Artificial Intelligence (AI) and Automation Practitioner Level 4

The Artificial Intelligence (AI) and Automation Practitioner Level 4 Apprenticeship is designed to give individuals the skills to identify, design and implement AI driven solutions that improve business efficiency and productivity. Apprentices will learn how to analyse current processes, identify inefficiencies, and use automation and AI tools to streamline workflows, reduce manual tasks, and integrate digital systems. Our next cohort starts 25th June 2026!

Find out more below & register your interest today.

Duration: 18 months + EPACourse Fee Information
Artificial intelligence (AI) and automation practitioner Level 4 Feature Image

Overview

This programme is suitable for organisations across all sectors that rely on digital systems and data driven processes, including operations, marketing, HR, finance, customer service, and public sector environments. It is particularly valuable for businesses looking to adopt AI in a practical, responsible way to save time, reduce costs, and enhance performance.

Apprentices typically work in roles focused on improving business processes, such as digital transformation, operations, or support functions, where they can apply AI and automation to real workplace challenges and drive measurable impact. This programme is built around one of the fastest growing and most transformative areas in the modern workplace. AI and automation are reshaping how businesses operate, make decisions, and deliver services. Through this innovative programme, learners will gain practical, relevant skills that are directly aligned with how businesses are evolving right now, ensuring they are prepared not just for the future of work as well as today’s roles.

Read More
Duration:18 months + EPA
Standard and Level:Artificial Intelligence (AI) and Automation Practitioner Level 4 standard
Entry requirements:
This programme is particularly suited to individuals in operational or business support roles who want to develop practical AI and automation skills, rather than those pursuing highly technical or coding focused careers.
Employers may also provide additional entry criteria.

Learners aged 18 who do not have exemptions will still be required to achieve Level 2 Functional Skills.  
 
Learners over 19 will have the option to either opt in or out of Functional Skills training and examinations. For those who choose to opt out, Fareport remains committed to supporting all learners in developing their literacy and numeracy skills by embedding these essential topics within the curriculum and assessments of the apprenticeship.  

To be eligible for an Apprenticeship you (or the apprentice) must:

  • Be living and working in England
  • Be 16 years old or above
  • Have the legal right to work in the UK
  • Have maintained UK residency for the last 3 years
  • Be employed in a real job; they may be an existing employee or a new hire
  • Work towards achieving an approved apprenticeship standard or framework
  • Work at least 30 hours a week
  • Be able to commit to the apprenticeship and its requirements
  • Not hold a prior qualification at the same or higher level in the same subject area
  • Not undertake or benefit from DfE funding during their apprenticeship programme, including Student Loans.
  • Have apprenticeship training and employment that lasts at least 12 months.
Cost:Fully funded through the Apprenticeship Levy or 95% government-funded for eligible employers, with minimal contribution required.

Knowledge


Knowledge (K) – The theoretical understanding an apprentice needs to perform their role effectively. This includes industry-specific principles, regulations, and best practices.

