Skills needed for developing / operationalising software and data-intensive systems using AI/ML
A representative sample of SFIA skills that can be applied to the development and operationalisation of software- and data-intensive systems using AI/ML.
Scope
- Depending on the organisation's take-up of AI/ML, initially targets a sub-section of your business, data, digital, technology and cybersecurity professionals i.e. those involved in building and deploying AI/ML applications
- Requires application of AI/ML knowledge in a selection of the the SFIA skills below - depending on the specifics of your organisation's use of AI/ML applications
- Focuses on translating AI/ML models into robust, secure, scalable, and maintainable production systems
Development
- Needs a combination of training, hands-on projects, and knowledge sharing
- Assessed through project outcomes, skills assessments, performance evaluations
Impact
- Critical for the successful implementation and maintenance of AI/ML systems
- Enables the organisation to exploit AI/ML for competitive advantage and innovation
- Ensures the reliability, security, scalability, and performance of AI/ML applications
SFIA skills for developing / operationalising software and data-intensive systems using AI/ML
The table below provides a representative sample of SFIA skills that can be applied to the development and operationalisation of software- and data-intensive systems using AI/ML. The purpose of this table is to illustrate how various SFIA skills contribute to different aspects of AI/ML projects and initiatives, from initial development to ongoing operations.
Depending on the nature of the skill and the type of AI/ML systems being developed, the required knowledge of AI/ML will vary.
- for some applications, AI and data literacy will be sufficient.
- others will require knowledge of specific AI tools and solutions,
- while more advanced applications will need deeper expertise in AI/ML technologies.
As an employer, you can use this table as a starting point to assess your organisation's AI/ML skill needs.
- Remember, SFIA is a flexible framework designed to guide skill management, not a prescriptive to do list.
Consider the following diagnostic questions as you review the table:
Breadth of skills
- Which of the SFIA skills listed in the table are most relevant to our AI/ML initiatives?
- Are there any key skills we are currently lacking in our organisation?
- How can we leverage our existing skill base for AI/ML projects, systems, products and services?
- What leadership, strategic, governance, risk management skills do we need to lead and oversee all AI/ML initiatives?
Depth of AI/ML knowledge
- For the skills identified as critical, what level of AI/ML knowledge do our staff need?
- Do we need deep expertise or is general awareness and literacy sufficient?
- In which skill areas do we need to invest in developing greater AI/ML specialisation?
Organisational context
- What is the nature of our AI/ML initiatives (e.g., adopting prebuilt models vs developing bespoke solutions)? How does this impact our skill requirements?
- Are there unique demands of our industry (e.g., healthcare, finance) that necessitate particular AI/ML-related skills?
- How do our AI/ML development and deployment practices (e.g. manual vs automated MLOps) shape the skills we need?
Skill gaps and development plans
- Based on our answers above, where are our most significant AI/ML skill gaps?
- What training, hiring, or outsourcing strategies should we employ to close those gaps?
- How will we keep our skill development plans current as AI/ML technologies and our business needs evolve?
Based on the responses to these questions, you can begin to define your organisation's specific AI/ML skill requirements.
- Use these insights to create targeted workforce development plans that invest in the right mix of AI literacy, knowledge and hands-on skills needed for your strategic imperatives regarding AI
- The goal is unlikely to be to turn everyone into an AI/ML expert, but to strategically build and deploy AI/ML skills in a way that aligns with your business objectives.
- SFIA provides a proven framework for assessing and managing those skills, but the specific path you take should be tailored to your organisation's context and needs.
