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Featured Case Studies
Case Study: How Tailored Brands anticipates demand and increases revenue with invent.ai in its tuxedo and formal wear rental business
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Case Study: How a leading shoe retailer unlocked $21.4M in sales with AI-powered inventory optimization
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Case Study: Fashion retailer reduces lost sales by 6% with AI-powered inventory optimization
Read MoreFrom planning to execution: 4 changes retailers can't ignore
Retail inventory planning is entering a new phase as rising demand volatility, shorter product lifecycles and the complexity of unified commerce expose the limits of traditional approaches.
Drawing on Gartner’s 2026 Market Guides for Forecasting, Allocation & Replenishment, this white paper highlights four key changes shaping the future of retail inventory planning. It reflects how advances in AI are transforming forecasting, allocation and replenishment from periodic activities into a continuous, connected system of decisions.
What you’ll learn in this guide:
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The shift from static rules to AI-driven decision-making models that continuously incorporate real-time and external signals
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How forecasting, allocation and replenishment are moving from siloed processes to unified decision-making across channels
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How AI is evolving from generating insights to executing routine decisions through agent-based systems with human oversight
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What it means to move from batch planning cycles to continuous, margin-aware decision-making in a fast-changing environment
Accelerate the shift to more adaptive, coordinated and execution-focused inventory decisions with AI.
Forecast Accuracy: Turning retail data into revenue-driving decisions
Retail forecasting only creates value when it leads to better action. This white paper explores how retailers can move beyond static forecasts and use SKU-store-day demand planning, probabilistic models and bias control to make more confident decisions about inventory placement, replenishment and buys. It also explains why forecasting performance should be judged by the business results it helps drive, not accuracy alone.
In this guide, you’ll learn:
- Why forecast accuracy alone does not guarantee better retail decisions
- How SKU-store-day forecasting supports better inventory decisions
- Where probabilistic models help reduce planning risk
- Why bias control is critical to avoiding stockouts and excess inventory
- How forecasting needs differ by product type and demand pattern
- Which metrics help measure stronger execution and retail performance
Turn retail data into revenue-driving decisions with accurate forecasts.
Retail Pricing Playbook: How to keep costs in check and tap into the power of AI
Retail pricing has entered a new era. Shifting market conditions, high prices and constantly changing customer expectations make setting prices far more complex than spreadsheets or static rules can handle.
The Retail Pricing Playbook explores how retailers can move beyond reactive pricing and build a data driven retail price strategy that adapts to demand, competition and customer behavior in real time.
In this playbook, you’ll learn:
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Why traditional approaches to retail pricing break down across channels, regions and customer segments
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How price elasticity, competitor pricing and market conditions shape modern pricing decisions
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Common pitfalls of discounting and time offers that quietly erode performance
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How dynamic pricing supports better price points without damaging brand perception
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What it takes to align pricing, inventory and promotions into one connected strategy
A practical guide for retailers ready to rethink pricing as a living system, not a static exercise.
Explainable AI in retail: The guide to moving from black box to glass box
Retail decisions are more complex than ever. Traditional AI is slow and can leave teams hesitant to act. Explainable AI makes recommendations transparent, understandable and actionable.
In this guide, you’ll learn:
- How multi-agentic AI reveals the “why” behind forecasts, allocations and pricing
- Practical steps to implement explainable AI in retail operations
- Real-world examples showing measurable results in sales, margins and efficiency
- Key metrics to track adoption, outcomes and governance
Turn AI from a black box into a trusted, glass-box decision partner.
Inventory Intelligence: Driving retail performance with AI
Shoppers expect the right product, in the right place, at the right time. Legacy forecasting can’t keep up—but AI can.
With AI-powered inventory decisioning, retailers connect forecasting, planning, pricing and allocation to cut costs, reduce stockouts and drive profitable growth.
In this white paper, you’ll learn how to:
- Expose the hidden costs of outdated inventory tools
- Balance inventory, planning and pricing with AI
- Audit your strategy to uncover blind spots
- Use AI to protect margin and reduce carrying costs
- Identify the must-have features in an inventory solution
Multi-Agentic AI: Turning insights into real-time action
Today’s retail environment moves at lightning speed—shoppers, supply chains and markets never pause. Multi-agentic AI doesn’t just provide recommendations, it acts, coordinating pricing, inventory, forecasting and execution in real-time to keep retailers ahead.
This white paper shows you how to:
- Overcome the limits of traditional AI in complex, interconnected retail
- Harness autonomous agents to make faster decisions
- See tangible results in margins, assortment optimization and forecast accuracy
- Build a scalable, intelligent decisioning system across your organization
Mastering Agentic AI: The retailer's guide for driving efficiency and innovation
After years of digital innovation, today’s retailers are facing a new directive: to move faster, operate more efficiently and deliver hyper-personalized experiences to shoppers.
Agentic AI has emerged as a powerful tool that doesn’t just predict and recommend actions, but makes decisions that drive efficiency, customer satisfaction and revenue growth.
In this white paper, we’ll explore:
- Why traditional AI tools can’t keep up with today’s complex retail challenges
- How Agentic AI elevates decision making and business outcomes
- Practical examples of how leading retailers are leveraging Agentic AI
- A roadmap for integrating Agentic AI into retail operations
Tariffs are here to stay, and retailers are adapting fast.
Invent.ai conducted a national survey to understand how retailers are responding to tariffs—specifically their effect on retail planning and execution.
Discover how sourcing, pricing, forecasting and AI adoption are evolving in response to ongoing tariff pressure. Download the full survey report to see the strategies leading retailers are putting in place.
Revolutionizing Returns: AI-driven strategies for fashion retailers
Returns are surging—and they’re hitting retail margins harder than ever. With rising volumes, shorter product life cycles, and shopper expectations for flexible returns, the old rules no longer apply.
Fashion retailers need a new approach—one that goes beyond prevention and focuses on precision. This white paper explores how AI can transform returns from a costly afterthought into a competitive advantage.
In this white paper, you’ll learn:
- Why traditional returns processes are failing in multichannel environments
- How AI helps forecast returns, reduce markdowns and reallocate goods before the season ends
- Ways leading retailers are optimizing reverse logistics with AI-driven positioning
- Practical examples of intelligent rerouting that drive higher resale revenue and customer satisfaction
