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Nearly every country in the world has some form of parliament. Parliamentary systems fall into two categories: bicameral and unicameral. Out of 188 national parliaments in the world, 81 are bicameral (162 chambers) and 107 are unicameral, making a total of 269 chambers of parliament with some 44,000 members of parliament. IPU membership is made up of 183 national parliaments
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This sub-guideline is part of the guideline Project portfolio management. The main guideline should be read first for context and an overview.
This sub-guideline introduces an example of a project portfolio management (PPM) methodology that parliaments could implement to support their AI programme.
The STEP approach
The STEP approach offers a structured way for parliaments to implement AI technologies and leverage their potential to enhance productivity and efficiency. Following this model allows parliaments to navigate the introduction of AI solutions with precision and foresight, ultimately driving positive outcomes and organizational success.
The STEP approach comprises four key stages:
Segmentation involves identifying and segmenting tasks suitable for AI integration, recognizing that no single AI system can fulfil all requirements.
Transition is the process of gradually incorporating AI systems into existing workflows, allowing for the deepening or upgrading of work roles in order to maximize the benefits of AI technologies.
Education plays a crucial role in ensuring staff readiness and competency in effective AI use, encompassing training on AI functionality, potential biases and best practices for integration.
Performance evaluation measures the impact and efficacy of AI systems in real-world contexts, providing valuable insights into their tangible benefits and areas for improvement.
These stages are shown Figure 1 and discussed in turn below.
Figure 1: The STEP approach
Segmentation
Segmentation offers a systematic framework for analysing activities within parliamentary proceedings by delineating related processes and tasks.
Segmentation entails categorizing tasks into three distinct types:
Tasks that AI cannot or should not perform according to decisions made by those in charge of AI governance
Tasks where AI can augment staff actions (augmentation)
Tasks that can be automated by AI (automation)
Crucially, this approach emphasizes that parliamentary staff should take the lead in task segmentation, leveraging their domain expertise to identify suitable tasks for AI integration. It also underscores the importance of staff experimentation with AI tools before widespread adoption, enabling them to assess suitability, usability and effectiveness in real-world contexts.
By adopting a proactive approach to task segmentation following a business analysis or a job competency analysis, parliaments can maximize the benefits of AI systems while ensuring alignment with organizational goals and priorities. Having a clear list of tasks identified for augmentation or automation will be crucial for the ideation phase. Within an AI PPM context, these tasks become the primary candidates for digital transformation.
Transition
The transition phase of the STEP approach focuses on changes to parliamentary staff members’ roles as tasks are augmented or automated through the integration of AI systems.
During this phase, resources freed up by AI implementation are carefully considered and assigned to one of three categories:
Job elimination: resources are redeployed to other functions within parliament
Job upgrading: tasks previously performed by more senior staff are now supported by AI-enabled systems
Job deepening: staff members have the opportunity to spend their time on tasks with greater added value, gain deeper knowledge in their domain, or engage in skill-building activities such as preparing training materials
By strategically managing this transition, parliaments can optimize resource allocation, enhance staff productivity and capitalize on the transformative potential of AI technologies.
Education
In the context of parliamentary AI integration, education is a multifaceted approach aimed at fostering a culture of continuous learning and skill development, with a particular focus on embedding an ethos of AI learning into the organizational culture. As part of this, parliaments should frequently review and update learning materials to ensure they keep pace with evolving AI trends and best practices, as well as revisiting the segmentation process regularly to identify new opportunities for AI integration.
Parliamentary staff can be enrolled on certifying courses that teach them the necessary knowledge and skills to effectively leverage AI solutions in their roles. These skills could include fine-tuning documents or data priority from the organization, mastering prompt engineering to create effective commands or prompts for AI systems, and evaluating the validity of predictions made by these systems.
Performance evaluation
The performance evaluation phase acknowledges that the introduction of AI systems in parliaments represents a shift rather than just a lift in operational dynamics. Performance evaluation encompasses the following aspects:
Recalibrating individual annual performance metrics to encompass factors such as speed, efficiency, accuracy and creativity, reflecting the contribution of AI systems and related training
Identifying and measuring appropriate performance indicators for AI systems to determine whether these systems are having a measurable impact that aligns with the organization’s strategic objectives
Regularly assessing the viability and effectiveness of AI systems, with feedback loops that provide valuable qualitative metrics to evaluate adoption rates and user satisfaction
Evaluating the effectiveness of AI-related training programmes, and continuously updating learning materials to ensure staff remain up to date with the latest AI advancements and methodologies
Closely monitoring the availability of new AI technologies and reviewing task segmentation accordingly
Summary
The STEP approach provides a structured and adaptable AI PPM method for parliaments. It helps ensure alignment with organizational goals, optimizing resource allocation and maximizing the overall impact of AI investments. By adopting this approach, parliaments can navigate the complex landscape of AI projects with confidence, driving innovation, efficiency and effectiveness in legislative processes and governance.
