AI Project Governance Framework | White Papers

Governing AI in Projects

Practical perspectives on governing AI use in project and programme environments, from establishing the case for AI Project Governance to understanding the PMO's emerging role in providing assurance.

Governing AI in Projects with the AI Project Governance Framework (AIPGF) - August 2026 White Paper
White Paper | August 2026

Governing AI in Projects with the AI Project Governance Framework (AIPGF)

A practical case for governing the use of AI assistance in projects and programmes. The paper explains why AI adoption creates new governance obligations and how the AIPGF provides a structured, adaptable approach.

  • Understand the governance gap created by AI-assisted project delivery.
  • Explore the AIPGF principles, values and lifecycle.
  • Understand how AI governance maturity can be assessed and improved.
  • Identify priorities for executive leadership.
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Governing AI in Projects: Why the PMO Should Take the Lead - August 2026 White Paper
White Paper | August 2026

Governing AI in Projects: Why the PMO Should Take the Lead

A focused examination of the PMO's emerging role in AI Project Governance, including how PMOs can embed governance into project delivery, provide assurance and improve governance maturity.

  • Understand why AI adoption has moved faster than project governance.
  • Explore the PMO's three responsibilities: Embed, Assure and Improve.
  • See how the AIPGF can support existing project governance arrangements.
  • Identify practical steps for establishing more consistent AI governance.
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Two perspectives. One governance challenge.

The first paper sets out the case for AI Project Governance and introduces the AIPGF. The second takes a closer look at the PMO and its potential role in embedding, assuring and improving AI governance across projects and programmes.

Start with the paper most relevant to your role

Whether you are establishing an organisation-wide approach to AI governance or considering what this means for your PMO, the papers provide a practical starting point.

Explore the AIPGF White Paper Explore the PMO White Paper
Complete the AIPG-CMM Self-Assessment
Get certified with the APMG on the AIPGF

with APMG International

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.

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.

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.

The five Core Values are Accountability, Sensibility, Collaboration, Curiosity and Continuous Improvement. Together, they guide responsible AI assistance throughout project delivery.

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.

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.

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.

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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