AI Use Case Funnel Management

What is an AI Use Case?

An AI use case describes the targeted application of data and AI methods to solve a specific problem. Its development and implementation aim to deliver measurable and value-adding results, whether through the optimization of existing processes and products or the development of new services.

A Data & AI use case can function as a standalone application or be part of a larger system, such as machine learning-based object recognition in autonomous vehicles. However, it can also be a business analytics reporting tool (e.g., a dashboard) or the use of generative AI (GenAI) in a specific area of application (e.g., textual correction of test reports from an automotive supplier). In this context, terms such as “Data and AI product” and “AI solution” are often used synonymously.

Use Case Funnel Management as Strategic Compass

It requires a clear roadmap for data and AI solutions to be successfully implemented. This is precisely where use case funnel management comes in. As a strategic compass, it enables all those responsible for data analytics and AI use cases to specifically review, evaluate, prioritize, and steer them through decisive “quality gates.” The analogy to the sales funnel is helpful here: just as a sales funnel identifies the most promising leads and accompanies them to a successful conclusion, the AI funnel ensures that your most promising ideas mature into measurable AI solutions.

Why Companies Need a Use Case Funnel Management

“Who is responsible for which use case?”“Is there a similar use case that is already productive?” and “Which use cases should we prioritize next to achieve the greatest added value?” These questions illustrate how difficult it is to maintain an overview without centralized funnel management. A use case funnel creates transparency and control and ensures that good ideas don't get lost in the chaos.

In today's data-driven world, introducing use case funnel management is not a luxury, but a strategic necessity. The benefits for your company are manifold:

• Strategic Alignment & Maximum Cost-Effectiveness: The AI Funnel ensures that every euro invested and every hour of work goes into data and AI use cases with the highest strategic and economic impact.
• Transparency & Control: You gain a clear, continuously updated overview of your entire data & AI portfolio. You see which projects are underway, which teams are involved, and, most importantly, the value they generate. This transparency keeps control firmly in your hands.
• Efficient Use of Resources: Budgets, valuable data scientists, and technical resources are optimally allocated and deployed. This avoids overload and underutilization, and your resources are concentrated where they provide the greatest benefit.
• Risk Minimization & Increased Agility: Thanks to quality gates, problems, technical challenges, and potential dead ends are identified and addressed early on. This allows for a faster adaptation to new circumstances and significantly reduces the risk of costly missteps.
• Improved Collaboration & Clear Communication: The structured approach and shared quality gates promote seamless collaboration and a common understanding between specialist departments, data science teams, and IT. Silo thinking is broken down and company-wide acceptance increases.
• Sustainable Scalability & Future Viability: A well-thought-out and systematic approach paves the way for the successful scaling of proven data analytics and AI solutions across the entire company. Your data & AI portfolio becomes predictable, manageable, and can grow continuously without losing efficiency.

Who is a Use Case Funnel Management for?

Whether it's AI portfolio managers, central data and AI teams, or middle management roles such as AI product owners and business analysts—wherever use cases are initiated, prioritized, or managed, structured funnel management creates clarity and efficiency. At the same time, C-level executives, marketing and sales, and IT and AI leads as consumers of the portfolio benefit from a unified, centralized view of all relevant initiatives.

Is it worth it?

Companies with at least 8–10 specific use cases or a steady stream of new ideas benefit particularly. Typical characteristics include a medium level of maturity, openness to innovation, disruptive markets, or highly regulated industries.

Maturity Phases of an AI Funnel

An AI Use Case Funnel Management is divided into clear phases, with each reflecting the maturity level of the use case. Each phase ends with a decisive “quality gate.” These gates ensure that only the most promising AI use cases reach the next level and that crucial quality gate assets are in place to make informed decisions and manage risks early on.

Phase 1: Idea

It all starts with an idea. This phase is about gathering an array of potential data and AI use cases. The goal is to capture as many ideas as possible, even vague ones. 

Quality Gate Phase 1: Initial Validation

Purpose: To ensure that the idea fits with the strategic goals, promises potential added value, and can be in the field of data and AI. It is an initial plausibility check to weed out unsuitable ideas at an early stage.

Quality Gate Assets

• Brief Description of the Idea: Concise formulation of the basic idea (1-2 sentences).
• Initial Business Impact Draft: Rough estimate of the potential benefit/problem to be solved.
• Rough Classification: Initial assignment to department and technology (e.g., AI/BA).
• Detailed Use Case Description: Detailed description of the problem, the target group, the desired solution, and the expected outcome.
• Description of the Target Group and Stakeholders: Who are the main users and who are the potential stakeholders?
• Hypothesis on Potential Benefits: A quantifiable assumption about the expected added value (e.g., “Increase in efficiency by X%”).
• Initial Risk Assessment: Identification of obstacles or dependencies.

