AI-Accelerated Engineering: How AI Can Transform the Product Owner Role | IJCT Volume 13 – Issue 4 | IJCT-V13I4P2
International Journal of Computer Techniques
ISSN 2394-2231
Author
Toby Chembakassery Mathew
Abstract
Generative AI is already changing software delivery in practice. Coding assistants, automated test generation, and AI-supported refactoring now let engineering teams move from intent to working software in a fraction of the time the same work took only a few years ago. The requirement definition side of delivery, owned by the Product Owner, has not kept pace. Problem discovery, business-rule analysis, prioritization, backlog elaboration, requirement capture, and outcome reporting are still performed largely by hand. The result is a velocity gap: an engineering function that can build faster than the product function can decide what to build. This paper argues that the Product Owner role is becoming the binding constraint on delivery throughput, and that the response is not to ask Product Owners to work harder but to apply AI to their own workflow. It presents a six-stage model of Product Owner work, identifies the dominant pain point and a corresponding AI intervention at each stage, and quantifies the resulting reallocation of effort. Under the model, roughly a third of a Product Owner’s time shifts away from mechanical, low-value tasks toward the discovery and alignment work that AI cannot perform independently. Because AI also compresses the discovery cycle itself, the same shift lets a Product Owner carry more discovery in parallel, raising the throughput of validated work the delivery pipeline can sustain.
Keywords
Product Ownership, Generative AI, Agile Delivery, Product Discovery, Requirements Elicitation, Backlog Management, Software Delivery Velocity, Delivery Throughput, AI Augmentation., Product Owner.
Conclusion
The acceleration of engineering by AI has relocated the bottleneck in software delivery. An engineering function that ships in days is throttled by a product function that still validates and defines in weeks, and no amount of additional engineering capacity resolves the mismatch. The constraint is upstream.
The answer is not to ask the Product Owner to absorb more mechanical work, nor to hand the role to a model. It is to give the Product Owner the same tools that reshaped engineering — for rule lookup, data access, backlog drafting, and status reporting and to put the freed time into the parts of the role that need judgment. The model puts that shift at about a third of a Product Owner’s week, roughly twelve hours, moving from mechanical work into discovery, validation, and alignment. That keeps the definition side of delivery in step with the execution side, and it lets a Product Owner spend more of the week on work that moves the organization’s objectives rather than on overhead.
References
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How to Cite This Paper
Toby Chembakassery Mathew (2026). AI-Accelerated Engineering: How AI Can Transform the Product Owner Role. International Journal of Computer Techniques, 13(4). ISSN: 2394-2231.
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