P. Wamuyu, and W. Mambo,

“African National Artiﬁcial Intelligence Strategies: A review, analysis and research agenda”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.

African National Artiﬁcial

Intelligence Strategies:

A review, analysis and

research agenda

ARTICLE HISTORY

Received 9 November 2024

Accepted 27 January 2025

Published 7 July 2025

Patrick Kanyi Wamuyu

United States International University-Africa

Computing Department

Nairobi, Kenya

kanyiwamuyu@yahoo.com

ORCID: 0000-0002-4241-2519

Wangai Njoroge Mambo

United States International University-Africa

Computing Department

Nairobi, Kenya

mambown@protonmail.com

ORCID: 0000-0001-9754-1273

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

This work is licensed under a Creative Commons

Attribution-NonCommercial-ShareAlike 4.0 International License.

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 2, July 2025

10.5281/zenodo.15742005

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 2, July - December 2025

African National Artificial Intelligence Strategies:

A review, analysis and research agenda

Patrick Kanyi Wamuyu

United States International University-Africa

Computing Department

Nairobi, Kenya

kanyiwamuyu@yahoo.com

Wangai Njoroge Mambo

United States International University-Africa

Computing Department

Nairobi, Kenya

mambown@protonmail.com

Abstract— Some countries have developed their national

artificial intelligence strategies (NAISs) while others have formed

task forces to develop them. This study reviewed elements and

concepts required to develop NAIS, related science, technology and

innovation (STI) strategies, policies and manifestos. Some of these

elements and concepts apply to both developing and developed

countries while some others are specific to one of them. STI

elements and concepts apply to artificial intelligence strategies since

AI technology is a specialization of STI technologies. The concepts

and elements identified by this study can aid strategy creators by

providing important insights for creating NAISs. For instance,

catch-up strategies based on learning from a country with similar

past technology, catch-up successes, and others who have created

NAISs are a low-cost way for developing and implementing NAISs.

Keywords— artificial intelligence, capability building, catch-

up, leapfrogging, national artificial intelligence strategy

I. INTRODUCTION

The artificial intelligence (AI) revolution is drastically

transforming the world, and several countries view it as a

transformative technology. AI solutions are useful to

everyone, and governments should strategize to create AI-

driven economies that solve diverse problems [1]. Developing

AI strategies and policies will align actors’ efforts and

resources towards this goal. In fact, technology strategies

enable countries to navigate the technology development

landscape to achieve strategic goals.

In this context, AI has been defined in literature in

different ways: As making computers act rationally and

humanly, or as making computers think like humans and

rationally [2]. Both identify AI as an enabling factor for

developing applications that imitate humans to increase

business, industries, governments and society

competitiveness.

Within the many countries that missed the first industrial

revolution, some were able to join the second and third

revolutions. Meanwhile others will join the fourth industrial

revolution, in which AI is known as one of its major drivers.

However, those that fail to join the fourth revolution in its

early phases will find it almost impossible to join later and will

face a high poverty and unemployment crisis. The gap

between those who catch up and those who fail is likely to be

exponentially larger than the gap created by the previous three

industrial revolutions.

Several national technology strategies have been

developed in the African continent. The latest of these

strategies are national innovation and ICT strategies.

However, most national ICT strategies do not even mention

AI. One way to start developing AI capabilities at low cost is

to add AI aspects to existing technology strategies. There is

few AI courses offered in African higher education

institutions. AI itself can be used to provide online courses at

lower cost than physical classes.

Creators of national artificial intelligence strategies

(NAISs) should start by assessing the country´s priorities,

strengths and weakness; the possibility of deployment with the

country’s limited resources and citizens’ aspirations [1]. A

NAIS building on citizens’ aspirations will get national

implementation support. In addition, citizens must be

educated about the benefits of AI technologies and NAIS.

Technology breakthroughs combined with large global

investments are likely to establish technology dominance.