AI/ML strategy, architecture and innovation |
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| Strategic planning | ITSP | Creating and maintaining organisational-level strategies to align overall business plans, actions, and resources with high-level business objectives. | This skill is applied when developing and executing a strategic plan for AI adoption, ensuring alignment with business goals, and securing the necessary resources and commitment. It helps organisations define a clear vision and roadmap for AI integration and value realisation. |
| Enterprise and business architecture | STPL | Aligning an organisation's technology strategy with its business mission, strategy, and processes and documenting this using architectural models. | This skill is applied when developing an enterprise architecture that incorporates AI technologies, ensuring alignment between AI initiatives and overall business strategies. It helps organisations create a coherent and integrated framework for AI adoption, considering factors such as data, applications, infrastructure, and security. |
| Solution architecture | ARCH | Developing and communicating a multi-dimensional solution architecture to deliver agreed business outcomes. | This skill is applied when designing and overseeing the implementation of AI solutions that align with business objectives, technical feasibility, and organisational constraints. It ensures that AI solutions are scalable, secure, and compliant with relevant standards and policies. |
| Information management | IRMG | Enabling the effective management and use of information assets. | This skill is applied when managing the information and data assets that power AI systems, ensuring that they are properly governed, secured, and leveraged for maximum value. It helps organisations establish the necessary information management practices and policies to support AI initiatives. |
| Information systems coordination | ISCO | Coordinating information and technology strategies where the adoption of a common approach would benefit the organisation. | This skill is applied when coordinating AI strategies and initiatives across the organisation, ensuring alignment, consistency, and synergy between different AI projects and stakeholders. It helps organisations achieve a cohesive and integrated approach to AI adoption. |
| Emerging technology monitoring | EMRG | Identifying and assessing new and emerging technologies, products, services, methods and techniques. | This skill is applied when staying informed about the latest developments in AI and assessing their potential impacts, threats, and opportunities for the organisation. It enables the creation of technology roadmaps that align organisational plans with emerging AI solutions and helps develop organisational guidelines for monitoring emerging AI technologies. |
| Formal research | RSCH | Systematically creating new knowledge by data gathering, innovation, experimentation, evaluation and dissemination. | This skill is applied when conducting AI research, exploring new AI technologies, techniques, and applications, and advancing the state of the art in AI. It helps organisations stay at the forefront of AI innovation and leverage the latest AI breakthroughs for competitive advantage. |
| Innovation management | INOV | Identifying, prioritising, incubating and exploiting opportunities provided by information, communication and digital technologies. | This skill is applied when driving AI innovation within the organisation, fostering a culture of creativity, experimentation, and continuous improvement. It helps organisations identify and capitalise on AI opportunities and develop innovative AI solutions. |
| Methods and tools | METL | Leads the adoption, management, and optimisation of methods and tools, ensuring effective use and alignment with organisational objectives. | It involves assessing, selecting, and implementing methods and tools, including those related to AI. This skill is applied when developing organisational policies, standards, and guidelines for AI methods and tools, setting direction, and leading in the introduction and use of AI techniques, methodologies, and tools to meet business requirements. |
| Consultancy | CNSL | Providing advice and recommendations, based on expertise and experience, to address client needs. | This skill is applied when providing expert guidance and recommendations on AI adoption, implementation, and governance to stakeholders within and outside the organisation. It helps organisations navigate the complexities of AI, make informed decisions, and develop effective AI strategies. |
| Specialist advice | TECH | Providing authoritative, professional advice and direction in a specialist area. | This skill is applied when providing expert guidance on AI technologies, best practices, and strategies to stakeholders across the organisation. It helps organisations make informed decisions about AI adoption, implementation, and governance based on specialised knowledge and expertise. |
AI/ML governance and risk management |
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| Stakeholder relationship management | RLMT | Influencing stakeholder attitudes, decisions, and actions for mutual benefit. | This skill is applied when engaging and collaborating with stakeholders throughout the AI adoption process, ensuring their needs and concerns are addressed, and securing their buy-in and support. It helps organisations build trust, manage expectations, and align AI initiatives with stakeholder interests. |
