AI Readiness Assessment: Does Your Team Have the Skills They Need?

As AI capabilities advance, the skills required for engineering success are evolving beyond traditional technical competencies. Recent research shows that employees are ready for AI, with leadership being the biggest barrier to success. But only 49% of employees feel equipped for their roles, revealing a critical AI skills gap between enthusiasm and preparedness.

Organizations need a clear AI skills framework to assess team AI readiness and identify where they need to grow.

The New AI Skills Framework

Codility’s Engineering Skills Model 2.0, developed by Ph.D-level I/O psychologists, introduces four AI readiness skill categories that define what it means to be AI-ready in today’s engineering landscape:

1. AI Literacy: Understanding the Foundation

AI Literacy encompasses foundational knowledge of artificial intelligence, including core machine learning concepts, large language model (LLM) architectures, and the capabilities and limitations of current AI systems. It’s about ensuring engineers recognize when AI can and can’t solve problems and that they grasp the ethical implications of AI usage.

Technical skills now become outdated in less than five years on average, making this foundational understanding essential for adapting to rapid AI evolution. Engineers with strong AI Literacy understand responsible AI principles including privacy, fairness, and accountability; critical considerations as AI systems become more deeply integrated into products.

2. AI Evaluation: Assessing Quality and Reliability

Creating AI-generated code is easy. Trusting it requires rigorous evaluation skills. The AI Evaluation category focuses on assessing the performance, reliability, and appropriateness of AI systems and their outputs.

Engineers proficient in AI Evaluation can test AI systems for accuracy, identify biases, and validate outputs for real-world applicability. They use systematic testing techniques including retrieval-augmented generation, semantic checks, and cross-validation.

3. AI Application: Putting AI to Work

The most critical skill gap isn’t knowing about AI, it’s knowing how to use it effectively. AI Application encompasses practical skills for leveraging AI tools and integrating AI capabilities into workflows and systems.

This includes prompt engineering (designing precise prompts that optimize AI outputs) and AI integration (incorporating machine learning capabilities through APIs, embedded agents, and model integration). 36% of employees now say role-related AI expertise is essential, reflecting growing recognition that AI application skills are becoming as fundamental as version control or debugging.

4. AI Building: Creating AI Solutions

While not every engineer needs to build AI systems from scratch, understanding how to design and implement AI-driven solutions is increasingly valuable. AI Building includes statistical modeling, model tuning, and machine learning operations across the full lifecycle from data preparation through deployment and maintenance.

Assessing Your Team’s AI Readiness

Understanding these four categories is the first step. The second is conducting an AI skills assessment to understand where your team currently stands. Consider these questions to evaluate AI skills:

• AI Literacy: Can your engineers explain how large language models work? Do they understand the ethical considerations around AI deployment?
• AI Evaluation: Are your engineers critically assessing AI-generated code before implementation? Do they have processes for testing AI outputs for bias and accuracy?
• AI Application: How many engineers are actively using AI tools in their daily workflows? Can they integrate AI capabilities into your existing systems?
• AI Building: Does your team have the expertise to design custom AI solutions when pre-built tools fall short?

Start by conducting a comprehensive AI skills assessment of your team’s current readiness across these four categories. Rather than relying on self-reported skill levels, use objective skill verification with a tool like Codility Skills Intelligence to identify gaps systematically.

Built directly on the Engineering Skills Model 2.0, Codility Skills Intelligence helps organizations gain verified insights into their technical workforce through skill-based assessments. You can evaluate your team’s engineering AI skills across all four categories with validated tasks, moving beyond subjectivity toward data-driven talent decisions.

The AI transformation of software engineering is accelerating, and teams with strong foundations in AI Literacy, AI Evaluation, AI Application, and AI Building will be the ones that thrive.

👉 Ready to assess AI skills in the hiring process or within your current teams? Book a Codility demo today.