  • K1: The role of organisational leadership in responsible AI adoption, including setting values, policy, and strategy. The business case for ethical AI adoption, including reputational risk, staff morale, and long-term sustainability.
  • K2: Legal and regulatory frameworks including employment rights, equality, and responsible automation, data protection and GDPR. Ethical principles and professional standards relevant to AI development such as fairness, transparency, and accountability.
  • K3: Understand the potential social and economic impacts of AI and automation on different roles, particularly for non-technical staff including change management principles.
  • K4: Approaches for identifying and implementing incremental change, including piloting, evaluating solutions in relation to organisational constraints such as budget, time, and resources.
  • K5: Methods to identify opportunities to enhance productivity such as improve processes, reduce waste, increase user or customer satisfaction or optimise outcomes.
  • K6: The importance of designing AI and automation systems that augment rather than replace human work, where feasible.
  • K7: The capabilities, benefits and risks of automation, AI and digital tools including responsible use, ethical considerations and the potential impact on the workforce.
  • K8: The capabilities, risks and implications of on-premise, cloud-based and third party solutions.
  • K9: AI and automation concepts, models and limitations. The impact adoption may have on workplace culture and wellbeing.
  • K10: Sources of error and algorithmic bias, including how they may be affected by choice of dataset and methodologies applied, and the impact on the user and or organisation. Fairness metrics and mitigation approaches.
  • K11: User requirements when designing and implementing AI and automation solutions including accessibility considerations.
  • K12: Product development lifecycle including consideration of user experience (UX) principles such as user centred design (UCD), data informed design and experimental testing.
  • K13: How to assess the viability of solutions, for example testing and evaluating solutions, using test data and results, feasibility (time, cost, data quality and process maturity), and user testing.
  • K14: Principles and application of testing methodologies and their application in practice.
  • K15: Principles of human oversight and human AI collaboration to achieve shared outcomes.
  • K16: Feedback and evaluation loops to improve systems, processes, productivity and performance including human in the loop safeguards.
  • K17: Principles for designing sustainable solutions to support organisational strategies and objectives.
  • K18: Governance principles to ensure accountability and compliance, including methods to identify system vulnerabilities and mitigate threats or risks to assets, data and cyber security.
  • K19: Engagement and training approaches used with non-technical staff to understand their roles, responsibilities, and concerns when AI automation solutions are proposed. Including best practice and methods to deliver training.
  • K20: Methods to develop resources such as manuals, short explainers, chat-based guidance, interactive wikis and training materials.
  • K21: Strategies for inclusive communication with stakeholders from diverse and non-technical backgrounds.
  • K22: Collaborative working principles to explore AI and automation solutions and implement prototypes, pilots or proof of concepts.
  • K23: Mitigation strategies for post-deployment issues such as overreliance and automation bias.
  • K24: Principles to support project and change management delivery.
  • K25: Approaches to maintaining up-to-date knowledge of existing, evolving and emerging technologies and sector trends for example peer learning, online forums, AI tool release notes.
  • K26: The benefits of wellbeing and safe working practices.
  • K27: Methods for assuring compliance in AI and automation projects, including documentation of model decision-making, conducting structured risk assessments, and aligning implementation with recognised AI assurance and governance frameworks. The importance of auditability, transparency, and accountability in organisational contexts.
  • K28: Principles and practices of algorithmic impact assessment and workforce equality monitoring, including methods to identify, assess, and mitigate potential disproportionate impacts of automation and AI systems on different workforce groups. Organisational responsibilities under equality and employment law, and methods to evidence fairness and transparency in adoption.
  • K29: Principles and practices for the long-term monitoring of AI and automation solutions, including detection and mitigation of risks such as model drift, emerging bias, degraded performance, and security vulnerabilities.

You can view the standard here.

Skills


Skills (S) – The practical abilities developed through training and hands-on experience. These are the technical and transferable skills required for the job.

  • S1: Review, establish, follow and or amend policies and procedures on data and information security.
  • S2: Follow ethical, responsible and safe working practices respecting confidentiality and sensitive organisational matters.
  • S3: Undertake analysis to identify if automation is viable. Including assessing risks such as data quality, process maturity and unintended consequences of AI automation projects, such as the impact on job roles.
  • S4: Engage with non-technical staff to understand their roles, responsibilities, and concerns when automation solutions are proposed and implemented. Adapt approach to support workforce needs when implementing solutions that impacts the workforce.
  • S5: Support with the introduction, adaption, and implementation of change. Contribute to constructive dialogue between leaders and employees about the adoption of AI and automation solutions.
  • S6: Review and complete workflow and process mapping to identify problems or inefficiencies and recommend solutions including pilots, incremental changes and scaling opportunities.
  • S7: Use automation design tools to suit the organisational context to configure, adapt and implement solutions for example Zapier, Make and Power Automate.
  • S8: Create and refine prompts for AI tools, using iterative testing to achieve accurate and useful outputs.
  • S9: Apply analytical and computational techniques using tools and datasets to design, evaluate, and optimise automation solutions.
  • S10: Integrate AI and automation technologies to collect, process, and manage data effectively, enabling intelligent and efficient system operation.
  • S11: Design, integrate, and test digital workflows and AI automation tools using APIs, connectors, or low-or no-code integration methods.
  • S12: Iterate solutions based on testing and feedback to ensure reliability, security, accessibility, and alignment with organisational needs.
  • S13: Identify opportunities to deliver automation. Support leaders in integrating ethical, empathetic approaches when decision-making.
  • S14: Support in the identification and evaluation of opportunities for increased productivity. For example, use of low-or no-code tools, streamlining processes and use of AI platforms.
  • S15: Make evidence based suggestions to support governance, outcomes and facilitate improvement for example cost benefit analysis.
  • S16: Report on productivity and efficiency savings and the opportunities for automation and where applicable when automation does not improve experience or processes.
  • S17: Contribute to sustainable and efficient AI and automation solutions.
  • S18: Support with the delivery of training to technical and non-technical user groups or audiences adapting content and format responding to feedback and organisational context.
  • S19: Contribute to the creation and or adaption of resources such as user guides, training materials, process documents to meet user requirements.
  • S20: Work collaboratively to deploy AI and automation strategies. Support where required to deal with the impact of automation for example retraining, redeployment, or upskilling of affected staff.
  • S21: Undertake data analysis, preparation, and conversion to support automation solutions.
  • S22: Present and communicate information including the translation of technical concepts into accessible materials to support clear dialogue with stakeholders.
  • S23: Work with others to achieve agreed outcomes or outputs. Provide evidence-based analysis and insight to leaders on the likely human impacts of automation projects.
  • S24: Use project management principles, techniques and tools to support the development of clear, balanced communications and briefings, articulating both opportunities and risks.
  • S25: Keep up to date with existing, evolving, emerging technologies and sector trends in AI, automation and technology including methods to evaluate vendor and supplier solutions.
  • S26: Apply ethical and human-centred design principles when scoping, developing, and deploying automation and AI solutions, underpinned by robust governance.
  • S27: Apply technical understanding to help align business needs with technical capabilities, supporting the development of solutions that are scalable, efficient, and aligned with the organisation’s strategic objectives.
  • S28: Undertake assurance activities to evidence responsible AI and automation, including maintaining clear documentation of design and decision-making, contributing to risk assessments, and applying assurance frameworks to support compliance with organisational, regulatory, and ethical standards.
  • S29: Apply algorithmic impact assessment and workforce equality monitoring techniques when scoping, implementing, and reviewing AI and automation projects. Gather and analyse relevant workforce data, identify potential equality risks, and contribute evidence-based recommendations to support fair and inclusive adoption.

