Artificial Intelligence and Start-Ups in Low- and Middle-Income Countries: Progress, Promises and Perils - GSMA
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Artificial Intelligence and Start-Ups in Low- and Middle-Income Countries: Progress, Promises and Perils Copyright © 2020 GSM Association
GSMA Mobile for Development
The GSMA represents the interests of mobile operators For more information, visit our website at
worldwide, uniting more than 750 operators and nearly www.gsma.com/mobilefordevelopment
400 companies in the broader mobile ecosystem, including
handset and device makers, software companies, equipment Twitter: GSMAm4d
providers and internet companies, as well as organisations
in adjacent industry sectors. The GSMA also produces the
industry-leading MWC events held annually in Barcelona, Authors:
Los Angeles and Shanghai, as well as the Mobile 360 Series Akanksha Sharma, GSMA
of regional conferences. Sam Ajadi, GSMA
Andreas Beavor, UrbanEmerge
For more information, please visit the GSMA corporate
website at www.gsma.com Contributors
Follow the GSMA on Twitter: @GSMA Susanna Acland, GSMA
Bian Li, The Hungry Lab
Ben Hawes, Independent Consultant
Alla Kos, Responsible AI
Acknowledgements:
We would like to thank all our respondents, external
UrbanEmerge is a sustainable and inclusive development consultants, start-ups and experts who were interviewed
consultancy, with a focus on emerging markets. As a core and consulted for this report.
group of partners with an extensive network of affiliated
consultants, UrbanEmerge advises public and private sector
clients in areas related to inclusive and resilient economic
growth, sustainable cities, circular economy and responsible
investment. These areas are supported by our cross-cutting
expertise in enabling technologies, including digital assistive
technologies and smart city solutions.
For more information, please visit the UrbanEmerge website
at www.urbanemerge.com
This material has been funded by UK aid from the UK
government; however, the views expressed do not
necessarily reflect the UK Government’s official policies.
Contents
Executive summary 2
Research objectives and scope 4
Overview of AI and its capabilities 6
Mapping AI innovations in LMICs 12
12
Use cases by sector 14
Use cases by SDG 15
Use cases by AI capability 16
Funding for AI use cases 20
Examples of AI use cases across the SDGs 20
Understanding AI business models in LMICs 24
Ethical use of AI in LMICs 33
Barriers and challenges to implementing AI in LMICs 34
Key takeaways 36
Executive summary
Executive
summary
Around the world, artificial intelligence (AI) is automating functions and
making new services possible with breakthroughs in cheap computing power,
cloud computing services, growth in big data and advancements in machine
learning (ML) and related processes. AI can radically alter and improve the way
governments, organisations and individuals provide services, access information
and improve their planning and operations.
This study examines the current use of AI in low- and The report concludes that while AI has the potential
middle-income countries (LMICs) across Sub-Saharan to achieve social good, positive outcomes are not
Africa, North Africa and South and Southeast Asia. guaranteed. There are many fundamental questions
The report maps a sample of 450 start-ups by sector about data protection, ingrained bias as a result of
in alignment with the UN Sustainable Development poor data collection methods, social inclusion and the
Goals (SDGs) and, based on interviews with AI responsible use of AI. AI enables new technologies to
experts in LMICs, we explore trends and challenges in improve efficiency and productivity, but it may also
business models, barriers to innovation and the deepen inequalities, hindering the achievement of the
ethical and responsible use of AI. UN SDGs. Since increased use of data introduces
further privacy and ethical concerns, AI solutions
Business intelligence and analytics emerged as a clear should be guided by sound privacy and ethical
leader in the use of AI, as it captures a wide range principles.
of business services solutions, from accounting and
decision making to customer service. Healthcare
is also a leader and benefits from AI solutions in
many ways, from sophisticated diagnosis and
treatment options to hospital management systems,
personalised lifestyle change recommendations and
healthy eating habits. These sectors were followed by
food and agriculture, financial services, education and
retail and consumer goods.
2
Executive summary
3
Research objectives and scope
Research
objectives
and scope
This study aims to understand the current and potential use of AI by start-
ups and small and medium enterprises (SMEs) in low- and middle-income
countries (LMICs) in four regions: Sub-Saharan Africa, North Africa and South
and Southeast Asia. We analyse the business models of companies using
predominantly home-grown AI solutions and examine which sectors and
countries are receiving the most funding.
The study answers the following research questions:
• What is the status of AI use in LMICs? • What are some of the barriers to implementing
AI solutions in LMICs?
• Which sectors, geographies and business models
are showing the most promise, and why? • How can AI be used ethically to accelerate the
achievement of the SDGs?
Summary of methodology
Key informant Mapping use cases
Literature review Case studies
interviews in Africa and Asia
A range of sources were 23 expert interviews The study includes A range of case studies
consulted to provide an were conducted with a sample of 450 AI start- were selected to provide
up-to-date summary of AI experts and up use cases in LMICs in a more detailed picture
the state of AI in LMICs, practitioners, including African and South and of the business models
as well as opportunities entrepreneurs, donor Southeast Asian being used by innovators,
and risks for sustainable organisations, project countries, most of which and the barriers they
development. beneficiaries and are early-stage, home- faced in implementing
technology users. grown solutions with a AI-based solutions in
focus on the SDGs. LMICs.
