Call for concept notes: Socio-economic impacts of artificial intelligence in Africa

Table of contents 

Concept notes: Socio-economic impacts of artificial intelligence in Africa 

1. About the funders and the focus area 

2. Overview of the call

3. Funding scope and duration

4. Eligibility criteria

5. Exceptions of projects

6. Submission process

7. Format and requirements for proposals

8. Evaluation criteria

9. Selection process

10. Requirements in subsequent stage

11. Post selection requirements

12. Timeline and communication of results

13. Information session, inquiries and FAQs

14. Permission for use and disclosure of information

Annex 1: Ensuring research ideas address gender equality and inclusion

Annex 2: Institutional Assessment Documentation 

Annex 3: LMIC countries in sub–Saharan Africa  

Annex 4: Research themes and topics 

About the funders and the focus area 

IDRC is a Crown corporation created in 1970 by the Parliament of Canada. IDRC supports and strengthens the capacity of people and institutions in developing countries to undertake the research that they identify as most urgent. It works with researchers and research users as they confront contemporary challenges within their own countries and contributes to global advances in their fields.  

The Centre’s 10-year strategy, Strategy 2030, affirms IDRC’s vision for a more sustainable and inclusive world, and commits the Centre to the following mission: IDRC will be a leader in research for development, investing in high-quality research and innovation, sharing knowledge for greater uptake and use and mobilizing alliances for more sustainable, prosperous and inclusive societies. (Please refer to IDRC’s Strategy 2030 for more information.) 

Gender equality and inclusion are central to all IDRC programs. In the following sections you will see how we plan to address this in all our programming. 

The United Kingdom’s Foreign, Commonwealth and Development Office (FCDO) leads the UK's diplomatic, development and consular work around the world. FCDO promotes the interests of British citizens, safeguards the UK’s security, defends our values, reduces poverty and tackles global challenges with our international partners. FCDO brings together the best of Britain’s international effort and demonstrates the UK acting as a force for good in the world.

FCDO research funding is overseen by the Research and Evidence Directorate (RED) which leads FCDO’s work on generating, synthesising, and using high-quality research and evidence to inform policy and programming. RED commissions and manages research, provides expert advice, and ensures that evidence underpins decision-making across the FCDO.

The AI4D program is a five-year partnership between IDRC and FCDO to support policy, innovations and expanded leadership to spur safe and responsible artificial intelligence (AI) development and use in Africa. The vision of the partnership is to support an inclusive, responsible AI ecosystem that improves quality of life for all in Africa and beyond.   

Responsible AI is safe, inclusive, rights-based, ethical and sustainable AI. 

AI4D is built around two critical outcomes that are needed to shape AI in the Global South as a force for good: (i) Global, regional, national responsible AI policies and regulations are enacted to stimulate AI that is safe, inclusive, ethical, rights based, green, and reflective of African (and other Global South) perspectives and which minimize potential harms from AI (such as human rights violations and increased social and gender inequalities) and (ii) Responsible AI innovations, that have been shown to address key development challenges in Africa and beyond (in the areas of gender, health, education, environment, etc.) are appropriately scaled. 

Overview of the call

Background and rationale

Artificial Intelligence (AI) is rapidly emerging as a transformative force across all sectors of society. In low- and middle-income countries (LMICs), particularly in Africa, AI technologies are beginning to shape new possibilities for economic development, social inclusion, and innovation. Enabled by growing computational power, improved connectivity and expanding data ecosystems, AI applications are being deployed to improve food systems, enhance education quality, address health and climate challenges, and support the emergence of new startups and digital economies. 

By some estimates, AI has the potential to inject USD 2.9 trillion (CAD 4 trillion) into the African economy by 2030, equating to a 3% annual increase in GDP. This economic boost could lift 11 million Africans out of poverty and create jobs for half a million people across the continent each year.^[1] 

Despite this promise, the benefits of AI are not evenly distributed, within and between countries. Like many technological advances, AI carries the risk of reinforcing existing inequalities and introducing new forms of exclusion. It can amplify structural biases, perpetuate gender disparities, displace workers, reduce the quality of jobs, contribute to the growth of precarious work arrangements and introduce opaque decision-making processes. For example, this may include differential impacts on labour based across industries, where AI-enabled process automation leads to job destruction for lower earning groups, while benefiting higher earning groups. In addition, labour regulations have been weak and slow to create dignified and fair conditions for workers broadly and particularly in data and AI value chains. AI systems can also be misused for surveillance, disinformation and other harmful purposes. The environmental cost of AI—particularly the energy demands of training large models—adds another layer of complexity to its responsible deployment.  

In Africa, these challenges are compounded by structural and demographic realities. The continent has a rapidly growing and youthful population, with millions entering the labour market each year in search of decent work. Africa’s labour force is expected to nearly double by 2050, and faces structural challenges, including high youth unemployment (26.1% NEET rate)^[2] and a dominant informal sector (85% of the workforce).^[3]  Women, while economically active, face disproportionate barriers to opportunity and control over resources. They are more likely to live in poverty, perform precarious or unpaid labour and be excluded from decision-making processes. It is estimated that between 60 to 80 million Africans live with some form of disability and that they are more likely to be unemployed and have lower levels of educational attainment^[4][5]. At the same time, young people and women are key drivers of change, contributing to the continent’s future through leadership, innovation and civic engagement Including individuals with disabilities will be paramount in ensuring equal opportunities for all. 

Digital exclusion remains a significant barrier. Mobile connectivity has expanded rapidly — according to GSMA^[6], rising from 20 million mobile subscriptions in 2020 to over 527 million by the end of 2023 alone  — but many communities still lack reliable access to digital infrastructure and services. This digital divide limits participation in the AI economy and risks leaving behind those who are already marginalized.  

This divide is amplified at the global level, where African data helps to fuel global innovation with limited local benefit. Most data and other critical infrastructure needed for AI is hosted in the Global North, in a small number of countries and firms. As these systems become further entrenched, there is a growing risk that African countries and the AI ecosystems within them will be consumers of AI instead of producers, representing economic and structural risks. This applies to taxation systems that are applied differently to global versus national and non-tech enterprises.  

Nonetheless, there are encouraging signs of progress. African countries are increasingly adopting national AI strategies. Investments in digital skills training are growing and the number of AI-related initiatives and companies on the continent is rising.  

There are significant gaps in knowledge and evidence on the socio-economic implications of AI and how these will surface in different contexts across the world. The current dialogue and research surrounding AI continue to be dominated by perspectives and entities from the Global North. Additionally, stakeholder groups across the AI ecosystem, such as Big Tech, think tanks, civil society and policy makers understand the risks and opportunities of AI differently.  

The future of AI in Africa is still being shaped. The policies, investments and research undertaken today will determine whether AI becomes a tool for inclusive development or a driver of deeper inequality. There are still major knowledge gaps that must be addressed to support evidence-based policymaking and responsible innovation that centre on human needs and experiences. These include understanding how AI is reshaping inequality and high-growth economic sectors and its implications for labour markets, growth and productivity. Compounding this are critical data gaps on the state of economic transformation in the current moment, where AI is proliferating rapidly and being adopted without policy and regulatory systems in place to oversee its use.  

It is difficult to understand what the socioeconomic implications of AI are now and in the short- to medium-term without data and information to paint a clear picture. The policy implications of AI’s potential to drive economic transformation are significant and there is a need for evidence to support critical decision- making. Understanding the risks and opportunities of AI and how they impact different groups across Africa calls for focused research and data collection to capture the reality of these transformations in real time. 

