The Complete Guide to Using AI in the Retail Industry in Colombia in 2025

Introduction: AI in Colombia's Retail Industry in 2025 - Colombia is moving fast from pilots to policy: the CONPES 4144 national AI policy lays out 106 actions and a multisector roadmap to boost ethical, inclusive AI adoption across commerce, while a new government bill submitted in July 2025 seeks to classify AI systems by risk and set enforceable governance rules for companies (CONPES 4144 national AI policy (Colombia), Colombia AI regulation bill (July 2025)).

For Colombian retailers the opportunity is practical and local - inventory optimization and demand-forecasting can cut overstock and spoilage in grocers and bakeries, while risk-based rules mean stores must pair data upgrades with clear AI governance (AI inventory optimization and demand forecasting in Colombian retail).

The next 12–36 months will be decisive: retailers who align strategy, data hygiene, and compliance will turn AI into measurable margin, not just a flashy pilot.

“The approval of CONPES 4144 reflects Colombia's commitment to the responsible adoption of emerging technologies, positioning the country at the forefront of innovation and digital transformation in the region.”

Colombia's regulatory picture for retail AI in 2025 is shifting from strategy to structure: CONPES 4144 provides a practical roadmap - six pillars (ethics and governance, data and infrastructure, R&D, talent, risk mitigation, and adoption) and 106 actions aimed at accelerating AI adoption across public and private sectors - backed by roughly COP 479,273 million in planned investment (CONPES 4144 National Artificial Intelligence Policy roadmap), while a separate Proposed Bill introduced in 2025 would add hard rules by classifying AI systems by risk (prohibited, high, limited, low), naming the Ministry of Science as a national authority, and setting obligations for transparency, impact assessments and human oversight (Colombia AI regulatory tracker and Proposed Bill summary).

For retailers this matters now: guidance from the Superintendence of Industry and Commerce (SIC) on data and AI already sets privacy and accountability expectations, and the draft risk framework means recommendation engines, credit-scoring or workforce-management models could soon face reporting, audit and documentation rules - so investments in cleaner data and clear governance are becoming as important as the models themselves.

The policy push aims to make AI a measured productivity tool for SMEs and grocers (for example, inventory-optimization systems that reduce overstock and spoilage), but until laws are finalized businesses should track both CONPES actions and congressional debate to manage compliance and opportunity.

“The approval of CONPES 4144 reflects Colombia's commitment to the responsible adoption of emerging technologies, positioning the country at the forefront of innovation and digital transformation in the region.”

Key institutions and stakeholders shaping retail AI in Colombia form a practical - and sometimes political - ecosystem that retailers must navigate: the Department of National Planning (DNP) and MinTIC drive the CONPES 4144 national AI policy that funds research, talent and infrastructure, while the recently submitted proposed Colombian AI bill naming the Ministry of Science, Technology and Innovation as the primary enforcement authority, with explicit sanctions for non‑compliance; together these bodies set the compliance bar that recommendation engines, credit-scoring tools and inventory systems will have to meet.

Industry and startups supply real‑world pilots and talent, universities and research centres feed R&D, and government initiatives - from Misión TIC to plans for two “Centers for AI Excellence” (Bogotá and Zipaquirá) and 100 microcenters - promise training and shared infrastructure, though implementation details remain contested.

For retail leaders, the takeaway is clear: align pilots with national roadmaps like the CONPES 4144 national AI policy, partner with academic and private labs for skills, and treat governance as seriously as ROI given the bill's potential fines and operational sanctions.

“Artificial intelligence is presented as a fundamental tool that can positively shape the future of our nation. But its development must be guided by solid ethical principles and a strategic vision that guarantees the well-being of all Colombians,” Olaya stated during the event held at the National University (Ministry of Science, Technology, and Innovation).

The AI revolution in Colombia's retail sector is less about sci‑fi robots and more about practical, margin‑focused tools that scale local wins: generative and data‑driven AI are powering hyper‑personalization (Colombian chain Farmatodo already uses gen‑AI to tailor product recommendations), smarter inventory and demand‑forecasting that cut overstock and spoilage for grocers and bakeries, and last‑mile routing that reduces failed drops in Bogotá and beyond (Farmatodo real-world generative AI use case - Google Cloud, Nucamp AI Essentials for Work bootcamp - inventory optimization for retailers).

