Reinventing product engineering with generative AI: A practical roadmap

Product teams are increasingly under pressure to drive new and innovative solutions faster than they have in the past for more sophisticated customers. The product development cycle from idea to deployment can be inefficient(s), creating a longer than desired time to market and a limitation on personalization at scale.

A 2024 McKinsey Digital report "The State of Product Development," reported that about 72% of product leaders view speed to product development as their greatest competitive pressure. 

Generative AI might be part of this solution to new and evolving challenges with product development. In recent research², 86% of executives indicated that technologies like generative AI are now essential for digital product design and development. This means the technology represents an immediate opportunity, not a future aspiration.

How Generative AI could transform the product lifecycle

Unlike conventional product development approaches that primarily rely on human intuition and limited data analysis, generative AI offers capabilities that could fundamentally reshape each phase of the product lifecycle:

• Discovery & Ideation: GenAI has the ability to analyse a large amount of customer feedback, market trends, and competitive intelligence to identify needs that might be overlooked. Organizations can take further use AI to analyse user behaviour, potentially uncovering niche segments and delivering highly targeted content experiences that enhance user engagement.
• Design & Prototyping: While traditional prototyping often takes weeks, the use of generative AI could enable rapid iteration by automatically generating design alternatives based on parameters and constraints.
• Development & Implementation: One of the most promising impacts could come in accelerating development through AI-powered code generation. The Gartner® Magic Quadrant™ for AI Code Assistants⁵ forecasts that by 2028, 90% of enterprise software engineers may use AI code assistants, up from less than 14% in early 2024. This marks more than just efficiency gains; it signals a fundamental shift in how software is built. A survey of 1,000 enterprise AI developers shows that 99% of them are already using coding assistants in some form.  
• Testing & Fine-tuning: Generative AI is proven to be effective in creating detailed test scenarios that human testers might overlook. By simulating a variety of user behaviours and edge cases, these systems could identify issues before products reach customers. This will further help in improving quality assurance processes.
• Continuous Improvement: Generative AI’s ability to enable hyper-personalization at scale through continuous product iteration is seen as one of the most important advantages. Through the analysis of individual customer interactions and preferences in real time, product teams can evolve offerings to match specific user needs. This will prove to unlock previously impossible levels of customization without needing significantly more development resources.

Bridging the implementation Gap

To successfully implement generative AI in product development, organizations need to address several critical challenges:

• Skills: Organizations should invest in upskilling talent while redefining roles to create effective models of human-AI collaboration. This will present a major opportunity for countries with rooted technical foundations. India and its robust STEM education system and growing technology ecosystem is well placed to lead in AI-augmented product development. The nation's developers and engineers have the potential to deliver end-to-end AI solutions that balance innovation with responsible implementation.
• Integration: Rather than replacing proven methodologies like Agile or Design Thinking, GenAI can enhance them. Organizations can integrate AI capabilities into existing workflows instead of creating separate processes. Technology companies could embed generative AI tools within existing sprint cycles, utilizing them during fine-tuning and code review phases to enhance quality without disrupting established team dynamics.
• Data Quality and Governance: The quality of generative AI systems depend on the quality of the data they are trained on. To get meaningful results, organizations must establish robust data governance frameworks to ensure that AI systems have access to high-quality, representative, and ethically collected information. This would require investment in both data infrastructure and AI capabilities.

Future outlook

As generative AI develops, product leaders will need to create systems where human and technological capabilities work in harmony. It will become increasingly important to not only train individuals on the use of AI tools but also to create awareness of the relative importance of technological advancement and understanding the deep, contextual needs of the customer. 

The future will be made for organizations that embrace the analytical power of generative AI while still holding onto the core aspects of being human. The human aspects of empathy, creativity, and ethical decision making will be paramount in creating meaningful/impactful products. Those organizations that can maintain this balance will be the organizations that will shape the future of product engineering excellence.

By Vishal Chahal, Vice President, IBM India Software Labs.