Funded by: Bill and Melinda Gates Foundation (BMGF)
Lead and Coordinated by: Nanko Madu, Lydia Ezenwa, Philip Adebayo, and Timothy Mugume.
Expert Consultants: Dr. Itoro Emembolu and Mr. John Kamara.
AI Anglophone Lead Researcher and Report Writer: Dr. Ing. Judith Leo.
AI Data Analysts: Eng. Samiiha Nalwooga, and John Wafula.
Foreward
AfriLabs’s mandate is to impact the continent over the next decade through four Strategic Pillars, one of which is Research, Evidence, and Learning. This pillar builds on innovative ways to move the continent forward through collaboration and research on what works, where it works, and how it works, and dissemination of learning across Africa and the wider innovation ecosystem, network, and experience gained so far with over 470 innovation centers across 227 cities. With this mandate and the rapidly growing Artificial Intelligence (Al) within the African innovation ecosystem, there has been an increasing number of Startups in the continent leveraging AI to build innovation that solves challenges in different sectors. To better understand the status, challenges, and opportunities that African Al startups and their ecosystem can tap into, AfriLabs through the support of the Bill and Melinda Gates Foundation (BMGF) has surveyed to map Al startups in African countries to understand the required actionable recommendations that promote excellence in the adoption and use of Al. Therefore, this report summarizes the findings that can be used as a guide to Al startups and their innovation ecosystem toward building sustainable socioeconomic development in Africa and the world at large.
Executive Summary
Artificial intelligence (AI) is a rapidly growing sec- tor within the African innovation ecosystem. In recent years, the continent has seen an increase in the number of startups focusing on AI and re- lated technologies. These startups are leveraging AI to build innovations that solve problems across different sectors such as agriculture, healthcare, and finance, among others. Despite the growth of AI startups in Africa, their potential for the ap- plication of AI technologies to revolutionize busi- nesses, increase efficiency, and solve social and industrial challenges is still very low. Therefore, the main objective of this project is to map and identify the status, challenges, and opportunities of AI startups in African countries which will pro- vide actionable recommendations that promote excellence in the adoption and use of AI leading to the social-economic growth and sustainable development in Africa.
erature and AfriLabs’ enormous datasets of Inno- vation hubs and associated networks of African startups, investors, hubs, corporates, public sec- tors, and humanitarian and development organi- zations. A snowballing approach was utilized to increase the coverage and diversity of the col- lected data and its stakeholders. The following data collection methods were deployed; desk research of stakeholders by country, review of AI stakeholders’ engagement through key informa- tion interviews and focus group discussions, case studies, and online surveys. A webinar session was used to discuss findings and get feedback and contributions from relevant stakeholders and contributors in the ecosystem. A total of 320 publications with over 900 AI stakeholders were identified of which 669 were AI startups and 165 participants participated in the secondary and primary data collection respectively. The collect- ed data was cleaned and annotated through the use of tools such as Python and MS Excel to un- derstand its dimensions and insights, and then,
The study utilized proprietary vocabulary to dis- cover and extract qualified startups from the lit-
4
PowerBI, Python, tableau, and DAX tools were used for data analysis and synthesis.
database for African AI startups, develop functional local, national, and regional AI-based tech commu- nities that promote capacity building, motivate in- vestors to invest in the AI startups, strengthen the role of women and female-led AI startups and girls in the tech sectors by addressing gender gaps in access of education, skills, finance, and network, al- locate budgetary provisions to support the devel- opment of AI startups, establish an African based datasets in each sector and lastly, identify infra- structure and support needed to mention a few that will enhance and promote excellence in the growth of AI startups leading to the sustainable so- cial-economic development of Africa countries.
The result shows 63% of the responses indicate that AI startups in African countries have formal educa- tion and are in the initial and intermediate stages of their growth. The major challenges that hinder the growth and establishment of AI startups include limited funding, regulatory barriers, lack of access to data, inadequate infrastructure including inter- net connectivity, and skills shortage. Therefore, the study recommends AI startups enhance collabo- rative networks for resource sharing within the AI stakeholder’s ecosystem, establish a network or
5
Table of Content
Executive Summary
4 7 8 9
List of Figures
Definition of Terminologies
1.0 Introduction
2.0 Literature Review
11
3.0 Key Definition and Methodology
13 13 13 15 16 17 17 17
3.1 Key Definition
3.1.1 AI Stakeholders
3.1.2 AI Readiness Index in Africa
3.2 Methodology
4.0 Results and Discussion
4.1 Results
4.1.1 Status of AI Startups in Africa
4.1.2 Opportunities for AI Startups in Africa 4.1.3 Challenges of AI Startups in Africa
26 28 29
4.1.4 AI Startups in Africa
4.2 Discussion
31
5.0 Conclusion and Recommendations
37 37 39 42 42 43 43 44
5.1 Conclusion
5.2 Recommendations
6.0 Limitation of the Study
7.0 Data Availability Statement
8.0 Funding
9.0 Conflict of Interest
Reference
6
List of Figures
SN Table Number
Table Name
1.
Figure 1
Description of AI Stakeholders and their Stages.