Dominant technologies like the Internet drive competing

technologies out of the market and force related technologies

to become compatible with it. If AI becomes dominant, then

ICT technologies including mobile applications and Internet

of Things (IoT) will have to become AI compatible to remain

competitive.

NAIS aligned with other specific and general technology

strategies, creates a system of strategies that work together to

achieve national objectives. Designing a new national strategy

should align with other strategies and those likely to be

developed soon.

The current research questions are:

1. What concepts, elements and catch-up lessons

identified in literature are necessary for creating

national AI strategies (NAIS)?

2. Which technology concepts, elements and catch-up

lessons learned are relevant for creating African

NAISs?

II. METHODOLOGY

The study used two research methods: the literature

review, research method [3] and Conjecture Analogy design

science method (CADSRM) [4]. The search was conducted to

find relevant NAIS studies. CADSRM analogy determined

similarities between literature and requirements for creating

African NAIS.

Most literature reviews focus on the past. Some identify

future research agendas, but few extend the literature by

ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

DOI:

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 2, July 2025

10.5281/zenodo.15742005

P. Wamuyu, and W. Mambo,

“African National Artificial Intelligence Strategies: A review, analysis and research agenda”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.

taking steps into the future. Reviews are based on what is

published, and extending into the future involves exploring the

unknown. A literature review is a springboard for future

research [5]. Strategies are tools for navigating an

organization´s future and a literature review provides a

springboard into that future.

A literature review is concept-centric and organized

around concepts. It should analyze the literature, identify

knowledge gaps and encourage researchers to address them

[3]. Gaps in knowledge for developing NAIS strategy limit the

efficiency and effectiveness of the strategies created.

The CADSRM research method supports research,

innovation and learning from one’s experience as well as the

experience of others. Developing countries (DCs) can benefit

by learning from their experience of areas they have

successfully leapfrogged, from other technological strategies

they have developed as well as from NAIS of the countries

that are leaders in AI technology.

The search string concatenated the terms: “African”,

“national science”, “technology”, “strategy”, “policy”,

“manifesto”, “innovation”, “system”, “adoption”, “lesson

learnt” and “technology success”. The search terms were

applied to Google, Taylor and Francis, IEEE explore and

Springer databases. The search found one hundred and sixty

(160) articles, which were then screened for relevance. This

resulted in forty-nine (49) articles being included based on

their abstracts. After a full-text reading, forty-five (45).

relevant articles remained. The CADSRM method was used

to identify concepts and elements in relevant articles for

creating an AI catch-up strategy for Africa.

III. RESULTS

The results are organized into sub sections: NAIS

concepts, related strategies and learning from past successes.

A. Elements of a national AI strategy

NAIS key pillars are AI in government and public

services; skills and education; research and development;

data, digital infrastructure and ethics [6]. The AI revolution

should be guided by ethics, as it aims to make computers act

and think intelligently like humans, which raises many ethical

issues. To join the AI race, countries that are behind should

start by providing resources to training institutions to build

knowledge and skills and develop AI infrastructure.

Educational institutions should integrate AI and related fields

in their curricula [7]. Skilled manpower is the most critical

component of building AI capability. Governments should

initiate AI projects, including some open source to create

avenues for beginners to acquire skills, experience and build

capabilities.

A comparative analysis of developing countries, NAIS can

help shed light on important elements that need to be included

or excluded in the strategy [8]. The comparison can help

identify the starting points of other countries and directions

they took, allowing strategy creators to select suitable

approaches for their countries. A government’s NAIS

strategy sets AI strategic directions for AI, shaping market

structure and societal outcomes [9]. Designing flexible

strategies helps overcome future challenges.

NAIS should set high-level priorities based on the

country’s future vision [10]. The vision determines a

country´s focus and aligns sets of strategies. Lessons learned

for creating national ICT strategies in DCs include involving

citizens and aligning strategies [11]. Changes in government

do not affect African national visions because they represent

national interest agreed upon by most stakeholders.