Behaviours


Behaviours (B) – The professional attitudes and values expected in the workplace. These include teamwork, adaptability, problem-solving, and ethical responsibility.

  • B1: Demonstrates empathy by actively considering the perspectives and concerns of staff who may be impacted by AI-driven change. Acts responsibly, recognising organisational efficiency goals with fairness to employees.
  • B2: Maintains professionalism and upholds confidentiality when discussing sensitive workforce impacts, showing respect for individual contributions.
  • B3: Demonstrates confidence in sharing concerns or alternative perspectives of self or others, even when under pressure to deliver efficiencies.
  • B4: Balances respect for leadership decisions with advocacy for employees.
  • B5: Support leaders to consider the impact of AI automation adoption, not just immediate organisational gains.
  • B6: Shows curiosity and initiative, experimenting with AI and automation, while ensuring such exploration is conducted safely, ethically, and with regard for potential impacts.
Once an apprentice has completed their apprenticeship, they will be ‘signed off’ by their employer/ provider as ready for end-point assessment of their knowledge and practical capabilities. In most cases, the assessment will be graded and must show the apprentice is fully competent and productive in the occupation.
Simon Gentile

Simon Gentile

Trainer

Simon joined Fareport Training in November 2024 as an experienced trainer, coach and mentor.  He has been working with level 3 and level 4 Data Analyst apprenticeship learners for around 5 years.

Prior to this, Simon has over 10 years practical experience working in Data Analytics in corporations and SMEs.

Simon is currently studying towards his CAVA assessor qualification.

Nicholas Beer

Nicholas Beer

Trainer

With a wealth of experience in the financial services sector, Nicholas is a passionate systems development professional who has excelled in various technical roles.  As a proficient MI/BI system developer and lead data analyst, Nicholas has a proven track record of building strong relationships through a friendly and open-minded approach.

Key personal skills include a love of learning, excellent communication, and a positive attitude.  Nicholas is known for his inclusive leadership, logical problem-solving, and enthusiastic embrace of change.  With extensive knowledge in software engineering and business process improvement, Nicholas brings a clear and enjoyable training style to software development best practices.  He looks forward to working with you.

Q. What funding is available if I am in the Devolved Nations” (Scotland, Wales, NI)
Q. What are the responsibilities of an employer?
Q. What are the benefits of apprenticeships?
Q. How much does an apprenticeship cost?
Q. What is the salary of an apprentice?
Q. What is an apprenticeship?
Q. Can I train locally?
Q. Why choose Fareport as your Training Provider?
Q. What is End-point Assessment?
Q. What are the employer responsibilities of an apprentice?
Q. Who are Apprentices for?
Q. What is Off-the-Job Training?

Why choose to learn with Fareport Training?

Fareport Training was established in 1981 in order to offer young people a route into work through work based training. In 2014 the business was purchased with support from entrepreneur Theo Paphitis by Natalie Cahill and Marinos Paphitis. Since then we have been building on Fareport’s excellent reputation for high quality training and delivering training and apprenticeships across England. We are proud to offer:

  • Expert-Led Instruction: Gain insights from industry leaders and seasoned professionals.
  • Cutting-Edge Curriculum: Stay ahead with the latest trends, tools, and techniques.
  • Flexible Learning Options: Balance your education with your professional and personal life.

Enquire Today!

Please send us your details by completing this short form below and our Business Development team will be in touch to answer any questions and advise you of the next steps.

Customer Testimonials

Real experiences, real success—hear from our learners and employers about how our training programmes have made a difference.

keyboard_arrow_up