2.
Figure 2
AI Readiness Assessment Pillars.
3.
Figure 3
Description of Methodology for Mapping AI Startups in Africa.
4.
Figure 4
Description of Demographic Results.
5.
Figure 5
Description of Geographical Distribution of Responses.
6.
Figure 6
Description of Sectoral Distribution of Responses.
7.
Figure 7
Description of AI Formal Education and AI Subfields Specialization.
8.
Figure 8
Description of Support and Resources Offered to AI Startups.
9.
Figure 9
Description of Factors that have contributed to the growth of AI. Description of Geographical Distribution of Responses.
10.
Figure 10
11.
Figure 11
Examples of AI startups in Africa.
12.
Figure 12
Examples of AI startups Case Studies in Africa.
7
Definition of Terminologies 1
Artificial Intelligence (AI) is a field within computer science that focuses on developing systems capable of executing tasks that would ordinarily require human intelligence. These tasks encompass learning, reasoning, solving problems, perceiving as well as language comprehension.
2 3 4 5 6
Machine Learning (ML) is a subset of Artificial Intelligence that entails developing algorithms that can learn and make predictions based on data.
Deep Learning is a subset of Machine Learning that analyses data using neural networks with several layers (deep networks). It’s very good at handling unstructured data like images and audio.
Natural Language Processing (NLP) is a branch of Artificial Intelligence that focuses on the interaction between computers and humans using natural language. The objective of NLP is for computers to be able to understand, interpret, and respond to human languages in a useful way.
Computer Vision is a domain in Artificial Intelligence which involves teaching computers to recognize and comprehend the visual world.
Robotic and Automation encompasses the building and management of robots, with robotics specifically focusing on this aspect. Automation, on the other hand, is the application of technology aimed at eliminating human involvement. Within the realm of Al, these fields aim to enhance processes, making them more intelligent and flexible.
7 8 9 10 11 12
Big Data are extraordinarily large data sets that can be computationally analyzed to identify patterns, trends, and correlations.
Business Intelligence is the application of technology to analyse data and provide useful insights for strategic decision-making.
Data Analytics is the science of examining unprocessed data to draw inferences about it is known as data analytics.
The Internet of Things (loT) refers to a system where physical objects, or ‘things,’ are embedded with sensors, software, and various technologies, enabling them to connect and share data with other devices and systems via the internet.
Fintech is a term that describes businesses that leverage technology to improve or automate financial services and operations
The Startup Ecosystem refers to the interconnected community of people (such as founders, investors, mentors), organizations (like educational institutions, governmental bodies, startup accelerators, and incubators), and activities (including creating business models, conducting market analysis, and developing products) that collectively support and nurture the evolution and expansion of new business ventures. Innovation Hubs and Incubators refers to establishments and initiatives structured to cultivate creativity and assist emerging companies and entrepreneurs in advancing their products or services. They offer essential resources such as workspace, guidance, networking opportunities, and financial support.
13
8
01 Introduction
Artificial Intelligence (AI) as a branch of science that studies and develops intelligent machines, is a significant component of the fourth industrial revolution that will lead to fundamental changes in the way people live, work and relate to one another (Jiang et al. 2021; Shen et al. 2021; Jaldi 2023). In recent years, AI is a rapidly growing sec- tor within the African innovation ecosystem with an increase in the number of startups focusing on the application of AI and its related technoligies (Law 2023; “AI in Africa: Unlocking Potential, Ig- niting Progress A Working Paper” 2023; Authur Gwagwa et al. 2020; Sedola, Pescino, and Greene 2021). These startups are leveraging AI to build innovations that address needs and gaps in the continent across different sectors such as agri- culture, healthcare, finance, education, fintech, climate change, public transport, logistics, en- terprise development, and language translation accelerating Africa’s capacity to achieve United Nation’s sustainable development goals (Stand-
ford University 2023; Disrupt Africa report 2022; Engines and Growth 2023; Sampene et al. 2022; Hofmann 2021). In Africa, AI startups have been accelerated by the AI ecosystem which include research institutions, universities, government in- stitutions, corporates, incubators/accelerators, investors, and public & private non-governmen- tal organizations (NGOs), through a holistic ap- proach that acts as a catalyst for propelling their status, challenges, and opportunities. Despite the growth of AI startups in Africa, the potential for the application of AI technologies to revolu- tionize businesses, increase efficiency, and solve social and industrial challenges is still very low (Hofmann 2021; Sinde et al. 2023; UNESCO 2022). To reap the full benefits of responsible AI in Af- rica, it is critical to investigate existing status, challenges, and opportunities for the AI start- ups and their innovation ecosystem to map out measures that will guide and foster effective and sustainable use, development, and adoption of
9
AI technology for the social economic develop- ment of Africa and the world at large. Therefore, this report presents the procedure and results of the investigation on the status, challenges, and opportunities of AI startups in African countries and then, maps out and provides actionable rec- ommendations that promote excellence in the
adoption, development, and use of responsible AI leading to sustainable social-economic de- velopment in Africa and the world at large. The report consists of five sections namely: introduc- tion, literature review, methodology, results and discussion, and lastly, recommendations and con- clusion.
