Traditional product development often starts as a series of handoffs; the product manager writes a detailed Product Requirements Document (PRD), design translates it into a solution, and engineering brings it to life. But by the time that document is “ready”, the assumptions have already shifted.
This breakdown happens because product development is often still viewed as predictable and linear. Write the requirements. Design the solution. Build the product. But modern product development is neither predictable nor linear. The reality is far more fluid and far more effective when embraced intentionally.
At project44, we’ve formalized what high-performing teams do naturally. We treat product development as an integrated, iterative process of discovery. Product, Design, Engineering, customers, and end users collaborate from the start, learning together as understanding evolves. AI has become a central part of this process. It helps us move faster, explore more options, and uncover insights earlier. More importantly, it deepens collaboration by allowing every function to contribute with greater clarity and creativity.
This approach has transformed how we define, design, and deliver solutions. It’s not about skipping steps; it’s about recognizing that progress happens when everyone contributes to shaping the right outcome from the beginning.
How work really happens
In theory, the PRD should capture everything a team needs to build the right product. In reality, it’s almost impossible for a Product Manager (PM) to finalize a PRD before Design and Engineering engage. The process is too dynamic, the unknowns too significant, and customer and end-user input too valuable to defer.
We’ve embraced this truth. Product, Design, and Engineering all begin working together early, each focusing on their area of expertise while influencing the others.
- Product defines business goals and customer needs through stakeholder conversations.
- Design conducts research, interprets user behavior, and explores solutions that balance user needs with business constraints.
- Engineering investigates technical opportunities and constraints, prototyping early to identify what’s feasible.
These activities happen in parallel, informing each other continuously. For example, a design concept might reveal a new business opportunity, or a technical exploration might expose a better user experience path. When all three disciplines collaborate in real time, teams converge on better solutions more naturally and with fewer cycles of rework.
The evolving PRD
In this model, the PRD is not a gate to pass before work begins. It’s a living document that evolves as teams learn.
A product manager typically starts with what’s known: customer goals, early assumptions, and a rough problem statement, and shares it openly. As Design conducts research and Engineering begins exploring technical feasibility, new insights emerge. Those insights feed directly back into the PRD, refining the problem, clarifying the opportunity, and aligning the team.
By the time the PRD feels complete, the team already has a shared understanding and validated direction. It’s not a static deliverable created in isolation; it’s a reflection of what the team has discovered together.
This shift removes unnecessary waiting and handoffs. Instead of treating planning and execution as separate phases, we see them as two sides of the same discovery process.
AI as an integral part of discovery
AI plays a significant role in enabling this way of working. It’s embedded throughout the product development process, from research to design, to engineering.
In research: Teams use AI to synthesize large volumes of data from user research, interviews, and feedback sessions.
In design: Designers use it to explore a wider range of solutions quickly, testing visual and interaction patterns before narrowing in on what’s most effective.
In engineering: Engineers use it to prototype and validate technical possibilities earlier, identifying constraints before they become blockers.
The value isn’t just speed, although that’s a major benefit. AI expands creativity and improves decision quality. It allows teams to see more of the problem space before committing to a solution, which ultimately leads to better outcomes for customers and end users.
At project44, AI has helped us move faster with smaller teams, explore more ideas, and improve the overall quality of our work. It’s not replacing expertise; it’s amplifying it.
Customers and end users as co-creators
Customers and end users are active participants in this process, not endpoints. We engage with them early and often, from shaping problem statements to validating early concepts.
This includes regular conversations during active discovery, co-development partnerships with select customers who help test early prototypes, and structured research methods like usability studies, contextual inquiries, and surveys.
The key is that customer and end user involvement isn’t a validation checkpoint at the end. It’s woven throughout discovery. We’re not asking customers, “Do you like what we built?” We’re asking, “Help us understand your world so we can build what you need, with you.”
By bringing customers and end users into the discovery process, we reduce the risk of late-stage surprises and strengthen the relationship between our teams and theirs. It also ensures our solutions work not just in theory but in the real-world environments where they’ll be used.
Making collaboration intentional
The biggest difference in our approach is that we’ve made this way of working intentional. Instead of treating cross-functional iteration as an exception, we’ve built our culture and processes around it.
By recognizing that progress comes from collaboration, and that AI enhances every stage of that collaboration, we’ve created a structure that scales with speed and precision. Each role contributes differently but works toward the same outcome: delivering meaningful, high-quality solutions that improve supply chain visibility and performance.
This model makes us a stronger execution partner for our customers and a more adaptive organization in a rapidly changing market.
Key takeaways
- Clarity comes from collaboration, not handoffs. The best solutions emerge when Product, Design, and Engineering work together from day one.
- The PRD is a living document, refined through shared discovery rather than finalized in isolation.
- AI is integral to research, design, and engineering, expanding what teams can explore and the quality of decisions they make.
- Customers and end users are partners throughout the process, helping to validate solutions from the start.
- Making collaboration intentional through clear processes and structural support improves quality, alignment, and speed.
Follow project44 for more insights on how we’re combining design, technology, and AI to redefine how modern product organizations build, learn, and deliver together.
