How Should eCommerce and Retail Respond to the Current Disruption in Software Engineering?

AI and automation breakthroughs are challenging not just the way we develop software, but the types of software that are being developed in the first place.

Many industry predictions place the value that will be unlocked by AI-driven optimization across merchandising, logistics, and personalization at hundreds of billions of dollars by 2030. The online retail space hasn’t exactly been stagnant in the recent past, but the changes that came along were more incremental, and in comparison the last couple of years have been outright disruptive.

Many retail organizations are not prepared to fully capture the many opportunities that AI presents. Tech debt, duct-taped commerce stacks, and legacy third-party platforms can hinder the adoption of the latest AI-based strategies. To compete in this era of AI agents, hyper-personalization, and automated optimization, the retail sector needs to adopt a paradigm shift in software engineering to emphasize agility and modern architectures while reimagining the relationship between retailers, development talent, and technology vendors.

The Priorities and Challenges for the CTO in Online Retail

The eCommerce industry has incredibly thin margins compared to other types of businesses in the tertiary sector yet has very high spend for digital technology on average. eCommerce platforms, payment systems, fulfillment, shopping and recommendation models, custom data analytics, aggressive personalization in advertising, and supply chain management are a few examples of the incredibly complex components of the tech stack that runs the average large online retailer, and each piece of this stack is getting increasingly complex thanks to modern AI advancements.

In order to keep returns on investment in new eCommerce technology somewhat proportional to spend, there are some challenges that must be addressed.

Legacy Systems Slow Innovation

A classic problem relevant in almost any industry, you can walk into a brick-and-mortar store for many large retailers and sometimes find point-of-sale systems running on CRT monitors or the distinctive beige yellowed by many years of sunlight from systems that have been in place for 25 years or more.

Though parts of their tech stack might have changed, it’s crazy to think that in some places their logistics systems are working with a point-of-sale system running on something like an IBM terminal running 4690 OS. These POS systems, probably referred to by the cashiers by the same acronym, are a visual reminder of how much the retail industry is a mix of modern and legacy systems glued together with many layers of middleware made by developers who are potentially retired or companies that no longer exist. The same is true of the online retail space, you just can’t see it physically as a customer.

Their ERP may be a heavily customized monolithic monster from 20 years ago, their fulfillment system may be built on the hopes and dreams of that time, which are simply well wishes when adjusted for inflation. Whatever the system, the longer it sits and the more entrenched it is, the harder it is to make it work with the coming changes to operational efficiency that AI promises.

Customers Are Spoiled

Customers want omnichannel shopping, and they expect it to be delivered tomorrow by default and make no distinction of importance, treating something they need next week like a donated human heart being transported for transplant. If they order Christmas decorations on October 31st, they expect them by November 1st, and might pass your store up because Amazon can get it to them by 4am.

Customers' expectations are shifting faster than development timelines can keep up with. Omnichannel shopping, next-day delivery being the norm, and hyper-personalization require real-time data processing, composable services, and rapid iteration - things that legacy components of a retailer’s system will struggle to support, even with another middleware shim.

A Shortage of Talent

Retailers often don’t have deep benches full of engineers with qualifications on the latest technologies, rather have talent focused on maintaining current systems. When trying to hire, they’re facing a competitive field with the tech giants slurping up talent for AI just like they’re slurping up power and computer memory with mind-boggling investment amounts. Retailers are navigating this complicated hiring market while being pressed to reduce the cost of human resources and improve margins at all costs.

The Engineering of a Complex Retail System

The online retail space is technologically intricate, even more than most consumers realize. A single custom action, like adding an item to a cart, applying a coupon, or asking for inventory availability in a brick-and-mortar location, can trigger dozens of processes across many systems. In the typical retail setup, just those three actions likely trigger the following systems:

• Inventory and demand forecasting
• Pricing engines
• Customer identity systems
• Tax compliance
• Warehouse management
• Supply chain visibility
• Personalization and recommendations
• Fraud detection
• Payment gateways

Despite so many systems working together to complete these actions, the tolerance for failure - both from customers and from the cost of mistakes that might not directly affect customers - is very low. A few seconds of latency or just occasional page breakage that a simple refresh can fix would lead to millions in losses during peak holiday shopping periods for the largest of retailers.

There’s also a compliance challenge that shapes the engineering of these systems. Online retailers must meet standards for things like payment processing, data privacy, consumer protection regulations, and industry-specific policies. Software engineering for online retail systems must incorporate strong privacy, security, and auditability standards during the software development lifecycle (SDLC).