10
According to the literature AI is rapidly evolv- ing as a key technological driver in Africa, ush- ering the continent into a new era of innovation and digital transformation (Okolo, Aruleba, and Obaido 2023). The adoption of AI across the di- verse landscapes of African countries showcases a commitment to leveraging technology to ad- dress both longstanding and emerging challeng- es (No et al. 2021). Among the core elements of AI are algorithmically controlled automated deci- sion-making (ADM) systems, or decision support systems, which are social technological frame- works that comprise decision-making models and the algorithms that translate the models into computable codes (Hofmann 2021; Authur Gwagwa et al. 2020; Fuster 2020; Centre for Re- sponsible Business Conduct in the Directorate for Financial and Enterprise Affairs 2019). In the African context, ADM systems are being used to improve efficiency, support evidence-based poli- cymaking, and address humanity’s most pressing issues which include a better-educated society, a more productive continent, less hungry nations, healthier populations and a society better placed 02 Literature Review
to tackle the effects of climate change (Authur Gwagwa et al. 2020). AI is being used across the continent to address needs and gaps in educa- tion, agriculture, health, fintech, climate change, public transport, logistics, enterprise develop- ment, and language translation, accelerating Afri- ca’s capacity to achieve UN sustainable develop- ment goals, 2030 (Hofmann 2021). For example, according to (Sedola, Pescino, and Greene 2021), a total investment of approximately $ 2.02 billion has been made towards the promotion and ac- celeration of AI activities in Africa. This has had an impact in sectors such as Agriculture, Education, Manufacturing, Banking & Finance, Healthcare, and Marketing. The education sector recorded the highest impact with a total of 216 entities, healthcare with 114 entities, and agriculture and manufacturing sectors recorded the lowest with approximately 53 entities. However, there was not enough data for some other countries in West, Central, and East Africa such as Ghana, Central African Republic, and Kenya to conclude that the agriculture and manufacturing sectors have the lowest AI adoption since most African countries
11
greatly benefit from these sectors. Therefore, fur- ther investigation should be conducted to under- stand the reasons for the low adoption and set the mitigation strategies to be taken. Additionally, the literature highlights that AI start- ups in the African continent have been acceler- ated by research institutions, universities, corpo- rates, and incubators/accelerators which through a holistic approach act as a catalyst to solve their challenges and offer opportunities in the African context. Moreover, policymaking is a very integral segment for the advancement of AI in Africa, how- ever, it is still in its embryonic stage, jeopardizing efforts that are meant to steer the AI ecosystem in Africa (Rogerson et al. 2022; Dwi Hadya Jayani 2019). Furthermore, the literature suggests that AI deployed in Africa tends to originate from out- side the continent lacking contextual relevance, particularly concerning cultural and infrastructur- al factors (UNESCO 2022; Oxford Business Group 2021; Eke 2023; Heumann et al. 2018; Law 2023; Arthur Gwagwa et al. 2021a; Sampene et al. 2022) The AI startups in Africa are on the rise, indicat- ing a growing interest and investment in AI tech- nologies. Therefore, identifying emerging trends provides insights into the current landscape and future trajectory of AI in Africa that can facilitate innovations that are culturally and contextually relevant to the needs of the various countries.
Therefore, the rationale for conducting this re- search is to map startups in Africa that adopt or apply AI innovations rooted in the recognition of AI as a transformative technology with the po- tential to drive socio-economic growth and sus- tainable development, transforming economies and societies for the better across the continent. By mapping the ecosystem players, the project seeks to provide insight into startups that are leading the way in AI innovation in Africa, pro- viding visibility of stakeholders and a platform to catalyze collaboration and knowledge sharing among AI startups, investors, policymakers, ac- ademia, and other stakeholders. This will facili- tate dialogue, networking, and the exchange of ideas, ultimately fostering a more dynamic and supportive ecosystem for AI innovation in Africa. Additionally, a review of the challenges and op- portunities faced by AI startups on the continent provides a nuanced understanding of the factors influencing the growth and success of AI start- ups, including regulatory environments, access to funding, talent availability, infrastructure, and market demand. Lastly, outline the recommenda- tions aiming to empower AI startups and stake- holders in the AI innovation ecosystem in Africa to enhance their impact and contribute more effec- tively to socio-economic growth and sustainable development on the African continent.
