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Imagine if a Demand Planner could prepare their meeting for S&OP in minutes instead of days. Or that a buyer could analyze hundreds of supplier contracts and detect hidden savings opportunities just by asking a question in natural language. This is no longer fictional, now generative AI is transforming the way companies plan, execute and respond to daily supply chain challenges.
According to the Study of Supply Chain Trends 2026, the 51.7% of companies consider Generative Artificial Intelligence as a technological adoption for this year 2026. This tells us that GenAI in the supply chain is not a one-off boom and has already established itself and gained popularity among the region's leading Supply Chain teams.
Traditional artificial intelligence models have been optimizing routes, generating forecasts and calculating inventory levels for years, generative AI goes one step further and includes new capabilities: producing text, graphics, analysis and recommendations based on large volumes of data, everything with natural language queries.
Generative AI isn't a replacement for analysts, it acts better as an intelligent co-pilot who understands context, explains decisions and speeds up teams' work.
In this article, you'll find an in-depth analysis of the main use cases, the risks you need to consider, and a roadmap to start implementing generative AI in your supply chain.
Generative AI in Supply Chain refers to the use of large language models (LLM) and associated technologies to assist in supply chain planning, execution and management. These models are able to interpret questions in natural language, connect to the company's transactional systems (ERP, APS, WMS, TMS) and generate answers, analysis, documentation and recommendations in an automated way.
Think of it as a expert assistant which can read your data, your documents and your processes, and return processed and contextualized information to you in seconds.
Classic AI in Supply Chain focuses on numerical and optimization tasks: models of Forecasting that predict demand, algorithms that calculate optimal transport routes, or solutions that determine levels of safety stock. These models produce numbers and calculated decisions.
Generative AI, on the other hand, produces content: text, explanations, summaries, draft documents, tables and graphs, analytical narratives. It doesn't compete with a statistical demand model. What it does is complement them:
The combination of the two is where the greatest impact is generated. We have seen that the teams that understand this (which is not a matter of choosing one or the other, but of coordinating them) are the ones that really use their full potential.
LLMs (such as ChatGPT, Gemini or Alaia) process and generate text understanding context and semantic relationships. When applied to the supply chain, these models are typically combined with a technique called RAG (Retrieval Augmented Generation), which allows them to access updated internal data such as ERP databases, policy documents, and incident records, before generating a response.
With RAG, the model can consult your specific inventory, contract or supplier performance data and generate relevant, accurate and contextualized answers to your operation.
The typical flow works like this:
Large vendors such as Microsoft and Google Cloud are already integrating generative co-pilots into their planning, execution and procurement solutions, allowing planners to interact via chat with their usual tools.
The most mature use cases today are concentrated in assistance to planners, analysis of contractual documents, automation of communications and management of internal knowledge. Let's go into detail for each domain.
This is one of the most mature and adopted domains, as it is one of the processes that saves the most time and can best employ an AI assistant.
Automatic generation of Forecast narratives: instead of a demand planner spending hours explaining why demand for a product rises or falls, the co-pilot automatically generates a narrative that includes the drivers that influence the variation: promotions, seasonality, external events.
Scenario analysis: faced with questions such as “what happens if demand falls by 10% in the Andean region?” , the co-pilot generates alternative scenarios with estimated impacts on inventory, service and costs, combining APS data with external variables.
Explanation of variations and external drivers: The LLM can monitor external sources (news, macroeconomic data, climate) and correlate them with changes in the forecast, generating explanations that previously required a week's manual analysis.
Example:
AlAia (the Generative AI assistant of Datup) answers questions such as “What are the 10 SKUs with the highest forecast bias this month and what hypotheses explain it?” using historical data. It can also automatically generate texts for S&OP meetings.
In the area of purchasing, generative AI is applied with very concrete and profitable results.
Generative AI doesn't replace routing solvers (UPS is still using ORION to optimize millions of routes), but it adds a valuable layer that solves very specific everyday problems.
Generative AI connects the front-office with the operational reality of the Supply Chain:
Emerging domain, but with enormous potential. The reality is that most companies manage Supply Chain risks reactively.
One of the Quick Wins clearer and with greater immediate impact. If you had to choose where to start, it would probably be here.
Dominion
GenAI's primary use case
Maturity 2025-2026
Commentary
Planning (Demands/S&OP)
Copilots for analysis and narratives
Alta
Wide range of large vendors; easy to pilot on existing data
Purchases and contracts
Contract analysis and RFP
Medium-high
Very cost-effective cases; requires good document governance
Logistics and transport
Exception Management and Communications
Media
Complements TMS/Solvers; quick wins in service and backoffice
Risk and Resilience
Narrative monitoring and stress tests
Emerging
Very promising, still in early adoption
Knowledge/training
Chat about SOPs, policies and playbooks
Alta
One of the first projects that organizations launch
Reports that used to take days can be generated in minutes. A Demand Planner that dedicated between 6 and 10 hours a week Preparing material for S&OP meetings can recover that time for higher-value analysis. HBR documented how large companies went from generating global demand reports in a week to having them in minutes. Think about what your team would do with those returned hours.