Dealing With the Burden of Legacy Systems

As mentioned before, decades-old legacy systems that cannot be easily replaced without major disruption to operations remain common. These ‘zombie systems’ manifest most commonly in:

• Fragmented commerce architectures
• Brittle ETL pipelines connecting old and new systems
• ERP-driven workflows rigidly tied to historical business processes
• Data models that cannot support real-time personalization
• Siloed in-store vs online infrastructure

Developers can spend a quarter to almost half of their time keeping these legacy systems even operational in the first place before having time to add any new features or capabilities. Many companies have spun up independent ‘keep the lights on’ teams to address the legacy maintenance issue. Since any part of these systems being non-operational for a short amount of time can have huge consequences, the dominant strategy for modernization is incremental replacement. Online retailers are adopting:

• Microservices that decouple from legacy cores
• API-first commerce
• Headless front ends
• Cloud-based fulfillment and OMS layers
• Managed services for search, recommendation, and payment processing

Adding modularity to enable eCommerce retailers to modernize their operations at a sustainable pace while gradually preparing for AI-native architectures.

How the Engineering Practices Around Online Retail Are Changing

Cloud-First Composable Commerce

Cloud-native platforms allow eCommerce retailers to scale fast for their peak traffic times, optimize costs, and leverage modern tooling. Composable commerce gives the freedom to those with the best service for each module of your store - checkout, search, inventory, promotions, and more are all ‘hot swappable’ without the need to rewrite the platform around their replacement.

Large online retailers have started to shift toward:

• Hybrid-cloud architectures
• Serverless computing to reduce infrastructure overhead
• Containerized microservices for omnichannel workloads
• Cloud development environments (CDEs) to standardize toolchains

The modularity that composable commerce gives is valuable for adopting AI across product, logistics, the customer experience, allowing in-place upgrades and rollbacks and technology changes without major problems.

True Agile Engineering, Not Sparkling Waterfall

There’s more to agile than just adding standups. Retailers often declare their development process agile, but still maintain long approval chains, siloed teams and departments, sequential release cadences, and fixed-scope planning cycles tied to merchandising seasons.

To keep up with the most advanced eCommerce retailers, moving forward you need to have cross-functional teams, continuous delivery pipelines, rapid experimentation frameworks, real-time monitoring and automated rollback measures. Your system should be built to allow faster experimentation and innovations while being responsive to market demand spikes or changes.

Shifting to Product-Led Development

Product-led development models are focused on the ownership of customer outcomes and not simply the delivery of technical requirements for a project-led development cycle. This is a critical shift for modern retail development.

Product-led development helps target continuous improvement of conversion rates, allows for experimentation on storefront UI/UX, dynamic pricing and personalization, omnichannel experience consistency, and continuous optimization of customer lifetime value.

The Inflection Point of Software Engineering in eCommerce and Retail

AI is creating a fundamental shift in how retail applications are developed. LLM-based tools like GitHub Copilot, Claude, Cursor, and AWS Q can now generate code, refactor legacy modules, document APIs, build test cases, and accelerate front-end development. Sometimes they can also do those things without making a fool of themselves and making everything worse. Increasingly, even.

When AI works, it dramatically improves time-to-value, which is especially important in retail operations with the burden of legacy systems or pasta factory codebases.

AI-Powered Testing

Retail systems are notoriously difficult to test effectively with edge cases created by promotions, seasonal logic, and complex inventory interactions. AI-based test generation and synthetic data creation are helping QA by making automated test generation easier, creating self-healing tests, offering real-time scenario simulation, and cross-channel behavioral testing.

AI in Design, Architecture, and DevOps

AI can help generate diagrams, recommend architectures and patterns, simulate load conditions, and optimize CI/CD pipelines. AI agents can also interpret natural-language requirements, write application logic, scaffold microservices, generate APIs, build data pipelines, create regression tests, perform code reviews, and more.

Even without having AI directly write code, software engineers in the retail space can realize incredible gains in speed and cost-effectiveness by using these tools in the non-code and boilerplate parts of the project.

Future-Proofing Talent Models for Retail Software Engineering

As a retailer, your talent strategy in the AI era is important when adapting legacy systems and adopting the latest technology, which often requires two distinct types of talent.

Bridging the Talent Gap Modernizing Legacy Systems

Many retail systems rely on old technology. It’s not uncommon to find really old versions of Java in use, long dead programming languages, or proprietary scripting languages. Legacy Enterprise Resource Planning (ERP)/Product Information Management (PIM)/Order Management System (OMS) frameworks are also a common problem.