For AI to become widely used, it should focus on mobile

phones which are widely available in sectors like education

and business that have large potential adopters [12]. This

creates the possibility of commercial success for applications

and AI becoming widely used.

B. Artificial intelligence strategy concepts

Mauritius was the first African country to develop NAIS

[13]; a few others have followed, while many have established

task forces. Experts from academia, industry and government

drafted Ghana’s AI innovation and adoption strategy [7]. The

three categories of experts ensure viewpoints of different

sectors are included. Forward thinking governments are

creating comprehensive NAIS to leverage AI’s transformative

power [6]. This process requires identifying areas where AI

will have transformative effects. Several countries are moving

quickly to be early AI adopters, especially regionally, to

obtain or maintain competitive advantage [13]. A SWOT

analysis is used to determine whether to create an AI strategy

that integrates both innovation and adoption or starts with

adoption followed by innovation.

Countries should start by building basic AI skills, then

proceed incrementally to more advanced skills. Assisted

intelligence helps people perform tasks better, augmented

intelligence assists people do what they could not, and

autonomous intelligence creates machines that replace

people’s roles [14]. Augmented intelligence and autonomous

machines represent the next generations of AI. The current

generation is predominantly assisted intelligence, with few

simple examples of augmented intelligence and even fewer

examples of autonomous intelligence. Innovation capability,

defined as the ability to generate innovative artifacts [15], will

be essential to create next AI generation technologies.

Strategies are future predictions modified as emerging trends

become clearer over

time.

Benchmarking is an analogical approach of learning and

building capabilities by extracting best practices, methods and

processes from organizations and countries that are more

successful to achieve similar performance [16]. Countries are

benchmarking their NAIS instead of reinventing what has

already been developed elsewhere. Benchmarking can be

done at regional [17] and at other levels. Innovation and

design literature indicates countries apply benchmarking to

national technology strategies, research, knowledge transfer

mechanisms, networks and clusters, [18]. NAIS shows how a

country plans to navigate the landscapes of the AI technology

revolution.

An entity imitates another’s better performing entity to

learn by analogy from it. Mix and match, also called

benchmarking, involves an imitator that mixes and matches

practices of several competing firms. On the other hand,

Copying the Best refers to an imitator identifying the best

performing firms and copying a subset of observable practices

from them [19]. Mix and Match produces a more diverse set

of best practices compared to Copy the Best. NAIS are

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observable as they are available to all AI actors in a country

and often available on the web.

African NAIS benchmarking against India, China, United

Kingdom, Canada and United Arab Emirates is a strategy

creation approach [20] in which best practices are selected

from benchmarking, adapted and adjusted when creating new

strategies. China’s strategy is to become the leading AI power

[21]; United Kingdom aims to improve its position as an AI

technology developer [22]; Canada seeks to improve AI

research and training profile [23]; India explores how to

leverage AI transformation power for inclusive growth

aligned with its government’s philosophy [8]; and the UAE

plans to use AI to improve government at all levels [24].

National strategies drive building skills, capabilities, business

ecosystems and establishing industries efficiently and

effectively. Efficiency maximizes the ratio of inputs to outputs

and effectiveness maximizes the number of goals met [25]. A

strategic plan sets the direction for a desired future destination.

Sampene et al. [20] categorizes the best practices in African

NAIS benchmark into inclusive growth, innovation,

continental leadership, improving AI training and technology

development. Countries worldwide apply NAIS

benchmarking to create competitive advantage [13]. However,

[20] do not indicate criteria for selecting the best practice

categories from NAIS strategies in the benchmark. More

research is needed on creating NAIS strategy benchmarks.

China is an exemplar of a large developing country in

catching-up AI; the United States (US) is a leader in basic and

applied research and development (RD) and invention. The

US is leading in quality of AI publications and the number of

patents. China’s and other countries’ AI catch-up capabilities

were created by learning from the US. The US strategy should

be included in the African NAIS benchmark as well as

strategies for South America and other countries.