4
Research objectives and scope
The Central Insights Unit mapped AI use cases to The use cases were selected based on the extent
create a representative sample. The 450 use cases in to which they enabled progress on sustainable
the sample should not be considered an exhaustive list development in line with the SDGs. To explore home-
of AI applications currently active in the target grown innovation and local entrepreneurship, we
countries. We are aware of over 3,000 use cases in focused on how start-ups and SMEs were using AI,
these countries, but the focus was narrowed down to rather than more established companies and mobile
450 use cases due to time and resource constraints. network operators (MNOs).
5
Overview of AI and its capabilities
Overview of
AI and its
capabilities
AI is a rapidly growing field, with a vast range of processes and applications. Some
of the underlying technology was first developed in the 1950s, but there has been a
step-change in the range and effectiveness of AI in the last decade. AI applications
have built on and enhanced many digital functions and can be developed with
digital data that may have initially been gathered for other purposes.
AI is the ability of a machine or computer to emulate sensory understanding and interaction; reasoning and
human tasks through learning and automation.1 The planning; optimisation of procedures and parameters;
UK Engineering and Physical Science Research Council autonomy; creativity; and extracting knowledge and
explains it as: “Artificial Intelligence technologies aim predictions from large, diverse digital data.”2
to reproduce or surpass abilities (in computational
systems) that would require ‘intelligence’ if humans were Solutions harnessing AI show promise in a variety of
to perform them. These include learning and adaptation; areas, as depicted in Figure 1.
Figure 1
Top AI use cases in LMICs
Administration and Government and
Climate change Education Agriculture
business processes public services
Finance and Disaster Cities and
Healthcare Identity
microlending management infrastructure
Source: GSMA
1 GSMA (9 September 2019), Mobile Big Data Analytics and AI for a Better Future.
Future
2 EPSRC (no date), Framework for Responsible Innovation.
Innovation
6
Overview of AI and its capabilities
Machine learning
Machine learning (ML) is a process that enables a system data sets are not easy to obtain, and sometimes
to automatically and cumulatively learn and improve researchers ask the algorithm questions they do
from experience, generally with more data. It is an not have answers to. In this instance, unsupervised
important foundational concept of AI that enables learning is applied. A neural network is required to
applications such as deep learning, natural language process a data set without explicit instructions for
processing (NLP), speech generation, computer vision, a specific outcome or correct answer. The neural
AI-optimised hardware, decision management (including network attempts to automatically find patterns in
categorisation and predictive analytics), biometrics, the data by extracting useful features and analysing
robotic process automation (RPA), virtual agents and structures. Data may be organised by clustering,
more. There are three classes of ML algorithms: anomaly detection, association or auto encoding.
For example, an unsupervised credit algorithm
• Supervised algorithms learn from data sets that might identify clusters of similar borrowers, but
have been labelled (e.g. pictures of animals) and would not make individual predictions on their
then predict how to sort new data. Supervised ML ability to repay the lender.
requires those building models to specify correct
• Semi-supervised algorithms typically use
answers from the training data, which the algorithm
some labelled data and more unlabelled data.
then learns to imitate. This is called a labelled data
Reinforcement ML algorithms are optimised with
set. For example, a credit-scoring algorithm might
rewards, which nudge them towards progressively
analyse the repayment history of past borrowers to
better performance. There are other approaches
determine which future borrowers are likely to
beyond this binary definition, such as reinforcement
default, guided by data that indicates likely
learning in which an objective is achieved through
outcomes.3
trial and error. Semi-supervised learning is also
• Unsupervised algorithms find their own patterns in possible.
data without labels. Clean and well-labelled
Types of AI capability
For this study, we have identified nine AI applications applications are complementary, and one service may
of machine learning (see Figure 2). Many of these use several of these techniques in combination.
Figure 2
Types of AI processes
Artificial intelligence
Machine learning
Supervised algorithms
Decision management
Virtual agents Deep learning
Robotic process automation Unsupervised algorithms
Biometrics Semi-supervised algorithms
Computer vision
Speech generation
AI-optimised hardware
Natural language processing
Source: GSMA and UrbanEmerge
3 USAID (2018), Reflecting the Past, Shaping the Future: Making AI Work for International Development.
Development
7
Overview of AI and its capabilities
Computer vision AI-optimised hardware
Computer vision focuses on replicating parts of the AI-optimised hardware refers to a class of
complex human vision system and enabling microprocessors or microchips designed to
computers to identify and process objects in images enable faster processing of AI applications. This
and videos in the same way humans do. type of hardware is designed or adapted for AI
workloads. One of the most common AI hardware
Computer vision is used in medical diagnostics,
architecture is graphic processing units (GPUs),
face recognition, automated vehicles and a
originally designed to manage complex gaming
wide range of monitoring systems, from satellite
visuals. GPUs have been particularly important in
monitoring of crops, livestock and environmental
accelerating training and inference as they can be
conditions to CCTV.
optimised for neural network operations.