Driving adoption are clear ROI levers highlighted by industry research: conversational shopping assistants and recipe/list builders for grocers, dynamic pricing and electronic shelf labels for convenience stores, and improved product discovery via visual search and virtual try‑ons to lower returns and boost conversion (Generative AI retail use cases - Publicis Sapient).

The common thread for Colombian retailers is data readiness and micro‑experiments: start with focused pilots tied to measurable KPIs, pair models with governance from CONPES‑aligned roadmaps, and scale the plays that actually move margin rather than novelty.

“If retailers aren't doing micro-experiments with generative AI, they will be left behind,” says Rakesh Ravuri, CTO at Publicis Sapient.

Top AI use cases for Colombia's retail sector are refreshingly pragmatic: start with inventory optimization and demand‑forecasting to slash overstock and spoilage in grocers and bakeries, then layer in personalized recommendations and visual search to lift conversion and lower returns - a playbook well described in

"AI for Retail in 2025" - Magenest report: AI for Retail in 2025

Next, apply generative and agentic AI for catalog copy, dynamic pricing and conversational shopping assistants - see practical enterprise examples in the Google Cloud roundup: 101 real‑world generative AI use cases - which local chains like Farmatodo are already using to tailor offers and content.

Operational wins matter just as much: shelf‑scanning robots, smart shelves, and last‑mile routing cut stockouts and failed drops across Colombian cities, while fraud detection and unified CDP-driven omnichannel stacks protect margin and unlock personalized campaigns - start small with measurable KPIs (for concrete Colombia-focused plays, see our inventory and routing primers) to turn pilots into sustained margin improvement without betting the store on unproven tech.

Technology and vendor choices for Colombian retailers are increasingly dominated by vision‑first, cloud‑native stacks that let stores move from pilots to production without rebuilding everything: NVIDIA's Metropolis platform supplies the building blocks for in‑store video analytics and intelligent‑store agents, while NVIDIA's retail workflows - like Retail Store Analytics for dwell‑time, heatmaps and queue insights and the Loss Prevention workflow for product recognition - ship pretrained models and microservices to jump‑start deployments (NVIDIA Metropolis overview, Retail loss prevention AI workflow).

The stack scales from Jetson edge boxes to cloud GPUs (and can be bundled through partners such as Advantech's Jetson‑based NVRs), while Google Cloud collaborations add managed inference and NIM microservices to make real‑time personalization and agent‑style shopping assistants practical at scale (Google Cloud and NVIDIA retail AI collaboration).

For Colombian grocers and convenience chains the payoffs are concrete: faster detection of shrinkage (models trained to index hundreds of thousands of SKUs, including high‑risk items like meat and alcohol), lower infrastructure overhead via optimized containers, and the option to deploy analytics at the edge for low‑latency routing and checkout improvements - so vendor selection should prioritize pretrained vision models, microservice architectures, and local integration partners that can manage edge fleets and OTA updates.

“NVIDIA's software on Google Cloud brings two of the best technology leaders together. NVIDIA's easy-to-use NIM microservices, available on Google Cloud, are secure and reliable, and help deploy high-performance AI model inference more quickly and affordably. NVIDIA NIM microservices and GPUs on GKE accelerated LiveX AI Agent's average answer/response generation speed by 6.1x, enabling real-time, human-like interactions for customer support, shopping assistance, and product education, boosting growth, retention and customer experience.” - Jia Li, Co‑Founder, Chief AI Officer, LiveX AI

Operational best practices for implementing AI in Colombian retail start with pragmatism: pair CONPES 4144's governance goals and the SIC's External Directive 002 with small, measurable pilots that prove value before scaling - think proof‑of‑value (not endless PoCs), tightly scoped inventory‑optimization or last‑mile routing experiments, and clear KPIs for shrinkage, spoilage or delivery success.

Make data work for the model instead of the other way round: establish fit‑for‑purpose pipelines, prioritize recent trusted sources, and avoid over‑sanitizing datasets so models stay robust to real store noise (yes, sometimes “dirty” data matters - the CIO primer even warns against scrubbing out odd but useful signals like the infamous pizza‑glue image that would break a photography model) (Colombia AI regulatory tracker - White & Case, Generative AI retail use cases and playbook - Publicis Sapient, Enterprise AI data cleanliness risks - CIO).

