The truth about the productivity of AI coding assistants in firmware and e-commerce

AI coding assistants have moved from the «impressive demo» stage to the business decision stage. For an e-commerce owner, the question is no longer whether AI can write code, but whether it can reduce costs, accelerate releases, improve quality and reduce risk in real systems: ERP integrations, custom checkout flows, WMS, POS, scanners, IoT warehouse devices, fulfillment automation and embedded software running on everyday devices. DesignNews« article on the »productivity myth” among firmware engineers points to a critical point: the value of AI coding assistants is not the same in every development environment. It’s one thing to create a simple web component and another to write firmware for hardware with limited memory, tight timing, drivers, RTOS, toolchains and physical debugging.

What the debate around AI coding assistants really shows

The central idea highlighted by DesignNews is that AI coding assistants should not be evaluated based on code typing speed. In firmware, the bottleneck is rarely writing lines. It is understanding the hardware, interpreting datasheets, using registers correctly, stability in edge cases, power consumption, memory management, interrupt timing, security, and verification on real devices. An LLMs for coding tool can suggest a function, a driver skeleton, or a test fixture, but it does not necessarily know about the specifics of the board, changes in the production line, compiler bugs, or the unorthodox behaviors of a sensor in response to temperature changes.

For e-commerce professionals, this is of immediate importance. Many online stores are investing in technology that connects the digital with the physical: portable scanners, smart labels, pick-to-light systems, temperature monitoring for food or pharmaceutical products, smart lockers, POS terminals, and custom shipping devices. If a development team uses AI programming tools without a disciplined code review and automated testing process, they can quickly deliver something that looks right, but later create delays, returns, downtime, or technical debt. Conversely, when the tools are included in an organized workflow, they can significantly help with boilerplate, documentation, refactoring, unit tests, migration scripts, debugging hypotheses, and faster understanding of legacy code.

Where does the promise of productivity end and the risk begin?

Productivity in software quality is not just measured by «how fast the code was written.» It is measured by lead time, defect rate, incident frequency, mean time to recovery, readability, maintainability, test coverage, and business outcome. Especially in embedded systems development, a seemingly small failure can lead to large operational costs: a misread barcode, a device that gets stuck during picking, a sensor that returns noise, a POS that fails at peak hours, or a firmware update that doesn’t complete properly. AI coding assistants are useful when they speed up familiar tasks, but dangerous when they produce convincing code that hasn’t been adequately tested.

A practical way to look at it is to distinguish between «low-risk» and «high-risk» tasks. Low-risk tasks include creating documentation, producing initial unit tests, converting a function to a cleaner form, explaining a legacy module, creating SQL queries for reporting, or writing mock data for testing. High-risk tasks include generating code that affects payments, security, personal data, firmware updates, authentication, inventory correctness, or real-time communication with hardware. Here, AI code generation should be considered a draft, not a final solution. Prompt engineering helps, but it does not replace the technical judgment, domain knowledge, and responsibility of the engineer.

The available data shows a mixed picture. The well-known controlled study for GitHub Copilot showed significant time reduction on a specific programming task, but that doesn’t mean the same percentage automatically transfers to firmware, security-sensitive, or mission-critical environments. As shown in the graph below, the study recorded a large difference in completion time, but the context was specific and not a complete representation of a complex enterprise or embedded project.

The data an e-commerce owner should look at

The adoption of AI coding assistants is on the rise, and this is not a theoretical trend. According to the Stack Overflow Developer Survey, the percentage of developers using or planning to use AI tools increased from 70% in 2023 to 76% in 2024. For an e-commerce business, this means that even if it doesn’t have a formal AI policy, internal or external development teams are likely already experimenting with such tools. So the right question is not «should we ban them or adopt them?», but «how do we integrate them with governance, metrics, and boundaries?».

Even more useful is linking AI tools to performance metrics. Google Cloud’s DORA survey for 2024 reports that a 25% increase in AI adoption was associated with improvements in documentation quality, code quality, code review speed, and delivery throughput, but also with a decrease in delivery stability. This is extremely important for decision makers: artificial intelligence can speed up workflows, but if not properly controlled, it can make delivery more unstable. In an e-commerce environment, where a bug in checkout or inventory sync directly affects sales and customer experience, stability is not a technical detail; it’s revenue protection.

This data leads to a more mature reading: AI coding assistants are not a magic wand that makes every 50% developer more efficient on every project. They are a multiplier of specific skills. A senior firmware engineer can use them to quickly generate scaffolding, compare approaches, write tests, or explain old code. A junior developer, without guidance, can accept wrong suggestions with great confidence. For e-commerce owners who collaborate with agencies or in-house teams, the crucial point is to demand transparency: which tools are used, on which tasks, with which review process, with which code security policy, and with which protection for commercial data.

