AI Competency Framework: How to Update Job Roles for the AI Era

Author Bio: Phil is the CEO of Evidenced and an expert in structured interviewing. Before founding Evidenced, he hired for software engineering teams at Amazon and spent several years teaching teams how to run effective interviews.

You're likely aware that artificial intelligence is no longer something of the future; how it affects company structure, organisational development and specific role competencies is evolving every day, across industries. So how do you make sure your job roles keep pace?

Let's explore how you can stay ahead by understanding AI's transformative impact on job roles, and how you can integrate these changes effectively into your organisational strategy via a strong, AI focused competency framework.

1. Understanding AI in the Workplace

1.1 How is AI Changing Job Roles?

AI is reshaping job roles by automating repetitive tasks and enhancing human capabilities. According to the World Economic Forum's Future of Jobs Report, this transition allows for more efficient processes but also necessitates a shift in human roles to more strategic and creative endeavours.

Here’s what this means for your organisation:

• Redefinition of job tasks: Tasks once centred around data entry or basic analysis are now automated, freeing up time for strategic planning.

• New roles emerging: Titles like AI Trainer, Data Analyst, and Ethical AI Officer are becoming commonplace.

• Skill enhancement: Employees need to adapt by learning to work with AI technologies, focusing on areas AI cannot yet conquer, such as nuanced decision-making and complex problem-solving.

1.2 The Importance of an AI Competency Framework

An AI Competency Framework is vital for systematically integrating AI into job roles. It's not just about having the latest technology - it's about ensuring your workforce is equipped to use that technology strategically.

Here's why your organisation should prioritise this framework:

• Alignment with organisational goals: Align AI capabilities with business objectives to ensure seamless integration into your current processes.

• Employee development: The framework supports targeted training and development to close the skills gap.

• Strategic foresight: Adapt to future AI advancements with a proactive approach to workforce planning.

2. Creating an AI Competency Framework for Your Organisation

2.1 Identifying Key Competencies for AI Integration

Identifying key competencies for AI integration starts by asking the right questions about your organisation’s current and future needs. Start by examining the existing skill sets within your team. Consider what's missing and what will be essential in the age of AI. Focus should be on:

• Taking stock of your team’s current skills – look at what people can actually do today, where the gaps are, and what will realistically be needed as AI becomes part of everyday work.

• Building practical technical confidence – not everyone needs to be an engineer, but having a working familiarity with tools like Python or understanding how AI models behave will help teams use them effectively.

• Applying AI to real problems – focus on using AI tools to solve concrete, day-to-day challenges (saving time, improving workflows, reducing manual work), rather than abstract or theoretical use cases.

To delve deeper, teams should conduct workshops or use collaborative tools to gather insights from various departments. This approach ensures that you're covering all angles. Data science, IT, HR, and even marketing can contribute valuable perspectives on what AI competencies will drive success. It's also useful to consider the wider landscape. Reviewing how others create an effective competency framework can provide actionable insights for your specific needs.

2.2 Skills Gap Analysis: Before and After AI

Conducting a skills gap analysis involves pointing a spotlight on the current proficiency levels within your organisation versus future AI requirements. The McKinsey Global Institute estimates that hundreds of millions of workers will need re-skilling by 2030. Begin by mapping out the specific competencies identified in the previous step against the existing skill sets. Here's a step-by-step approach:

• Start with current day-to-day work – map the tasks people own and the skills they rely on to deliver them.

• Overlay relevant AI capabilities – identify where AI literacy, automation, data interpretation, or technical skills would improve that work.

• Turn gaps into actions – use the comparison to decide what to up-skill, where to bring in outside support, and which roles may need to evolve.

After pinpointing these gaps, you'll be armed with the knowledge to bolster your workforce through targeted training.

Pro-tip: Conduct skill gap reviews biannually to stay adept at responding to sudden changes in technology or shifts in industry focus that demands new AI competencies.

2.3 Developing Training Programmes for Up-skilling

Once you've identified the gaps, developing structured training programmes will be instrumental in up-skilling your team. Begin by curating a blend of internal workshops, online courses, and mentorship initiatives. Here’s how you can structure a comprehensive training programme:

• Level 1: Foundational AI literacy courses focusing on AI concepts and vocabulary.

• Level 2: Intermediate workshops focusing on applying AI in business processes.

• Level 3: Specialised AI tools and programming workshops, tailored to individual team roles.

Tailor these programmes to suit departmental needs, ensuring each role gets the bespoke training it needs to thrive in an AI-driven environment. Encourage a culture of continuous learning by incentivising completion of courses with certifications, and periodically update your training materials to keep pace with technological advances.

3. Implementing and Updating Job Roles for the AI Era

3.1 How to Create and Implement New AI Roles

As organisations begin to adopt AI more seriously, many find that existing roles don’t fully cover the new types of work required. Rather than forcing AI responsibilities into already stretched positions, there’s increasing value in defining clear, focused roles that can drive adoption, ensure quality, and connect technical capabilities to real business outcomes. The key is to introduce these roles in a way that supports day-to-day operations, complements existing teams, and evolves as AI use matures.

• Start with real business needs – identify where AI can improve efficiency, decision-making, or customer experience before defining any new roles.

• Design roles around outcomes, not hype – be clear on what each role is responsible for delivering (e.g. reducing manual work, improving model performance, enabling teams to use AI tools).

• Embed roles into existing teams – avoid creating isolated “AI silos”; instead, place these roles within product, ops, or commercial teams so they stay close to real problems.

• Balance hiring and upskilling – retrain existing employees where possible, and hire externally for highly specialised skills.

• Start small and iterate – pilot new roles on specific projects, then refine responsibilities as you learn what actually works.

Examples of New or Emerging AI Roles include:

• AI Product Manager – defines how AI features are built and used within products.

• Prompt Engineer – designs and optimises prompts to get reliable outputs from AI systems.

• AI/ML Engineer – builds, deploys, and maintains machine learning models in production.

• AI Ethics / Responsible AI Lead – ensures AI systems are fair, compliant, and transparent.

3.2 Monitoring and Evolving AI Roles

Keeping AI roles relevant and effective requires proactive monitoring. Regularly collecting data on how AI-enhanced positions are performing allows you to tweak roles as required. Identify trends and bottlenecks to pre-empt challenges with:

• Routine Feedback Gatherings: Schedule recurring meetings or surveys to collect employee feedback on AI integration effectiveness. Questions should cover both difficulties and successes.

• Benchmark against Competitors: Compare your AI role integration with industry standards. Keeping up with advancements ensures your organisation remains competitive.

• Utilise Performance Metrics: Track progress using established KPIs. Assess whether roles meet company objectives, adjusting responsibilities or training as necessary.

To conclude, the strategic implementation of AI competencies reshapes job roles, aligning them with new technologies to drive business success. Tailoring roles and competencies to your organisation's specific needs ensures a competitive edge while fostering a culture of learning and adaptability.

Don't know how to roll out your new competency framework?

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