How to build AI skilling initiatives that last: the AI for All Workforce Skilling Policy Toolkit

Introducing the AI for All Workforce Skilling Policy Toolkit

Turning AI ambition into real workforce impact takes more than good intentions; it takes practical tools. Access Partnership has worked with AVPN to develop the AI for All Workforce Skilling Policy Toolkit: a resource designed to help policymakers across Asia Pacific design, scale, and sustain inclusive AI skilling initiatives.

The Toolkit supports the AI Opportunity Fund: Asia Pacific, launched by AVPN in partnership with Google.org and the Asian Development Bank, to help equip the region’s workforce with essential AI skills for an evolving work landscape.

The Toolkit aims to guide the design and development of AI skilling initiatives for broad-based participation while also ensuring close alignment with economy and industry needs, supported by three key objectives.

Identify essential fit-for-purpose AI skills needed to adapt to evolving job landscapes
Establish a tailored approach towards developing skilling initiatives for sectoral and demographic groups (e.g., Micro, Small, and Medium Enterprises (MSMEs))
Design actionable and agile steps to build AI capabilities at scale

The four-pillar framework

The Framework underpinning this Toolkit consists of four pillars. This enables continuous iteration and improvement, with findings informing future programme design and providing policymakers with a flexible foundation that can be adapted to local needs and priorities.

Identify the skills needed for AI

Develop a skills taxonomy that enables policymakers to define and classify skills transformed by AI, and identify emerging AI skills that align with national and sectoral policies and priorities.

Assess gaps in skilling ecosystems

Conduct a gap assessment to identify gaps in existing skilling programmes, ensuring that future skilling programmes remain targeted.

Develop and implement a workforce skilling roadmap

Design an actionable AI workforce skilling roadmap, prioritising the key actions to be taken by various stakeholders.

Monitor and evaluate progress

Identify and develop key performance indicators (KPIs) as part of the MEL (monitoring, evaluation, and learning) approach to continuously refine the framework.

Built for every stage of AI readiness

Recognising that countries are at different stages of AI readiness, the Framework includes three progression pathways to help policymakers with a structured approach to identify where they are and how they can best apply the Framework to design, scale, and sustain AI skilling initiatives. To support this process, each pillar is accompanied by a set of guiding questions tailored to each progression pathway, helping policymakers identify priority areas for action and determine the steps needed to advance towards the next stage of AI skilling ecosystem maturity.

Starting action

Establishing the foundational capabilities, partnerships, and processes needed to develop an AI skilling ecosystem.

Scaling efforts

Strengthening coordination, expanding existing initiatives, and building the systems required to scale AI skilling efforts.

Sustaining impact

Enhancing institutional capabilities, embedding continuous improvement processes, and ensuring long-term impact from established AI skilling initiatives.

The Framework is also complemented by case studies that showcase how different countries and organisations have approached AI skilling, offering practical examples and lessons that policymakers can draw upon.

From framework to action

Together, these features provide policymakers with a practical roadmap to translate AI ambitions into actionable initiatives. To explore the full Framework, progression pathways, guiding questions, and case studies, download the full AI Workforce Skilling Policy Toolkit here:

AI Workforce Skilling Policy Toolkit

Developed in collaboration with AVPN, Access Partnership is committed to supporting stakeholders across the AI ecosystem in building inclusive, responsible, and impactful approaches to AI adoption.