C. Science, technology and innovation strategy, policy and

manifesto concepts

AI history is filled with many promises, some of which

were not realized. AI inability to meet its promises is a risk

to be managed.

African national information technology (IT) strategies

should include general public’s needs, be adaptable,

adjustable and have a section on every domain of IT, including

national development and training needs for five years or more

[26]. NAIS is an IT technology strategy, and having these

characteristics is an added advantage. AI technology

industries are in different stages of industry formation cycle.

Each stage has different requirements. The innovation stage

requires start-ups to use experimentation and trial and error,

imitation stage requires informal research and development,

while the growth stage requires formal RD by large firms [27].

Firms deciding to start developing specific AI technology

must determine the stage of the industry formation cycle and

the entry strategy.

The Informal sector is the largest employer in most

developing countries, but its return on investment is very low.

Innovation in the informal sector is based on grassroots

innovation that innovates by trial and error and experimenting.

The next generation of AI technology revolution will support

grassroots innovation movements [28] and countries that

embrace AI grassroots innovation will be able to drastically

improve informal sector productivity. Informal sector

grassroots innovation should be a major part of developing

countries’ NAIS.

The NAIS primary long-term goal is to ensure leadership

position, using AI to increase global competiveness and

address society challenges and development needs [8]. These

are critical goals that every country aspires to achieve.

However, competition for technology leadership is cutthroat

and only countries with sufficient resources have good

chances of being technology leaders. Some countries, instead

of focusing on becoming overall AI technology leaders, are

focusing on specific AI subfields and application domains.

For example, Canada’s AI strategy seeks to make it a global

leader in AI education domain. Countries that are left far

behind in AI should seek to become emerging or DCs

technology leaders or followers.

AI strategies based on catch-up models can enable

countries to leapfrog and reduce the gap with technology

leaders based on incremental innovations. Radical AI

innovation strategies in developing countries have a very low

probability of success but can enable DCs to become

technology leaders. They require large investments that are

almost impossible to get and have high failure rates, making

them very risky. The failure of DC’s AI radical innovation

project would have dire and almost irrecoverable

consequences in all economic sectors.

Recommendations for rooting STI and AI in society for

transformational revolution are: Rejecting knowledge

dependence to make Africa a major producer and a funder of

AI research, encouraging bottom-up innovation and new

forms of innovation [29]. New forms of innovation like

inclusive and grassroots innovations can be included in NAIS.

To bridge the gap between scientists, technologists and

industry, governments should promote transdisciplinary

thinking and research centers [29]. Transdisciplinary artificial

intelligence research can bridge different knowledge and skills

silos enabling the utilization of knowledge locked in silos.

The NAIS strategy has elements of STI strategy goals:

stimulate networking, create awareness, advice policy makers

and include wide range of stakeholders [30]. Networking

enables sharing knowledge and expertise. AI human networks

enable knowledge to flow between members of a network for

spreading awareness. Policy makers consult existing

strategies to ensure that government agencies are working

towards a common goal.

African countries can develop innovation capability by

leveraging informal sector knowledge, indigenous

knowledge, biodiversity and biotechnology [31]. Next AI

generation will automate grassroots innovation based on

collaboration of AI and human systems [28]. Every country is

technology and knowledge leader in its indigenous technology

and knowledge. Grassroots innovation uses informal and

indigenous knowledge, and it is an important element in

African STI capability building [31]. Mauritius’ NAIS puts

little emphasis on informal sector [13]. This sector is

important for the continent’s development. Countries ignore

this sector at their own risk.

The pilot analysis of Nigerian STI policy indicated it

pursued five industrialization strategies: focusing on

appropriate technology RD, developing local design and

production capacity, fostering interactions among

universities, research institutions, industries and investors to

generate innovations and promote entrepreneurial innovation

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P. Wamuyu, and W. Mambo,

“African National Artificial Intelligence Strategies: A review, analysis and research agenda”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.