Key objectives

This call for concept notes invites research projects in Sub-Saharan Africa that aim to fill evidence and data gaps, deepen understanding, and inform policies on the impact of AI on formal and informal employment and labour markets, productivity shifts and the risks of exclusion. These projects should examine what economic resilience entails for low-resourced countries in the digital age by addressing critical knowledge gaps, exploring the differentiated socioeconomic implications of AI and contributing to shaping policies and practices that ensure AI is a driver of jobs and economic growth through concrete recommendations and policy engagement. 

The specific objectives are to: 

1. Generate rigorous, context-specific data and evidence on the socioeconomic impacts of AI.  

This includes the differential impacts of AI on labour markets, livelihoods and productivity across sub-Saharan Africa and as part of the global economy.  Without locally relevant evidence, AI governance risks being shaped by external actors and values. Existing research does not adequately reflect the diversity and realities of the African continent, and current AI policies and strategies are being developed with an inadequate evidence base on the consequences for jobs, productivity and inequality. This initiative will generate evidence to support the development of responsible AI policies with a strong emphasis on gender equality, disability inclusion and the experiences of marginalized populations. It will amplify African evidence and experiences in the global AI discourse.  

2. Inform inclusive policymaking on AI.  

AI is reshaping economies and labour markets in real time, but most existing research is either speculative or based on Global North contexts. This initiative will provide policy and decision makers with timely, contextual evidence and data on how AI interacts with employment, productivity and inequality in Africa, to support the development of inclusive, rights-based AI policies and strategies across the continent that increase decent opportunities and support economic resilience to change.  

3. Integrate a strong gender equality, disability and inclusion (GEDI) lens in the generation and translation of knowledge on the socioeconomic impacts of AI.  

Research will prioritize the pressing and policy-relevant challenges, particularly those affecting vulnerable and underrepresented groups, including women, youth, people with disabilities and rural communities. These are critical inputs into AI policies and strategies, to ensure economic benefits accrue for all and enhance resilience and opportunities. 

Thematic focus areas  

Concept notes should address one or several of the main themes specified below, with the types of empirical questions that fit within them. Applicants are encouraged to draw on the detailed research gaps and questions identified in the Learning Agenda in Annex 4. Questions provided below (and in Annex 4) are not exhaustive, and concept notes for research projects can address any of these questions, combinations of questions, and/or other questions fitting within the overall themes. Across all themes and research questions, we are seeking rigorous evidence of what is happening to inequality as a result of the proliferation of AI.   

1. Labour and Livelihoods: There is a need to better understand AI's role in creating or displacing jobs, and on the quality and precarity of jobs, particularly in regions with young and growing populations like Africa, where there is a continuous influx of new entrants into the labour market, a high rate of informality and increasing rates of platform and data work that can blur the lines between formal and informal jobs. While some initial assessments exist, particularly at the global level or focused on the Global North, rigorous evidence on AI’s differential impacts in Africa is insufficient. Indicative research questions include:

• What is the net impact of AI on formal and informal employment in Africa, particularly across sectors and demographic groups?
• How is AI affecting gender dynamics in the labour market, youth employment outcomes and rural livelihoods?
• How is AI transforming job roles, especially entry-level positions and youth employment?
• What are the implications of AI for informal workers, including job security, income stability and access to protections?

2. Productivity and Economic Transformation: AI is poised to reshape productivity and economic structures in Africa, with the potential to boost GDP by USD 2.9 trillion (CAD 4 trillion) by 2030, but it also risks deepening inequalities, displacing vulnerable workers and entrenching digital dependency. Rigorous, context-specific evidence is needed on whether AI supports pathways to economic upgrading, whether that is linked to social upgrading and how AI affects different sectors, populations and economies — especially the informal sector — to inform equitable, inclusive and locally grounded policy responses. Indicative research questions include:

• How is AI adoption affecting productivity at the firm, sector and national levels?
• What is the potential of AI to enhance productivity in the informal economy and support greater economic integration?
• In what ways does AI adoption affect value creation and alter the relationship between labour and capital?
• What are the conditions under which AI-driven productivity gains translate into improved livelihoods and reduced inequality, particularly for women and other vulnerable groups?

3. Poverty and Inequality: AI has the potential to perpetuate or amplify existing power imbalances between groups of people, especially if the benefits of the technology are concentrated in privileged groups and the risks are concentrated in vulnerable groups. Indicative research questions include:

• What are the risks of AI reinforcing or creating new forms of exclusion and inequality in the labour market, access to services and in economies more broadly?
• What are the differential socioeconomic impacts of AI of different demographic groups (women, young people, migrants and displaced persons, people with disabilities, etc.)?
• What governance mechanisms are needed to ensure that AI enhances the equity and effectiveness of public service delivery and contributes to inclusive economic development?

4. Global Inequality and AI Colonialism: This theme explores how AI systems and infrastructures may reinforce global power imbalances, perpetuate digital dependency and marginalize local knowledge and communities in sub-Saharan Africa. Research should examine pathways to resist AI colonialism and promote equitable, locally driven AI development for economic growth. Indicative research questions include:

• How can data governance safeguard data ownership rights and minimizse practices that lead to economic extraction in AI development?
• What policy and investment strategies can foster local AI innovation and reduce dependency on foreign technologies? 

Cross-cutting considerations 

Submitted proposals must describe how the cross-cutting considerations presented below will all be integrated into the design and implementation of the proposed research. While it may not be possible to address all considerations at the same level of depth, these will be considered in the selection process.  

• Gender equality, disability and inclusion: Proposals must adopt an intersectional lens to examine how AI affects different groups in society, particularly those who are historically marginalized. Definitions and application of gender equality, gender analysis, gender norms, gender power relations/dynamics are included here. This includes:
  • Investigating how AI may reinforce or mitigate structural inequalities based on gender, disability, socioeconomic status, geography, ethnicity, migration status, language and other factors of exclusion.
  • Exploring the differentiated impacts of AI on informal workers, youth, women and persons with disabilities.
  • Ensuring inclusive research processes, including participatory methods and the involvement of affected communities in shaping research questions and interpreting findings.
  • Where relevant, identifying how AI systems may produce or exacerbate algorithmic bias, exclusion, or harm to vulnerable populations.
• Data gaps and methodological rigour: Given the scarcity of context-specific, disaggregated data on AI’s socioeconomic impacts in Africa, proposals should:
  • Clearly identify existing data limitations and propose strategies to address them (e.g., primary data collection, partnerships with local institutions, use of innovative data sources).
  • Prioritize the generation of empirical evidence that is disaggregated by gender, age, geography, disability status and other relevant social markers.
  • Employ rigorous, context-sensitive methodologies that reflect the lived realities of diverse African populations, including those in the informal economy.
• Knowledge translation: Projects must include a clear strategy for translating research findings into actionable insights for diverse stakeholders, including policymakers, civil society and affected communities. This includes:
  • Identifying key audiences and decision-makers.
  • Outlining plans for stakeholder engagement throughout the research lifecycle.
  • Proposing mechanisms for communicating findings in accessible, context-appropriate formats.
  • Demonstrating how the research will contribute to national, regional, or continental policy debates on AI and development. 