When hiring new engineers, most on the job market will prefer to work with modern systems and languages and use cloud-native tooling. Finding someone who specializes in legacy software requires paying premium rates to find rare snowflakes who know the exact thing you need them to work with, or paying for potentially years of on-the-job training before they’re effective if they are not already specialized in a particular legacy technology.

AI is challenging this gap. It’s now possible for a competent modern developer to interpret and modernize something like a COBOL system with the assistance of AI. Subject matter experts are still the best people for the job, but they’re no longer without alternatives.

Upskilling Existing Teams for AI-Native Development

Even a relatively modern developer may need some training or self-learning time to get familiar with all of the latest AI technologies, as they are often fundamentally different from what they were working with before.

The modern retail software engineer needs machine learning fundamentals, prompt engineering skills, data-engineering skills, understanding of agents and Model Context Protocol, as well as an understanding of the AI-powered systems for things like personalization and recommendation systems that modern competitors are developing.

Combating Software Engineering Attrition by Giving Product Ownership

There are often morale issues in retail engineering teams because the business teams drive requirements, engineering teams lack decision-making authority, and time to experiment is scarce and is discouraged.

This can be helped by adopting product-driven development and finding other ways to improve developer experience by giving them more agency and ownership over product development.

Rethinking Your Relationship With Third-Party Vendors

In the eCommerce industry, retailers depend heavily on vendors of things like commerce platforms, payment processing, search engines, loyalty program systems, logistics, and more. There are, of course, many things you shouldn’t take the burden of developing for yourself, but the reliance on these vendors comes with challenges. You often pay for products that have partially overlapping capabilities, there can be high integration overhead (sometimes intentionally so) and of course the classic vendor lock-in. In addition, not owning the responsibility of key systems for critical shopping seasons can be nerve-wracking for managers.

Evolving Vendor Strategy as a Retailer

Consolidation

Reducing the fragmentation of services you rely on from vendors improves interoperability, performance, cost simplicity, data consistency, and reduces points of failure.

Prioritizing Extensible, Composable Solutions

Choose vendors that offer robust APIs with fair pricing, event-driven architectures, shared data models, and plug-and-play components.

Expect Vendors to Be AI-Native

Shoving AI into every orifice of your product or service is not a good strategy, and you should not seek (or trust) vendors that do that. Still, your software partners should still actively use AI in some key places. AI is extremely useful for the online retail industry when leveraged for personalization, fraud detection, search result quality, recommendation engines, and the automation of certain workflows. The workflow automation is especially for back-office tasks. In particular, AI excels at on-the-fly data conversion, filling in the gap where there is not an existing tool to automate moving data between formats or platforms.

Moving Toward the Future of Software Engineering in eCommerce and Retail

Real-time data, highly modular software, and AI will shape the next decade. To be prepared for the near future, retailers should:

• Modernize legacy pieces of their software stack
• Adopt cloud-native and composable architectures
• Invest heavily in developer experience (DX)
• Rethink talent strategies around AI and product-led engineering
• Consolidate third-party partnerships
• Establish strong AI governance and trust frameworks

The last point in particular is of the utmost importance for any department that handles potentially sensitive information, like information covered under an NDA or customer records. Even for employees whose job hasn’t seen the official integration of AI-based automation, it’s extremely important to provide a ‘sanctioned’ AI chatbot. Even if the policy is actively to not use or trust AI for certain tasks, employees will find a way to use it without getting caught. If you provide an official internal AI chatbot, you have full control over the security aspect of it.

You can choose any level of trust, ranging from running an open-weight model on your own hardware to simply using a paid service that has data protection commitments, you don’t want employees dumping potentially sensitive information on whatever their favorite AI tool is. It’s hard to tell someone to cut grass with scissors when they use a lawnmower at home. When faced with something like a particularly annoying and hard-to-automate data entry task, if not given a tool they will find their own.

Why CodeClouds

Consumer expectations are changing and the length of innovation cycles is shortening. A modern software engineering strategy for eCommerce is not optional. With the nature, scope, and speed of the changes AI is bringing to the online retail space, delaying modernizing is likely more harmful than it has ever been since the world was searching with AltaVista. It is likely that AI is to online retail what online retail was to brick-and-mortar.

CodeClouds has been in custom eCommerce development for more than 16 years. In that time we’ve developed for DTC, B2C, B2B2C, and everywhere in between. We can help your company make sense of the changing online shopping landscape and help you join the modern eCommerce era.