With clear explanations of why demand changes, what risks exist in the supplier base or what is the impact of a disruption in the chain, generative AI allows decisions with more information and greater speed.
From generating draft RFPs to updating systems from carrier emails, generative AI eliminates repetitive manual work that consumes valuable resources. Purchasing, customer service and logistics operations teams are the main beneficiaries.
The ability to generate explained alerts about emerging risks, propose contingency plans and document lessons learned in an automated way significantly increases the speed of response to disruptions, a critical factor in today's supply chains.
Large Supply Chain suppliers are offering generative copilots embedded directly in their suites. These co-pilots access data already consolidated in the company's data lake or data warehouse, call on existing planning engines (APS, optimizers) and offer a conversational and explanatory layer on top of that.
SAP Joule acts as a copilot on SAP IBP, allowing planners to interact with the planning system using natural language questions.
Microsoft Copilot integrates with Dynamics 365 Supply Chain Management to provide contextual insights and suggestions.
This is the most commonly used technical pattern for custom implementations. RAG combines an LLM with an information retrieval system that searches for relevant data in internal documents and databases before generating a response.
The flow works like this:
Platforms such as Azure OpenAI Service, Google Vertex AI and AWS Bedrock offer infrastructure ready to implement this pattern.
For the Supply Chain co-pilot to be useful, they need access to up-to-date transactional system data. This is achieved by:
The quality of the integration directly determines the quality of the copilot's responses. If integration is weak, the answers will be weak.
LLMs can generate answers that sound great, but are wrong. In Supply Chain, where there is a big operation in the game, we have to rectify the data with which we make decisions. The main mitigation is to use RAG with your own data and always maintain human judgment.
There is a risk of sensitive information (supplier prices, business conditions, customer data) leaking if public models are used without properly isolating the data. Emerging regulations such as the future EU AI Act require transparency and control over AI-assisted decisions.
It can also be mitigated by relying on vendors that meet data security requirements (ISO 27001:2022 certification), the international standard for information security management, which guarantees strong data protection, confidentiality and control practices.
One of the main barriers to digital transformation is organizational adoption, in the absence of prepared talent, according to the Supply Chain Trends Study carried out recently.
Many teams see generative AI as a black box. Without clear roles, defined usage policies, and adequate training, the risk of misuse is high. In our experience, Change management is as important as technology, and this includes training staff for proper use. We've seen flawless implementations that died because no one took care of getting users to actually use them.
An LLM can generate brilliant explanations for erroneous inputs, amplifying biases or errors instead of correcting them. If master data is out of date, if historical data has errors, or if systems are not well integrated, generative AI will amplify those problems with a compelling narrative which can lead to poor decisions. This is particularly dangerous, a bad number in an Excel is seen as a bad number. A bad number wrapped in a coherent AI-generated narrative... is seen as a solid recommendation.
For a retail or manufacturing company in Latin America, our recommendation is to work in waves, starting with Low-risk, high-visibility quick wins. It's not about “putting AI” into everything, but about orchestrating specific use cases that support the service, cost and resilience strategy.
Identify the key activities to optimize: how much time do you spend preparing meetings? How much craftsmanship is there in the shopping process? How saturated is customer service? Translate every pain into a measurable value hypothesis:
“If I automate the preparation of S&OP reports, I save X hours/week and free up analysis capacity for Y.”
Typical quick wins to start with are:
Selection Criteria: low regulatory risk, internal use, little engagement with critical financial decisions, and high volume of current manual work.
Choose the base platform, and the LLM model together with your IT team. Define what data will be used, how it will be anonymized when necessary, and what access and auditing policies will be applied. This step is essential and should not be skipped.
Or you can implement external solutions such as Datup, where we access your data securely, this will save you time and headaches if you don't have a team dedicated to these types of technology.
Schedule a discovery call and find out if Datup adapts to your operations.
Launch a pilot with a mixed squad that includes Supply Chain, IT, Data/AI and business profiles. Work with a limited group of end users and define Clear KPIs from the start:
When a case works and the KPIs support it, integrate it robustly with source systems (stable connectors to ERP/APS/WMS/TMS) and establish a managed operating model (MLOPS/LLMOPS). Expand the reach to other geographies or business units and add new use cases by reusing components already built.
Create a small “AI Product Office” for Supply Chain that prioritizes cases, defines standards, manages risks and measures impact on an ongoing basis. It trains Planners, buyers and managers in how to interact with AI: ask good questions, validate answers and use it as a support, not as an oracle or something without human judgment.
Use generative AI to explain, document and create possible scenarios about the results of your current models, not to plan from the start. Your human judgment is still the planning engine; generative AI is the copilot that makes it more accessible and understandable.
Attendees who read data and documents to produce summaries, explanations, and meeting materials often have great ROI and low risk. Start there before tackling cases involving critical financial decisions.