12
3.1 Key Definition The following two criteria AI stakeholders and AI readiness index in Africa have been deployed in the study to achieve the results. 3.1.1 AI Stakeholders By definition, AI Stakeholder refers to an in- dividual, group of people, or company that has invested in AI, either in the form of di- rect support for research and development or a vested interest in the success of the AI, or who is affected by or has an interest in AI and its social impact initiative(s) (Sinde et al. 2023; “AI for Good: Global Impact” 2020; Hoffman et al. 2023). They may include users, community members, partners, funders, in- vestors, policymakers, regulators, research- ers, and others (Arthur Gwagwa et al. 2021b; Truby 2020). Based on the literature, Africa’s tech boom is often linked to fintech, but the largest tech acquisition in the continent during the last years was AI-focused. Indeed, AI technology is growing across Africa, with over 2,400 companies specializing in AI, 41% 03 Key Definitions and Methodology
of which are startups. Estimates indicate that the technology could contribute $1.2 billion to the continent’s GDP by 2030 if it could only capture 10% of the fast-growing global AI market (Gadzala 2018; Rogerson et al. 2022; Hoffman et al. 2023). This configuration has contributed to the emergence of an AI eco- system in Africa. Most of this progress can be found among AI-related startups, academic, Government, corporate, social, and incuba- tors/accelerator institutions. Therefore, for the nature of this study, the stakeholders have been categorized into firms, academia, and government. Additionally, these stake- holders are in different stages for AI adop- tion, which include beginners (initial stage), experimenters (intermediate stage), and in- novators (final stage) as briefly described in Figure 1. However, despite their stages, each of them contributes to the understanding of the status of AI in the country. Therefore, this study has considered all stakeholders in all three stages of AI adoption to understand the status of AI at a country level and eventu- ally at the African continent level.
13
Firms
Academia
Government
Encompasses companies in the private sector engaged in the AI domain. Varying from emerging startups to established multinational corporations
Encompasses any education- al entity such as universities, colleges and research institu- tions that contributes to the field of Artificial Intelligence, be it through conducting re- search developing an dusing the technology or educating professionals in AI
Encompasses entities such as policy-making bodies, regu- latory agencies and organiza- tions within the public sector that play a role in either reg- ulating or facilitating the use and development of AI.
With a deep understanding and multiple live use cases. innovators are driving enter- prise-wide adoption of so- phisticated techniques like deep learning with strong executive support while ac- tively attracting and retain- ing top talent
Understand AI and have live use cases, but still imple- menting point solutions with- out a defined methodical approach to conceptialising and implementing
Recognise the need but waiting to see demonstrated benefits across the industry before launching
Figure 1: Description of AI Stakeholders and their Stage
14
3.1.2 AI Readiness Index in Africa Overall, the average AI readiness index for all countries in Africa stood at 26.91 which is still below the halfway mark (Gwagwa et al. 2021; Rogerson et al. 2022). Only Egypt, Kenya, South Africa, and Tunisia recorded index values above 40 but none of them anywhere above 50. Generally, there is still a lot of need and effort to expedite the use and adoption of AI activities in Africa (Dwi Hadya Jayani 2019; Akello 2022; Rogerson et al. 2022). Therefore, based on the literature, the following summarizes the criteria that are supposed to be looked into to determine the AI readiness index of a country; (Adams 2022; Broadridge 2019; Sedola, Pescino, and Greene 2021; Intel Corporation 2018). In a nutshell, to assess the readiness of AI in a particular country the following five main criteria should be taken into consideration which include knowing the available strategy, staff, skills, systems, and structure for AI adoption and implementation as briefly described in Figure 2.
Staff: Includes Change Management and Cultural Read- iness which are important blocks for AI readiness. The organizations must ensure their goals, processes and culture are aligned with AI principles, interns of its adop- tions, use and development Skills: Includes Workforce Readiness and Skills Develop- ment. All countries should train their staff on technical, ethnical, and regulatory aspects of AI, as well as on how to intepret, analyse, and use data effectively AI solutions for making informed decisions. Systems: Includes Infrastructure and Computing Pow- er. The power of AI lies in its ability to process large amounts of data quickly and accurately, therefore, coun- tries preparing for AI readiness need to consider the in- frastructure requirements for AI along with computing resources. Structure: Includes Data Strategy and Governance. AI solutions are data-hungry, hence they rely on high-qual- ity data and responsible use, development and deploy- ment. Therefore, all countries should observe these Structure to serve as a key methods for guiding AI to reflect the values and goals of a country and African continent at large. Strategy: Includes Ethical and Responsible AI Practice and Collaborative AI Ecosystem. All countries should observe these Strategies towards AI adoption, use, de- velopment and implementation in order to accelerate social economic development of their country and Af- rica at large.
Strategy
Staff
Structure
AI readiness Criterias
Skills
Systems
Figure 2: AI Readiness Assessment Pillars
15
3.2 Methodology The methodology of this study was guided by the vision to map AI startups in African countries with a focus on wide and diverse coverage in both rural and urban areas, and inclusivity in the AI innovation ecosystem dedicated to understanding their status in terms of their funding mechanisms, fundraising journeys, products, traction, challenges and opportunities that will lead to the promotion of excellence in the adoption, development, implementation, and use of AI that will lead to sustainable social-economic development in Africa. The mapping for AI startups in Africa utilized proprietary vocabulary to discover and extract qualified startups from the literature, online resources, and AfriLabs’ enormous datasets of African startups, investors, hubs, corporates, public sectors, and humanitarian and development organizations with the in- tegration of snowballing approach to have wide and diverse coverage of the data. Throughout the process, iterative methods were used in each stage to review and incorporate missing and new required information leading to a comprehensive final report. The following Figure 3 briefly describes the methods applied toward fulfilling the vision of this project;
1
2
3
Primary Data Collection E ngage African AI stakeholders through the use of key informant interviews (KIIs), Focus Group Discussions (FGCs), case studies, Startups and Hubs Referral.
