Human-Centricity
AI enhances human capability, but humans remain accountable for decisions and outcomes.
White Paper | September 2025
Governing AI in Projects with the AI Project Governance Framework (AIPGF)
AI is becoming embedded in project delivery. This white paper introduces a pragmatic, structured and adaptable approach for governing AI use in projects and programmes.
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The governance gap
AI adoption creates opportunities. It also creates governance obligations.
AI-powered tools are changing how projects are conceived, planned and delivered. The paper identifies risks including transparency gaps, inefficiency, compliance exposure, reputational risk, bias and financial loss. Its central proposition is that AI adoption should be governed rather than left unmanaged.
The AIPGF
Three principles underpin the Framework
Transparency
AI decisions should be explainable, auditable and trusted.
Adaptability
Governance should scale with AI adoption, AI automation and organisational AI governance maturity.
Core Values
Five values guide responsible AI assistance
Accountability
Every AI decision is explainable and attributable.
Sensibility
Balance AI outputs with human judgement.
Collaboration
Encourage effective collaboration between teams and AI tools.
Curiosity
Explore AI innovations responsibly.
Continuous Improvement
Regularly review and refine AI use.
Project lifecycle
Governance throughout the project lifecycle
The AIPGF uses three lifecycle stages that can map to an organisation's chosen project management approach, allowing the Framework to be integrated with different project methods.
Stage 1
Foundation
- Establish AI assistance objectives and scope.
- Select relevant AI tools.
- Assess data availability and quality.
- Enable the team.
- Identify and manage AI-related risks.
Stage 2
Activation
- Operationalise the AI Assistance Plan.
- Facilitate ethical, efficient and effective human-AI collaboration.
- Monitor AI effectiveness.
- Continue to anticipate and mitigate AI-related risks.
- Manage AI-related issues and keep stakeholders updated.
Stage 3
Evaluation
- Evaluate AI impact and AI decision-making processes.
- Document and share lessons learned.
- Identify improvements in AI adoption, tool selection and training.
AIPG-CMM
Assessing AI governance maturity
The AIPGF recommends the AI Project Governance Capability Maturity Model (AIPG-CMM) to benchmark governance of AI usage in projects and programmes and to prioritise actions for continuous improvement.
Level 1: Ad Hoc
Governance of AI usage in projects is largely non-existent, sporadic or reactive.
Governance of AI usage in projects is largely non-existent, sporadic or reactive.
Level 2: Initialised
AI governance processes in projects are minimally defined and only occasionally implemented.
AI governance processes in projects are minimally defined and only occasionally implemented.
Level 3: Standardised
AI governance processes are documented, repeatable and consistent across projects within parts of the organisation.
AI governance processes are documented, repeatable and consistent across projects within parts of the organisation.
Level 4: Enterprised
AI governance processes are institutionalised, integrated across the organisation and measured regularly.
AI governance processes are institutionalised, integrated across the organisation and measured regularly.
Level 5: Optimised
AI governance is fully integrated across the organisation's project ecosystem and continuously refined.
AI governance is fully integrated across the organisation's project ecosystem and continuously refined.
For executives
Executive leadership priorities
The paper argues that AI governance cannot be delegated solely to project teams or technical specialists. Executives set the tone, provide sponsorship and help ensure that governance frameworks are embedded into organisational practice.
Benchmark maturity
Understand the current level of AI governance maturity across the organisation's project ecosystem.
Champion implementation
Signal that responsible and transparent AI use in projects and programmes is a strategic priority.
Allocate roles and responsibilities
Define who is responsible for AI oversight across project, compliance and data functions.
Build capability
Invest in AI literacy and governance competence across project leadership and delivery teams.
Demonstrate accountability
Provide assurance to relevant stakeholders that AI use in projects is governed responsibly, transparently and ethically.
How well is your organisation governing AI use across its projects and programmes?
Read the full white paper to explore the AIPGF, its lifecycle, governance maturity model and the priorities for executive leadership.
