By Sanjay Sehgal, Founder, Chairman, and CEO, Aziro (Formerly known as MSys Technologies)
Product Engineering Services assume the significance of strategic proportions as their role shifts from software development to anticipating change and precisely executing it.
Product Engineering in the AI-driven era demands significant transformations, especially those deeply rooted in clarity, agility, and continuous evolution. Today’s landscape warrants product engineering services (PES) to be crucial in building strategic capabilities. Rather than just building software, PES must boast capabilities such as sensing change, learning from data, and responding with precision. Thankfully, AI plays a crucial role in this transformation and catalyses the shift in PES through intelligence and adaptability.
From Code Delivery to Intelligent Problem Solving
Days are gone when product engineering means following a straight line from A to B. The old-school approach of writing code to match requirements is not yielding results. Further, the methodical perspective of building and testing everything no longer cuts it. AI has completely shaken things up. These days, engineers are diving deep into system-level challenges that would’ve seemed like science fiction a decade ago. The real magic happens as AI anticipates what users will do next, crunches performance data on the fly, and adapts to how people use products in the wild.
Engineering teams need more than just technical chops now – they must understand the business side of things and get into users’ heads. Just cranking out code and calling it a day doesn’t work today. AI has made the process completely living and breathing. Technology helps teams work together, letting data guide the way, and act decisively to keep their ears to the ground while receiving user feedback. AI has also made it easy for the development teams to figure out what works for the users and how development teams can integrate feedback for more resilient results. Instead of sticking religiously to a roadmap, AI has enabled developers to use predictive insights to shape their development efforts.
AI Is Now Core to the Modern Engineering Toolkit
Generative AI, large language models (LLMs), and intelligent automation tools are now part of the developer’s toolbox. They help engineers write code, test features, and even find bugs before deployment takes place. And the value just goes beyond productivity. AI speeds up experimentation, prototypes features, runs simulations, and understands downstream impacts before letting teams release their final proposition into the market.
The use of AI reduces human error, too. With AI-powered optimization, engineering teams can deliver more reliable software with fewer regressions. This doesn’t mean engineers are being replaced. On the contrary, their role is expanding to encompass more strategic work. Engineers are now expected to be system architects, data analysts, and product strategists. They use AI to build more innovative products and to supercharge themselves.
Multi-Agentic AI for Autonomous Collaboration
Many teams are adopting a multi-agentic AI approach, wherein multiple AI agents operate autonomously yet collaboratively to manage different stages of the software development lifecycle. These agents specialize in requirements interpretation, architecture suggestion, code generation, testing, and deployment. Working in parallel and learning from shared outcomes, they reduce development time and improve consistency. This model enables faster decision-making, especially in complex environments where human oversight alone can become a bottleneck.
Engineering Success Now Demands Cross-Functional Synergy
In the age of AI, product engineering can’t be a silo. The lines between engineering, data science, design, and business are blurring. AI models need clean data, and engineering teams are responsible for making that happen. Decisions on what to build and how to build it require input from marketing, product, and even customer support. There’s shared ownership now. This cross-functional approach requires new ways of working. Agile, DevOps, and continuous integration/continuous delivery (CI/CD) are evolving to support AI model training, testing, and deployment. Teams must co-develop and co-validate their assumptions, learning from shared datasets and real-world usage.
Platform Thinking Is Replacing One-Off Projects
AI is also changing how companies think about scale. Instead of building isolated projects, organizations are now creating platforms. A good example is AI-enabled product frameworks that can be reused across lines of business—whether it’s a standard analytics engine, a unified user identity service, or a natural language interface. This means a whole new ball game for engineering service providers. The value isn’t just in delivering on time and budget; it’s in helping clients build long-term capabilities. Partnerships that provide reusable components, data pipelines, and flexible APIs will stand out in this environment.
Talent, Tools, and Culture Define Competitive Advantage
Hiring AI-enabled engineers is part of the equation, but fostering a culture of experimentation, continuous learning, and adaptability is just as essential. Companies today need to move beyond traditional skill matrices. They need to assess teams on how well they work with AI tools, how well they interpret data, and how quickly they can pivot on customer feedback. Culture eats strategy for breakfast – but in the AI era, it also eats your velocity, innovation, and relevance.
Looking Ahead: Building with AI at the Center
Product engineering is not a phase – it’s a fundamental shift in how we think about products. The top echelons of firms, including CEOs and business leaders, need to get this and adjust to the philosophy. Product engineering is now a strategic function where human creativity meets machine intelligence and will be the competitive advantage for businesses. Software Developer Firms need to recognize that they are no longer building software. They are building systems that learn, evolve, and anticipate. The teams that get this will shape the next generation of digital experiences. The time has come for the Digital Teams to lead with clarity, build with purpose, and deliver with intelligence so that better value can be realized across the stakeholders of the development ecosystem.