Secondary Data Collection
Initial Preparation
Conduct thematic desk research, verify data authentically across various AI, publish articles, and tech-focused online sources.
Setup ethics and confidentiality recruiting team, prepre research tool, and identify participants.
7
Data Reporting and Dissemination
Report data using tools such as PowerBI and Tableau and Data dissemination to AI-based Afrilabs Gatherings such as Clean Tech Conferences, Africa Tech Summits, TechCrunch Disrupt, and Africa Tech Festival, and also, African and Global AI conferences and journals to mention a few
AI Stakeholders Engage- ment: Conduct in-depth KIIs and FGDs with key stakeholders
Case Studies: Conduct outreach to short- listed AI startups and create case studies that highlight their status, challenges and opportunities
6
Data Analysis ans Synthesis
Use snowballing technique to identify existing AI start- ups and its AI stakeholders
Conduct qualitative and quanti- tative analysis using tools such as PowerBI, Python, and DAX to analyse and synthesize the clean collected data
Surveys: Deploy online surveys to AI stakeholders in AfriLabs community and AI stakehold- ers in Africa.
5
4
Iterative Review of Collected Data Follow an iterative strategy to review and refine findings and primary data for final reporting
Data cleaning and Annotation
Webinars: Host identified thought- ful-leaders within AI and techh community in Africa.
Data cleaning and annotation through the use of tools such as Python and Excel.
Figure 3: Description of Methodology for Mapping AI Startups in Africa
16
04 Results and Discussion
4.1 Results 4.1.1 Status of AI Startups in Africa
(i) Results of Demographic Survey Analysis Based on the demographic analysis as detailed in Figure 4, one hundred and sixty respondents participated in the data collection at a rate of 48% under firms, 39% under academia and 13% under government categories, where 69% of them were male and 31% female in fifty-four (54) countries in Africa, at an average of 25 to 34 years old.
17
160 Participants
69% 31% Male Female
Average age 25-34
13%
2,>= Participants per country
39%
54 in Africa
Firms Academia Government
48%
Distribution of AI stakeholders categories
Government
Academia
Firms
21%
70
60
50
40
30
73%
20
10
0
18 - 24
25 - 34
35 - 44
45 - 54
55 and above
Distribution of Age and Gender
Distribution of AI Firms categories
AI StartupsOther
Firms Categories
Male
Female
Percentage
Figure 4: Description of Demographical Results
18
(ii) Geographical Distribution of Responses During the data collection only twenty-nine (29) countries participated in the primary data collection as detailed in Figure 5, however the finding from the remaining twenty-five 25 countries were found from the literature whose results have been included in the findings of this report.
40
Responses from 29 Countries
30
30
Tanzania Kenya South Africa
Sierra Leone Cameroun Namibia Sao Tome Principe
20
25
Uganda Nigeria Zambia Rwanda Egypt Senegal
10
Tunisia Malawi Togo Algeria South Sudan Democratic Republic of Congo Eswatini Mozambique
20
0
15
-10
Zimbabwe Botswana Central African Republic Guinea Morocco Ghana
10
-20
Ethiopia Gambia
5
-30
-10
-20
0
10
20
30
40
50
Figure 5: Description of Geographical Distribution of Responses
(iii) Sectoral Distribution of Responses According to the results, most startups utilize AI primarily in Healthcare, followed by Finance, Agriculture, and Education. Similarly, in Academia, many AI innovations focus on addressing is- sues in Healthcare, with Agriculture and Education also being significant areas of application as detailed in Figure 6.
3%
11%
12%
24%
28%
Healthcare Finance Agriculture Education Manufacturing Marketing
11%
Research Institutions
Startups
21%
18%
20%
12%
19%
24%
19
Figure 6: Description of Sectoral Distribution of Responses.
(iv) Description of AI Formal Education and AI Subfields Specialization The study findings indicate that 63% of the respondents have formal education on AI, with 37% at the final stage, 27% at the intermediate stage, and 36% at the initial stage of AI adoption. The AI Start-ups in Africa have focused on the following AI Subfields Specializations; whereby majority apply Machine Learning techniques in their production at 30% ; Deep Learning at 17; Natural Language Processing at 15% ; Computer Vision at 12% ; Generative AI at 10%; Reinforcement Learning at 9% ; and Robotics at 7% as detailed in Figure 7.
