Cloud & AI for the Enterprise
Infrastructure dependencies determine how easily a use case can scale across environments, regions, and teams. You see them in the systems the workflow relies on, the data pipelines it needs, the hardware it requires, and the architectural assumptions baked into the solution. Some use cases depend on lightweight, cloud‑native components that scale naturally. Others rely … Read more
Vertical scalability reflects how well a system grows by increasing the power of a single unit — more CPU, more memory, faster storage, or more powerful GPUs. You see it in architectures that rely on larger machines rather than more machines. When vertical scalability is strong, performance improves predictably as resources increase. When it’s weak, … Read more
Horizontal scalability reflects how well a system grows by adding more units — more servers, more containers, more nodes, more parallel workers. You see it in architectures designed to distribute load rather than concentrate it. When horizontal scalability is strong, the organization can expand capacity simply by adding more instances. When it’s weak, growth becomes … Read more
Scalability describes how well an AI or cloud capability grows with your business. You see it in how easily a workflow handles more users, more data, more complexity, or more locations without breaking down. Some tools work beautifully in a pilot but struggle when usage expands. Others are designed for scale from day one, absorbing … Read more
Risk scoring gives you a structured way to quantify the exposure associated with an AI or cloud use case. Instead of relying on broad labels like low‑risk or high‑risk, you can assign a score that reflects data sensitivity, workflow criticality, regulatory expectations, and the potential impact of errors. This score becomes a practical tool for … Read more
Operational risk reflects how much disruption a use case can create if something goes wrong. You see it in workflows where errors slow production, delay shipments, affect customer commitments, or introduce instability into core processes. Some use cases operate at the edges of the business with minimal impact. Others sit in the heart of daily … Read more
Data privacy considerations shape how safely and responsibly AI and cloud capabilities can be deployed. You see their impact in how data is collected, stored, accessed, and used across workflows. Some use cases rely on low‑sensitivity data with minimal restrictions. Others depend on personal, confidential, or regulated information that requires strict controls. This benchmark helps … Read more
Regulatory sensitivity reflects how closely a use case is tied to laws, standards, and industry‑specific compliance requirements. You see it in workflows where data handling, decision logic, or model outputs must meet strict rules. Some use cases operate in environments with minimal oversight. Others sit in domains where even small deviations create exposure. This benchmark … Read more
High‑risk use cases sit in the parts of the business where errors carry real consequences. These are the workflows where data is sensitive, decisions are high‑impact, and regulatory or operational controls are strict. You see these use cases in credit decisions, clinical recommendations, safety‑critical operations, and any workflow where automation directly influences outcomes. Even when … Read more
Low‑risk use cases give you the fastest path to visible value because they operate in controlled environments with limited exposure. These are the workflows where data sensitivity is low, the impact of errors is manageable, and the decisions supported by the tool don’t carry regulatory or operational consequences. You see these use cases in internal … Read more