Step-by-Step evaluation guide before investing in AI coding assistants

The first step is to clearly define what problem you want to solve. Don’t start by buying a tool. Start with a business goal: faster releases to your Shopify or WooCommerce store, fewer bugs in an ERP integration, better documentation of custom modules, faster QA, reduced onboarding time for new developers, or improved embedded software in warehouse devices. For each goal, set a baseline. If an integration currently takes 20 days to go from development to production, that’s your baseline measurement. If you currently have 12 production incidents per quarter, that’s your benchmark.

The second step is to classify tasks based on risk. Create three categories: permitted use, approved use, and prohibited or restricted use. In the first category, put documentation, boilerplate, test data, unit test drafts, refactoring suggestions, and code explanation. In the second, put business logic, API integrations, migration scripts, and performance optimizations. In the third, put payment flows, authentication, personal data, production credentials, cryptography, firmware updates, and operations that can stop a warehouse or checkout. This policy doesn’t have to be complicated; it just needs to be written, understandable, and enforceable.

The third step is to build a small 30- to 45-day pilot. Choose two or three real use cases, not artificial demos. For example: generating unit tests for an ERP connector, documenting legacy checkout customization, and refactoring an inventory sync module. Use GitHub Copilot or another similar tool in a controlled environment and record before-and-after metrics: lead time, pull request cycle time, review comments, defect leakage, test coverage, developer satisfaction, and time taken for fixes. The goal is not to prove that the AI «works,» but to see where it creates pure profit and where it transfers cost to the review.

The fourth step is to strengthen code review. Every code generated by AI coding assistants should be treated as code from a new team member: it may be useful, but it does not go unreviewed. Ask your team to check correctness, security, performance, edge cases, dependencies and licensing. For firmware engineers, add additional checklists: memory footprint, timing, interrupt safety, hardware assumptions, compiler warnings, datasheet alignment and behavior in failure modes. For e-commerce development, add checks for checkout impact, GDPR, PCI-related areas, inventory consistency, API rate limits and rollback plan.

The fifth step is to connect AI use with automated testing. If AI programming tools increase code generation speed but do not increase test coverage, the risk increases. Invest in unit tests, integration tests, contract tests for APIs, end-to-end tests for critical customer journeys, and post-release monitoring. For hardware-connected systems, add hardware-in-the-loop testing where possible. Automation is not a luxury. It is the mechanism that turns speed into reliable productivity.

The sixth step is to measure ROI in a cold way. Calculate licensing costs, training time, changes to the review process, and potential increased QA effort. Compare these with time savings, fewer bugs, faster deployments, better documentation, and less onboarding. If the tool primarily helps with documentation and testing, that can be valuable even if it doesn’t dramatically reduce development time. However, if it increases review cycles or creates unstable releases, then the apparent productivity is a miscalculation.

Practical conclusions for development teams and digital leaders

AI coding assistants deserve a place in the modern development stack, but not as an autopilot. Used properly, they are more like an experienced assistant that speeds up individual tasks and less like a replacement for mechanical thinking. For firmware engineers, the value lies in reducing time on repetitive tasks, creating better tests, and accelerating code understanding, not in producing uncontrolled firmware. For e-commerce owners, the value lies in faster and more disciplined implementation of technology changes that affect sales, logistics, and customer experience.

The healthiest strategy is to start small, measure rigorously, and scale only where there is evidence. Don’t buy into a productivity narrative without governance. Ask your team or technology partner to show you before-and-after metrics, not just impressions. Ask what part of the code was written with AI, what was checked manually, what is covered by tests, and what rollback plan is in place. When these become part of the process, AI coding assistants can be a real help. When ignored, they create a new type of technical debt: code that was written quickly, was easily accepted, and cost a lot later.

For TWO DOTS, the practical message to every e-commerce business is clear: AI in development is not about impressing, but about operational maturity. If you have the right architecture, clean processes, metrics, code review, automated testing, and experienced people, AI tools can become a significant accelerator. If you don’t have these foundations, the speed they promise can turn into instability. And in e-commerce, instability always costs more than the initial estimate shows.

Sources: DesignNews: The Productivity Myth: What AI Coding Assistants Actually Deliver for Firmware Engineers | Peng et al.: The Impact of AI on Developer Productivity, 2023 | Stack Overflow Developer Survey 2023 | Stack Overflow Developer Survey 2024 | Google Cloud DORA Report 2024 | NIST AI Risk Management Framework