Funding scope and duration

As a result of this call, up to four grants of up to CAD 1 million will be issued. The project duration will not exceed 36 months, including all research activities and final reporting. Proposals will be selected with consideration for adequate cross-regional representation and to address all priority thematic areas. Multi-country research is encouraged. 

IDRC reserves the right to fund additional proposals from this call if/when more funding becomes available.  

IDRC is under no obligation to issue any funds prior to the applicant returning a fully executed grant agreement to IDRC.   

All grants are subject to sufficient funds being made available to IDRC by the Parliament of Canada or under a donor partnership agreement with a particular external funder. 

IDRC reserves the right to cancel this call for concept notes at any time without prior notice and/or to not issue any grants under this process. 

Grant negotiations may also be influenced by operational considerations, e.g., Canadian law; knowledge of research settings; ability to monitor research activities; conditions that may make it difficult, dangerous or onerous for IDRC to carry out its objectives or to exercise proper stewardship of its resources.  

Eligibility criteria

Only concept notes that meet the eligibility criteria will be considered. 

Concept notes must be submitted by organizations registered or incorporated in African LMIC countries, as listed in Annex 3. These could include, inter alia, research institutions, universities, think tanks, network secretariats, associations, civil society organizations, non-profits, or the private sector.  

In case of a consortium application, individual members can be based in different countries and the non-leading members may be based in countries other than LMIC countries in sub-Saharan Africa. However, the leading institution can only be based in an eligible country (please see Annex 3 for a list of eligible countries). IDRC will establish a contractual agreement with the designated lead institution only and that institution will need to specify their arrangements with other consortia partners within their budgets (either as sub-contractors or sub-grantees).   

Note that this call for proposals is not open to individuals or UN agencies, government ministries and agencies, but it is open to public research institutes and public university centres, labs and departments.

The following eligibility criteria also apply: 

1. Research team composition: Proposals may be submitted by individual organizations, or by consortia of up to five organizations. Proposals from consortia must name one lead organization, which can sub-grant to the others. Proposals from, or that include, private sector partners should demonstrate how private sector resources – financial or technical knowhow - will contribute to the project. Organizations/consortia must have a strong presence and track record of work in Africa. 
2. Team expertise: Teams must demonstrate relevant expertise in social science research, AI and digital governance and related fields. Multidisciplinary teams and collaborations across institutions are encouraged, especially those that include early-career researchers, women, and underrepresented groups.
3. Institutional capacity: The lead organization must have the administrative and financial capacity to manage the grant and comply with reporting requirements.
4. Geographic focus: Research must be focused on sub-Saharan African contexts, with clear relevance to local or regional policy, governance, or development priorities. 

Applicants must have independent legal status (or “legal personality”) and be capable of contracting in their own right and name, receiving and administering funds and have authority to direct proposed project activities. Applicants must be able to demonstrate legal status through written documentation. Legal status will only be reviewed if and when applicants are selected following technical selection.  

Expectations of projects 

 Proposals will be evaluated based on the following criteria:  

A) High quality research for impact  

Assessed against the following four quality dimensions (refer to IDRC’s Research Quality Plus (RQ+) framework for more details): 

1. Scientific rigour: extent to which the research design demonstrates accepted standards of technical merit for its domain and discipline. This involves an assessment of the structural quality of the research protocol, including the following: the study is framed by examination of current knowledge on the issue, clear presentation of research questions and data collection strategies (that would enable reproduction), adherence to methodological standards for the type of research, identification of relevant analytical frameworks and well-considered reporting and sharing.
2. Research legitimacy: extent to which the research proposal accounts for the concerns and insights of relevant stakeholders and addresses potential environmental consequences. IDRC has defined three sub-dimensions for assessing the legitimacy of the proposed research: 

• Addressing potentially negative consequences – appropriateness of proposed strategies to address the risk of negative consequences of the research process or expected outcomes.  

• Gender equality and inclusion – [see B) below]. 

• Engagement with local knowledge – extent to which the research proposal is contextually grounded, relative to the appropriate scale (community-level, national, regional, or global) at which the research is designed.  

3. Research importance: is the value of the research questions for intended users and uses. IDRC has defined two sub-dimensions for assessing research importance:

• Originality – potential to contribute to theory and/or practice in terms of innovation in generating new knowledge relative to current state of the research field or context.
• Relevance – extent to which the proposed research design and expected outputs and outcomes address existing social and/or environmental problems.

4. Positioning for use: extent to which the research design has a knowledge sharing plan that will enhance the probability of use and impact. IDRC has defined two sub-dimensions for assessing positioning for use:

• User engagement – degree to which the research proposal has incorporated plans to build meaningful, two-way connections with intended knowledge users at appropriate stages of the research process.
• Openness and actionability – appropriateness and feasibility of the plans in the proposal for sharing research data and results. This includes the extent to which the knowledge sharing plan has considered tailoring products to be timely, useful, comprehensible and attractive to knowledge users, as well as following guidance on whether a data management plan is required.  

B) IDRC gender equality, disability and inclusion considerations 

IDRC strives for equality in all aspects of its work. We support the generation of knowledge — including by individuals from diverse genders, communities, histories and experiences — that tackles the systems which perpetuate inequalities based on identity. Inequalities exist across multiple and intersecting categories of identity, including, but not limited to, the following: gender, disability, sexuality, age, class, race, caste, ethnicity, citizenship status, religion and ability. Taking an intersectional approach to gender equality recognizes these differences and understands diversity as central to advancing equality. Given that gender inequality is a significant barrier across all dimensions of diversity, IDRC invests specific efforts in ensuring its work promotes gender equality and inclusion.  

For additional background, please see IDRC’s Equality Statement. 

Accordingly, proposals should demonstrate how gender equality, disability and inclusion will be promoted and adopted using an intersectional approach, both with respect to the following: (i) team composition and organizations comprising the research team; and (ii) the design and implementation of the proposed research.  

More specifically, research proposals should integrate an intersectional lens to examine how AI impacts different groups differently, based on gender, disability, age, geography, socioeconomic status and other forms of marginalization. This includes barriers to participation in AI-enabled economies, inclusive research practices, disaggregated data, and a focus on how research will seek to promote an inclusive digital transformation in the region.   

Proposals should also demonstrate how gender equality, disability and inclusion (GEDI) will be promoted, using an intersectional approach, with respect to team composition and organizations comprising the research team, monitoring, evaluation and learning, and knowledge mobilization processes. Gender-blind proposals will not be considered. 

The projects funded out of this call will become part of the AI4D program. They will be invited to participate in joint learning and synthesis activities with other projects and to extend their knowledge mobilization strategies with other partners. They will be required to link their results to the AI4D-wide results framework and track some common results in their monitoring, evaluation and learning strategies.  

Applicants should refer to Annex 1 for a series of guiding questions that can be used to help guide the GEDI aspects of your concept note.  

C) Southern leadership 

IDRC’s mandate is to promote inclusive development in the Global South. Projects that are led by researchers from the Global South will be given greater preference. The AI4D initiative, as a central principle, seeks to amplify African voices in global debates about AI impacts and governance and to ensure African researchers are shaping global AI agendas. 

Other complementary criteria:  

• Level of community involvement and leadership in the research.
• The proposed research adopts a systems approach to strengthen –, rather than create silos of – information or action.
• Extent to which the proposed project is likely to build the coping capacity and resilience of the affected communities.
• Existing capacity of participating institution(s) to carry out the research, including financial and administrative capacity.
• Feasibility of achieving project goals and objectives, as well as appropriateness of proposed human and financial resources.
• Where relevant, support from other agencies or institutions (formal letters of support are required as proof).
• Attention to ethical considerations and potential risks.
• Potential for, or commitment of, local contribution and in-kind resources. 

