AI Copilots & Code Generation for the IoT: Transforming Embedded Development with Intelligent Assistants

Inside this Report

AI has fundamentally reshaped software development. Development tool providers have successfully leveraged the rapid evolution of generative AI and natural language processing to help engineers automate large portions of the coding process and accelerate prototyping. Despite massive productivity benefits, automation comes with inherent security and quality risks that force embedded engineering organizations to approach AI-powered assistants with caution. Commercial solutions that can effectively blend security, quality, and process acceleration through custom guardrails, tool integrations, best practices guidance, and model refinement will reap early share in this young but rapidly emerging space for AI copilots and code generation solutions.

This report delivers a comprehensive analysis of the AI copilots and code generation ecosystem as it applies to IoT and embedded software development. It examines the capabilities and limitations of current agentic AI and AI coding tools, their integration with popular IDEs, DevOps pipelines, and embedded toolchains, and the extent to which these tools can meet the performance and regulatory requirements of IoT and edge computing deployments. The report also includes an analysis of relevant mergers and acquisitions, LLM ecosystems, licensing strategies, agentic IDEs, concerns with AI generated code, and profiles of leading vendors. The study includes market sizing and forecasts from 2024 to 2029 with commentary and segmentations by product type (general purpose versus application-specialized solutions), region vertical market, and leading vendors.

What Questions are Addressed?

• What factors are driving demand for AI-enhanced copilots and code generation solutions?
• How can development tool providers strengthen their portfolios to capitalize on demand for AI- powered development solutions?
• Which vertical markets will lead market growth in this burgeoning sector?
• When will safety- and security-critical industries adopt AI code generation at scale?
• Why do engineers favor agentic solutions over lightweight assistants?
• Which companies are driving product innovation and influencing the market?

Who Should Read this Report?

This report was written for those making critical decisions regarding product, market, channel, and competitive strategy and tactics. This report is intended for senior decision-makers who are developing, or are a part of the ecosystem of, AI assistants and code generation tools, including:

Technology Providers in this Report:

Demand-side Research Overview

VDC launches numerous surveys of the IoT and embedded engineering ecosystem every year using an online survey platform. To support this research, VDC leverages its in-house panel of more than 30,000 individuals from various roles and industries across the world. Our global Voice of the Engineer survey recently captured insights from a total of 600 qualified respondents. This survey was used to inform our insight into key trends, preferences, and predictions within the engineering community.

Executive Summary

AI code generation is emerging as one of the most disruptive forces in IoT software development since the advent of open source. Enterprise/IT organizations eagerly adopted AI-powered coding tools with little hesitation, but demand for code generation capabilities from embedded engineering organizations has lagged behind, resulting in a blossoming opportunity for AI copilot and code generation vendors beginning primarily in 2025. AI copilots accelerate software development, helping engineering organizations cope with the increasing complexity of software codebases and their core role in product-level differentiation. For engineering and product development organizations across industries, AI promises to bridge skill gaps, reduce time to market, and improve developer productivity.

This acceleration in automated coding, however, also increases the need for rigorous quality assurance, compliance checks, and additional security. Currently, there is a large gap in the market for a complete solution that offers safety-critical software testing and analysis alongside standards-compliant code generation. AI-generated code can introduce vulnerabilities, licensing risks, or inefficiencies that are difficult to detect without robust testing and software composition analysis (SCA) in the background. Many of the leading AI development tool vendors do not have partnerships or experience in embedded software development, creating an opportunity for organizations with a long tenure in embedded engineering to partner with AI leaders to safely and securely bring AI-generated code to the IoT for all use cases.

Copilots and code generation will take hold in embedded engineering over the next five years. In the near term, adoption will be strongest in non-safety-critical IoT segments such as communications & networking, consumer electronics, and smart home, where AI-assisted coding can quickly prove ROI without extensive regulatory overhead. As certification bodies and standards organizations formalize guidelines for AI-generated code, safety-critical engineering organizations will adopt copilots more eagerly. To capture a portion of the growing safety-critical market share, vendors must add compliance support, code provenance tracking, and integrate with popular software verification and validation tools.

Key Findings:

• Demand for application- and domain-specialized code generation will accelerate rapidly as embedded engineering organizations embrace AI to add greater amounts of software-driven value to their products.
• As secure, purpose-built AI copilots go to market, the automotive vertical will grow the fastest as OEMs transition toward software-defined vehicle architectures and value-added software features that generate recurring revenue.
• Agentic AI will not only transform code generation but also the complete software development lifecycle as it automates design planning, QA, and project management.
• The Americas is a home market for many of the world's leading AI copilot and code generation solution vendors, contributing to its early market leadership.
• VDC's Voice of the Engineer survey data shows that AI tooling is effectively accelerating project timelines, helping engineers meet and exceed deadlines.

Report Excerpt

AI Usage Improves Project Schedule Adherence

Organizations leveraging AI for code generation are measurably outperforming their peers in project execution timelines. Engineering organizations employing AI-generated code are significantly more likely to beat expectations, with 38% reportedly ahead of their project schedules (2.1x more likely than organizations not using AI code generation). This discrepancy reflects AI's ability to automate foundational coding tasks, accelerate iteration cycles, and reduce delays caused by manual development bottlenecks.

The sharp difference in three to six month delays (3.0% of AI users versus 10.9% of non-AI users) and overall reduction in delays among AI code users suggest that engineering organizations benefit from AI's ability to preempt errors and improve code reliability earlier in the lifecycle. AI code generation tools that generate boilerplate or repetitive code components allow engineers to focus on architecture, integration, and optimization, which are key elements for fueling product innovation and differentiation in traditional workflows. In edge AI contexts, where deployment environments are heterogeneous and performance tuning is critical, complex task automation (e.g., model integration or hardware abstraction) enables teams to compress development cycles and better align with shifting project requirements. AI-integrated software development strategies free up developers to work proactively on value-creating features. As a result, solution providers should position AI code generation not just as a developer aid, but as a catalyst for predictable, repeatable acceleration, which is especially compelling in embedded markets defined by deployment complexity and constrained engineering resources.