[31]. These strategies generate new knowledge and develop

capabilities to apply knowledge to innovate. Kenya’s STI

policy is to create an STI innovation culture, while Nigerian

objective is to inculcate a culture of innovation [31]. Kenyan

and Nigerian STI pilot studies indicated commonalities and

different emphasis on distinct aspects. Organizations, national

and regional innovation systems integration into global value

chains are developing into increasing global innovation

systems [30]. Increased networking and collaboration among

different African innovation system actors facilitate the

commercialization of research and innovation [32].

Integrating African STIs ecosystems into national innovation

systems through global networks will benefit from global

research and experience.

D. Learning from past technological successes

Experience is important in technology development, and it

is the reason why organizations often prefer employing

experts over novices. Novices are good for generating radical

ideas, many of which fail but those that succeed have a large

impact. Experts are better at generating incremental ideas,

many of which succeed but have relatively smaller impact

[33]. Expertise levels apply to individuals, organizations and

countries. Countries joining the AI race rely on ideas and

efforts of both novices and experts.

Africa is beginning to significantly learn and master many

AI technologies. A novice can only create new technology and

management innovation like a strategy by reference to an

example [34]. It is, therefore, more efficient and effective for

Africa to learn from its past technological successes as well as

learn from other countries by imitating.

The African countries used low-cost catch-up strategies to

build their initial education and technology systems after

independence. They had to build working systems within a

short time and with very limited resources. Catch-up literature

is vast and two Kenyan catch-up cases, one in education and

other mobile money transfer, are used. Analogy-inspired

approaches provide a way of systematically learning and

leveraging similar past experiences to solve new problems.

The most widely used analogy-based learning approach is

design by analogy, also called analogy-inspired design.

Design by analogy can enable learning from past success, the

experiences of others and from biology through innovation

and research [35]. The African AI strategy benchmark

provides a means to learn from suitable strategies of other

countries.

Kenya faced the challenge of introducing computing

degree programs because of the lack of skilled trainers,

practitioners and computing infrastructure. Without them, the

mobile telecommunication and mobile money transfer

revolution would not have succeeded. Kenya started a

postgraduate computing degree by introducing a one-year

postgraduate diploma in AI. One-year postgraduate diploma

courses are more affordable and produce graduates more

quickly than master’s and PhD programs. This enables those

without a computing background to be able to join the field.

The initial idea was to start with a postgraduate diploma to

create necessary foundations for starting master’s programs.

However, today many African countries have few AI master’s

degrees and face a lack of critical mass of graduates to carry

out the AI revolution.

Kenya used frugal improvisation to transform personnel

from science, technology, engineering and mathematics

disciplines, who had studied in computing areas in Master’s

and PhDs degrees, into undergraduate computing lecturers.

The first postgraduate degree was introduced at the University

of Nairobi, providing the foundations for introducing

computing master’s degrees. Many students from different

non-computing disciplines were able to use this degree to

transition into computing fields and become computing

practitioners. Some went into pursuing master’s and PhD

degrees abroad, and later returned to help create the critical

mass to start computing master’s and PhD degree programs.

While this was not an ideal solution, it was low-cost and

affordable, making graduate computing programs feasible.

Sometimes, affordability is the most critical success factor for

catching up.

A country can analyze whether it can manage with an

average upgradable solution or wait for a better solution that

it is not guaranteed to create. Another example of education

is Jean School at Kabete, which at independence, used

frugality to improvise teaching aids and necessary equipment

[36]. The institution has grown to become one of the major

technical colleges in Kenya. Frugal innovation and

improvisation were possibly the most successful strategies

that triggered the establishment of technology foundations of

African countries at independence. Frugal innovation,

combined with design thinking, can support DCs innovation

and entrepreneurship [36] needed to establish AI industries.

