February 27, 2026 By: Karthik Balasubramanian
Generative AI has moved past experimentation and into the toolkit of product and supply chain leaders who must make high-stakes decisions about what to keep on the shelf and what to cut. For experts working on assortments, assortment planning, and lifecycle management, the conversation today is less about whether to adopt AI and more about how to embed it into the SKU decision process so that trade-offs between revenue, margin, complexity and service level are explicit, measurable and repeatable.
Recent industry evidence shows the scale of that shift. Gartner projected that more than 80% of enterprises will have used generative AI APIs or deployed GenAI-enabled applications by 2026, a level of adoption that makes AI a de facto capability for modern product operations.
Rethinking SKU Optimization with AI
At its core, SKU optimization with AI reframes the problem from “which SKUs to remove?” to “what is the optimal portfolio under explicit objectives and realistic constraints?” Generative AI for SKU optimization augments classical approaches such as ABC/XYZ segmentation, contribution-margin trees and Pareto analysis by synthesizing structured telemetry (POS, returns, promotions, lead times) with unstructured signals (social trends, reviews, weather, competitor assortment moves) and producing scenario-rich recommendations. The value is not only more accurate forecasts but also better decisions.
McKinsey’s analysis of AI-driven forecasting shows that applying AI in supply chains can reduce forecast errors by roughly 20–50 per cent and translate into meaningful reductions in lost sales and stockouts, which are outcomes that materially change SKU-level economics.
The Operational Shifts in AI-Driven SKU Management
Practically, AI-driven SKU management introduces several shifts in how teams operate.
- Continuous and Experimental Modeling: Modeling moves from one-off batch runs to continuous, experiment-friendly systems that test SKU removals or additions.
- Data-First Decision Rights: Decision rights become data-first: category managers receive not only a ranked list of candidates for SKU rationalization with Generative AI but also counterfactual simulations showing margin, turnover and service-level impacts at store or cluster level.
- Governance And Explainability: Governance and explainability rise in importance; Gen AI output must be auditable and grounded in the business rules that matter to procurement, merchandising and finance.
These changes require a concerted investment in master data, causal feature sets and a tight feedback loop from execution back into the models.
Real-Life Implementations of Gen AI in SKU Optimization
Leading companies are transforming product decisions with data-driven insights, simulation, and automated rationalization by Gen AI application in SKU optimization, illustrating how AI simplifies complexity, improves portfolio agility, and strengthens customer-centric decision-making.
- World’s largest retailer: The organization deployed Generative AI models that were trained on historical sales, seasonality, and store-level patterns to simulate the impact of adding or removing SKUs. The system highlighted which products were easily substitutable and which were critical for customer retention. This enabled category managers to streamline duplicate SKUs in snacks and dairy while protecting region-specific high loyalty items. This helped reduce inventory costs, make shelves more productive, and improve regional customization.
- Global Beverage Leader: Used product portfolio optimization using Gen AI to simplify its vast beverage lineup. The AI engine modelled demand scenarios, cannibalization risks, and consumer shifts if certain SKUs were removed. Based on these simulations, the company discontinued overlapping or low growth SKUs while preserving products essential to market share. This resulted in a leaner portfolio, reduced production complexity, and more focused marketing investments.
- Global FMCG Conglomerate: Implemented SKU rationalization with Generative AI to enhance agility across its global FMCG portfolio. The system continuously analyzed POS data, promotions, supplier metrics, and social sentiment to predict which SKUs were becoming inefficient or nearing lifecycle end. Recommendations included retiring, refreshing, or bundling SKUs based on demand and brand relevance. Reducing slow-moving inventory, lowering waste, and faster response to emerging trends.
These implementations show that AI-driven SKU management is not an experimental capability but a proven path to operational efficiency. By blending predictive analytics, simulation, and explainability, organizations are making SKU decisions that are faster, more transparent, and better aligned with market realities, setting a new standard for AI for product lifecycle management in the years ahead.
Building the Right Operating Model for Success
From an operating model perspective, successful teams combine three capabilities:
- Trusted Data Plumbing: Ensuring SKU attributes, hierarchies, and historical data are clean, consistent, and accessible.
- An Experimentation Culture: Encouraging teams to test AI insights rather than treat them as black boxes.
- A Human in the Loop Control Layer: Allowing category owners to retain decision authority, especially for prestige or seasonal SKUs where brand and supplier relationships weigh heavily.
Product portfolio optimization using Gen AI becomes effective when data engineers standardize SKU attributes and hierarchies, data scientists build causal demand and cannibalization models, and category owners retain veto and refinement authority. The human-in-the-loop element is especially crucial for seasonal, promotional, or prestige SKUs where brand, strategy and supplier relationships create constraints that models alone cannot assess.
Quantifying the Business Impact
There is a measurable payoff. Beyond forecast accuracy, AI-enabled assortment and inventory optimizations tend to reduce carrying costs and improve turns. McKinsey’s distribution and operations research highlights inventory reductions and logistics gains when AI is applied to planning and replenishment, with many practitioners reporting inventory decreases of 20 % to 30% and cost gains across the distribution network. Those uplift numbers convert directly into the financial slack needed to experiment with new SKUs and to shrink assortments without harming availability.
Design Principles for Effective Implementation
For teams standing up these capabilities, three design principles help accelerate value capture.
- Adopt Outcome-Led Metrics: Measure recommended-SKU adoption rates, realized margin impact, forecast drift post-deployment, and the delta in working capital tied to SKU changes.
- Bake Explainability into Product Releases: Models should surface the top three drivers for any SKU recommendation and quantify uncertainty so trading and procurement can negotiate with suppliers armed with risk-aware scenarios.
- Treat Optimization as a Continuous Program: Treating SKU rationalization with Generative AI as an ongoing program, not a project, continuous learning from promotions, lifecycle decay and new-channel behavior keeps the portfolio aligned to evolving demand.
Extending AI Across the Product Lifecycle
AI for product lifecycle management is not limited to assortment or forecasting; it can power supplier scorecards, automated end-of-life triggers, and synthetic testing of new SKUs before they reach pilots. When these pieces are stitched together, organizations stop guessing which SKUs are “safe” to remove and start making defensible, data-backed portfolio decisions that drive margin and reduce complexity.
Gen AI and SKU optimization is not a single technology play but an operating transformation, measurement becomes more granular, experimentation more routine, and decisions more auditable. For experts managing complex portfolios, the opportunity is clear. Apply AI where it improves signal extraction, preserves human judgment for strategic trade-offs, and embeds governance so that every SKU change is replicable and reversible. The companies that master that balance will convert modelled confidence into tangible reductions in waste, improved turns, and healthier product portfolios for 2026 and beyond.