Yes|No
37 % 27 %
7 % Robotics
Final Stage
63%
37%
Formal Education on AI
9 % Reinforcement learning
Intermediate Stage
36 %
Final Stage
30 % Machine Learning
10 % Generative AI
AI Adoption
Description of AI Subfields Specialization in Africa
12 % Computer Vision
The AI Start-ups in Africa have focused on the following AI Subfields specializations; whereby majority apply Machine Learning techniques in their production at 30%; Deep Learning at 17%; Natural Language Processing at 15%; Comput- er Vision at 12%; Generative AI at 10%; Rein- forcement Learning at 9%; and Robotics at 7%
15 % Natural Language Processing
17 % Deep Learning
Figure 7: Description of AI Formal Education and AI Subfields Specialization.
ics processing units (GPUs), tensor processing units (TPUs), and others, increases with growing AI algorithm complexity. For the past decade, the world has witnessed unparalleled levels of in- vestment in digital infrastructure which has been brought due to increase in smartphone penetra- tion, industry digitalization and the rise of cloud computing, however the rapid evolution of AI has
(v) Description of Needed AI Infrastructure The emergence of AI is fueling the surge in the need for digital infrastructure and comput- ing power worldwide. The degree of computing power is crucial in developing and deploying AI models, particularly those involving computations and massive datasets. The need for high-perfor- mance computing (HPC) devices, such as graph-
20
further elevated the demand for computing pow- er. Digital infrastructure for AI encompasses hard- ware and software elements that provide a wide range of capabilities including but not limited to data storage and management, cloud computing, sufficient computational power, fast and robust communication networks, reliable electricity/ power connections, security and privacy of infra- structure, scalability and flexibility, programming languages and AI system development tools, op- erating systems, virtualization, and business intel- ligence tools (Kuleto et al. 2021; Arakpogun et al. 2021; van Buren, Chew, and Eggers 2020; Sinde et al. 2023) All these components are required for unlocking the full potential of AI, driving innova- tion, addressing complex challenges in the coun- try and eventually providing effective socio-eco- nomic development. There are currently several initiatives under way in Africa, for instance African countries are adopt- ing digital strategies to create an enabling en- vironment for digitalization. Private sector firms including companies such as Amazon, Google, and Microsoft, are investing in African data cen- ters, rapidly expanding Africa’s hosting capacity even as they provide customers with data-relat- ed services such as cloud computing and stor- age—especially in South Africa, Nigeria, Kenya, and Ghana. For example, the Government estab- lished the Tanzania National Internet Data Center (NIDC) to not only provide scalable storage and compute resources, but also gives Tanzanian en- tities access to stacks of AI and Machine Learning services which are constantly updated, there- by eliminating the need to write many of these services from scratch. The country has attracted investments in public data centers also from the private sector which in turn has resulted in licens- ing of more public data centers so as to provide access to computing resources which are useful to process complex algorithms, run applications, and analyze large datasets in real-time. Overall, African country’s main objective is to have robust and reliable digital infrastructure for a safe and secure foundation for AI development and use. The following are some of the initiatives done in
Africa (bidin A 2017; Bank 2021; On et al. 2022; URT 2021; 2023; “The United Republic of Tanza- nia) Tanzania Mainland” 2016).
A. Boosting Local Investments in Data Centers
As part of the African countries initiatives towards supporting cloud computing services, the coun- tries have established state-of-art tier-3 Data Cen- ters which has been connected to the National ICT Backbone with the support of international submarine cables making it a strategic hub for East Africa and beyond. The data center has been able to serve both public and private institutions in the country. For example, by December, 2023, Tanzania had licensed two (2) public data centers from the private sector showing the result of ef- forts made in attracting more investments in the area however with the anticipated growth of AI applications in the country and digital transfor-
21
C. Provide affordable access to compute capacity and Tools for AI
mation, there is a need to increase investments in data centers in the country capable to meet the high computing capacity demands brought by AI.
Computing power is a fundamental element to the research, development and use of AI. It is therefore of paramount importance to study and understand the country’s needs for AI comput- ing capacity needs in order to ensure that accessing computing power is not a barrier to AI research and innovation, commercialization and deployment of AI. Therefore, most of the African Governments have established tailored strategies to curb the needs of computing power capacity while ensuring the available resources are cost-ef- fective and affordable. These initiatives include developing government and private sector part- nerships to develop supercomputing centers to support AI innovation and research as well as pro- vide funding and financial incentives to allow eas- ier access to AI development tools and platforms and hence lower the barrier to entry for AI de- velopers and researchers in Africa. This includes software frameworks, libraries, and development kits for machine learning, deep learning, natural language processing, and computer vision.
B. Improving network & connectivity for inclusivity to digital infrastructure for AI African countries have been adapting well to the evolution and adaptation of broadband technolo- gies for provision of high-speed internet services in their countries. Through their Government ini- tiatives towards extending broadband network connectivity to bridge the digital divide and en- hance usage of ICT and AI applications for sus- tainable socio-economic development marked a milestone through its establishment of the Na- tional ICT Broadband Backbone (NICTBB). For example, in Tanzania since 2009, the country is connected to the NICTBB and the plan is to ex- tend the NICTBB to the district level with a total of 15,000km by 2025. Currently the NICTBB has been extended to all twenty-six (26) regions and forty-three (43) out of one hundred and thirty-nine (139) district headquarters. The Government in- tends to increase connection to 99 districts by June, 2024. Tanzania also has the strategic posi- tion advantage geographically being surrounded by landlocked countries and therefore provided with an opportunity to provide submarine con- nectivity to the neighboring landlocked countries which are Rwanda, Burundi, Uganda, Malawi and The democratic Republic of Congo which can boost the investments in data centers as well. Through these initiatives the Government man- ages to provide access ICT services through the deployment of Communication infrastructure across the country hence bridging digital divide and eventually support learning AI and use of AI solutions in the country.
