Project portfolio management

Audience
This guideline is intended for parliamentary staff including senior leaders who may not have a technical background in AI but who are involved in the management or oversight of AI projects.
About this guideline
This guideline examines the practice of managing a portfolio of AI projects.
The ability to effectively manage a diverse array of AI projects is increasingly important as AI becomes more commonplace in parliaments. By strategically prioritizing and aligning AI projects with organizational goals, parliaments can maximize their impact.
This guideline therefore aims to equip parliaments with the knowledge and tools they need to navigate the complexities of AI project portfolio management, ensuring that AI technologies are implemented successfully and in line with ethical principles.
What is project portfolio management?
Project portfolio management (PPM) refers to the centralized management of a parliament’s programmes, projects and related activities. It is used to meet strategic objectives by optimizing resource allocation, balancing risks and maximizing overall value.
Why project portfolio management is relevant to AI governance
AI project portfolio management (AI PPM) entails overseeing a diverse array of AI technologies and initiatives in order to optimize performance and achieve strategic objectives as part of ongoing strategic governance.
One key consideration in AI PPM is recognizing that AI systems are designed to evolve over time, adapting to changing needs and circumstances. With multiple large language models (LLMs) available in the field of generative AI, parliaments must carefully evaluate and select the most suitable AI technologies to support their objectives.
Effective AI PPM involves ensuring that AI initiatives are aligned with organizational goals, while also prioritizing transparency, accountability and ethical considerations. By adopting a proactive approach to AI PPM, parliaments can harness the full potential of AI to drive innovation, efficiency and effectiveness in legislative processes and governance.
From segmentation to portfolio management
AI PPM provides a structured approach to strategically selecting, prioritizing and overseeing AI projects within parliaments. Unlike traditional project management, which focuses on individual projects, AI PPM takes a holistic view, considering the collective impact of AI projects and their alignment with organizational goals and priorities.
At its core, AI PPM involves identifying, evaluating and prioritizing AI projects based on their potential value, feasibility and alignment with strategic objectives. Each potential or existing AI project can be evaluated against seven key criteria:
- Strategic alignment: Consider whether the project aligns with parliament’s overarching strategy and goals. Projects that contribute directly to achieving strategic objectives should be given higher priority.
- Measurable impact: Prioritize projects with clear, objective and measurable impact metrics in order to ensure a tangible return on investment.
- Augment or replace: Determine whether the project will augment current human operations or replace existing manual processes entirely. Projects that enhance human capabilities or efficiency should be prioritized over those that solely aim to automate existing processes.
- Nature of the problem: Evaluate whether the problem being addressed is suitable for AI-driven solutions. Projects that align with the capabilities of AI technologies and have clear problem-solving potential should be prioritized.
- Data availability: Assess the availability and quality of data required for the project. Projects for which the necessary data is already available or can easily be acquired should be prioritized, as data availability is essential for AI model training and performance.
- Technological capability and skills: Consider whether parliament possesses the technological infrastructure and skill set required to develop, deploy and scale up the solution successfully. Projects that align with existing technological capabilities and expertise should be prioritized in order to minimize implementation challenges.
- Ethical considerations: Finally, ensure that all ethical considerations – including bias mitigation, privacy protection and transparency – have been thoroughly evaluated for each project and that the project aligns with agreed organizational values.
Prioritization
Once parliament has identified and evaluated AI projects, the next important step is to review the institution’s (potential) AI portfolio as a whole and to prioritize the workstream:
- Evaluate how each AI project aligns with parliament’s overall strategic goals and rank them according to potential value and impact.
- Assess resource availability, conflicts and constraints.
- Evaluate the potential benefits and risks of each AI project and prioritize those with favourable risk-reward ratios.
- Identify projects that are prerequisites for others or that could create synergies if implemented together, and consider prioritizing those that unlock value in other projects or create a foundation for future initiatives.
- Understand the time sensitivity of each project, looking to balance quick wins and long-term strategic value.
- Assess the impact of each project on key stakeholders (both internal and external) and prioritize those with high levels of stakeholder support.
Methodologies and frameworks for PPM
There are many standards and frameworks that organizations can use to support the implementation of a PPM approach. Some examples are given below:
The Standard for Portfolio Management (SPM): a standard developed by the Project Management Institute (PMI)
Disciplined Agile (DA): a toolkit, also developed by PMI, that includes portfolio management practices
https://www.pmi.org/learning/library/pathway-organizational-project-management-maturity-8221Organizational Project Management Maturity Model (OPM3): another model developed by PMI
Projects IN Controlled Environments (PRINCE2): a project management method that also has implications for portfolio management
Lean Portfolio Management: a method that is part of the Lean-Agile approach
Hoshin Kanri: a strategic planning process that can be applied to portfolio management
Objectives and Key Results (OKRs): an approach that can be used to align portfolios with organizational goals
Other approaches that can be used for PPM include Balanced Scorecard and Theory of Constraints.
Parliaments can use their existing methodologies to support AI PPM, or they can adopt an established external framework that fits well with their culture and working methods.
For further guidance on developing or adopting a framework for PPM, refer to the sub-guideline Project portfolio management: The STEP approach.
Find out more
- IPU Centre for Innovation in Parliament (CIP), IT Governance Hub: Framing the development of IT governance for parliaments
The Guidelines for AI in parliaments are published by the IPU in collaboration with the Parliamentary Data Science Hub in the IPU’s Centre for Innovation in Parliament. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence. It may be freely shared and reused with acknowledgement of the IPU. For more information about the IPU’s work on artificial intelligence, please visit www.ipu.org/AI or contact [email protected].
Related content
About the guidelines | The role of AI in parliaments | Introducing AI applications | Inter-parliamentary cooperation for AI | Strategic actions towards AI governance | Risks and challenges for parliaments | Generic risks and biases | Ethical principles | Risk management | Alignment with national and international AI frameworks and standards | Project portfolio management | Data governance | Systems development | Security management | Training for Data Literacy and AI Literacy | Glossary of terms