Build governance into each rollout: classify systems by risk, run privacy impact studies for high‑risk uses, document decisions and assign a “Responsible for AI,” and pair vendor edge/cloud stacks with operational processes for OTA updates and model monitoring.

Finally, invest in data literacy and change management so store teams treat models as tools to improve margin - not magic - and include workforce retraining plans that align with the Proposed Bill's transition goals to keep stores compliant and resilient as rules evolve.

“If retailers aren't doing micro-experiments with generative AI, they will be left behind,” says Rakesh Ravuri, CTO at Publicis Sapient.

Risk is the quiet cost of moving fast with AI in Colombian retail: although CONPES 4144 sets a national roadmap and the Superintendence of Industry and Commerce has issued External Directive 002 on data and AI, there are still no final, sector‑specific AI laws - yet a stack of proposed bills would soon require risk classification, mandatory impact studies, transparency, and a named “Responsible for AI” for any system used in Colombia (Colombia AI regulatory tracker (White & Case), Colombia AI bill overview (Baker McKenzie)).

For retailers that means practical compliance steps now: map and classify every AI touchpoint (recommendation engines, dynamic pricing, workforce management), run privacy and fundamental‑rights impact assessments for high‑risk uses, codify vendor obligations and data quality standards under Law 1581, and keep rigorous documentation - because the Proposed Bill contemplates steep penalties, including fines up to the equivalent of 3,000 monthly minimum wages and suspensions or closures for non‑compliance.

A vivid rule of thumb: if a model can change pricing, credit, or hiring, treat it as high‑risk - start with small, well‑documented pilots, built‑in human oversight, and retraining plans so AI improves margin without risking regulatory shock.

In five years Colombia's retail landscape will feel less like an experiment and more like an optimized machine: regional forecasts show Latin America's AI-in-retail market ramping dramatically from roughly USD 498M in 2024 toward multi‑billion dollars over the decade, and Colombia - part of the region's ~24% share - will see that capital translate into everyday store‑level gains (personalized recommendations, demand forecasting and unified omnichannel stacks) rather than gimmicks (Credence Research report on Latin America AI in Retail market).

Expect concrete margin drivers to dominate: inventory models that slash spoilage and overstock for grocers and bakeries, AI routing and neighborhood micro‑fulfillment hubs that cut failed drops and delivery times (industry data finds many retailers planning expanded automated micro‑fulfillment over the next five years), and generative tools that automate catalog copy and customer service while boosting conversion (Shopify analysis of global ecommerce and micro-fulfillment trends).

Scaling those wins will hinge on heavy under‑the‑hood investments - compute, power and cooling upgrades at stores, distribution centres and data centres are non‑negotiable if AI workloads are to run reliably (Vertiv analysis of AI infrastructure challenges for retail).

The practical takeaway for Colombian retailers: treat AI as operational infrastructure - run tight micro‑experiments, invest in the cooling and edge compute that keep models live, and pair deployment with workforce retraining so the “robots humming in micro‑fulfillment hubs” become the vivid sign that AI is turning into sustained margin, not just a flashy pilot.

Practical checklist & conclusion: getting started means trading big bets for tight, measurable steps - start by choosing one high‑value, low‑risk pilot (inventory optimization or last‑mile routing are ideal Colombia starters), then run an AI readiness assessment to map gaps across people, processes, applications and data (use the LeanIX AI readiness checklist to make sure no pillar is missed: LeanIX AI readiness checklist); next, treat data as the project's backbone - inventory sources, set quality standards, version datasets and validate training data using an AI data‑readiness framework like Actian's checklist (Actian AI data readiness checklist) so models aren't built on fragmented or biased inputs.

Secure executive buy‑in, assign a “Responsible for AI,” and classify each system by risk (pricing, credit or hiring systems = high risk) so pilots include privacy/impact reviews and vendor contracts that bind observability and OTA updates.

Measure infra needs early (edge boxes, cloud GPUs, cooling) and run a tight micro‑experiment in one Bogotá or Medellín store with clear KPIs - shrinkage, spoilage or delivery success - before scaling.

Finally, close the loop with people: upskill frontline and ops teams (consider the practical AI Essentials for Work bootcamp (15 Weeks) - Nucamp to train nontechnical staff), document results, and convert proven pilots into repeatable processes so AI becomes operational margin, not just a flashy proof‑of‑concept.