← All DORA Capabilities

AI Literacy

Climate for Learning

Artificial intelligence literacy determines whether an organisation can responsibly adopt and benefit from AI technologies across its engineering practice. As AI capabilities become embedded in development tools, platforms, and products, engineers must understand how these systems work, where they can add value, and where their limitations lie. Without a baseline level of AI literacy, teams risk misusing AI, introducing security or data risks, and making architectural decisions that are poorly informed or difficult to sustain.

A mature approach to AI literacy focuses on building capability progressively across the engineering workforce. Engineers should develop the ability to critically evaluate AI outputs, apply AI to accelerate engineering work, and design systems that integrate AI safely and effectively. Organisations that treat AI literacy as a strategic capability unlock new forms of productivity and innovation. Those that neglect it risk fragmented adoption, unmanaged risk, and missed opportunities to improve engineering performance and product value.

AI Awareness
(Engineers understand what AI is and where it fits)

Description

Think of this level as AI users.


Observable Characteristics

  • Understand basic AI terminology (LLMs, embeddings, tokens, inference, training)
  • Understand strengths and limitations of generative models
  • Know typical use cases (code generation, summarisation, classification, search)
  • Understand risks such as hallucinations, bias, data leakage
  • Use AI tools for simple productivity tasks

Outcomes & Risks

  • Uses Copilot or Claude for small coding tasks
  • Uses AI to summarise documentation or logs
  • Understands that outputs require validation
AI Augmented Engineering
(Engineers use AI as a productivity multiplier)

Description

At this level engineers are AI augmented developers.


Observable Characteristics

  • Effective prompting and context construction
  • Using AI for test generation, refactoring, documentation
  • Debugging with AI assistance
  • Understanding token limits and prompt structure
  • Evaluating AI output quality

Outcomes & Risks

  • Uses AI to generate test scaffolding
  • Uses AI to accelerate code reviews
  • Uses AI for exploratory design thinking
AI Application Engineering
(Engineers can build AI powered features into products)

Description

At this level engineers become AI application builders.


Observable Characteristics

  • Working with APIs from models (OpenAI, Anthropic, etc.)
  • Prompt engineering for product features
  • RAG (Retrieval Augmented Generation)
  • Vector databases and embeddings
  • Basic evaluation techniques

Outcomes & Risks

  • Builds chat interfaces over internal knowledge bases
  • Implements semantic search
  • Creates AI powered summarisation or classification services
AI Systems Engineering
(Engineers design production grade AI systems)

Description

Here engineers act as AI platform engineers.


Observable Characteristics

  • Model selection and trade offs (cost, latency, accuracy)
  • Observability for AI systems
  • Prompt versioning and evaluation pipelines
  • Guardrails and safety layers
  • Cost management and performance tuning

Outcomes & Risks

  • Designs scalable RAG pipelines
  • Implements AI monitoring and evaluation metrics
  • Handles prompt drift and model upgrades
AI Native Engineering
(Engineers design systems where AI is a core architectural component)

Description

At this level engineers are AI system architects.


Observable Characteristics

  • Multi agent systems
  • AI orchestration frameworks
  • Model fine tuning or custom models
  • AI product strategy thinking
  • Designing AI enabled workflows

Outcomes & Risks

  • Builds agent based systems
  • Designs AI assisted development pipelines
  • Integrates AI into core business processes
Ensure engineers develop the literacy required to safely apply, integrate, and design AI-enabled capabilities within modern software systems.