disruptor to jolt the company into its next chapter, Target elevated Michael Fiddelke from within. Investors read it as a sign of continuity during a moment that seemed to demand reinvention.
But the earnings call told a different story. Similar to the themes explored in the recent Walmart automation article, Target is working to overhaul the foundations of its operating model. The company is leaning heavily into AI for trend identification and consumer simulation, modernizing the digital experience, restructuring the fulfillment network, and deploying machine learning to improve inventory accuracy. None of these moves will fix comps next quarter. But together they signal a surprisingly aggressive long-term rebuild of how Target works.
Below are the five pillars driving this transformation.
1. AI as the New Merchandising Engine
Target believes that its path back to growth requires restoring its authority as a design-led merchant. To do that, it is using AI to change how assortments are imagined, tested, and brought to market.
Trend Brain, a new GenAI system, analyzes color, material, social conversations, and industry signals to identify emerging styles much earlier than a human-only process can. In parallel, the company is using synthetic audiences that mimic real consumer groups so marketers and merchants can test product appeal before items ever hit stores.
This ecosystem shortens cycle times and reduces guesswork. For categories like toys, gaming, music, and collectibles, the early results are encouraging. The company highlighted that where trend-forward newness has been deployed with speed and focus, guest response has improved.
2. AI for Guest Experience and Digital Discovery
AI is also beginning to shape how guests interact with the brand. Target introduced a GenAI-powered gift finder inside the app that helps shoppers articulate needs and receive curated answers in natural language. It is also testing integrations with conversational commerce platforms, positioning itself to become one of the first retailers where a guest can chat, build a multi-item basket, and select drive-up or same-day delivery directly through AI-enabled pathways.
This approach extends the strategy behind the Walmart automation article, which argued that automation rewires not only back-end processes but also the way customers discover and evaluate products. Target’s vision is to make its large digital reach more personalized and more intuitive, especially during seasonal occasions when discovery matters most.
3. Redesigning the Fulfillment Network through Market-Based Orchestration
The most sweeping operational change at Target is happening deep inside its fulfillment engine. While the company’s financials have drawn most of the attention, the real story sits in how Target is reorganizing the physical and digital flow of merchandise. The Chicago pilot is the clearest illustration of how Target intends to rebuild scale advantages through software, capacity mapping, and labor optimization.
Under the new model, stores no longer carry identical roles in the fulfillment hierarchy. In high-traffic locations, where the in-person experience drives the most value, teams are being intentionally freed from the most labor-intensive digital picking and packing. These stores can redirect more labor hours toward guest service, in-stock accuracy on the sales floor, and presentation. This resolves a tension that large-format retailers have always battled. Digital growth often consumes in-store labor at the expense of the physical experience.
Lower-volume stores, meanwhile, are being reimagined as localized shipping hubs. Their larger backrooms and lower guest density allow these locations to absorb a greater share of brown box fulfillment. The result is more efficient load balancing across a market. Instead of every store operating as a general-purpose node, each location is assigned a role that aligns with its physical constraints and demand characteristics.
Target offered tangible evidence that the model is working. The pilot region saw improvements in fulfillment speed, more predictable staffing needs, and lower average cost per order. These results were strong enough that the company is now expanding the model to 35 additional markets. When placed alongside the company’s progress in same-day delivery, next-day shipping, and AI-assisted guest discovery, the fulfillment redesign becomes part of a broader operating system shift. It aligns merchandising, forecasting, and labor in a more coordinated way, something that becomes more apparent when viewed against the details in the full Target earnings article.
This is not a robotics-heavy transformation. It is a network-wide rebalancing that uses data and software to reassign work to the most efficient nodes. In an environment where inventory turns, labor availability, and digital convenience shape the competitive frontier, Target’s approach gives stores distinct identities within a unified market strategy. It is the kind of structural move that takes years to build but can quietly reshape how a retailer grows.
4. Machine Learning Forecasting and Inventory Flow
The fourth lever is the least visible but potentially the most important. Target has invested in machine learning models that improve forecasting and inventory placement. These tools monitor flow from supplier to shelf, making replenishment more accurate and reducing the risk of overstock in discretionary categories or out-of-stocks in essentials.
The company reported that on-shelf availability for its top 5,000 items improved by more than 150 basis points year over year. Better forecasting also helps reduce markdown exposure, which was a major drag on gross margin this quarter. When the company has a clearer understanding of demand and better control over inventory positioning, the entire P&L becomes more stable.
This is where Target’s merchandising, supply chain, and fulfillment strategies converge. AI helps the company predict and curate demand. Machine learning helps ensure the right products are in the right places. A more flexible fulfillment network ensures the last mile can be served efficiently. These systems reinforce one another.
5. CapEx as the Financial Backbone of the Transformation
Target is not relying on strategy alone. It is backing the operational overhaul with a notable step-up in capital spending. The company expects roughly four billion dollars in CapEx this year, with close to three billion already deployed. That pace is set to accelerate meaningfully. Target plans to increase CapEx by approximately 25 percent in 2026, or about one billion dollars more than this year, in order to fund store remodels, new larger-format locations, category resets across the chain, and major upgrades to technology and digital fulfillment capabilities.
These investments support the other levers in the transformation. Remodels reinforce the renewed focus on merchandising authority and in-store experience. Bigger boxes expand market capacity for fulfillment and new categories. Technology investment underpins AI-enabled forecasting, digital discovery, and better labor tools. In total, CapEx forms the financial backbone of the reset Target is trying to build, ensuring the strategic ideas are matched by real infrastructure.
The Crossroads Ahead
Target’s financial performance has not yet reflected the scale of the work underway, and skeptical investors are still waiting for proof that the strategy can deliver results. But the architecture being built across AI, forecasting, digital experience, and fulfillment is more than incremental improvement. It is an attempt to rebuild the company’s operating core in ways that could fundamentally change how Target competes. The question for the next two years is not whether the company has the right ideas, but whether it can execute at the pace required to turn a difficult present into a more resilient future.
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