Submission process

IDRC invites eligible applicants to submit concept notes of 3,900 words or less. Applicants should submit an electronic application through the Survey Monkey application platform for this call before the deadline.  

• Applications must be received by no later than September 17, 2025 at 11:59 p.m. Eastern Daylight Time or EDT. Applications received after the deadline will not be considered.
• Applications can be submitted in either English or French. 

An acknowledgement of receipt of your submission will be sent to all applicants whose application was received before the deadline. 

Format and requirements for proposals 

The application form for this call for concept notes includes nine fields that applicants will need to complete, considering the maximum number of words allocated per question. 

The concept note must be organized according to the following sections: 

Additional documents and information  

As part of the application process, applicants will also be required to submit the following individual/institutional documents:

• Short one-page CV of the principal investigator and proposed team members
• Signed letters of commitment: one from each applicant organization
• Legal registration
• Optional documents - this may include letters of reference, letters of commitment from each participating institution confirming their role and responsibilities, workplans, Consortium Letter (if proposing a consortium), Key Stakeholder Endorsement and legal documents.
• High-level budget in local currency
  • To be prepared and submitted based on the IDRC budget template. As a guide, please consult the - General Terms and Conditions for a Grant Agreement | IDRC - International Development Research Centre.
  • IDRC does not make grants for basic operating expenses, endowments, or facilities for individual school districts, colleges, universities, or human services organizations. It does not generally make grants to individuals or make program-related investments.

Disclosure of Artificial Intelligence (AI) Use

• As part of IDRC’s commitment to transparency and fairness in the evaluation process, we ask all applicants to disclose whether any form of generative AI — including tools for writing, coding, design or data analysis — was used in the preparation of their application materials. This information will not affect the evaluation of applications but will help us better understand current practices and ensure responsible use of emerging technologies. Kindly note the following link from the Tri-Councils: Guidance on the use of Artificial Intelligence in the development and review of research grant proposals.

Conditions of Funding 

• The applicant must consent to the use and disclosure of full application and nominative information at the time of application, for purposes of relevance review and/or funding decisions by the relevant partners.  
• Applicants must meet minimum requirements to receive an IDRC grant. Any selected proponents shall be required to sign IDRC’s standard Grant Agreement, as amended by IDRC from time to time. The grant agreement will provide a schedule for submitting interim and final technical and financial reports. 
• Grant recipients will be required to submit technical and financial reports to IDRC. The frequency and information required in these reports will be described in the grant agreements.  

IDRC reserves the right to rescind its selection of a project if it is deemed that the information provided in the application is false or misleading. 

Evaluation Criteria 

Selection process 

Responding to this call is the first step in the application process for potentially securing funding for your proposal.   

Applications will first be screened for eligibility using the eligibility criteria outlined above. 

In the next stage, applications will be reviewed and short-listed by an internal AI4D review committee. This committee is comprised of IDRC program staff with expertise in different relevant disciplines, including with expertise in gender, disability and inclusion, in knowledge translation and in specific thematic areas of the call. The committee will assess the applications according to the evaluation criteria outlined above. 

Only short-listed teams will be asked to submit full research proposals for review and funding consideration. Full proposals will then be reviewed by a peer-review committee, according to the evaluation criteria provided.  

The technical selection of a proposal does not constitute a formal commitment by IDRC to fund the project. IDRC will have no obligation to issue any funds prior to the applicant returning an executed grant agreement issued to them by IDRC. See “Outline of the Selection process” below for further information.  

Outline of the selection process for concept notes 

Requirements in subsequent stage 

Those teams that are subsequently invited to submit full proposals will be requested to elaborate on their concept note. While it may not be possible to address all considerations in the same depth, they will be assessed in the selection process.  

A) Research ethics and safeguarding 

Research work must be carried out in accordance with high ethical standards, in keeping with IDRC’s Corporate Principles on Research Ethics. The grant agreement further outlines applicable ethics standards.  

Prior to commencing research, applicants may need to obtain approval from an official institutional or national research ethics body. In contexts where there is no official institutional or national research ethics body, the applications will need to propose how they plan on setting up an ethics committee for the project.  

B) Capacity strengthening 

Projects that combine research with capacity strengthening of researchers, civil society organizations, research users and community members are strongly encouraged.  

Examples of capacity strengthening activities include training, mentoring, networking, opportunities for publishing, presenting, or engaging with researcher users and opportunities to take on new roles and responsibilities, among others.  

Capacity strengthening can focus on a range of research-related skills such as the ability to identify and analyze development challenges; conceive, conduct, manage and communicate high quality research; and/or share and use the knowledge and innovation generated by research to address challenges over time and in a sustainable manner. Strengthening leadership skills, particularly for marginalized or underrepresented students, early-career researchers, or emerging community leaders, is also an important capacity strengthening consideration.  

Projects that have a mix of experienced and early-career researchers are also encouraged. 

C) Open Access and data management plan 

Applicants funded through this program will be expected to comply with IDRC’s Open Access Policy and IDRC Open Data Statement of Principles. 

IDRC encourages the use of Data Management Plans (DMPs) in our programming. We have two templates – Stage 1 and 2 DMPs. Stage 1 DMPs requires less detailed information and a Stage 2 DMP assumes applicants have a good understanding of their data collection and management plans. 

Applicants will be asked to submit a Stage 1 Data Management Plan. Applicants who are funded through this opportunity will be required to update their DMP taking into consideration comments received three months after the project commencement date. The DMP templates can be found here.  

D) Required network collaboration 

To foster deeper shared learning and collaboration across countries and projects, project teams will join coordination and cross-learning activities with the network of the wider AI4D initiative. Project teams will be expected to participate in a series of joint activities with AI4D members. Some of these activities will be in person, and we ask that a travel budget for network events be included.  

E) Knowledge sharing and scaling 

Knowledge sharing 

A key objective of IDRC’s Strategy 2030 is to share knowledge for greater uptake and use – increasing the reach and impact that IDRC-supported research has in driving solutions, and in influencing national, regional, and global development agendas, including through synthesizing, and communicating results.  

Applicants must explain how their concept note responds to an emerging need, knowledge gap or demand and they must demonstrate intentionality and identify opportunities to move knowledge (research evidence) into action (policy, social and behavioural change, etc.).  

Applications must include a knowledge sharing strategy that identifies key knowledge users, and that describes the anticipated approach to engage these strategic stakeholders (ideally throughout the research process) to support research uptake and use and/or to scale impact (by optimizing impact beyond original project boundaries). Note that IDRC anticipates supporting the implementation of knowledge sharing plans which are integrated into project proposals – provided the resources required are clearly described, appropriate, and incorporated as part of the overall project budget. 

While this should be addressed in the concept note, it can be elaborated in the full proposal should you advance to that stage of the competition. 

Post selection requirements  

Proposal and budget finalization  

Successful proponents will be invited to submit a full proposal to IDRC. Communication on deadlines will be shared at that time. Prior to finalizing a grant agreement, IDRC reserves the right to request any revisions to the submitted proposal and budget. A revised proposal with the necessary revisions must be returned in a timely manner to IDRC.  

