Use this category only when the content is materially about the category.
Definition: Artificial Intelligence means using machine capabilities such as learning, reasoning, problem-solving, data analysis, automation, or forecasting to support organisational decisions, workflows, or product/software delivery. The focus must be on AI itself, not merely on digital tools or general improvement.
Must have:
- Explicit discussion of AI or machine intelligence performing tasks normally requiring human intelligence, such as learning, reasoning, or problem-solving.
- Use of AI-generated data insights, real-time analysis, or forecasting to improve decision-making.
- Automation of repetitive work, workflows, resource allocation, or delivery activities through AI.
- Application of AI within Agile, DevOps, product development, or organisational delivery practices.
- Claimed outcomes tied to AI, such as reduced lead times, improved forecast accuracy, increased efficiency, innovation, or better customer-aligned products.
Strong fit:
- Primary: The category is the main subject and at least two Must have items are discussed.
- Secondary: The category is a substantial supporting theme and at least one Must have item is discussed.
Weak fit:
- Tertiary: AI is mentioned or adjacent, but the content is mostly about Agile, DevOps, delivery, data, automation, or innovation in general.
- Ignored: AI is absent, only implied, or supported only by generic domain language.
Exclude:
- Data Analytics or Business Intelligence where the focus is dashboards, reporting, or metrics without AI capabilities.
- Automation or DevOps tooling that does not use AI, machine learning, reasoning, or predictive analysis.
- Agile, Lean, or continuous improvement practices where AI is not a material mechanism.
- Generic discussion of innovation, efficiency, decision-making, collaboration, resilience, software development, or digital transformation; generic terms are insufficient on their own.