Phase 2: Concept

In this phase, the use cases described above are examined in more depth. The potential business case is analyzed in detail, and technical feasibility is assessed based on data availability and an initial analytical concept.

Quality Gate Phase 2: Prioritization & Strategic Alignment

Purpose: Classification of the use case within the overall portfolio and decision on which use cases should be pursued further based on their strategic relevance and potential.

Quality Gate Assets

• Detailed Business Case: Comprehensive presentation of the expected ROI, costs, and value contribution.
• Feasibility Study: Assessment of technical feasibility and data availability.
• Resource Requirements Analysis: Estimation of personnel, technology, and budget requirements for a PoC/MVP.
• Detailed Risk Assessment: Detailed identification and assessment of technical and organizational risks.
• Use Case Scorecard: Evaluation of the use case based on predefined criteria (value, feasibility, risk, strategic fit).
• Portfolio Analysis: Visualization of the use case in the context of the entire data & AI portfolio.
• Decision Log: Documentation of the go/no-go decision and the reasoning behind it.

Phase 3: Proof of Concept (PoC) & Prototype

For the qualified use cases, concrete planning now begins with initial practical testing. The aim is to validate key assumptions and reduce technical risks at an early stage before larger investments are made. The analytical concept not only serves as proof of feasibility but is also tested in the form of a prototype to ensure both implementability and user-friendliness.

Quality Gate Phase 3: Go/No-Go for Full Development

Purpose: The final and often most far-reaching decision on investing in the full development of the use case based on the validated findings of the PoC.

Quality Gate Assets

• PoC Results Report: Documentation of findings, results, and limitations.
• Technical Validation: Proof of technical feasibility (e.g., model performance, data integration).
• Refined Business Case: Adjustment of financial forecasts based on the PoC results.
• Detailed Implementation Plan (Roadmap): Concrete plan for the entire development, rollout, and operation.
• Resource Approval for Development: Confirmation of budgets and personnel.

Phase 4: Pilot & Minimum Viable Product (MVP)

Once approval has been granted, development and iterative implementation of the AI solution can begin. Agile methods are used here, and close cooperation between the business departments and the development teams is crucial.

Quality Gate Phase 4: Solution Integration & Acceptance

Purpose: To ensure that the developed solution meets the defined requirements, can be integrated smoothly, and is operational.

Quality Gate Assets:

• Completed Solution (MVP/Production Version): The developed AI system.
• Comprehensive Test Reports: Documentation of unit, integration, and acceptance tests.
• Security & Compliance Audit: Proof of compliance with relevant security and data protection standards.
• Integration Documentation: Description of the interfaces and the integration process.
• Operational Concept (MLOps/DevOps): Plans for monitoring, maintenance, and support of the solution.
• Final Rollout Plan: Detailed plan for gradual or complete implementation.

Phase 5: Rollout

The final phase involves the company-wide rollout of the implemented solution. The decisive factors here are not only the introduction itself, but also the continuous monitoring of performance in the productive environment and the precise measurement of the actual added value achieved. This phase therefore does not mark the end. Rather, it establishes essential feedback loops for ongoing optimization. 

Quality Gate Phase 5: Performance Review & Continuous Optimization

Purpose: To ensure long-term value contribution and identify optimization potential or new use cases.

Quality Gate Assets

• Performance Reports & ROI Analysis: Measurement of the actual business impact in live operation.
• Feedback From Users & Stakeholders: Collection of experiences and suggestions for improvement.
• Roadmap for Further Development: Plan for future features, improvements, or new use cases.
• Documentation of Lessons Learned: Systematic recording of insights for future projects to continuously improve funnel management itself.

Finding the Right Tool for Your Use Case Funnel Management

Successful Use Case Funnel Management depends not only on the right strategy, but also on the right tools. Initial attempts are often made using standard solutions such as Excel or general task management tools such as Jira. This may be sufficient for getting started, but as maturity increases and the number of data and AI initiatives grows, these approaches quickly reach their limits.

Choosing the right tool depends heavily on company-specific processes, responsibilities, and requirements, especially in the context of data and AI. Below, we look at common options and their advantages and disadvantages.

Excel and Google Sheets

Spreadsheet programs such as Excel or Google Sheets are ubiquitous and familiar to many users. They offer a flexible way to collect and organize data in a structured manner. They can be a quick solution, especially for startups or very small teams.

Advantages

• Low Entry Barrier & Costs: Usually already available in the company, usage is straightforward and requires little additional investment.
• Flexibility: Customizable columns, filter, and sorting functions allow for quick adaptation to simple needs.
• Easy Collaboration: Multiple users can work on a document at the same time (especially with Google Sheets), which facilitates basic exchange.

