The ROI of AI-assisted product development
What every business leader needs to know
Published on: July 8, 2025
Last update: July 10, 2025
AI-assisted product development is now a practical part of modern developer workflows, not just a future idea. It’s reshaping how software teams build products by shifting engineers from writing every line of code to curating intelligent systems. But what is the real return on investment (ROI) for businesses that adopt AI tools in product development?
In this blog, I break down the key value drivers, share practical examples, and explain the leadership mindset needed to capture AI’s potential for your product teams.
From writing code to curating systems: A new way of working
In the past few years, engineers have moved from writing code to curating systems. Instead of manually typing out every line, developers now spend a significant portion of their time working alongside AI tools. They focus on defining the problem, crafting clear prompts, reviewing AI outputs, and refining the results.
This shift has resulted in massive efficiency gains. For example, what might have taken three hours of manual coding two years ago can now be accomplished within minutes by providing the right instructions to an AI tool, reviewing the generated code, and iterating quickly. This process can reduce effort by half in most cases.
This efficiency saves time, reduces development costs, and enables teams to focus on strategic initiatives like design and user experience.
Instead of manually typing out every line, developers now spend a significant portion of their time working alongside AI tools. They focus on defining the problem, crafting clear prompts, reviewing AI outputs, and refining the results.
What is AI-assisted product development in 2025? (and beyond)
AI-assisted development is still in its early days. Think of it like the internet in 1998: full of potential but rapidly evolving. Because the product development landscape is changing quickly, the definition of AI-assisted product development is always shifting as well.
At its core, however, AI-assisted development means augmenting traditional coding workflows with AI-powered tools that automate routine tasks, speed up prototyping, and improve testing.
Today’s tools, such as GitHub Copilot for AI coding assistance, Figma with a Model Context Protocol (MCP) setup for design collaboration, Anthropic’s AI models for advanced reasoning, and OpenAI’s suite, are helping teams deliver software faster and with less manual effort.
In parallel, existing tools like SonarQube, though not AI-based, play a key role in validating that AI-generated code meets quality and security standards.
As new tools emerge, product teams continually experiment to find better ways of incorporating AI into their processes. This ongoing evolution improves speed, innovation, and quality for teams.
AI isn’t just changing what teams build; it’s changing how they build, resulting in a complete rewiring of the product operating model.
Where does AI create the biggest strategic impact for product development?
AI-assisted product development delivers value across several critical business dimensions:
- Faster time to market: Automating repetitive coding tasks allows teams to ship new features and products more quickly.
- Improved team efficiency: By reducing manual effort, engineers can spend more time on complex problems and creative solutions.
- Accelerated innovation: AI enhances idea validation and rapid prototyping, enabling teams to test and iterate faster. This supports a fail-fast mindset, identifying what doesn’t work early, learning quickly, and reallocating effort to the most promising ideas. With lower costs and shorter cycles, teams can experiment more boldly without slowing down delivery.
- Higher quality: AI-driven testing and code reviews help identify bugs early and reduce production errors.
Innovation is the most important benefit. At the rate our world and technology are evolving and becoming more sophisticated, it can be hard to keep up. By using AI to generate hypotheses and validate them quickly, teams can outpace competitors and deliver more value to customers.
Measuring ROI of AI-augmented development: Why velocity alone is not enough
Many business leaders focus on velocity, or how quickly code is written or deployed. However, velocity by itself is an incomplete measure of success.
Moving faster means little if it results in lower quality, higher cost of maintenance, or more bugs. True ROI from AI comes when teams achieve both speed and quality improvements. This means delivering features faster without sacrificing stability or user experience.
Leaders should evaluate AI’s impact based on several factors:
- How quickly teams can innovate with AI tools, supporting a validated learning approach that tests hypotheses based on real feedback rather than assumptions.
- The quality and reliability of AI-generated code.
- Cost savings from reduced manual development time.
- Business outcomes such as quicker customer satisfaction and revenue growth.
Moving faster means little if it results in lower quality, higher cost of maintenance, or more bugs. True ROI from AI comes when teams achieve both speed and quality improvements. This means delivering features faster without sacrificing stability or user experience.
Lessons learned from real-world examples
At Modus Create, we ran an AI tooling experiment and found that using AI tools reduced delivery time by 60% while maintaining the same level of code quality. The key was a thoughtful approach to integrating AI, breaking down problems into smaller tasks AI can handle, and using human review to refine outputs.
AI-assisted product development works best when teams understand its strengths and limitations. It is not magic, and it requires skillful orchestration.
Teams that invest time in experimentation, training, and learning initiatives see the fastest gains. For instance, junior developers new to a domain may not immediately benefit from AI tools because they lack the context to evaluate and refine AI outputs effectively. In contrast, experienced teams reason on how to break complex problems into smaller action items that AI can iterate over to produce faster and higher-quality work.
How does AI enhance collaboration across product and technology teams?
AI-assisted product development transforms how developers, designers, and product managers work together. Clear, detailed communication becomes even more critical because AI depends on well-structured inputs and context.
Designers and product managers must provide thorough documentation of requirements, user stories, and expected behavior. Developers then feed this information into AI tools to generate code or prototypes rapidly.
For leaders, this means investing in tools and practices that promote intentional communication and documentation. Building these habits leads to smoother collaboration and more effective AI use.
What should you look for when investing in AI tools?
When adopting AI tools, business leaders should aim to enhance existing workflows rather than overhaul them. Tools that require massive changes in how developers work risk reducing productivity during adoption.
Effective AI tools for product teams should:
- Integrate seamlessly with current developer environments.
- Be backed by quality AI models, such as Anthropic’s Claude Sonnet.
- Support experimentation without forcing immediate production use.
A smart starting point is automating boilerplate code or building internal tools to automate a specific process. These use cases show quick ROI and allow teams to gain familiarity with AI assistance before expanding to more complex tasks.
In general, business leaders should keep their focus on:
- Defining clear problems to solve with AI, rather than adopting AI for its own sake.
- Prioritizing AI tools that empower humans instead of replacing critical human judgment.
- Testing AI outputs extensively before deploying in production, especially for quality and customer impact.
- Watching for tools that support “human in the loop” workflows, where AI assists but humans remain the decision makers.
AI is a powerful amplifier, but it requires human guidance to deliver true value.
Building a culture that embraces AI
Let’s face it: technology alone does not deliver ROI. Effective AI adoption depends on leadership fostering a culture that encourages experimentation and tolerates failure.
CIOs and other executives must create environments where product teams can try out new AI tools without fear of fallout from early missteps. This means celebrating learning and progress, rather than solely rewarding outcomes.
Reskilling is essential for AI-augmented product development to work. Investing in new disciplines such as prompt engineering and AI problem-solving will help teams unlock AI’s full potential. Business leaders who promote continuous learning and iteration will be the ones to create the foundation for long-term AI success.
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