| Governance | GOVN | Defining and operating a framework for making decisions, managing stakeholder relationships, and identifying legitimate authority. | This skill is applied when establishing a governance framework that encompasses AI-related decision-making, ensuring compliance with relevant laws, regulations, and ethical principles. It helps determine the requirements for appropriate AI governance, communicating benefits, opportunities, costs, and risks to stakeholders. |
| Risk management | BURM | Planning and implementing organisation-wide processes and procedures for the management of risk to the success or integrity of the enterprise. | This skill is applied when identifying, classifying, and prioritising risks associated with AI adoption, such as data privacy, algorithmic bias, and ethical concerns. It helps develop and implement organisational approaches to managing AI-related risks, ensuring the integrity of the business, its products, services, and end-users. |
| Continuity management | COPL | Developing, implementing and testing a business continuity framework. | This skill is applied when ensuring the continuity and resilience of AI systems in the face of disruptions, disasters, or failures. It helps organisations develop and implement plans to maintain the availability and performance of AI solutions, minimise the impact of incidents, and ensure rapid recovery. |
| Information and data compliance | PEDP | Implementing and promoting compliance with information and data management legislation. | This skill is applied when ensuring that AI initiatives comply with relevant laws, regulations, and ethical principles related to data privacy, security, and governance. It helps organisations navigate the complex legal and regulatory landscape surrounding AI and data use. |
| AI and data ethics | AIDE | Implementing and promoting ethical practices in the design, development, deployment, and use of AI and data technologies. | This skill is applied when ensuring that AI initiatives comply with ethical guidelines, addressing concerns such as bias, fairness, transparency, and data privacy. This helps organisations build trustworthy AI systems, building stakeholder confidence and ensuring responsible AI use. |
Business analysis and process optimisation |
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| Business situation analysis | BUSA | Investigating business situations to define recommendations for improvement action. | This skill is applied when analysing the potential impact and opportunities of AI adoption on business processes, operations, and strategies. It helps organisations identify areas where AI can drive efficiency, innovation, and competitive advantage, and develop actionable recommendations for AI implementation. |
| Feasibility assessment | FEAS | Defining, evaluating and describing business change options for financial, technical and business feasibility, and strategic alignment. | This skill is applied when assessing the feasibility of AI initiatives, considering factors such as cost, technical viability, and alignment with business objectives. It helps organisations make informed decisions about investing in and pursuing AI projects. |
| Business process improvement | BPRE | Creating new and potentially disruptive approaches to performing business activities. | This skill is applied when identifying opportunities to leverage AI technologies for improving business processes, enhancing efficiency, and driving innovation. It helps organisations redesign and optimise their processes to take full advantage of AI capabilities. |
| Requirements definition and management | REQM | Managing requirements through the entire delivery and operational life cycle. | This skill is applied when eliciting, analysing, and managing the requirements for AI systems, ensuring that they align with business objectives and user needs. It helps organisations clearly define and prioritise the requirements for AI initiatives, enabling effective development and deployment. |
| Acceptance testing | BPTS | Investigating systems, products, business processes or services to validate and verify that they deliver the expected business value or outcomes. | This skill is applied when ensuring that AI solutions meet the acceptance criteria and deliver the intended business value. It helps organisations validate that AI initiatives align with business objectives and stakeholder expectations before final deployment. |
| User research | URCH | Identifying users' behaviours, needs and motivations using observational research methods. | This skill is applied when conducting research to understand user requirements, preferences, and pain points related to AI applications, and informing the design and development of user-centred AI solutions. It helps organisations gather valuable user insights to guide AI innovation and improvement. |
Project and change management for AI/ML initiatives |
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| Portfolio management | POMG | Developing and applying a management framework to define and deliver a portfolio of programmes, projects and/or ongoing services. | This skill is applied when managing a portfolio of AI initiatives, ensuring alignment with business objectives, optimal resource allocation, and effective risk management. It helps organisations prioritise and coordinate their AI investments to maximise value and achieve strategic goals. |