Country clearance requirements  

In some cases, IDRC has scientific and technical cooperation agreements with the governments of the countries where we support projects. Where such agreements exist, IDRC may require additional or alternative approval processes to be followed to comply with such agreements. Otherwise, grantees must follow the prevailing approval procedure as required by the government authority. This is often administered by a coordinating or nodal agency of the government and varies by jurisdiction. 

An IDRC grant administration representative will advise the selected applicant if any country procedures need to be followed. A grant agreement will only be issued if and once country clearance(s) is/are obtained. IDRC reserves the right to not pursue the funding of a selected project if the country approval is not secured within six months after IDRC officially announces approval of the project, as this would jeopardize the timely completion of the initiative.  

After an institutional assessment of an applicant’s organization is performed, IDRC may identify operational or financial weaknesses that could pose some administrative risks to the proposed project. In such cases, IDRC reserves the right to request the applicant’s organization to partner with another institution as a condition of receiving the grant.    

Sub recipients  

In cases where the recipient will manage sub-grantees, the country requirements that apply to sub-grantees are also documented in the grant agreement. It becomes the responsibility of the grantee to ensure that sub-grantees meet these requirements.  

Country risk   

IDRC funds research in locations that respond to the corporate and programmatic plans and objectives approved by IDRC’s Board of Governors. Project proposals and risk mitigation measures may need to be revised where: 

• project activities may be affected by legal restrictions on transferring funds or other resources to specific entities;
• due to physical remoteness, physical risks to IDRC employees in particular regions, or other inaccessibility factors prevent IDRC from properly monitoring and supporting the project; or
• applicable laws and regulations prevent institutions from accessing funds.   

Grant Agreement  

Any selected proponents must sign IDRC’s standard Grant Agreement to receive funds. Please refer to the General Terms and Conditions for a  Grant Agreement. The grant agreement will provide a schedule for submitting interim and final technical and financial reports. Although there is no limit on the number of co-applicants in one application, IDRC will only negotiate grant agreements with the organization of the lead applicant. 

Open Access Policy 

IDRC embraces the principle of sharing research data and encourages researchers to make their data openly available. We will support researchers seeking to share their research data and we will proactively work with researchers who are generating significant data to make it open and accessible. 

IDRC believes that open research data can accelerate collaboration and scientific discovery and supports the fundamental scientific requirement of allowing others to confirm or challenge research results; and as a public research funder we should work to remove barriers to research results (see IDRC’s Open Access Policy) and the underlying data that informs it. 

Timeline and communication of results

Submission process (approximately six weeks) 

Call launch: August 7, 2025 

Information session/webinar: August 27, 2025  

Deadline for submitting proposals; receipt of proposals acknowledged: September 17, 2025 

Selection process (approximately five weeks – for two-phase process, add an additional eight weeks) 

Initial eligibility screening by IDRC: September 24, 2025 

Ineligible applicants informed: September 26, 2025 

Internal review by committee: October 16, 2025 

Shortlisted successful concept notes informed they have been selected to submit a full proposal: October 17, 2025  

Unsuccessful applicants notified: October 17, 2025 

Applicants submit full proposals: November 17, 2025  

Information session, inquiries and FAQs 

Following the launch of the call for proposals, IDRC organized an information session to address any queries from potential applicants. We invite you to watch the recordings, available in English or French.  The PowerPoint deck for the presentation is available in here.

Any additional inquiries related to the call and application process should be sent by e-mail to ai4dafrica@idrc.ca. All inquiries should be received on or before September 5, 2025 at 5 p.m. EDT to receive a response prior to the deadline date. 

Any inquiries which affect all applicants received on or before the above-mentioned deadline will be added to the FAQs with IDRC’s responses to those inquiries, and without revealing the source of the inquiries. 

Permission for use and disclosure of information 

As a Canadian Crown corporation, IDRC is subject to Canada’s Access to Information Act and the Privacy Act. Consequently, any submissions in response to this call for concept notes will be managed by IDRC in a manner consistent with applicable legislation and IDRC’s Privacy Policy, including IDRC's obligations to disclose documents requested by members of the public or requests for personal information. For more information on how IDRC manages information in accordance with this legislation can be accessed here.

All applicants, as part of the application process through SurveyMonkey Apply, are required to sign IDRC’s Privacy Statement and Terms of Use as well as any terms and conditions of SurveyMonkey at the time the application is submitted. 

ANNEX 1 – Ensuring research ideas address gender equality and inclusion

IDRC strives for equality in all aspects of its work. Inequalities exist across multiple and intersecting categories of identity, including, but not limited to, gender, sexuality, age, class, race, caste, ethnicity, citizenship status, religion and ability. 

Achieving equality varies by place and must be situated within the socio-cultural, political and economic contexts of the different regions where IDRC works. Equally, inequalities are not static and can vary and change over time.  

To promote gender equality and inclusion, it is critical for research projects to strongly consider investigating the roles of sex, gender and other diverse identities and experiences and their relationship to the history, structures and functioning of these systems. 

IDRC recognizes the importance of striking a balance between ambition and pragmatism. Actions to address gender and other inequalities require doing the groundwork to interrogate and surface the ultimate root causes of inequality; at the same time, changing gendered structural dynamics takes time, trust and long-term commitments to policies and practices. 

The questions below are intended to guide you in reflecting how your research addresses social and gender equality and inclusion, and how you can strengthen these dimensions in your proposal. 

1. Does your proposal intend to understand and address social and gender inequalities and their underlying causes?
2. In the context of your proposal, what are the power structures and power dynamics that exist between men and women and other groups which underpin gender inequality? What are some possible avenues to address and change these conditions?
3. In the context of your research problem, how is this affected by identities or experiences such as race, ethnicity, socio-economic class, income levels and where individuals live (e.g., rural, urban settings)?
4. Is there a logical theory of change of how your research objectives will promote or lead to greater gender equality and/or inclusion? What impact will your research proposal have on these aspects?
5. Do you have a stand-alone objective on addressing gender equality and inclusion? How are other objectives framed in relation to addressing gender equality and inclusion?
6. How will the proposed conceptual framework(s), research design, and related research methods address, and analyze the root causes and context-specific factors contributing to intersectional forms of gender inequality? Which individuals and groups should be engaged in co-creating this research design and its implementation – to what extent and how will they be engaged?
7. Has your project identified clear outcomes and indicators with respect to gender equality and inclusion? Are these integrated into project measurement tools? For example, do you plan to collect and analyze sex-disaggregated data? What about gender-disaggregated data? Have you planned to undertake a pre- and post-project gender analysis?
8. Does the proposal’s knowledge translation plan integrate sex and gender considerations (including intersectionality) in how the iterative processes of engagement, analysis, synthesis, product development and knowledge facilitation are designed and operationalized?
9. Do the members of your research team understand contextual gender equality and inclusion issues? Do you have the right skills and experience in your team? Which of your team members will take the lead in designing, implementing, monitoring and assessing your project’s objectives to address gender inequality and inclusion?
10. Does your research team have a good balance between male and female scientists or scientists of other identities?
11. Have you clearly budgeted for gender equality and inclusion activities and staffing? Have you allocated sufficient time and resources to strengthen the capacity of your team, partners and other stakeholders on gender and inclusion issues? 