Disadvantages

• Lack of Scalability: With many use cases or complex processes, it quickly becomes confusing, error-prone, and difficult to maintain.
• Lack of Automation: Manual maintenance is time-consuming; there are no automatic notifications, workflows, or dashboards to actively support the process.
• No Versioning/History: Changes are difficult to track, which can lead to conflicts and data loss in collaborative work.
• Increased Security Risks: Sensitive information can be more easily shared unintentionally, and access control is limited.
• No Specialization or Best Practices: There is a lack of specific functions and predefined structures that are optimal for managing data & AI use cases.
• No Visual Representation: Complex relationships and progress in the funnel are difficult to display clearly.
• No Department-Specific Restrictions: The granularity of permissions is often insufficient for governance requirements in larger teams.

Jira and Related Tools

Jira is a widely used tool for project and issue tracking, particularly popular among agile software development teams. It enables detailed task tracking, assignment of responsibilities, and visualization of workflows. It can also be used to map use case funnels by representing each use case as an “epic” or “initiative.”

Advantages

• Structured Workflows: Customizable workflows and status transitions (e.g., “Idea,” “Qualified,” “In Development”) enable process mapping.
• Clear Task and Responsibility Management: Clear assignment of tasks and progress tracking are core functions.
• Reporting & Dashboards: Offers extensive options for creating reports and overview dashboards to track project metrics.
• Integrations: Numerous interfaces to other development tools facilitate integration into existing ecosystems.

Disadvantages

• Complexity & Training: Jira can be overwhelming for non-developers or business users. Configuration is often difficult and unclear, requiring a training period.
• Focus on Tasks: The focus is primarily on managing tasks and tickets, rather than on the strategic evaluation of the business value of AI use cases or the entire portfolio.
• Not Suitable for Change Management: It is not designed to promote broad acceptance and communication across all departments or to comprehensively support cultural change.
• Licensing Costs: Can incur significant costs for larger teams and more complex requirements.
• Not Specialized in AI Use Cases: Specific evaluation metrics or visualizations for data & AI projects (e.g., data availability, model performance risk) often have to be configured manually or implemented as workarounds, which is prone to errors.
• Lack of End-to-End Visibility: It is difficult to map and control the entire funnel process from the initial idea to value in operation in a comprehensive and coherent manner.

Tailored Funnel Management Solutions

Specialized platforms such as Casebase.ai are specifically designed for managing data analytics and AI use cases. They support the entire funnel process—from idea generation and evaluation to operational monitoring—and specifically address the challenges of AI portfolio management. The focus is on business requirements, strategic prioritization, and ensuring added value.

Tools such as Jira can still be used for technical implementation. The key is the close integration of both systems: the specialized platform defines the strategic use cases with goals and metrics, while the task management tool controls the operational work packages and workflows. This way, the platform remains the “single source of truth” for strategy and prioritization, while Jira ensures implementation in the teams.

Advantages

• Customized Functionality for Data & AI:Developed with a specific focus on data & AI use cases. This includes predefined evaluation metrics (e.g., accurate ROI estimation for AI, technical complexity of AI models, MLOps maturity), risk assessments, and governance checks that are missing from standard tools.
• Holistic Funnel View:Provides a clear, consistent overview of all phases and the maturity level of each use case in the funnel – from the initial idea to scaling.
• Automated Workflows & Quality Gates: Supports compliance with defined processes through automatic status transitions, notifications, and checklists for each “quality gate,” significantly increasing process discipline.
• Improved Prioritization Framework: Enables data-driven, transparent, and strategically sound prioritization based on criteria relevant to AI projects.
• Optimized Collaboration & Governance: Promotes seamless collaboration between business and technical teams and supports governance of the entire AI portfolio through integrated compliance and risk management capabilities.
• Integration of Relevant AI Metrics: Can directly integrate and visualize specific metrics such as model performance, data quality, or MLOps maturity.
• Intuitive Visualizations: Often provides specialized dashboards and reports that clearly illustrate the progress and value contribution of AI initiatives in a way that is understandable to all stakeholders.

Disadvantages

•  
  Specialized Solution: Requires implementation and adaptation to the specific processes of the company, as they are less “plug-and-play” than Excel.
• Increased Costs: Generally higher licensing costs compared to standard tools such as Excel or Jira, but these can be offset by efficiency gains and avoided misinvestments.
• Dependence on the Provider: Customizations or further developments are tied to the platform's range of functions and roadmap.

Casebase: Our AI Use Case Funnel Management Solution

Looking Ahead

In summary, without structured use case funnel management, scaling data and AI in companies quickly becomes an unmanageable challenge. It is the key to a transparent and value-adding AI portfolio. Given the growing pressure to derive efficiency and measurable value from your data and AI investments, now is the right time to take proactive action and establish this system before complexity and costs get out of hand.

Look forward to the second part of our series! There, we will highlight why these principles are particularly important for agentic AI use cases, what new challenges await you here, and how management is related to microservices and core assets.