Revolutionizing Product Management with AI: From Ideation to Implementation

Artificial intelligence and large language models (AI/LLMs) have emerged as powerful tools that can transform how products are conceived, developed, and delivered. A fascinating new paper by Gayathri Shriram of TCS and Mark Anning of Openreach published in the Enterprise Technology Leadership Journal (Spring 2025) explores how these technologies can revolutionize the entire product life cycle.

Their paper, “Revolutionizing Product Management: From Ideation to Implementation Utilizing AI/LLMs,” offers a comprehensive framework for leveraging AI/LLMs throughout the product management process. The authors present proven prompts and methodologies that can help product owners and managers streamline their work, enhance productivity, and ultimately deliver better products to market.

The Problem: Time-Strapped Product Owners

The authors identify a critical challenge in product management: product owners (POs) are perpetually time-constrained. This is especially true for experienced POs who often support newer team members while managing their own heavy workloads. Meanwhile, less experienced POs may neglect key practices or apply them without sufficient rigor, requiring additional guidance from senior colleagues and agile coaches.

This creates a catch-22 situation where those with the most expertise have the least time to share it, while those needing the most support struggle to develop comprehensive skills independently. The authors hypothesized that AI/LLMs could bridge this gap, saving valuable time while improving the quality and consistency of product management activities.

The Solution: AI-Enhanced Product Management

Through their initiative “Charting the Course to Requirements Excellence in DevOps,” conducted across Openreach Tribes in 2024, the authors experimented with AI/LLM implementation in product management. Their goal was twofold: make product owners’ lives better by saving time and achieve sustainable improvements in value realization, workflow efficiency, and product quality.

The results were impressive—they observed approximately 26% time savings in generating product artifacts and a significant reduction in defects through the application of AI/LLMs, particularly in the solution space.

A Framework for the Product Lifecycle

The paper organizes product management activities into four phases—discover, define, develop, and deliver—and provides specific AI/LLM prompts for key tasks within each phase:

Discover

• Brainstorming ideas
• Creating OKRs and metrics
• Prioritizing tasks
• Developing the product vision

Define

• Creating personas and empathy maps
• Developing requirements

Develop

• Creating journey maps and story maps
• Disaggregating high-level features into stories
• Writing user stories and acceptance criteria
• Developing product roadmaps
• Creating product requirements documents
• Generating release notes
• Drafting communication emails
• Creating survey questions for customer feedback

Deliver

• Classifying and summarizing bugs
• Summarizing customer feedback and soliciting suggestions

For each task, the authors provide detailed AI/LLM prompts and demonstrate their effectiveness through the journey of a fictional product owner named “Steve.” He leverages these tools to develop a unified communication platform app for telecom engineers, designed to meet Ofcom Quality of Service targets and improve customer satisfaction.

Benefits Beyond Time Savings

While saving POs’ time was a primary goal, the authors discovered additional advantages:

1. Enhanced Productivity: AI/LLMs automate routine tasks, allowing product managers to focus on strategic decisions and innovation.
2. Improved Quality: The systematic approach enabled by AI/LLMs brings increased diligence to tasks that might otherwise be rushed or overlooked, leading to fewer defects and better products.
3. Better Collaboration: Structured outputs from AI/LLMs create a common language and format for communication among team members and stakeholders.
4. User-Centric Approach: Tools like AI-generated empathy maps, impact maps, and journey maps help ensure products are both technically sound and aligned with user needs.

Real-World Application

The paper presents a cohesive story of how product owner Steve uses AI/LLMs to navigate each phase of product development. For instance, when Steve needs to create a product vision, he uses an AI/LLM prompt that generates a comprehensive vision statement with key components, long-term goals, and success metrics.

In another example, when preparing for a product launch, Steve uses AI/LLMs to draft release notes and communication emails to management, ensuring consistent messaging and proper highlighting of benefits in terms of objectives and key results (OKRs).

What makes this approach particularly valuable is its practicality. The authors aren’t suggesting that AI/LLMs replace human judgment or collaboration—rather, they position these tools as enablers that free up human capacity for higher-value activities like strategic thinking and stakeholder engagement.

A Collaborative Tool, Not a Replacement

The authors are careful to emphasize that AI/LLMs can’t replace the product owner’s role but instead serve as collaborative tools—similar to pair programming in software development. Many of the techniques articulated through their prompts must still be conducted alongside users, stakeholders, and developers.

This positions AI/LLMs not as a threat to product management professionals but as force multipliers that can help them achieve more with limited resources. The approach allows both new and experienced product owners to maintain high standards while balancing competing demands on their time.

Implementation Insights

For organizations looking to adopt similar approaches, the paper offers several valuable insights:

1. Start with the Right Prompts: The effectiveness of AI/LLMs in product management depends significantly on well-crafted prompts. The authors provide tested examples that can be adapted to specific organizational needs.
2. Focus on High-Value Activities: Not all product management tasks benefit equally from AI/LLM assistance. The paper helps identify where these tools offer the most significant returns.
3. Integrate with Existing Processes: The AI/LLM approach works best when integrated with established product management methodologies rather than replacing them entirely.
4. Measure the Impact: The authors tracked time savings and quality improvements to demonstrate the value of their approach. Similar metrics can help other organizations justify and refine their AI/LLM implementations.

Conclusion

“Revolutionizing Product Management” offers a compelling vision for how AI/LLMs can transform product management practices. By providing concrete examples and practical guidance, the authors have created a valuable resource for organizations seeking to enhance their product development capabilities in an increasingly competitive marketplace.

The paper’s structured approach to integrating AI/LLMs across the product life cycle offers a blueprint that can be adapted to various industries and organizational contexts. As AI technologies continue to evolve, the framework presented by Shriram and Anning provides a solid foundation for ongoing innovation in product management.

For product managers, executives, and technology leaders looking to leverage AI/LLMs to improve their product development processes, this paper offers both strategic insights and tactical guidance. The integration of AI tools into product management isn’t just about efficiency—it’s about raising the bar for product quality and user satisfaction while enabling product teams to accomplish more with limited resources.

- About The Authors

Leah Brown

Managing Editor at IT Revolution working on publishing books and guidance papers for the modern business leader. I also oversee the production of the IT Revolution blog, combining the best of responsible, human-centered content with the assistance of AI tools.

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