Why the Best Path to ROI for Generative AI is Simplicity in Design

The growing need to justify AI projects with clear ROI calculations has led many organizations to adopt a simplified approach that focuses on enhancing existing applications and metrics, rather than building entirely new AI systems. This shift is helping IT teams deliver value faster while reducing the scope and complexity of AI projects.

Key Takeaways: Leveraging existing applications and metrics as the foundation for AI projects offers several compelling benefits:

• Teams can bypass the need to create and gain consensus on new ROI calculations, allowing them to deliver prototypes much faster.
• Using mostly the same data and application logic simplifies compliance and security processes, which often delay larger-scope AI projects.
• Starting with greater clarity around the design and value-add of the AI enhancement leads to a shorter design process and fewer post-commit changes.

Addressing Management Concerns: As executive management increasingly demands ROI justification for AI projects, IT teams are adapting their strategies:

• Focusing on improving metrics that are already being measured provides a clear path to demonstrating the value of AI enhancements.
• Reducing project scope to target the most compelling improvements helps teams deliver larger ROI while managing the complexities of AI development.

A Pragmatic Shift: While this simplified approach may not be suitable for every AI project, it offers a valuable shortcut for many organizations:

• CIOs are recognizing the challenges surrounding the design and justification of entirely new AI applications.
• Concentrating efforts on enhancing existing systems and metrics is proving to be an effective way to deliver tangible results more quickly.

Broader Implications: The trend towards simplifying AI projects by building upon existing applications and metrics reflects a maturing understanding of how to effectively integrate AI into business processes:

• As organizations gain more experience with AI, they are learning to balance the transformative potential of the technology with the practical challenges of implementation.
• By focusing on incremental improvements rather than wholesale reinvention, IT teams can deliver value more consistently while managing the risks and uncertainties associated with AI development.

This pragmatic approach to AI adoption is likely to become increasingly common as more organizations seek to harness the power of AI while ensuring that their investments deliver measurable returns. However, it will be important for IT leaders to strike a balance between this incremental approach and the pursuit of more ambitious, transformative AI projects that have the potential to create entirely new sources of value for the business.