Please note that these are some myths or assumptions that will be important to avoid in your proposal: 

• Assuming that women, or certain groups, do a task so that they will benefit is not adequate. Instead, it will be important for your project to identify any gender inequalities and outline steps by which your research will help re-define power dynamics.
• Adding “especially women and marginalized groups” after each of your objectives is not adequate. You must be able to define how gender dynamics are present in your research objectives. Research rigour and quality is critical.
• The women in your team will not always qualify as the gender expert. Get real gender expertise and partnerships that bring in the necessary skills.
• Equally, addressing gender in the project is not only the responsibility of these gender experts – rather the entire team must understand the gender dynamics at play in your research.
• Addressing gender takes real resources. Saying gender cannot be integrated because you do not have sufficient resources is not acceptable. Budget resources for gender at the outset. 

ANNEX 2 – Institutional Assessment Documentation 

Successful applicants will be required to provide the following documents to allow IDRC to undertake an institutional assessment prior to confirmation of funding: 

1.  Most recent audited financial statements*, including but not limited to:

• Balance Sheet, Statement of Income and Expenses or Profit and Loss and Statement of Cash Flow;
• Notes to the Financial Statements;
• Audit Report;
• Any Management or Internal Control Letters and related follow-up response. 

*The latest financial statements duly authorized by a financial officer if an audited statement is not available. 

2. Current organizational chart.
3. Human resources manuals.
4. Finance and administration manuals.
5. Policy/procedure for procurement.
6. List of active external donors and their current contributions.
7. Latest annual report. 

ANNEX 3 – LMIC countries in sub–Saharan Africa  

For this funding opportunity, the following list indicates eligible countries where the lead institution must be located. Applications from organizations not based in these countries will not be considered for funding. This list was informed by the World Bank country classifications, IDRC’s current grant-making experiences, our agreements with national authorities and external factors beyond our control that may restrict organizations from performing the expected functions of a lead organization. 

* Applications with a Lead Institution based in these countries are eligible but may be subject to a further stage of approval within IDRC. 

ANNEX 4 – Research themes and topics 

The learning agenda was shaped by a mixed-methods study conducted by Genesis Analytics between February and June 2025, aimed at identifying the most pressing research gaps concerning the socio-economic effects of AI across Africa. 

The learning agenda is organized across four themes. Within each theme, topics, their associated research questions and indicative research outcomes are presented 

Inequality is the primary cross-cutting theme of this agenda. The impacts of AI are not uniform. Instead, they are shaped by intersecting identities including gender, ethnicity, age, education, geography and more. Research should accordingly adopt an intersectional approach to analyse these differential outcomes. In doing so, the research will support policymakers in ensuring inclusivity and the protection of vulnerable populations. Additionally, the complex nature of AI's impact necessitates interdisciplinary research, combining methodologies from economics, sociology, data science and policy studies to generate a holistic understanding.   

Theme 1: Labour and livelihoods  

Africa faces a significant challenge with unemployment, a situation projected to worsen due to ongoing demographic shifts. By 2050, Africa is expected to be home to nearly 59% of the world's working-age population.^[7] The working-age cohort (20-64 years) alone is projected to almost double from 883 million in 2024 to 1.6 billion by 2050.^[8] Youth unemployment presents a pressing concern across the continent, with 26.1% of Africa’s youth (15-24) not in employment, education or training (NEET), equating to around 72 million people, compared to 9.6% across countries in the European Union.^[9] Africa’s workforce is concentrated in the informal sector which accounts for 85% of the total labour force.^[10]   

AI is already impacting work across Africa and these impacts are expected to evolve. Global research suggests that AI will induce significant structural changes in labour markets. The World Economic Forum's (WEF) 2025 Future of Work report projects that 170 million jobs could be created while 92 million are displaced over the next five years, globally.^[11] In South Africa, digitization, machine learning and automation could displace as many as 3.3 million existing jobs by 2020, but has the potential to create up to 4.5 million jobs, a net gain of 1.2 million jobs.^[12]  

However, there is insufficient rigorous evidence about AI’s differential impacts on African job markets. Specifically, the nuances of its impact on different sectors, worker demographics and firm size remain poorly understood. Interviewees consistently noted these knowledge gaps. More granularly, the key research gaps are as follows:   

1. The impact on net employment figures including across and within sectors.
2. The impact on opportunity pathways and access to work specifically entry-level roles and opportunities for MSMEs and independent gig workers The impact of AI on poverty and economic vulnerability at large.  

Topic 1.1: Impact of AI on formal employment opportunities  

A key research gap is understanding the scale and nature of AI's impact on employment in Africa. On one hand, optimistic reports offer projections of 230 million new digital jobs across Africa, suggesting a future of opportunity and job creation.^[13] On the other hand, sector-specific studies present  more cautionary findings; for example, one analysis indicated that full AI adoption in Nigeria’s agricultural sector could displace over 20 million jobs.^[14] A central hypothesis in more recent research is that AI is not simply creating or destroying jobs, but fundamentally transforming roles and tasks, which in turn affects the demand for skills and overall work readiness.   

Core Research Gaps   

Studies suggest that while emerging markets generally have lower exposure to AI, tasks requiring higher-cognitive skills are disproportionately at risk.^[15] This indicates that higher-skilled jobs could be particularly vulnerable. However, a critical lack of granular, contextual research into the impact of AI on Africa's employment rates currently hinders the development of effective policy to address this potential challenge.

1. Net figures and a macro-level understanding of cross sectoral impacts: While global and some pan-African reports offer high-level projections, there is a lack of rigorous analysis to understand the potential net effect on employment across Africa. Policymakers lack a clear, evidence-based picture of which sectors are likely to be the biggest net job creators and which face the most significant losses at a macro level, making it difficult to prioritize national economic and industrial strategies.  Crucially, research is missing that looks beyond the direct automation of existing roles but to include job creation in upstream and downstream sectors of the AI value chain. For instance, AI's significant demand for data and energy could spur growth in green jobs for the renewable energy transition, while the need for local 1. Net figures and a macro-level understanding of cross-sectoral impacts: While global and some pan-African reports offer high-level projections, there is a lack of rigorous analysis to understand the potential net effect on employment across Africa. Policymakers lack a clear, evidence-based picture of which sectors are likely to be the biggest net job creators and which face the most significant losses at a macro level, making it difficult to prioritize national economic and industrial strategies. Crucially, research is missing that looks beyond the direct automation of existing roles but to include job creation in upstream and downstream sectors of the AI value chain. For instance, AI's significant demand for data and energy could spur growth in green jobs for the renewable energy transition, while the need for local data creates opportunities for new roles in data collection, annotation, and monetisation. These lines of enquiry remain underexplored.   

2. Identifying AI’s impact on job creation and displacement at the sector-level. Macro-level figures, even if accurate, are insufficient. The impact of AI will vary dramatically between sectors due to differences in task composition and adoptability. The recent paper by Caribou Digital and Genesis Analytics explores the impact of AI on Africa’s business process outsourcing (BPO) sector and found that 40% of tasks are at risk of automation and women face a 10% higher automation risk than men.^[16]   

3. The impact of AI on pathways to earning opportunities and work readiness: A critical and often overlooked area is the vulnerability of entry-level jobs, the traditional gateway for young people into the formal labour market. Bloomberg finds that AI could replace more than 50% of the tasks performed by market research analysts (53%) and sales representatives (67%), compared to just 9% and 21% for their managerial counterparts.^[17] Similarly, evidence from online freelance platforms like Upwork shows a 21% reduction in the number of jobs available for African freelancers since 2022, a trend that particularly affects entry-level opportunities.^[18] While these global studies indicate that entry-level roles are particularly threatened by automation exposure, there is a gap in understanding in more detail how entry-level roles across the continent and across sectors will be impacted. This may include examining how AI affects apprenticeships and informal skill-building pathways that often serve as alternatives to formal entry-level positions, understanding the differential impact across sectors, evaluating whether AI is creating new labour market entry points.  