C. Promote Green-Energy powered Data Centers
Most of the African countries have unstable na- tional grids and respective environmental con- cerns that hinder substantial investments in the data center industry. To address the power issue, most of the African governments have created a regulatory and legal environment that supports data center providers to partner with Indepen- dent Power Producers (IPPs) and embraced alter- native energy sources to ensure sustainable pow- ered data centers.
22
(vi) Overview of Support and Resources offered to AI Startups by Hubs
Business Advisory
Investor Linkages
Education
Funding
Mentorship
Support and Resources AI Start-ups
Government Policies
Market Linkages
Accelerator/ Incubator Programs
Legal Support
Co-working spaces
Figure 8: Description of Support and Resources Offered to AI Startups.
23
The following Table 1briefly explains the identified top ten (10) support and resources offered to AI startups in Africa;
Table 1: Description of Top Ten (10) Support and Resources offered to AI startups in Africa.
SN Support and Resources Offered to AI Startups
Description
1.
Education
Africa's Tech Hubs are increasingly concentrating on developing sophis- ticated technical skills such as programming, AI, Data Science, Machine Learning, and other emerging technologies. These centers frequently act as focal places for education, innovation, and entrepreneurship, of- fering training programs and workshops to startups and people. An ex- ample is kLab in Rwanda, Clintonel Innovations in Nigeria, TechQuest STEM Academy in Nigeria, iLabAfrica in Kenya, data Lab (dLab) in Tanza- nia, Sprint in Egypt, iHub, a tech hub located in Nairobi, Kenya (Engines and Growth 2023; Artificial Intelligence for Africa Report 2018). It men- tors and develops entrepreneurs' and programmers' talents by offering workshops and training courses on a variety of computer disciplines, including AI and data science. Universities and research institutions play a pivotal role in the AI eco- system by generating new knowledge, innovating technologies, and producing graduates with relevant AI skills. These institutions are often at the forefront of cutting-edge research that pushes the boundaries of what AI can achieve. Mentorship is vital for developing AI skills in tech hubs across Africa. It comes from industry experts, academic researchers, entrepreneurs, and investors. Industry experts provide practical knowledge, academic researchers offer theoretical guidance, entrepreneurs teach startup founders about entrepre- neurship, and investors and business leaders offer guidance on business de- velopment, marketing, and understanding market dynamics. Tech hubs provide access to AI mentors through various programs and initia- tives. These include structured programs, workshops and seminars, online platforms, networking events, community initiatives, and partnerships with educational institutions and corporations. These programs help participants pair with mentors based on their educational needs and career aspirations.
2.
Mentorship
24
3.
Government Policies
Government policies are crucial in fostering AI startups by ensuring robust data privacy, security, and Intellectual Property protections. Countries like Egypt and Mauritius have enhanced their data protection regulations to meet international standards like General Data Protection Regulation (GDPR), boosting consumer trust and facilitating international business col- laborations. Countries with robust cybercrime laws offer a secure environ- ment for AI innovation, safeguarding startups from potential data breaches and cyber threats (Adams 2022). Nigeria enacted the data protection act in 2023 and established the Nigeria Data Protection Commission to regu- late data processing. Moreover, Countries like Egypt, Mauritius, and Rwanda have implemented National AI regulations, promoting AI development and integration into key sectors. These strategies include educational reforms, startup incentives, and significant AI research investments. This not only fos- ters a regulated environment but also attracts global tech investments, driv- ing growth and innovation (Adams 2022; UNESCO 2022; Law 2023). Hubs offers legal and business advisory services to startups, helping them navigate regulatory hurdles, contracts, intellectual property issues, and oth- er legal matters. Providing a collaborative workspace fosters a conducive environment for startups to work, network, and share ideas. Co-working spaces offered by hubs can reduce overhead costs and promote interaction among entrepre- neurs.
4.
Legal Support
5.
Co-working Spaces
6. Business Advisory Some hubs assist startups in expanding their operations to other African markets, offering market research, partnerships, and market entry strate- gies.
7.
Investor/ Industrial Linkages
Hubs assist startups in refining their pitches and connecting with po- tential investors. This support is crucial for securing funding and scaling operations. Partnerships between startups, established tech companies, and other sectors have facilitated knowledge exchange and innovation. Such col- laborations often lead to the development of new AI applications that can transform industries. Access to financial resources is paramount for startups. Hubs in Africa play a crucial role in providing funding opportunities, whether through grants, investment connections, or access to venture capital firms. Some hubs assist startups in expanding their operations to other African markets, offering market research, partnerships, and market entry strate- gies. These structured programs provide startups with intensive mentoring, re- sources, and networking opportunities over a defined period, helping them accelerate their growth.
8.
Funding
9.
Market Linkages
10.