| Programme management | PGMG | Identifying, planning and coordinating a set of related projects and activities in support of specific business strategies and objectives. | This skill is applied when managing AI programmes that comprise multiple interrelated projects, ensuring strategic alignment, benefit realisation, and effective coordination. It helps organisations deliver complex AI initiatives in a structured and cohesive manner. |
| Project management | PRMG | Delivering agreed outcomes from projects using appropriate management techniques, collaboration, leadership and governance. | This skill is applied when managing AI projects effectively, ensuring that they are delivered on time, within budget, and to the required quality standards. It helps organisations plan, execute, and control AI initiatives, managing risks, resources, and stakeholders effectively. |
| Portfolio, programme and project support | PROF | Providing support and guidance on portfolio, programme and project management processes, procedures, tools and techniques. | This skill is applied when supporting the planning, execution, and governance of AI projects and programmes, ensuring they are aligned with organisational objectives and delivered effectively. It helps organisations manage AI initiatives in a structured and coordinated manner. |
| Change control | CHMG | Assessing risks associated with proposed changes and ensuring changes to products, services or systems are controlled and coordinated. | This skill is applied when managing the risks and impacts of introducing AI technologies into an organisation, ensuring that changes are properly assessed, planned, and implemented. It helps organisations maintain stability and minimise disruption as they adopt and integrate AI solutions. |
| Organisational change management | CIPM | Planning, designing and implementing activities to transition the organisation and people to the required future state. | This skill is applied when managing the organisational changes associated with introducing AI technologies, ensuring that stakeholders are prepared, engaged, and supportive of the transition. It helps organisations navigate the people-side of AI adoption, driving successful implementation and user acceptance. |
Data science, engineering and analytics |
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| Data science | DATS | Applying mathematics, statistics, data mining and predictive modelling techniques to gain insights, predict behaviours and generate value from data. | This skill is applied when developing and implementing AI solutions that leverage data science techniques. It helps organisations harness the power of data to drive innovation and business value through AI. |
| Data analytics | DAAN | Enabling data-driven decision making by extracting, analysing and communicating insights from structured and unstructured data. | This skill is applied when leveraging data to support AI initiatives. It involves extracting, processing, and analysing both structured and unstructured data to derive actionable insights that inform decision-making. It helps organisations enhance AI systems' performance and create value from data. |
| Data engineering | DENG | Designing, building, operationalising, securing and monitoring data pipelines and data stores. | This skill is applied when creating and maintaining the data pipelines necessary to support AI initiatives. It ensures that data is properly collected, processed, stored, and made available for AI applications, while adhering to security, compliance, scalability, and reliability requirements. |
| Machine learning | MLNG | Developing systems that learn through experience and by the use of data. | This skill is applied when designing, implementing, testing, and improving machine learning architectures and systems that power AI applications. It helps organisations leverage machine learning techniques to create intelligent systems that can learn and adapt over time. |
| Numerical analysis | NUAN | Creating, analysing, implementing, testing and improving algorithms for numerically solving mathematical problems. | This skill is applied when developing and optimising numerical algorithms used in AI applications, such as machine learning, data analytics, and scientific simulations. It helps organisations ensure the accuracy, stability, and efficiency of the mathematical foundations underlying their AI solutions. |
| High-performance computing | HPCC | Using advanced computer systems and special programming techniques to solve complex computational problems. | This skill is applied when leveraging high-performance computing infrastructure and techniques to support AI workloads, such as deep learning, large-scale data processing, and complex simulations. It helps organisations harness the power of parallel computing to accelerate AI computations and handle massive datasets. |
| Database design | DBDS | Specifying, designing and maintaining mechanisms for storing and accessing data. | This skill is applied when designing and optimising the database architectures that support AI applications, ensuring efficient data storage, retrieval, and management. It helps organisations provide scalable, secure, and performant data platforms for AI workloads. |
| Data management | DATM | Developing and implementing plans, policies, and practices that control, protect and optimise the value and governance of data assets. | This skill is applied when ensuring that data is effectively managed throughout its lifecycle to support AI initiatives. It includes activities such as data governance, data quality assurance, data security, and compliance with relevant legislation and ethical principles. |
