People Professional Technologies

About this package

People Professional Technologies examines how digital tools support the people function, from recruitment and employee records to learning, performance, pay and engagement. It considers both the benefits and constraints of technology: reducing administration, improving workforce insight and shaping employee experience, while recognising that a system can save time for one team yet create difficulties for its users. The starting point is people-centred selection, with attention to process fit, usability and organisational needs rather than supplier features alone. The package explains the purposes of human resource information systems (HRIS), human capital management (HCM) platforms and connected specialist tools. Learners examine applicant tracking, candidate assessment, learning management and learning experience platforms, performance systems, surveys, communication and recognition tools, payroll and reward. Employee records, organisational hierarchies, self-service and workflows provide the foundation for understanding how these systems connect. Data-flow mapping and configuration activities address reliable records, validation and effective dates, including why an approved contractual change must reach payroll before the process is genuinely complete. Implementation is treated as a change to working practices, responsibilities and skills, not simply the introduction of software. The content addresses ownership, capability gaps, data quality, resistance, training and adoption. It also develops critical use of people analytics: identifying workforce patterns, forming evidence-based recommendations and recognising where the available data cannot support a conclusion. AI and automation are explored through recruitment screening, learning recommendations and employee support, alongside bias, transparency, privacy, security, employee trust and GDPR principles. The final part connects technology choices to investment appraisal and longer-term transformation. Learners consider total cost of ownership, efficiency gains, adoption evidence and employee experience, keeping financial savings distinct from usable staff time and other benefits. They then bring these considerations into prioritised roadmaps with defined capabilities, stakeholder responsibilities and success measures. Emerging skills platforms, continuous listening and AI-enabled decision support are assessed in relation to better services and workforce decisions, rather than treated as ends in themselves.

Who it’s for

This package suits people professionals, HR practitioners and learners preparing for people-function roles who need to understand, select or help introduce workplace technologies. It is also relevant to those contributing to people analytics, system improvements or digital transformation planning.

What’s covered

1. Technology’s role and people-centred selection

   Examine how technology supports or constrains people practices across the employee lifecycle. Evaluate systems against intended processes, user needs, accessibility and organisational requirements rather than feature lists alone.

2. Implementation, capability and adoption

   Explore how to introduce technology through changes to responsibilities, skills and working practices. Implementation planning covers ownership, capability gaps, resistance, user training and adoption measures.

3. Core people systems and HRIS/HCM

   Categorise people technologies by their primary purpose and distinguish core employment records from specialist functions. Examine employee records, organisational hierarchies, workflows and self-service as foundations for connected processes.

4. Recruitment platforms and candidate assessment

   Follow recruitment information from the request to recruit through vacancy management, candidate communication and assessment. Consider how applicant tracking systems link applications, recruitment stages and evidence to the correct vacancy.

5. Learning and performance technologies

   Explore learning management systems, learning experience platforms and goal management. Distinguish evidence of completed learning from evidence of job performance, and consider how each should inform decisions.

6. Employee engagement technologies

   Examine survey, communication and recognition tools and their connections to core employee data. Consider how to interpret their outputs without confusing recorded activity with employee commitment or exposing respondents.

7. Payroll, reward and connected data

   Trace how employment, salary and benefits information feeds payroll and reward processes. Explore why accurate records, effective dates and reliable integration matter even when a data transfer runs without technical faults.

8. Workflow design and configuration

   Map tasks, decisions and responsibilities before configuring a people-system workflow. Address process rules, data validation and user experience, using contractual-hours changes to connect approval with completed employee-record and payroll updates.

9. People analytics and evidential limits

   Analyse workforce patterns using dashboards, reports and other analytics outputs. Develop recommendations that distinguish what the evidence supports from what remains unknown.

10. AI and automation in people practices

    Explore AI screening, candidate matching, learning recommendations and employee support. Distinguish fixed-rule automation from systems that learn patterns from data, and consider the risks of plausible but unsuitable outputs.

11. Responsible technology, GDPR and security

    Assess bias, proxy discrimination, transparency, privacy, security and employee trust. Consider safeguards that protect people’s opportunities and ability to challenge decisions, aligned with GDPR principles.

12. Investment appraisal and strategic transformation

    Evaluate total cost of ownership, financial returns, usable staff time, adoption and employee outcomes. Develop prioritised transformation roadmaps and assess service redesign, decision rights, skills platforms, continuous listening and AI-enabled decision support.

Learning outcomes

By completing this package, learners can:

• Explain how technology supports or constrains people practices across the employee lifecycle, using examples of its effects on efficiency, workforce insight and employee experience.
• Categorise HRIS/HCM, payroll, recruitment, learning, performance and employee experience technologies by their primary functions and the people processes they support.
• Analyse the structure of an HRIS/HCM platform, including employee records, organisational hierarchies, workflows and self-service, and map its data flows to connected systems to establish a single source of truth.
• Apply process-design and configuration principles to a specified people-system workflow, addressing integration, data validation and user experience requirements.
• Evaluate people-technology options against defined criteria for process fit, integration, usability and organisational needs, justifying a selection for a given workplace scenario.
• Develop an implementation plan for a people-technology change that assigns ownership, addresses data quality and resistance, and specifies digital literacy development, user training and adoption measures.
• Analyse workforce data and people-analytics outputs to identify patterns, formulate evidence-based recommendations and distinguish supported conclusions from claims that exceed the available evidence.
• Assess proposed uses of AI and automation in recruitment, learning and employee support for bias, transparency, privacy, security and employee trust, recommending safeguards aligned with GDPR principles.
• Evaluate the value and limitations of a people-technology investment using financial costs, efficiency gains, adoption evidence and employee experience measures to justify a recommendation.
• Create a prioritised digital transformation roadmap for a people function that defines required capabilities, stakeholder responsibilities and success measures, and appraises skills platforms, continuous listening and AI-enabled decision support.

Frequently asked questions

Related training packages