Moreover, exposure to automation varies across sectors: for instance, entry-level, rote roles in tech-enabled services and financial services may face higher displacement risks than where human interaction remains central, such as care work. These variations are poorly understood in African labour markets, where sector-specific data on automation risk is limited.  

Indicative Research Questions and Research Outcomes  

Topic 1.2: Impact on opportunity pathways and access to work 

Beyond the net effect on job numbers and qualitative changes in tasks, it is crucial to understand how AI is reshaping the pathways and access to work itself. This topic delves into the practical experience of these shifts at the firm and individual level. It examines whether individuals, particularly youth, are being equipped for an AI-enabled workplace, and explores the quality and nature of opportunities for the small enterprises and independent workers who constitute the majority of Africa's workforce.

Core Research Gaps   

The impact of AI on pathways to earning opportunities and work readiness: A critical and often overlooked area is the vulnerability of entry-level jobs, the traditional gateway for young people into the formal labour market. Bloomberg finds that AI could replace more than 50% of the tasks performed by market research analysts (53%) and sales representatives (67%), compared to just 9% and 21% for their managerial counterparts. 13 Similarly, evidence from online freelance platforms like Upwork shows a 21% reduction in the number of jobs available for African freelancers since 2022, a trend that particularly affects entry-level opportunities. 14 While these global studies indicate that entry-level roles are particularly threatened by automation exposure, there is a gap in understanding in more detail how entry-level roles across the continent and across sectors will be impacted. This may include examining how AI affects apprenticeships and informal skill-building pathways that often serve as alternatives to formal entry-level positions, understanding the differential impact across sectors, and evaluating whether AI is creating new labour market entry points. Moreover, exposure to automation varies across sectors: for instance, entry-level, rote roles in tech-enabled services and financial services may face higher displacement risks than where human interaction remains central, such as care work. These variations are poorly understood in African labour markets, where sector-specific data on automation risk is limited. Impact of AI on MSME business opportunities and independent workers’ ability to access meaningful and dignified work. While global discourse often focuses on large-scale corporate automation, it is essential to understand the implications of AI for smaller economic actors, who collectively form the backbone of Africa’s economy. An estimated 85% of the continent's workforce work in the informal economy which primarily comprises MSMEs as well as independent workers (including gig and platform workers). 15 It is crucial to recognise the diversity within the MSME category, which ranges from a single-person survivalist business that constitutes the majority, to growth-oriented. 

 Indicative Research Questions and Research Outcomes  

Topic 1.3: Impact of AI on poverty   

The changes occurring in both formal and informal labour markets due to AI are hypothesized to have a direct bearing on poverty and economic vulnerability. Sub-Saharan Africa faces a significant poverty crisis, with World Bank forecasts indicating that by 2030, the region could be home to 90% of the world's extremely poor, the only global region where extreme poverty is projected to increase.^[19] Whether AI lifts people out of poverty by creating new, higher-value opportunities or exacerbates it by displacing workers and suppressing wages is a critical, unanswered question. This research area connects the impacts on labour directly to household-level economic outcomes.   

Core Research Gaps  

1. The link between AI-driven livelihood changes and poverty levels: There is a major evidence gap in empirically connecting the adoption of AI in the economy to measurable changes in poverty rates and household resilience. While it is assumed that positive changes in employment and income will reduce poverty, this link needs to be explicitly investigated to guide pro-poor policy. Analysis could link macro or sectoral data on AI adoption with micro-level household survey data. The tangible data points emerging from this research could include calculations on the change in the poverty headcount ratio and the poverty gap in regions with high versus lower AI penetration.   

Indicative Research Questions and Research Outcomes  

Theme 2: Productivity   

Macroeconomic productivity gains are critical for Africa to reduce poverty, create jobs, achieve sustainable growth and build resilience against shocks. Productivity is a measure of the efficiency with which a country combines capital and labour to produce more with the same level of inputs.^[20] For example, a factory that produces twice as many goods with the same number of workers and machines has doubled its productivity. Productivity is important because it is a key determinant of living standards in the long term. Increasing productivity over time allows businesses to produce more goods and services per unit of input. This ultimately enables higher wages, aids economic growth, increases profitability and boosts tax revenues.^[21]

While the continent’s economic growth is accelerating, with projections of 3.9% in 2025 and 4% in 2026, these gains are set against structural economic weaknesses, including small domestic markets, limited local value addition, reshoring of manufacturing, dependence on primary commodities and automation through AI.^[22] The recent decline in Official Development Assistance (ODA), projected to fall by as much as 40-60% by 2030, further underscores the urgent need for the continent to strengthen its domestic resource mobilization and internal economic foundations.^[23] Boosting domestic productivity is therefore necessary for generating government revenue and ensuring long-term resilience and growth.   

AI presents a monumental economic opportunity. Projections indicate that AI could contribute up to USD15.7 trillion (CAD21.4 trillion) to the global economy, and while estimates vary, the potential gains for Africa are substantial.^[24] Research from IDRC and Genesis Analytics has estimated a potential USD2.9 trillion (CAD4 trillion) boost for the continent, while McKinsey forecasts that Gen AI alone could unlock between USD61 billion (CAD83.3 billion) and USD103 billion (CAD140.6 billion) in value.^[25] 

However, there is major uncertainty regarding how, or even if, this productivity dividend will be realized. General forecasts fail to capture the granular realities of the impact of AI adoption across Africa. A deeper and more nuanced understanding is required to examine the potential productivity impacts for African economies. Within this theme, core research gaps can be categorised as:   

• The impact of AI on formal sector productivity, from firm-level dynamics to its aggregate effect on macroeconomic growth and structural transformation 

Topic 2.1: Impact of AI on Formal Sector Productivity   

Agenda 2063, the African Union's development blueprint, places structural transformation at the core of its vision for “The Africa We Want”. ^[26] This process of shifting economies from a reliance on primary commodities to diversified, high-value industrial and service sectors is critical for the continent's future. AI, with its capacity to boost productivity across sectors, presents a powerful engine for this transformation. The central hypothesis is that AI adoption in key formal sectors can drive significant productivity gains, leading to GDP growth and enhanced global competitiveness.   

Core Research Gaps   

While the potential of AI is widely acknowledged, the evidence base required for strategic policy and investment is incomplete.   