| Data modelling and design | DTAN | Developing models and diagrams to represent and communicate data requirements and data assets. | This skill is applied when designing the data models that underpin AI systems, ensuring they are efficient, scalable, and aligned with business requirements. It helps organisations structure and organise their data assets to support AI development and operations effectively. |
Software development and testing for AI/ML systems |
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| Programming/software development | PROG | Developing software components to deliver value to stakeholders. | This skill is applied when creating the software components and applications that power AI systems, using appropriate development methods, tools, and techniques. It ensures that AI software is developed in a structured, efficient, and maintainable manner, adhering to best practices and quality standards. |
| Software design | SWDN | Specifying, architecting and designing software to meet defined requirements by following agreed standards and principles. | This skill is applied when designing the architecture and components of AI software systems, considering factors such as performance, scalability, security, and maintainability. It helps ensure that AI software is designed to meet the specific requirements of the organisation and its users. |
| Software configuration | PORT | Designing and deploying software product configurations into software environments or platforms. | This skill is applied when configuring AI software components, models, and environments to ensure optimal performance, scalability, and interoperability. It helps organisations manage the complexity of AI software configurations and ensure smooth deployment and operation of AI solutions. |
| Functional testing | TEST | Investigating products, systems and services to assess behaviour and whether this meets specified or unspecified functional requirements and characteristics. | This skill is applied when testing AI systems to ensure that they function as intended, meet the specified requirements, and deliver the expected outcomes. It helps organisations validate the functionality and reliability of AI solutions before deployment. |
| Non-functional testing | NFTS | Investigating products, systems and services to assess behaviour and whether this meets specified or unspecified non-functional requirements and characteristics | This skill is applied when evaluating AI systems to ensure they meet non-functional requirements such as performance, scalability, reliability, usability, and security. It helps ensure that AI applications perform efficiently under expected workloads, maintain security standards, provide a consistent user experience, and are resilient to failures. Non-functional testing ensures that AI solutions are robust, dependable, and provide value beyond mere functional correctness. |
| User experience design | HCEV | Producing design concepts and prototypes for user interactions with and experiences of a product, system or service. | This skill is applied when designing intuitive, user-friendly, and effective user interfaces and interactions for AI applications, considering factors such as usability, accessibility, and user engagement. It helps organisations create AI experiences that are seamless, delightful, and aligned with user expectations. |
| User experience evaluation | USEV | Validating systems, products or services against user experience goals, metrics and targets. | This skill is applied when evaluating the usability, effectiveness, and user satisfaction of AI applications through user testing, feedback, and analytics. It helps organisations identify areas for improvement and optimise the user experience of their AI offerings. |
ML operations and service management |
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| Infrastructure operations | ITOP | Provisioning, deploying, configuring, operating, and optimising technology infrastructure across physical, virtual, and cloud-based environments. | This skill is applied when ensuring that the necessary infrastructure is in place to support AI applications, including computing resources, storage, networking, and security measures. It helps organisations build and maintain a scalable, reliable, and secure infrastructure foundation for AI initiatives. |
| System software administration | SYSP | Installing, managing, and maintaining operating systems, data management, office automation, and utility software across various infrastructure environments. | This skill is applied when managing the system software components that support AI applications, ensuring their stability, performance, and compatibility. It helps organisations provide a reliable and optimised software environment for AI operations. |
| Configuration management | CFMG | Planning, identifying, controlling, accounting for and auditing of configuration items (CIs) and their interrelationships. | This skill is applied when managing the configuration of AI systems, ensuring the integrity, coherence, and traceability of CI's including models, data sets, codebases, and infrastructure components. It helps organisations maintain control over AI configurations, manage changes, and ensure compliance with standards and policies. |
