An Open GenAI Business Case Analysis Best Practice: A Methodology for Data Program Transformation

Methodology Executive Summary

Read more on Xentity Blog: From Months to Momentum: Xentity’s AI-Driven Strategy for Faster Data Program Transformation

This site/document presents a standardized methodology for conducting a comprehensive business case analysis for Generative AI (GenAI) initiatives. Its purpose is to provide public agencies and other organizations with an open, repeatable, and business-driven process to ensure that investments in GenAI are strategically aligned, technically sound, ethically responsible, and deliver measurable mission value. The intended audience includes program managers, IT strategists, data scientists, and agency leadership who are tasked with evaluating and proposing GenAI integrated with AI, Analytics, and data workflow ssolutions.

The methodology outlined herein is a phased approach that guides users from initial concept to a full “Modernization Blueprint.” 

Adopting this best practice offers significant benefits, including:

  • Tighter alignment of GenAI, AI, Analytics, and data solution investments with core mission objectives.
  • Improved decision-making through a structured and evidence-based analysis.
  • Enhanced stakeholder buy-in and communication.
  • Proactive identification and mitigation of technical, operational, and ethical risks.
  • A clear, data-informed projection of return on investment (ROI).

This standard leverages the principles of the Methodology for Business Transformation (MBT) and is informed by the practical application of these concepts in the OGC Climate and Disaster Resilience Pilot reports on GenAI in Wildland Fire Management.

The Need for a Structured Approach

Generative AI integrated with AI, Analytics and data workflow solutions offers transformative potential, with the ability to scale data processing beyond human capabilities and generate novel text, images, and data-driven insights in response to complex prompts. From enhancing disaster response planning to automating complex analysis, the opportunities are vast. However, the rapid evolution of GenAI also presents significant challenges. Issues such as model inaccuracies, “hallucinations,” goal drift, high costs, and the need for specialized talent require a disciplined and structured approach to adoption.

Without a rigorous business case, organizations risk investing in solutions that are poorly aligned with user needs, technically unsustainable, or ethically unsound. This best practice provides the necessary framework to navigate this complexity, ensuring that GenAI is not just a technological pursuit but a strategic enabler of the organization’s mission. The process detailed here is based on the methods used to produce successful analyses, such as the OGC Engineering Reports on GenAI for Wildland Fire, which assessed user needs, data sources, technology components, and implementation challenges to provide actionable recommendations.

Guiding Principles

To ensure success, the GenAI business case analysis process shall be guided by the following core principles:

  • Mission-Driven: The primary driver for any GenAI initiative must be its direct alignment with the organization’s mission and strategic goals. The analysis must clearly articulate how the proposed solution will advance specific business objectives, whether it involves improving situational awareness for disaster response, optimizing resource allocation, or enhancing community resilience.
  • User-Centric and Stakeholder-Engaged: The process must be centered on the needs and workflows of the end-users. As demonstrated in the wildland fire use cases, success depends on involving stakeholders—from national planners to field-level personnel—throughout the process to ensure the solution is practical, trustworthy, and solves real-world problems. A “human-in-the-loop” approach is often essential for validating AI-generated insights and building trust.
  • Data-Informed: Decisions must be rooted in evidence. This requires a thorough assessment of the core data needed to train and operate GenAI models. The analysis must evaluate the availability and quality of varied data types, including unstructured documents, tabular datasets, knowledge graphs, and raster collections.
  • Responsible and Ethical AI: A commitment to responsible AI must be integrated from the beginning. This involves proactively addressing security, privacy, and ethical considerations. The framework must include plans for ensuring data provenance, providing traceability for AI-generated content, handling sensitive data like Personally Identifiable Information (PII), and mitigating model bias.
  • Iterative and Agile: GenAI technology and organizational needs evolve rapidly. Therefore, this methodology promotes a phased, iterative approach over a monolithic one. It encourages developing proofs-of-concept and pilots to test assumptions, gather feedback, and refine solutions before committing to full-scale deployment. This aligns with the understanding that a continuous training and labeled data improvement lifecycle is necessary to maintain model accuracy and relevance over time.

The approach leverages the Methodology for Business Transformation (MBT)

First, a thorough understanding of the MBT is crucial. From the information available on the Xentity website, the MBT is a business-driven, enterprise architecture approach to transformation and integrated change. It emphasizes a structured, phased methodology to ensure that business transformation is aligned with strategic goals and delivers measurable value.

The key tenets of the MBT appear to be:

  • Business-Driven: The process starts with understanding the business needs, drivers, and strategic context.
  • Segmented Approach: It breaks down the transformation into manageable “segments” for analysis and implementation.
  • Stakeholder-Centric: It heavily involves stakeholders throughout the process to ensure buy-in and that the solutions meet user needs.
  • Performance-Focused: It aims to align products and services directly with performance drivers and improve efficiencies.
  • Iterative and Adaptive: The methodology has evolved through lessons learned, indicating a culture of continuous improvement.

The MBT appears to have several steps, including:

  • Initiation and Scoping: Defining the scope and objectives of the transformation effort.
  • Analysis (Business and Technical): Analyzing the “as-is” state and defining the “to-be” state.
  • Developing Recommendations: Authoring a “Modernization Blueprint” with actionable recommendations.

Implementation and Governance: Integrating the blueprint into the enterprise plan and portfolio, with a focus on governance and change management.