Accelerator/ Incubation Programs
25
(vii) Description of Factors that have contributed to the growth of AI
Funding & Investments
Education & Capacity Building
Government Support & Policies
AI innovation Ecosystems
Industrial Collaboration
Access to AI Talents & Skills
Financial support is crucial for the na- scent stages of AI startups. Increased funding and invest- ment have provid- ed the necessary capital to support research and de- velopment, scale operations, and at- tract further finan- cial backing.
Universities and resear institutions play a pivotal role in the AI ecosys- tem by generat- ing new knowl- edge, innovating technologies, and producing gradu- ates with relevant AI skills. Capacity building has nu- tured a sustainable growth model for AI in Africa.
Supportive gov- ernment policies and initiatives have significantly con- tributed to the AI landscape. These include funding for technology hubs, tax incentives for AI startup, and the establishment of regulations that encourage fair and ethical AI develop- ment.
The AI innovation ecosystems have assisted to bring together AI ex- perts, research- ers, entrpreneurs, and investors to develop and commercialize AI technology-driven solutions.
Partnerships be- tween startups, es- tablishd tech com- panies, and other sectors have facil- itated knowledge exchange and in- novation, Such col- laborations often lead to the devel- opment of new AI applications that can transformin- dustries.
The available of skilled profes- sionals equipped with AI expertise is essential for the growth of the in- dustry. Training programs have helped develop a pool of talent that can drive AI initia- tives forward.
Figure 9: Description of Factors that have contributed to the growth of AI.
4.1.2 Opportunities for AI Startups in Africa
Based on the findings of this study, out of 165 have predicted the growth and investment rates of AI start- ups in 5 years to come will be high at 77% and 62% respectively. Additionally, findings indicate that the AI startups in Africa have several opportunities for them to do well in enhancing different socioeconomic activities. Despite the low AI adoption rate and existing challenges hindering the growth of the use and application of AI in Africa, there is still a lot of development and experienced scenarios both within Africa and outside the African continent which has shown great benefit that a country can get through the use and application of responsible AI. These opportunities include;
Learning Patterns in Data:
Helping: AI can potentially as- sist and support indi- viduals and industries, particularly in address- ing complex challeng- es, emerged as a re- curring theme.
Access to Services: AI presents significant opportunities to en- hance access to crit- ical services, ranging from healthcare, and agriculture to financial services, thereby con- tributing to societal development.
Improving: Leveraging AI in our existing activities, pro- cesses, and systems enhances efficiency, effectiveness, and overall performance.
The ability of AI to dis- cern patterns within vast datasets provides an opportunity for gain- ing valuable insights and driving informed decision-making across various sectors.
26
These opportunities can be applied in various social economic sectors such as in;
Healthcare The prospect of improving healthcare outcomes through AI-powered diagnostic tools and telemedicine platforms is a transformative opportunity.
Agriculture AI holds promise in
revolutionizing agriculture by optimizing processes, aiding decision-making, and enhancing overall productivity.
Social Economic Sectors
Access to Finance Within the financial sector, AI can establish increased trust systems, improve fraud detection, and facilitate financial services for underserved populations.
Resources AI presents opportunities for resource optimization, including water and energy conservation, in sectors such as agriculture and manufacturing.
27
4.1.3 Challenges of AI Startups in Africa
Africa as a developing continent faces challenge across all levels of the AI readiness index. Therefore, the following are the top six challenges that hinder the adoption and successful use and application of AI in Africa as described in Figure 10. These challenges include; limited funds, regulatory barriers, lack of data access, lack of competitive business environment and innovation, shortage of skilled and educated work- force, and inadequate infrastructure.
Regulatory Barriers
Lack of Data Access
Limited Funds
The persistent challenge of securing adequate funding remains a barrier, hindering the growth and scalability of Al Initiatives. Limited Funds.
The absence of supportive regulatory environment and complex legal frameworks add layer of complexity to the de- velopment of Al technologies. Regulatory Barriers
Majority of AI difliculties in Startups face accessing the required context specif- ic datasets that are crucial for training and refining Al models. Lack of Data Ac- cess
Inadequate Infrastructure
Lack of competitive business environment and innovation
Shortage of skilled and educated workforce
There is lack of well estab- lished Al Stakeholders’ Eco- system and sufficient R&D infrastructure and resources to support innovation Lack of Competitive Business Envi- ronment and Innovation
A shortage of specialized talent in the Al and tech in- dustries hampers the devel- opment and implementation of Al solutions. Shortage of Skilled and Educated Work- force
The absence or inadequacy of of infrastructure including data storage, high speed in- ternet and cloud computing poses significant-hurdle. In- adequate Infrastructure
Figure 10: Description of Geographical Distribution of Responses.
28
Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Page 8 Page 9 Page 10 Page 11 Page 12 Page 13 Page 14 Page 15 Page 16 Page 17 Page 18 Page 19 Page 20 Page 21 Page 22 Page 23 Page 24 Page 25 Page 26 Page 27 Page 28 Page 29 Page 30 Page 31 Page 32 Page 33 Page 34 Page 35 Page 36 Page 37 Page 38 Page 39 Page 40 Page 41 Page 42 Page 43 Page 44 Page 45 Page 46 Page 47Made with FlippingBook - Online Brochure Maker