Download the White PaperHow Mature is your Governance of AI in Projects?
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Frequently Asked Questions
What is the AI Project Governance Framework (AIPGF)?
The AIPGF is a pragmatic, structured and adaptable framework for governing AI use in projects and programmes. It is designed to help organisations use AI responsibly, transparently and effectively while maintaining appropriate human accountability.
Why is AI governance needed in projects?
AI-powered tools are increasingly being used across project delivery, creating opportunities as well as risks. The paper identifies potential risks including transparency gaps, inefficiency, compliance exposure, reputational risk, bias and financial loss. AI governance provides a structured way to manage these risks while enabling organisations to realise the benefits of AI assistance.
What principles underpin the AIPGF?
The AIPGF is based on three principles: Human-Centricity, Transparency and Adaptability.
Human-Centricity means AI enhances human capability while humans remain accountable for decisions and outcomes.
Transparency means AI decisions should be explainable, auditable and trusted.
Adaptability means governance should scale with AI adoption, AI automation and organisational AI governance maturity.
What are the five Core Values of the AIPGF?
The five Core Values are Accountability, Sensibility, Collaboration, Curiosity and Continuous Improvement. Together, they guide responsible AI assistance throughout project delivery.
How does the AIPGF work across the project lifecycle?
The AIPGF uses three lifecycle stages: Foundation, Activation and Evaluation.
Foundation establishes the purpose and scope of AI assistance, identifies appropriate tools, assesses data readiness, enables the team and identifies AI-related risks.
Activation operationalises the AI Assistance Plan, supports effective human-AI collaboration, monitors effectiveness, manages emerging risks and issues, and keeps stakeholders informed.
Evaluation assesses AI impact and decision-making, captures lessons learned and identifies improvements in AI adoption, tool selection and training.
Can the AIPGF be used with different project management methods?
Yes. The paper describes the AIPGF as adaptable to different project management approaches. Its lifecycle can be mapped to an organisation’s chosen methodology rather than requiring a separate project management method.
What is the AIPG-CMM?
The AI Project Governance Capability Maturity Model (AIPG-CMM) is a maturity model recommended by the AIPGF for benchmarking governance of AI usage in projects and programmes and prioritising actions for continuous improvement.
What are the AIPG-CMM maturity levels?
The AIPG-CMM has five maturity levels: Ad Hoc, Initialised, Standardised, Enterprised and Optimised.
Ad Hoc: Governance is largely non-existent, sporadic or reactive.
Initialised: Governance processes are minimally defined and only occasionally implemented.
Standardised: Governance processes are documented, repeatable and consistent across projects within parts of the organisation.
Enterprised: Governance processes are institutionalised, integrated across the organisation and measured regularly.
Optimised: Governance is fully integrated across the organisation’s project ecosystem and continuously refined.
The AI Project Governance Framework (AIPGF) offers a sensible methodology for facilitating ethical, efficient and effective human-AI project collaboration.
- Can be integrated with a chosen project management methodology or approach, such as Agile, PRINCE2, PMBOK or hybrid approaches.
- Provides structured and scalable AI governance, supporting projects and programmes of varying size, complexity, risk and AI adoption maturity.
- Facilitates and encourages a high standard of ethical, efficient and effective use of AI in projects and programmes.
By implementing the Framework, organisations can systematically govern AI use across their portfolio of projects and programmes, as their AI adoption scales and as AI tools evolve. The accompanying AI Project Governance Capability Maturity Model (AIPG-CMM) can be used to establish maturity benchmarks and actions towards continuous improvement.
Disclaimer
The AIPGF is intended to provide practical guidance for governing the use of AI in projects and programmes. The author (Emanuela Giangregorio) expressly disclaims all liability to any person or organisation arising directly or indirectly from the use of, or for any errors or omissions in, the AIPGF guidance. The adoption and application of the guidance is at organisation discretion and is their sole responsibility.
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