OneStock & AI: Shaping the next generation of commerce
AUTHOR
Guillaume Vanbrugghe
Leading Product marketing
OneStock
It’s already a cliche that the potential for Artificial Intelligence to transform retail is huge, but at OneStock our philosophy is clear: AI will only become truly valuable when it solves real business problems. This is why our AI vision is built around three strategic pillars, each engineered to drive measurable impact and higher profitability for retailers. And this entire vision is underpinned by a single, game-changing architectural innovation: the OneStock MCP Server.
Pillar 1: Enabling the future of agentic commerce – The next revenue engine
Agentic commerce is no longer looking like a passing trend; it’s a fundamental structural shift in how consumers will shop and transact and leading analysts agree: McKinsey & Company reports on how generative AI is reshaping the customer experience across commerce.
Further, as Emily Pfeiffer from Forrester describes in her blog , ”Is Agentic Commerce a Thing?”, this dynamic manifests itself in two main forms:
As external Agent Experiences: those that happen outside your brand on general platforms (e.g., via ChatGPT, Gemini, or Perplexity).
As owned Agent Experiences: those embedded within your brand’s own channels, giving you full control over the customer journey and data.
The OneStock imperative
As a Distributed Order Management System (DOM/OMS), OneStock is a key enabler for this new channel. It is the master source for inventory availability, the customer (or delivery) promise, and the order lifecycle. By providing this critical, real-time data through the new MCP Server tools (for owned agents) and a robust product data feed (for external agents), OneStock allows AI agents to propose a complete and awesome pre-purchase and post-purchase experience.
The intelligent ecosystem
However, OneStock is only one critical component. The agentic experience is only as powerful as the unified data that sits behind it. Other components are just as important in this new ecosystem:
Product Information Management (PIM) solutions, such as Akeneo, provide the enriched product data (specifications, attributes, imagery) necessary for the agent to provide complete product information and unify it across all sales channels.
Search engines, such as Algolia, provide the essential real-time tools for improving the search experience and unifying it across all channels.
Marketplace platforms also play a key role. Mirakl, for example. With years of experience orchestrating complex eCommerce platform ecosystems, connecting thousands of sellers, managing distributed catalogs, and facilitating seamless transactions, they’ve built deep expertise in the infrastructure challenges that agentic commerce will need to solve. This foundation positioned them to launch Mirakl Nexus, a neutral infrastructure designed to connect merchants to AI agents, enabling autonomous discovery, transactions, and post-sales management across platform ecosystems.
Pillar 2: Massively increase operational efficiency – The cost saver
AI is just as crucial for internal and customer-facing teams as it is for the end-consumer experience. As an industry, it’s imperative we continue to improve the daily experience of retail teams to gain time, reduce errors, and cut operating costs. The MCP server facilitates the creation of AI agents that solve specific, common pain points across the organization
- For Business Users: Faster system configuration and instant and actionable AI-driven insights from operational metrics
- For Customer Service: Agent tools for quicker searching and filtering and for instant cross-system action on orders, thus speeding up resolution times.
- For Store Associates: Guided fulfillment agents for faster, more accurate in-store picking and packing.
- For System Integrators: Better monitoring and behavioral understanding and a standardized MCP interface for faster development and integration.
Every AI-driven action relies on product information that is complete, consistent, and trustworthy, yet creating a foundation of reliable product data is one of the biggest problems businesses face today; Gartner estimates that poor data quality costs organizations an average of $12.9 million annually, while Forrester links fragmented data to lost revenue, slower time-to-market, and higher return rates. AI becomes a force multiplier in both directions. When fed inaccurate or inconsistent data, it accelerates the impact of those problems, making bad decisions faster, amplifying operational inefficiencies, and eroding profit margins. But when AI operates on clean, enriched, and well-structured product data, its value compounds: forecasting becomes sharper, personalization more effective, inventory decisions more precise, and the customer experience more profitable.This is why investing in trustworthy product data is one of the most strategic moves a business can make today. High-quality product information powers AI, ensuring every automated action and every predictive model is grounded in truth. In this way, reliable product data becomes the quiet engine behind AI-driven profitability.
By Akeneo
Pillar 3: Make better decisions for profitability – The optimizer
The future of order management is predictive and self-optimizing. Leveraging advanced AI models, OneStock is intensely focused on capabilities that directly boost your bottom line:
- Smarter Stock Reliability: Automatic safety stock and buffer adjustments based on real-time demand and proactive anomaly detection.
- Promise Accuracy Optimization: Dynamic fine-tuning of delivery estimates based on historical carrier and warehouse performance.
- Intelligent Fulfillment Orchestration: Auto-rerouting of at-risk orders and optimizing routing logic for the highest margin.
- Predictive Operational Forecasting: Anticipating order volumes and resource needs to optimize staffing and warehouse operations.
The architectural foundation: The OneStock MCP Server
Pillars 1 and 2, Agentic Commerce and Operational Efficiency, require a new kind of foundation. Traditional API-based architectures were never designed for the dynamic, multi-step orchestration required by AI agents. They are too often based on manual workflows and fragile and costly integrations.
This is why OneStock has introduced its MCP server, released in October 2025 (Find the OneStock MCP documentation here). This server is the missing link that allows AI to fully leverage OMS intelligence at scale. Equipped with declarative tools covering all pre-purchase and post-purchase experiences, the MCP server provides the standardized, intelligent interface necessary for seamless AI-to-System communication. Read more on our blog about MCP server.
Conclusion: AI needs to – and can – solve real business problems
OneStock’s AI strategy is built on a clear, pragmatic vision: to enable agentic commerce, increase operational efficiency, and improve profitability, all powered by a modern, unified architecture ready for AI. As a pioneer of the Model Context Protocol (MCP), OneStock is providing the architectural foundation for the next decade of commerce. The future of retail is agentic.