| Release management | RELM | Managing the release of new and updated services into production, ensuring alignment with business objectives and compliance standards. | This skill is applied when managing the deployment and release of AI models, algorithms, and applications, ensuring they are properly tested, validated, and aligned with business requirements. It helps organisations maintain control over the introduction of AI changes and minimise risks associated with deployments. |
| Asset management | ASMG | Managing the full life cycle of assets from acquisition, operation, maintenance to disposal. | This skill is applied when managing the assets involved in AI initiatives, including data, models, algorithms, and infrastructure. It helps organisations ensure the proper governance, security, and optimisation of AI assets throughout their lifecycle. |
| Capacity management | CPMG | Ensuring that service components have the capacity and performance to meet current and planned business needs. | This skill is applied when managing the capacity and performance of AI systems, ensuring they can handle the required workload, scale effectively, and meet performance expectations. It helps organisations optimise resource utilisation and ensure the smooth operation of their AI solutions. |
| Availability management | AVMT | Ensuring that services deliver agreed levels of availability to meet the current and future needs of the business. | This skill is applied when managing the availability of AI systems, ensuring they are reliable, resilient, and capable of meeting service level agreements. It helps organisations maintain the continuity and performance of their AI solutions, minimising downtime and disruption to business operations. |
| Incident management | USUP | Coordinating responses to incident reports, minimising negative impacts and restoring service as quickly as possible. | This skill is applied when coordinating responses to incident reports related to operational AI/ML systems. It involves quickly identifying, diagnosing, and addressing issues that arise during the deployment and use of AI/ML models in production environments. The goal is to minimise negative impacts on business operations and restore service as quickly as possible, ensuring the continued reliability and performance of AI/ML applications. |
| Problem management | PBMG | Managing the life cycle of all problems that have occurred or could occur in delivering a service. | This skill is applied when proactively identifying and resolving problems related to AI systems, preventing future incidents, and minimising the impact of issues that cannot be prevented. It helps organisations continuously improve the reliability and performance of their AI solutions. |
| Service level management | SLMO | Agreeing targets for service levels and assessing, monitoring, and managing the delivery of services against the targets. | This skill is applied when managing the performance and quality of AI services, ensuring they meet agreed service level agreements and customer expectations. It helps organisations maintain the reliability, responsiveness, and value of their AI offerings. |
| Service catalogue management | SCMG | Providing a source of consistent information about available services and products to customers and users. | This skill is applied when managing the catalogue of AI services and products, ensuring they are accurately described, categorised, and communicated to stakeholders. It helps organisations provide clear and reliable information about their AI offerings, facilitating service discovery, request, and delivery. |
| Service acceptance | SEAC | Managing the process to obtain formal confirmation that service acceptance criteria have been met. | This skill is applied when ensuring that AI services meet the defined requirements, quality standards, and customer expectations before they are deployed into production. It helps organisations validate the readiness and fitness for purpose of their AI offerings. |
Information security and assurance for the use of AI/ML |
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| Information security | SCTY | Defining and operating a framework of security controls and security management strategies. | This skill is applied when ensuring the security of AI systems and the data they process, protecting against threats such as unauthorised access, data breaches, and cyber attacks. It helps organisations establish and maintain a robust security framework for their AI initiatives. |
| Information assurance | INAS | Protecting against and managing risks related to the use, storage and transmission of data and information systems. | This skill is applied when ensuring the security, confidentiality, integrity, and availability of data and systems involved in AI initiatives. It helps organisations manage the risks associated with AI, such as data privacy, algorithmic bias, and ethical concerns. |
| Security operations | SCAD | Manages and administers security measures, leveraging tools and intelligence to protect assets, ensuring compliance and operational integrity. | This skill is applied when ensuring the security and resilience of AI systems, monitoring for threats and vulnerabilities, and responding to security incidents. It helps organisations maintain the confidentiality, integrity, and availability of their AI assets and data. |
