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Artificial Intelligence (AI) and Automation Practitioner

Find where AI and automation can save your business time, then plan, build and run it safely, fairly and within the law.

  • 9 modules
  • 87 sections
  • 58 capabilities
  • Manager sign-off at every module
  • Certificate on completion
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What's inside

9 modules, 87 sections.

Short modules, each split into sections that fit a working day. Open a module to see what it covers.

    1. What AI And Automation Tools Are
    2. Specialist and Composite AI Tools
    3. What AI Decisions Look Like
    4. Responsible AI Use
    5. Workforce, Culture and Wellbeing Impacts of AI Adoption
    6. Where AI Lives – On-premise, Cloud, and Third Party
    7. GDPR
    8. Data Security
    9. Data Protection and GDPR in AI Work
    10. Equality, Diversity and Inclusion
    11. Equality, Employment and Fair Automation
    12. How AI Systems are Built and Operate
    13. Human Oversight and the Role of People in AI Work
    14. The Leadership Layer of AI Decisions
    15. Understanding AI and Automation Individualisation Quiz
    16. Sources of Error and Algorithmic Bias
    17. Fairness, Metrics and Mitigation
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    1. Process Mapping
    2. Data Quality for AI and Automation
    3. Lean Principles
    4. Methods to Identify Opportunities to Enhance Productivity
    5. Identifying Opportunities to Deliver Automation
    6. Assessing the Viability of an AI or Automation Opportunity
    7. Incremental Change and Piloting
    8. Evidence and Cost-Benefit Methods
    9. Making the Case: Building, Framing, and Delivering

    1. Identifying Stakeholders
    2. Stakeholder Mapping
    3. Stakeholder Engagement
    4. Stakeholder Risk and Reality Checks
    5. Stakeholder Management in a Change Initiative
    6. Stakeholder Management in the Project Lifecycle
    7. Collating, Interpreting, and Communicating Data
    8. How to Communicate Information Effectively Through Different Media
    9. Shannon Weaver Model of Communication
    10. Transactional Analysis
    11. Engaging Non-Technical Staff with Automations

    1. Understanding Users and Accessibility
    2. Gathering and Structuring Requirements
    3. Applying User-Centred Design and UX Principles
    4. The Technical Shape of AI and Automation Solutions
    5. Aligning Technical Solutions to Business Needs
    6. Principles for Designing Sustainable AI and Automation Solutions
    7. Evaluating Vendors and Suppliers
    8. Staying Up to Date with AI and Automation Trends

    1. Project Management
    2. Project Team Roles
    3. Creating a Project Plan
    4. WBS & RACI Matrix
    5. Managing Project Risk and Resources
    6. Monitoring Project Progress
    7. Using Digital Tools To Deliver a Project

    1. Automations with Low-code/No-Code – Part 1 of 3
    2. Automations with Low-code/No-Code – Part 2 of 3
    3. Automations with Low-code/No-Code – Part 3 of 3
    4. Designing and Iterating Prompts for Reliable Outputs
    5. Reading APIs For Integration Work
    6. Integrating AI and Automation
    7. Working with Data in Spreadsheets
    8. Preparing and Working with Data
    9. Collaborative Working Principles When Testing

    1. Testing Methods
    2. Designing, Running, and Producing Readiness Decisions
    3. Feedback Loops and Human-in-the-loop Safeguards
    4. Iterating Based on Testing and Feedback
    5. Contributing to Sustainable AI and Automation
    6. Managing Complex Stakeholder Situations

    1. Documenting Decisions
    2. Risk Assessments
    3. Data and Information Security Principles
    4. Algorithmic Impact Assessment and Equality Monitoring
    5. Applying Algorithmic Impact Assessment and Equality Monitoring
    6. Monitoring AI for Drift, Bias, and Degradation
    7. Overreliance and Automation Bias
    8. Ethical and Safe Working
    9. The Benefits of Wellbeing and Safe Working
    10. Supporting Leaders to Make Ethical AI and Automation Decisions

    1. Delivering training to Users
    2. Creating Resources for Users
    3. Supporting Change Management in AI and Automation
    4. Providing Human Impacts and Insights to Leaders
    5. Supporting Leaders to Make Decisions
    6. Translating Technical Concepts
    7. Handling Resistance To Change
    8. ADKAR and Kotter’s 8 Steps
    9. Change Curve
    10. Changes

Outcomes

What your people will be able to do

58 capabilities

  • Leadership and the business case for responsible AI
  • Legal, regulatory and ethical frameworks for AI
  • Social and economic impacts of AI on job roles
  • Piloting and evaluating incremental change
  • Methods to identify productivity improvements
  • Designing AI to augment rather than replace human work
  • Capabilities, benefits and risks of AI and automation
  • On-premise, cloud and third-party solutions
  • AI concepts, limitations and impact on workplace culture
  • Sources of error, algorithmic bias and fairness
  • User and accessibility requirements for AI solutions
  • Product development lifecycle and UX principles
  • Assessing the viability of solutions
  • Testing methodologies in practice
  • Human oversight and human-AI collaboration
  • Feedback loops and human-in-the-loop safeguards
  • Designing sustainable solutions for organisational goals
  • Governance, compliance and security risk mitigation
  • Approaches to engaging and training non-technical staff
  • Methods for developing manuals and training materials
  • Inclusive communication with diverse stakeholders
  • Collaborating on AI prototypes, pilots and proofs of concept
  • Managing overreliance and automation bias after deployment
  • Project and change management principles
  • Ways to keep up with evolving technology and trends
  • Wellbeing and safe working practices
  • Methods for assuring compliance in AI projects
  • Algorithmic impact assessment and equality monitoring
  • Long-term monitoring of AI and automation solutions
  • Managing data and information security policies
  • Working ethically, safely and confidentially
  • Analysing automation viability and risks
  • Engaging non-technical staff affected by automation
  • Supporting change and dialogue on AI adoption
  • Process mapping and recommending improvements
  • Configuring AI solutions with automation design tools
  • Creating and refining prompts for AI tools
  • Analytical techniques to design and optimise automation
  • Using AI and automation to collect and manage data
  • Designing, integrating and testing digital workflows
  • Iterating solutions from testing and feedback
  • Automation opportunities and ethical decision-making
  • Helping identify and evaluate productivity gains
  • Evidence-based suggestions for governance and improvement
  • Reporting on productivity and efficiency savings
  • Contributing to sustainable, efficient AI solutions
  • Supporting training delivery for different audiences
  • Creating and adapting user guides and resources
  • Deploying AI collaboratively and supporting affected staff
  • Data analysis, preparation and conversion
  • Presenting technical concepts accessibly to stakeholders
  • Working with others and advising leaders on human impact
  • Using project management for balanced briefings
  • Tracking technology trends and evaluating vendors
  • Ethical, human-centred design of AI solutions
  • Aligning business needs with technical capabilities
  • Evidencing responsible AI through assurance activities
  • Applying algorithmic impact and equality assessments

Ways of working

  • Shows empathy and fairness to staff affected by AI
  • Maintains professionalism and confidentiality
  • Confidently raises concerns, even under pressure
  • Balances respect for leaders with advocacy for employees
  • Helps leaders consider AI impact beyond immediate gains
  • Explores AI with curiosity, safely and ethically

Each person rates themselves against these at the start, and again at the end of every module. Their manager approves every rating.

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