Why Product Management Is The Strategic Glue Your Business Needs

Shyam Alok is an experienced AI-digital transformation & PM leader at Object Technology Solutions in the logistics & supply chain industry.

Despite the promise of AI in reshaping industries, businesses often struggle to transition from pilot projects to enterprise-scale deployments. According to industry reports, up to 85% of AI projects fail to deliver the intended business outcomes. These failures frequently stem not from technical limitations but from a mismatch with organizational goals, poor execution frameworks and inadequate stakeholder buy-in.

During my experience as a director of digital transformation, I’ve seen that product management (PM), with its emphasis on customer-centricity, iterative development and cross-functional collaboration, provides the discipline necessary to navigate these challenges. By treating AI initiatives as products rather than standalone projects, organizations can enable alignment and sustained value delivery.

The Challenges

AI-driven digital transformations often fall short due to the following roadblocks:

• Misaligned Priorities: AI initiatives frequently focus on technical experimentation rather than addressing high-impact business problems.

• Poor Adoption: Solutions that lack usability or fail to demonstrate immediate value often encounter resistance from end users and stakeholders.

• Limited Scalability: Many AI projects remain stuck in pilot phases due to fragmented data systems, siloed teams and insufficient infrastructure.

AI isn’t merely a technology—it’s a transformation enabler. Without structured execution—the kind that PM provides—AI risks becoming another overhyped, underdelivering investment.

The Benefits

The following are core principles of PM and how they’re relevant to AI in digital transformations:

• Customer-Focused Methodology: AI solutions tend to be user-focused thanks to PM. For instance, I included cross-departmental stakeholders early on in the creation of a logistics optimization project that I was managing. Because of the increased trust and lower opposition, it was possible to make iterative improvements that brought technological solutions into line with practical workflows.

• Development Through Iteration: Using Agile approaches, PM encourages continual improvement and quick prototyping. We implemented a gradual rollout of an AI-based inventory management technology in one effort. Early users’ input guided later revisions, lowering risk and improving usability.

• Frameworks For Prioritization: The MCI (moat, cost and innovation) framework is one of the most straightforward priority frameworks that PM offers. Warren Buffett popularized the idea of an economic moat, which describes a company’s capacity to sustain competitive advantages over rivals to safeguard long-term earnings and market dominance. Teams can concentrate on initiatives that optimize return on investment by assessing them according to their strategic impact, cost-efficiency and innovation potential.

• Interdepartmental Cooperation: PM acts as a liaison between business executives and technical teams. My team collaborated with logistics managers and data scientists on a supply chain optimization project to smoothly incorporate AI solutions into current procedures, enhancing delivery schedules and cutting expenses.

Transforming AI From A Project To A Product

Viewing AI initiatives as products with life cycles fosters a mindset focused on continuous improvement and measurable outcomes. Structured execution involves creating clear roadmaps, prioritizing initiatives based on business impact, ensuring iterative feedback loops and aligning technical efforts with organizational goals. This disciplined approach prevents resource misallocation and enhances the likelihood of scalable success.

AI As A Product Life Cycle

• Define the problem statement and validate its importance through stakeholder input.

• Build a minimum viable product (MVP) focusing on high-priority features.

• Ensure solutions are scalable across departments and geographies.

• Incorporate user feedback for continuous improvement.

Prioritization Using MCI And VFS Frameworks

• Using the MCI (moat, cost and innovation) framework, identify opportunities where AI can enhance the organization’s unique value proposition, reduce costs or drive innovation.

• Using the VFS (viable, feasible and scalable) framework, evaluate initiatives based on their technical feasibility, financial viability and scalability potential.

Cross-Functional Collaboration

• Establish dedicated AI steering committees with representatives from business, technology and operations.

• Use shared metrics to align teams and track progress, such as time-to-market, ROI, user adoption rates and model accuracy and performance. These metrics offer a consistent framework for assessing achievement and pinpointing areas in need of development.

Real-World Use Case

A mid-sized retail company that I work with had trouble predicting demand, which resulted in frequent stockouts and overstocking. We started with a single product category and worked closely with stakeholders to characterize the problem using PM concepts. As an MVP, we created an AI-driven forecasting model, tested it repeatedly and made adjustments in response to user feedback. After validation, the solution expanded to more locations and categories. This strategy increased inventory turnover and decreased stockouts by 30%, yielding a quantifiable return on investment and boosting stakeholder trust in AI-driven projects.

Conclusion

In addition to overcoming some of the typical obstacles that hinder AI project success, organizations that implement PM principles will be able to fully utilize AI’s promise for innovation and expansion. So, begin now by determining a high-priority issue, involving stakeholders and applying PM frameworks to produce quantifiable, enduring effects.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?