| Vulnerability assessment | VUAS | Identifying and classifying security vulnerabilities in networks, systems and applications and mitigating or eliminating their impact. | This skill is applied when assessing the security vulnerabilities of AI systems, identifying potential risks and weaknesses, and implementing measures to mitigate them. It helps organisations proactively identify and address AI security risks to ensure the integrity and resilience of their AI implementations. |
| Penetration testing | PENT | Testing the effectiveness of security controls by emulating the tools and techniques of likely attackers. | This skill is applied when ensuring the security and resilience of AI systems, identifying vulnerabilities, and mitigating risks. It helps organisations proactively identify and address security weaknesses in their AI implementations, protecting against potential attacks and breaches. |
Learning and development |
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| Learning design and development | TMCR | Designing and developing resources to transfer knowledge, develop skills and change behaviours. | This skill is applied when creating learning materials and resources that cover AI-related topics, such as AI literacy, AI ethics, and AI applications in various domains. It ensures that the workforce receives the necessary training to understand and work effectively with AI technologies. |
| Learning delivery | ETDL | Transferring knowledge, developing skills and changing behaviours using a range of techniques, resources and media. | This skill is applied when delivering AI-related learning activities to various audiences, using appropriate learning delivery techniques to enable learners to develop AI-related skills, capabilities, and knowledge. It helps ensure that the workforce is well-equipped to adopt and use AI technologies in their roles. |
| Competency assessment | LEDA | Assessing knowledge, skills, competency and behaviours by any means, whether formal or informal, against frameworks such as SFIA. | This skill is applied when evaluating the AI-related competencies of the workforce, identifying skill gaps, and providing guidance for professional development planning. It helps organisations align their workforce's competencies with the requirements for successful AI adoption and implementation |
Supplier and contract management |
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| Sourcing | SORC | Managing, or providing advice on, the procurement or commissioning of products and services. | This skill is applied when sourcing AI technologies, tools, and services from external providers, ensuring they meet organisational requirements, standards, and values. It helps organisations make informed decisions about AI procurement, considering factors such as cost, quality, security, and ethics. |
| Supplier management | SUPP | Aligning the organisation’s supplier performance objectives and activities with sourcing strategies and plans, balancing costs, efficiencies and service quality. | This skill is applied when managing suppliers to ensure that suppliers provide the necessary resources, tools, and services to support the development, deployment, and maintenance of AI/ML models. |
| Contract management | ITCM | Managing and operating formal contracts, addressing supplier and client needs in product and service provision. | This skill is applied when managing contracts related to AI products, services, and partnerships, ensuring they are properly executed, monitored, and governed. It helps organisations ensure the smooth delivery of AI solutions and maintain effective relationships with AI vendors and partners. |
Organisational design and workforce planning |
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| Organisation design and implementation | ORDI | Planning, designing and implementing an integrated organisation structure and culture. | This skill is applied when facilitating changes needed to adapt to new technologies like AI and new operating models and business processes. It ensures that the organisation's structure, roles, jobs, and career paths align with the adoption of AI technologies. Organisation design and implementation also help create a supportive and collaborative culture that embraces AI and drives improved organisational performance. |
| Job analysis and design | JADN | Planning, analysing, and designing job roles and structures to align with organisational goals and culture. | This skill is applied when designing or redesigning jobs to integrate AI technologies, processes, or operational needs. It helps identify the workforce required for current and future AI-related activities and develops career pathways to enable the retention of staff as they progress through their professional development in AI-related roles. |
| Workforce planning | WFPL | Strategically projecting the demand for people and skills and proactively planning the workforce supply to meet organisational needs. | This skill is applied when identifying the current and future workforce requirements for AI-related activities, adopting or developing a skills and capabilities framework for AI roles, and developing plans to close gaps between the current state and future state of the workforce in terms of AI skills and competencies. |
| Resourcing | RESC | Acquiring, deploying and onboarding resources. | This skill is applied when sourcing and acquiring talent with AI-related skills and competencies, deploying them effectively within the organisation, and ensuring a smooth onboarding process to support AI initiatives. It helps organisations build and maintain a workforce that is well-equipped to drive AI adoption and implementation. |