Research on frugal AI innovation could help both in

introducing AI in the informal technology sector, and in

automating other ignored areas.

Africa’s leapfrogging in mobile communications, money

transfer, solar energy and lessons from

Chinese education and

technology are applicable to the design of DCs leapfrogging

strategies [37]. Technology leapfrogging refers to skipping

low-grade and costly technologies and industries in favor of

more effective and advanced technologies [38]. Leapfrogging

experience creates mental models and builds capabilities

within a country. Nations can build on these mental models

and capabilities from previous technology leapfrogging.

Kenya’s MPESA mobile money transfer leapfrogging

leveraged existing mental models, such as mobile vehicle

banking and the practice of employees working in towns

sending money by public transport vehicles to their rural

relatives [39]. The most important resource for technological

catch-up, leapfrogging and job creation for Africa’s large

unemployed youth in Africa is human capital [37]. Some

evidence of this is all African countries have slogans like “the

youth is the greatest asset these countries have”.

AI is a major driver of Industry 4.0. All national

technology strategies should be aligned since they work

towards the same STI strategic goals. The commonly studied

strategy alignment in computing is between business and IT

strategies. Aligning business and IT strategies is an effective

way to create efficiency and achieve targets for any business

[40]. Organizations that align their strategies benefit from

related organization elements working as a system, which

creates synergy. African NAISs can follow either bottom-up

or top-down strategies [41]. Strategy creators can decide

which bottom-up, top-down, middle-out or a combination of

strategies are appropriate.

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For DCs, incremental innovation is better than radical

innovation as it is cheaper, less risky and enables firms to

innovate by imitating solutions from other parts of the world

[42]. They creatively imitate and retranslate existing solutions

from other industries such as technologies, patents, specific

knowledge, capabilities, general principles, business

processes and whole business models [43]. Chinese, Japanese

and South Korean SMEs have used imitation by breaking

down and reassembling products or creatively imitating or

importing and improving upon the best knowledge,

experiences and solutions [44]. In Japan, the culture of

imitation has existed for five centuries, where individual

entrepreneurs invented a product, then other entrepreneurs

imitated inventors and firms commercialized invented

solutions [44]. Imitation that learns by analogy is often

cheaper than invention. A pattern of expertise levels derived

from Japanese martial artists, consisting of learning, detaching

and transcending was used as the basis for creating agile

method developers’ capability-building model [45]. The

Agile methods have since been adapted for AI technology

development. The first example is learning by analogy from

an indigenous system on how to create a modern technology

innovation. The second example is how artificial intelligence

learned how to apply a software engineering method.

The mindset that imitation is embarrassing is misleading.

Business leaders should see imitation not as an inhibitor but

as an enabler of innovation, but risks are not to be ignored

[46]. Imitation is often viewed by business, society and

academia negatively as something to avoid. Imitation that is

unethical or that violates intellectual property is negative.

Blind imitation copies non-beneficial elements while smart

imitation copies only useful elements and invents others.

Several countries, such as China, Japan and South Korea as

well as many firms have been able to catch-up based on

imitation. One-way startups create imitation competitive

advantage through learning by analogy from technology

leaders’ new product development: cheaply and with fewer

resources, to create their own innovations [47]. Startups can

also learn by imitating processes, start-up creation and growth

approaches from developed countries. For example, Tanzania

can learn from European and Asian SMEs along with start-ups

experiences on how to combine imitation and innovation to

overcome limited research and development capacity; and

infrastructure constraints to catch-up in science, technology

and innovation [48]. AI Invention strategy is largely preferred

over Imitation strategy in Africa. Tencent used an imitation

strategy to develop its first product by copying good elements

from an American company and substituting not-so-good

elements with innovative others to grow [49]. Literature

provides many examples of individuals, groups, firms,

communities and countries catching up through learning by

imitation.

