White paper #002: A Well-Architected Framework for public procurement

Upgrading institutional capacity in the age of AI

Software that does not constantly evolve is outcompeted, vulnerable to threats, and abandoned by users. The same can be true for institutions.

Public procurement is how governments acquire the tools and technologies they need to function. Yet, outdated procurement systems are slow, rigid, and inefficient, making governments less capable of addressing today’s challenges—especially in the AI era. This paper proposes a Well-Architected Framework for Public Procurement, inspired by software architecture, to make procurement more adaptive, responsive, and purpose-driven.

What a well-architected procurement system offers

A well-architected procurement system optimises institutional design to enable adaptability, agility, accuracy, and accessibility:

• Adaptability – Procurement must shift from rigid rule-following to dynamic risk management, ensuring governments can continuously update their capabilities.
• Agility – Instead of one-time purchases, procurement should be designed as an iterative process with feedback loops that refine and improve over time.
• Accuracy – Procurement models should match the characteristics of what is being purchased. Buying cloud computing should not follow the same process as buying office furniture.
• Accessibility – Procurement systems should be transparent, open, and navigable, reducing barriers for startups and new entrants while ensuring fair competition.

Building institutional capacity for the AI age

To implement a new procurement framework, procurement systems must invest in four key enablers:

• Rules – Shift from compliance-driven complexity to flexible, outcome-focused regulation.
• Culture – Promote a problem-solving mindset that embraces risk management, not avoidance.
• Talent – Elevate procurement to a strategic function, equipping specialists with skills in negotiation, technology, and market analysis.
• Data – Use analytics and AI to track performance, detect inefficiencies, and improve decision-making.

This paper benefited from feedback in a closed TIAL session, with valuable contributions from Anir Chowdhury, Julie Guichard, Andrea Halmos, Malcolm Harbour, Laura Kirchner, Aaron Maniam, Sir Geoff Mulgan, Julian Olsen, Anita Poort, Martin Rand, Giulio Quaggiotto, and Angeliki Vourdaki.

Read more about the TIAL series of white papers here.