| Performance management | PEMT | Improving organisational performance by developing the performance of individuals and workgroups to meet agreed objectives with measurable results. | This skill is applied when setting workgroup objectives aligned with AI adoption, supporting individual growth to achieve AI-related objectives, and developing effective working relations within and between workgroups to facilitate the successful implementation of AI initiatives. |
Quality and measurement |
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| Quality management | QUMG | Defining and operating a management framework of processes and working practices to deliver the organisation's quality objectives. | This skill is applied when establishing and maintaining a quality management system for AI initiatives, ensuring consistent and reliable delivery of AI solutions that meet business and customer requirements. It helps organisations embed quality principles and practices throughout the AI lifecycle. |
| Quality assurance | QUAS | Assuring, through ongoing and periodic assessments and reviews, that the organisation’s quality objectives are being met. | This skill is applied when ensuring the quality and reliability of AI systems, processes, and outputs, through rigorous testing, validation, and verification. It helps organisations maintain high standards of AI quality and instil confidence in their AI solutions. |
| Measurement | MEAS | Developing and operating a measurement capability to support agreed organisational information needs. | This skill is applied when measuring the performance, impact, and value of AI initiatives, using appropriate metrics, processes, and tools. It helps organisations track the success of their AI projects, identify areas for improvement, and make data-driven decisions about AI investments and strategies. |
Hardware and embedded systems |
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| Hardware design | HWDE | Specifying and designing hardware systems and components to meet defined requirements by following agreed design principles and standards. | This skill is applied when designing the hardware infrastructure that supports AI applications, such as GPUs, TPUs, and edge computing devices, ensuring optimal performance, efficiency, and compatibility. It helps organisations provide the necessary hardware foundation for AI workloads. |
| Real-time/embedded systems development | RESD | Designing and developing reliable real-time software typically within embedded systems. | This skill is applied when developing AI applications that operate in real-time or embedded environments, such as autonomous vehicles, IoT devices, and industrial control systems, ensuring their reliability, safety, and performance. It helps organisations leverage AI capabilities in mission-critical and resource-constrained scenarios. |
Safety and compliance |
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| Safety engineering | SFEN | Applying appropriate methods to assure safety during all life cycle phases of safety-related systems developments. | This skill is applied when engineering AI systems that are safe, reliable, and compliant with safety standards and regulations. It helps organisations design and develop AI solutions that prioritise safety and minimise risks to users and the environment. |
| Safety assessment | SFAS | Assessing safety-related software and hardware systems to determine compliance with standards and required levels of safety integrity. | This skill is applied when ensuring the safety and reliability of AI systems, particularly in safety-critical domains such as healthcare, transportation, and industrial control. It helps organisations identify and mitigate safety risks associated with AI implementations. |
Financial management |
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| Investment appraisal | INVA | Assessing the attractiveness of possible investments or projects. | This skill is applied when evaluating the potential benefits, costs, and risks of AI investments and projects, and making informed decisions about resource allocation. It helps organisations prioritise AI initiatives based on their strategic alignment, financial viability, and expected returns. |
| Financial analysis | FIAN | Conducting in-depth analysis of financial data to derive insights and support decision-making. | This skill is applied when conducting financial analysis related to AI initiatives, such as cost-benefit analysis, ROI assessment, and budgeting. It helps organisations make data-driven decisions about AI investments, monitor financial performance, and optimise costs and benefits. |
| Budgeting and forecasting | BUDF | Developing and managing financial budgets and forecasts to enable effective decision-making and resource allocation. | This skill is applied when planning and managing the financial aspects of AI initiatives, including budgeting for AI investments, forecasting costs and benefits, and monitoring actual performance against plans. It helps organisations ensure the financial viability and sustainability of their AI projects. |
| Cost management | COMG | Planning, controlling and analysing costs to enable the effective use of financial resources. | This skill is applied when managing the costs associated with AI initiatives, including infrastructure, talent, development, and operations. It helps organisations optimise AI costs, identify cost-saving opportunities, and ensure the cost-effectiveness of their AI investments. |