IV. CONCLUSION

Some of the core concepts for African countries in creating

NAIS strategies include capability building, learning from

past technology building successes like mobile money

transfer; and using follower and catch-up strategies. These

concepts are necessary to create strategies with sound

theoretical foundation that could work.

African countries can learn from their past successes to

upgrade AI education, research and development, and to set

up necessary infrastructure. The technology leapfrogging

strategy was the force that drove mobile money revolution.

These are exemplary technology leapfrogging cases.

NAIS can help align national thinking and efforts in a

common direction, providing a more effective and efficient

way of building national AI capabilities. Failing to plan is

planning to fail. A strategy is a form of planning and failure to

create an AI catch-up strategy is, in effect, like creating an AI

strategy to fail. Making large investments in AI without

establishing prerequisite foundations is likely to lead to

failure. Knowledge and research should be the basis of such

investments.

In the past, AI made many great promises, some were

realized while others were empty promises. Today, AI still

presents as many threats as opportunities. To deal with these,

most of AI technology leaders invest short-term in less risky

incremental innovations and long-term in highly risky radical

innovations. Followers invest in incremental innovations or in

building capabilities to build commercial applications and

continuously upgrade applications and capabilities.

There is little research on African information and

communication technology catch-up, although it is an

important source of learning. Literature reviews can provide

foundations for future catch-up research and provide a quick

reference for those interested in catch-up research like policy

makers.

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10.5281/zenodo.15742005

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ISSN:1390-9266 e-ISSN:1390-9134 LAJC 2025

LATIN-AMERICAN JOURNAL OF COMPUTING (LAJC), Vol XII, Issue 2, July 2025

AUTHORS

Prof. Patrick Kanyi Wamuyu is an Associate Professor of Information

Technology at United States International University-Africa, Nairobi,

Kenya. Dr. Wamuyu earned his Ph.D. degree in Information Systems

and Technology from the University of KwaZulu-Natal, Durban,

South Africa. He completed his postdoc research at the Freie

Universität, Berlin, Germany and the Indian Institute of Information

Technology, Allahabad, India. His research focuses on a broad range

of topics related to Information and Communication Technologies

for Development (ICT4D), Social Media Use and Consumption,

E-business Infrastructures, ICT Innovations and Entrepreneurship,

Wireless Sensor Networks and Databases. His academic publications

include books, book chapters, peer reviewed journal articles, and

refereed conference proceedings. He has over twenty-seven years

of experience in the computing and information technology industry

that have taken him from software development, running his own

Information Technology Enterprise to the academic world. He has

advised many graduate (Masters and Ph.D.) and undergraduate

students. Currently serves as the Associate Dean, School of Graduate

Studies. When he is not in academia, Patrick enjoys hiking, traveling,

and volleyball.

Wangai Njoroge Mambo started his computing career in government

and industry, and then began teaching and research at Kenyan

institute of administration thereafter moved to universities. He worked

at Kabarak University, Kenyatta University and several other Kenyan

universities. He obtained master’s degree in computer applications

from Zhejiang University, Hangzhou, China and BSc (Chem and Math's)

degree from university of Nairobi, Kenya. Currently he is an adjunct

lecturer computing department, United States International University

Africa. His research interests are artiﬁcial intelligence, transdisciplinary

intelligent software engineering, trans-ﬁelds, software innovation

and indigenous knowledge. His work explores intersection between

these ﬁelds and indigenous knowledge His research has appeared

in multiple peer reviewed journals including artiﬁcial intelligence,

computers science and robotics, transdisciplinary engineering and

science, African journal of innovation and entrepreneurship journal

among others.

Patrick Kanyi Wamuyu

Wangai Njoroge Mambo

P. Wamuyu, and W. Mambo,

“African National Artiﬁcial Intelligence Strategies: A review, analysis and research agenda”,

Latin-American Journal of Computing (LAJC), vol. 12, no. 2, 2025.