Generative AI in retail ceased to be an isolated experiment a long time ago. Retailers use conversational assistants to reduce customer service times, models that generate product descriptions at scale, and algorithms that recommend buying combinations in real time.
In our blog, we have already seen concrete examples and the integration requirements to bring these projects to production in the guide on Generative AI in retail: real examples and integration requirements.
The problem is that a lot of teams are trying to build that AI layer on messy data and about an ecosystem full of patches: loose plugins, integrations tailored by small agencies, legacy middleware and connectors that break with each change.
A recent and very comprehensive analysis of Google Cloud, based on McKinsey data, shows that 70% of organizations that adopt generative AI face difficulties with data governance, integration and volume of training data, which slows down the jump from pilots to production. That is to say: the obstacle is not the models, but the data and the way in which it is connected.
This article is the practical “prequel”: how to prepare your data and integrations formerly of spending the first dollar on AI models. If today your stack is a “Frankenstein” of plugins, custom developments and isolated connectors, any generative AI project will only amplify inconsistency, stock errors and poor customer experiences.
The good news: tidying up the data and integration layer is a structurable job. And, when it is supported by an iPaaS such as Weavee on Microsoft Azure, it becomes much faster than continuing to accumulate patches.
The key concept to understand this point is that of Data readiness. It's not just about “having a lot of data”, but about having:
When that's missing, the same thing that Google Cloud describes happens: the company wants to scale GenAI, but its data strategy doesn't allow it because the information is dispersed, with uneven quality and limited accessibility.
If we look at the daily life of a medium/large retailer in LATAM, the symptoms are known:
In this context, a generative AI chatbot mounted “on top” can only provide incoherent answers: it recommends products out of stock, is unaware of purchases made in a physical store or does not understand the rules of current promotions.
McKinsey sums it up well when he explains that conversational assistants in retail only truly work when the model is connected to Base SKU, to customer data and to personalization engines; otherwise, the bot stays generic and doesn't convert.
If you haven't already done so, we recommend supplementing this reading with our guide on Generative AI in retail: real examples and the integration requirements to bring it to production, where we map the use cases and a plan in 10 steps.
Do you want to evaluate if your data is ready for generative AI? Let's talk and schedule a test with Weavee.
Let's move from diagnosis to action. The practical question is: What steps to take before talking about AI models and providers?
Inspired by frameworks such as IBM's step-by-step guide to generative AI and Google Cloud's recommendations for building solid databases, the natural order is: inventory, evaluate quality, unify, govern, and then choose models.
IBM is direct: any serious GenAI initiative starts with take inventory and evaluate data sources relevant to your objectives, before thinking about the model.
Translated to retail, that involves mapping at least:
This inventory is not an Excel to “have in store”. It is the input to design how these systems are connected in an integration hub such as Weavee Universal Connection, and to prioritize which data domains are critical for early generative AI use cases.
If you want to learn more about the role of ERP and omnichannel in this photo, we recommend our guide “ERP and omnichannel: the ideal combination for retailers in Latin America”.
Having a lot of low-quality data only trains models to make mistakes faster. IBM insists that Data quality directly impacts the performance of generative AI models, and that data engineers must lead the evaluation and preparation processes.
In retail, typical problems are:
The Weavee iPaaS incorporates a Real-time data transformation which allows you to normalize taxonomies, unify identifiers (for example, SKU vs. Item ID), validate formats and apply consistent business rules across channels.
While sorting this layer, it makes sense to also review the peaks in demand: our guide “How to prepare your e-commerce for Black Friday: integration and automation to the rescue” shows how the quality of data and integrations directly affects the ability to survive massive campaigns.
Google Cloud summarizes a key principle: For AI to deliver value, you must first connect data to AI, ideally through a unified platform that acts as a “single source of truth”, without having to copy everything to a single repository.
In practice, this means:
Here you enter Weavee Universal Connection, the iPaaS capability that acts as a hub between ERP, CRM, e-commerce, WMS and POS, decoupling producers and consumers through queues, data contracts and centralized business rules.
This same approach is what we use when helping clients modernize the relationship between ERP and e-commerce, as we explained in “Modernizing your e-commerce without redoing it: how to orchestrate the current channel with your ERP”.
As the volume of data connected to AI models increases, so does the risk: breaches, improper access, unauthorized use of personal data, excessive retention. Google Cloud insists that, in order to scale GenAI, a robust framework of data governance and security that covers the entire life cycle.
In our experience with retailers, a minimum governance framework for generative AI should include:
Weavee is based on the infrastructure of Microsoft Azure and implements security practices such as encryption in transit (HTTPS/TLS) and at rest, secret management with Azure Key Vault, integration with identity services such as Entra ID and compliance with standards such as ISO 27001, ISO 27018, SOC 1/2/3 and FedRAMP, among others.
If safety is one of your brakes, you may be interested in delving into our guide “Cybersecurity in integrations: good practices to protect your data”.
DIY, plugins and patches: the real cost of “saving” on integration for AI
It's tempting to follow the familiar path:
The problem is that each patch adds operational friction, hidden costs and blind spots. In our articles on middleware modernization and headless commerce, we show how these approaches end up skyrocketing the TCO, make monitoring difficult and become extremely fragile in the face of version changes or peaks such as Black Friday.
Taken to generative AI, the risk multiplies:
Instead, an iPaaS like Weavee allows:
That's why at Weavee, we insist on modernize integration first, and only then add GenAI. In many cases, the ROI of ordering integrations is seen even before the first AI pilot is launched.
The conclusion is simple, but it requires discipline:
In retail, generative AI only generates value when it is powered by reliable and connected data.
Before investing more in models, it's time to tidy up your data, modernize integration, and establish a robust governance and security framework. That's exactly the layer that Weavee is already helping to build in retailers in Latin America.
If you want to evaluate the level of “data readiness” of your operation and design a GENAI-ready architecture, Ask for a test with Weavee and let's talk about your current stack, your use cases and how our iPaaS on Azure can help you go from isolated pilots to production results.