Our client, a leading German telecommunications company, needed a consistent way to turn AI ideas into governed products and identify potential cost savings and revenue opportunities. Working together, we developed an Alfabet-based approach that links demand, use cases, and AI products with portfolio governance, technical architecture, and business value.
01 The Challenge
Why AI Products Need More Than Just a List of Ideas
AI initiatives often start in different teams, with varying levels of detail and different paths to implementation. Without a shared process, teams may pursue similar ideas independently and overlook data or models that could be reused. As a result, decision-makers lack a complete picture of costs, value, risks, and technical dependencies.
Our customer therefore needed more than just a list of AI use cases. The goal was to establish a clear path from the initial idea to a product that can be delivered, operated, improved, and eventually retired. Responsibilities, governance requirements, and the connection between business needs and the technical product had to remain clear throughout.
02 The Approach
A structured path from idea to portfolio
Seize the opportunity
New AI ideas are submitted to Alphabet through a standardized request process. Each request includes information on its owners, the sponsoring IT unit, stakeholders, operational responsibility, and expected business value. Its status indicates whether the idea is open, being assessed, under review, approved, or complete.
Assessment and prioritization take place within the demand process. The outcome is clearly documented: the idea is either rejected or becomes an approved AI use case. This creates a traceable handoff from exploration to delivery without the need for a separate shadow list.
Shape the AI product
The operating model distinguishes between three related elements. A demand tracks an idea from discovery to decision, while an AI use case describes how AI will address an approved business need. The AI product is the solution that is ultimately delivered and operated.
This distinction is important because a single AI product can support multiple use cases. The product has its own architecture, ownership, and lifecycle, while each use case retains its specific business purpose, value, and governance context. Products and use cases are assigned separate identifiers but remain connected through a many-to-many relationship.
Alfabet links each product to its use cases, data products, technology platform, hosting components, AI models, and supporting technologies. Teams can identify dependencies, opportunities for reuse, and the people responsible for the product in operation.
Manage the entire lifecycle
The AI Product Operating Model covers discovery, delivery, maintenance, and retirement. Discovery begins with an initial assessment and continues with a detailed review of business and technical feasibility, while delivery covers development and rollout. Once the product is in operation, maintenance includes monitoring, fixes, and operational support.
Governance is built into each stage, beginning with an early assessment that filters ideas before significant effort is invested. Product councils document decisions regarding further assessment and delivery, while portfolio, project, privacy, cybersecurity, and AI risk processes provide the necessary approvals.
The playbook also provides a structured framework for prioritization. It takes into account strategic fit and expected value, as well as confidence, reusability, implementation costs, and the effort required from IT and AI teams. This helps direct limited resources toward initiatives with credible value and a realistic path to delivery.
Manage products as a portfolio
Alfabet reports provide an overview of the AI portfolio and illustrate the interdependencies between use cases, products, data, and technology. Color coding highlights relevant attributes, while Kanban views track progress through the process. Business value is presented alongside technical and governance information.
Decision-makers can see which opportunities are being evaluated, which products are in the delivery or operational phases, and which use cases rely on the same assets. This allows teams to compare similar initiatives before developing separate products. It also highlights shared data, platforms, and models, turning reuse into a deliberate portfolio decision.
The product bridges the gap between business value and real-world implementation. It transforms an approved AI use case into something that can be owned, operated, and improved.
03 The Outcome
A Connected Foundation for the AI Portfolio
The implemented Alfabet model provides our customer with a structured foundation for managing AI requirements, products, and the broader portfolio. Rather than remaining isolated entries, ideas, use cases, and AI products remain linked to the relevant data, models, technologies, owners, risks, and business value.
The operating model playbook defines the broader process built on this foundation. Although some elements continue to evolve, the direction is clear: a single, transparent path from an idea to a governed AI product, with decisions and dependencies visible throughout its lifecycle.
AI portfolio management is one of the use cases we implement in Bizzdesign Alfabet. Explore Alfabet's capabilities or talk to us about your own operating model.