1. Macro-level understanding of productivity gains of AI: McKinsey’s research into the economic value of Gen AI across sectors finds that 40% of surveyed institutions have either started to experiment with Gen AI or have already implemented significant solutions.^[27] While it is clear that businesses across the continent have started to harness AI tools, there is little research on the productivity impacts of these technologies. Current research offers compelling but incomplete continent-wide data, but typically focus on a subset of AI or are too broad to identify which specific sectors are likely to be the primary national growth drivers.  
   Research in this sub-topic could produce calculations around Total Factor Productivity (TFP) calculations for key African economies, comparing trends in sectors with high AI penetration versus low AI penetration. Research could also include a decomposition analysis to isolate the contribution of AI to labour productivity growth, distinct from capital investment, allowing for a greater understanding of how AI impacts ‘output by worker.  Furthermore, a research gap exists in providing a comprehensive analysis of the broader societal impacts of macro productivity gains. For example, research could model the potential "gender dividend" unlocked if AI-driven efficiencies in social services, childcare, or health administration lower the cost and increase the accessibility of care, enabling greater female participation in the workforce and boosting overall economic productivity and tax revenue.
2. Systematic Measurement of Firm- and Sector-Level Productivity Impact. While studies from organisations like the GSMA provide valuable case studies of current AI applications in Africa, there is a lack of systematic research that measures the impact of AI on increasing productivity at the sector and firm level. While robust impact testing in the agricultural sector has shown that AI significantly increases productivity, beyond agriculture, there is a lack of evidence to understand firm-level adoption, operational efficiency and innovation impacts in other key sectors such as mining, telecommunications, banking, retail and more.^[28] Additionally, sectoral studies to date have typically focused at the national level.   
   Research here could utilize firm-level panel data generating metrics such as; 1) percentage change in operational efficiency, e.g., reduction in production time, cost per transaction or  2) return on investment (ROI) for AI capital expenditure. 
3. Impact of AI on the productivity of MSMEs and individual workers: The central hypothesis is that AI can provide transformative tools for MSMEs and individual workers, helping to overcome traditional barriers, boost productivity, and increase their economic contribution. While the research question in Labour and Livelihoods examines AI’s impact on MSMEs and independent workers through the lens of income, job quality, and opportunity, here the focus shifts to examining AI's potential to boost the productivity of MSMEs and independent workers and, in turn, their aggregate contribution to macro-economic output. While emerging anecdotal evidence and case studies highlight the potential of AI tools which can optimise inventory, provide dynamic pricing suggestions or facilitate micro-credit through alternative data scoring, there is little to no research on if these aggregate to influence macroeconomic growth.

Indicative Research Questions and Research Outcomes  

Theme 3: Africa in the Global AI Ecosystem   

AI is reshaping the global economy, but its benefits and burdens are being distributed unevenly, threatening to deepen existing divides between and within countries. PWC’s 2017 report projects that 85% of AI-driven economic gains could accrue to North America, China, and Europe compared to 8% to ‘Africa, Oceania and the rest of Asia.^[29] This is not just an economic risk but a structural one, giving rise to concerns about "AI colonialism", where Africa is a consumer rather than producer of AI technology.   

In order to promote policy around fostering more equitable global AI ecosystems research is required across one key area:   

1. The impact of current AI deployment on Africa’s role within the global AI ecosystem, focusing on the economic implications of data ownership, value chains, and wealth extraction   

Sub-Theme 4.1: The impact of AI on Africa’s role within the global AI ecosystem  

The central hypothesis is that without deliberate intervention, the current trajectory of AI development will entrench new forms of dependency, sometimes characterized as "AI colonialism". This is hypothesized to occur through two primary channels: first, through extractive economic models where African data is harvested to create value and wealth that accrues disproportionately to global technology firms; and second, through the widespread deployment of culturally misaligned AI models that predominantly reflect Western norms, languages and contexts, potentially marginalizing local knowledge systems.  

Core Research Gaps  

1. Data economy and extractive relationships: While the risk of "extractive relationships" is frequently noted, there is a lack of empirical research that systematically maps the AI data value chain as it relates to Africa. There is a gap in understanding how data from African users is collected, processed and monetized by global firms and what the net economic impact, in terms of value created versus value extracted, is for African economies. This evidence gap places African policymakers at a severe disadvantage in global negotiations. Without a clear, empirical understanding of the value of their citizens' data and how that value is being extracted, they cannot formulate effective data governance policies, negotiate fair terms for market access, or design strategies to capture a greater share of the AI value chain for their own economies. 
2. Mapping "Homegrown" vs. "Imported" AI: Consultations consistently highlighted the need to make Africa a “producer” rather than ”consumer” of AI tools. Descriptive statistics that measure the prevalence of locally developed versus imported AI technologies in use across Africa could be harnessed to understand the future trends and impacts of locally-developed AI. This research could produce a "State of the African AI Industry" report, detailing the market share of local versus international AI vendors in key sectors.   

Indicative Research Questions and Research Output  

[1] IDRC and Genesis Analytics (2024) AI in Africa: The state and needs of the ecosystem. Executive Summary. Available here.

[2] ILOSTAT (2023) African youth face pressing challenges in the transition from school to work. Available here. 

[3] AUDA-NEPAD (2025) AI and the Future of Work. Available here.  

[4] Challenges Facing Persons with Disabilities in Sub-Saharan Africa – in 5 charts

[5] Disability News: Africa and South Africa

[6] The Mobile Economy Sub-Saharan Africa 2023 - The Mobile Economy 

[7] IDRC and Genesis Analytics (2024) AI in Africa: The state and needs of the ecosystem. Executive Summary. Available Here.  

[8] New African Magazine (2024) Reaping the demographic dividend requires time, money and effort. Available Here. 

[9] ILOSTAT (2023) African youth face pressing challenges in the transition from school to work. Available Here. 

[10] AUDA-NEPAD (2025) AI and the Future of Work. Available Here.

[11] WEF (2025) The Future of Jobs Report. Available Here. 

[12] McKinsey (2019) The Future of Work in South Africa: In Brief. Available Here.

[13] WEF (2025) The Future of Jobs Report. Available Here; SAP (2025) AI Skills Development in Africa: New Report Findings Revealed. Available Here. 

[14] VerivAfrica (2025) The Future of Work: AI’s Impact on Nigeria’s Job Market. Available Here. 

[15] IMF (2024) AI will transform the Global Economy. Let’s make sure it benefits humanity. Available Here. 

[16] Caribou and Genesis Analytics (2025) Preparing for Ai in the BPO and ITES Sector in Africa. Available Here. 

[17] Bloomberg (2024) A white-collar world without juniors. Available Here. 

[18] Caribou and Genesis Analytics (2025) Preparing for Ai in the BPO and ITES Sector in Africa. Available Here. 

[19] World Bank (2024) Pathways out of Poverty. Available Here. 

[20] Reserve Bank of Australia (2024) Productivity: Explainer. Available Here.  

[21] Office for National Statistics (2025) How Productive is your Businesses? Available Here. 

[22] African Development Bank Group (2025) African Economic Outlook 2025 - Africa’s short-term outlook is resilient despite growing economic and political headwinds. Available Here. 

[23] Dalberg (2025) Our Five- Year Projections on Overseas Development Aid Funding. Available Here. 

[24] PWC (2019) Sizing the Prize: PWCs Global Artificial Intelligence Study: Exploiting the AI Revolution. Available Here.  

[25] IDRC and Genesis Analytics (2024) AI in Africa: The state and needs of the ecosystem. Executive Summary. Available Here; McKinsey (2025) Leading, not lagging: Africa’s gen AI opportunity. Available Here 

[26] African Union (2013) Agenda 2063: The Africa We Want. Available Here. 

[27] McKinsey (2025) Leading, not lagging: Africa’s gen AI opportunity. Available Here.

[28] Olivier Donfouet & Ibrahim Ngouhouo (2024) Impact of artificial intelligence on the total productivity of agricultural factors in Africa. Available Here. 

[29] PWC (2019) Sizing the Prize: PWCs Global Artificial Intelligence Study: Exploiting the AI Revolution. Available Here.