Rather than using a “pure” model with general knowledge, combine it with your document and internal database. This significantly reduces hallucinations and increases the relevance of the answers to your specific context.
AlAia, is a Generative AI assistant who is already trained for the supply chain. Accessing it will save training work, and you will have immediate results just by connecting it to the operational data of your company's supply chain.
It explicitly defines which decisions can be automated and which require human review and approval. In the Supply Chain, where errors have real operational and financial consequences, human judgment is still indispensable.
Measure impact from day 1
Record time saved by Planners, reduction of errors in contracts, internal tool NPS and any other relevant indicator. Use these numbers to decide what to scale and what to adjust. What isn't measured can't be improved, and investment in generative AI needs quantitative support to hold on.
Datup It is one of the pioneering companies in the inclusion of artificial intelligence in the supply chain, from AI-based demand forecasts, to inventory optimization and distribution with intelligent recommendations, they also have an AI assistant trained specifically for day-to-day supply chain operations. It already has more than 30 companies in 15 countries that have adopted Generative AI in their operations, and who are constantly improving with the latest technological advances.
Microsoft has implemented language models internally to manage the complexity of its hardware and server supply chain for Azure. Planning teams use co-pilots to analyze global demand variations, generate consolidated reports, and propose inventory adjustments in multiple regions.
Documented by Harvard Business Review, a global automotive OEM implemented a specifically trained LLM in its supplier contract base. The system identified unapplied volume discounts, unexecuted favorable clauses, and renegotiation opportunities that generated savings of millions of dollars in the first year. This case illustrates something we often see: the money is already there, in your own contracts... only that no one has time to read them all with a magnifying glass.
Forecasting, optimization and LLM models will work in an integrated way. The future is not “generative AI or classical AI”, but comprehensive platforms where each type of AI plays its role: predictive AI anticipates, optimization calculates and generative AI explains, documents and assists.
The next evolutionary step is AI agents able to detect an exception (a delay, a stock break, a regulatory change), analyze the impact, propose solutions and execute corrective actions with minimal human supervision. This is already being tested in controlled environments and will be an operational reality in the next 2-3 years.
Latin America presents unique opportunities for generative AI in Supply Chain: complex supply chains with multiple countries, high variability in demand, challenging logistics infrastructure and high-turnover operational teams. Knowledge and training use cases, exception management, and assistance to planners have a particularly high impact potential in the region.
The increasing availability of cloud platforms in the region (Azure, GCP and AWS have data centers and commercial presence in multiple countries in Latam) and the maturity of data teams in leading retail and manufacturing companies mean that May 2026 be an propitious time to start structured pilots or adopt tools from specialized vendors such as Datup.
No. Generative AI works as a copilot that amplifies the capabilities of planners, not as a substitute. Critical supply chain decisions require human judgment, knowledge of the business context, and responsibility that a language model cannot assume. What does change is the planner's profile: he'll spend less time compiling data and more time analyzing, deciding and leading.
The cost varies significantly depending on the scope. An initial pilot based on RAG with internal data can be launched with a modest investment in cloud platform and hours of a mixed team (supply chain + IT + data) for 2-3 months. Or, to save time and money, they can access AlAia with Datup, where it is a monthly subscription (depending on the size of your operation and contracted modules), without a permanence clause, which makes it faster and more accessible to those companies that do not have specialized personnel in AI.
ROI is often quickly justified in saving planning hours and reducing errors.
Major vendors include Datup (with its AlaiA conversational assistant), Microsoft (Azure OpenAI+ Copilot for Dynamics 365), and Google Cloud (Vertex AI). The choice depends on the technology stack you already use, the equipment you have and the budget you have to dedicate to it.
A well-designed pilot can generate visible results in 6 to 12 weeks. Use cases that are quicker to implement (such as a chat about internal knowledge or a co-pilot to prepare S&OP meetings) can show value in the first few weeks. More complex cases such as contract analysis or logistics exception management may require 3 to 6 months to reach a level of operational maturity.
It may be, but it requires precautions. It is essential to use models deployed in controlled environments (private instances in the cloud, not public models), to establish clear policies for what data can be processed, to implement anonymization when necessary, and to maintain audit records. Platforms like Datup offer data security and compliance guarantees.
Generative AI is a practical tool that is already generating real value in demand planning, purchasing, manufacturing, logistics, customer service, risk management and internal training. The key is to understand it as a co-pilot and not as a replacement for the systems and equipment you already use today.
The benefits are a drastic reduction in analysis times, better quality in decision-making, automation of administrative tasks and greater agility in the face of disruptions. But to capture that value, you need a disciplined implementation: start with low-risk quick wins, build on your own data with RAG or hire a specialized provider, always keep a human in the loop and measure impact from day one.
Felipe has specialized in the application of artificial intelligence to optimize supply chains, helping companies to predict demand, manage inventories and determine the ideal times to buy raw materials.
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