Natural language processing
(NLP) Deep learning platforms
NLP enables computers to read a text, hear
Deep learning platforms are a subset of machine
and interpret speech, gauge sentiment and
learning developed to deliver solutions to
prioritise and connect to appropriate subjects and
complex problems. The ‘deep’ aspect refers to
resources.
the structure of the system, which has multiple
The most familiar application is an automated call layers of ML processing called neural networks.
centre that sorts calls by category and directs the There is an input layer, multiple ‘hidden’ layers and
caller to recorded responses. NLP has become an output layer. The greater interconnection and
more widespread with voice assistants like Siri sophistication of deep learning systems compared
and Alexa. NLP uses machine learning to improve to simpler ML systems means that deep learning
these voice assistants and deliver personalisation is particularly good at dealing with unlabelled and
at scale. unstructured data, such as data coming in from
multiple real-world sources like sensor systems or
Decision management internet traffic.
(including predictive analytics) Deep learning enables applications in complex
environments, including autonomous movement,
Decision management refers to automated
translation of spoken language, price forecasting
systems that accept inputs, perform analysis
and medical diagnosis from images.
and deliver decisions based on those inputs.
These systems provide decision making without
human intervention, reducing the need for Speech generation
focused human labour. It can be particularly (or synthesis)
effective with detailed but repetitive decisions,
Speech generation is computer-generated human
for example, in financial services.
speech that combines recorded elements of speech
to deliver language. Speech generation or synthesis
Decision management systems can convert big
is used in assistive technologies for people with a
data (e.g. on customer behaviour) to predict
range of disabilities. Text-to-speech helps people
trends. Combined with machine learning,
with visual impairments and those with literacy and
decision management can help make business
reading difficulties to read text. It can give people
decisions more effective.
with speech disabilities a new voice. It is also used
in automated translation.
8Overview of AI and its capabilities
Biometrics RPA enables organisations to reduce labour costs
and errors and can be lower cost and easier to
Biometrics are the physical characteristics and implement than other AI applications. RPA can
behaviours of people used for analysis by AI. also be enhanced with higher level AI applications
The premise of biometrics is that individuals can like NLP. Large-scale application of RPA carries
be accurately identified by intrinsic physical or social risks because it tends to directly replace
behavioural traits, which are usually used for functions (and even jobs) performed by people.
identification, assurance and controlling access. An example is the production of suitable business
forms or guidelines based on the needs of the
Biometrics can support large-scale systems for
user and the likely outcomes that someone of their
identity assurance, which can be of great value
profile would require.
in environments that lack other reliable identity
systems. However, the personal nature of the data
requires that effective data protection is in place. Virtual agents/chatbots
Ethical issues can also arise if a system performs
unevenly across groups, for example, it does not
A virtual agent is a dedicated software system
perform equally for all skin tones. Biometrics can
that interacts with people and can be used
also be used to assist and monitor livestock and
in a team, service or information interface.
wildlife.
The most familiar form of virtual agents is an
automated representation of a customer service
Robotic process automation (RPA) representative. Virtual agents identify appropriate
responses and provide them in an informative
conversation.
RPA is a means of automating a business process.
RPA tools are used to develop software, or a Chatbots tend to be oriented to a tightly defined
‘robot’, that analyses and captures a process set of processes for a business or public sector
and then uses that process to deliver the same organisation. While virtual assistants like Siri and
transaction. RPA can be used to deliver a Alexa are oriented to individual users, and
simple response to a particular type of email or personalisation of individual needs and interests
application process and can also be combined into improves over time, virtual agents help deliver
complex systems. personalisation at scale, greatly reducing costs for
healthcare and financial advice.
9Overview of AI and its capabilities
COVID-19 and the changing dynamics of AI
Over the course of our research in the second quarter of 2020, the world
experienced unprecedented disruption due to the COVID-19 pandemic. As more
traditional ways of working have ground to a halt, the digital economy has become
vital to economic resilience and the ability to continue providing goods and services.
In light of the new normal, there is an urgent need to The need to deploy distance learning systems due
rethink data and predictions. Machine learning models to COVID-19 is becoming more urgent and there are
trained on pre-COVID-19 events and movements many ways to create more responsive and bespoke
of people and goods will no longer be accurate. services using ML processes and virtual agents. In
However, the disruption from COVID-19 presents Africa, global research partnership EdTech Hub has
governments, companies and other organisations in played an active role in supporting EdTech innovation
LMICs with opportunities. It is clear that the digital and research. COVID-19 has also highlighted the
economy is being accelerated, thanks to a shift to importance of a secure local food supply and
online communication and online provision of services contingency planning. A shift towards more digital
to reduce physical contact and international travel. It supply chains that connect producers to vendors and
is not yet clear if these shifts are positive or negative, markets can improve food security and the reliability
including the greater use of AI and associated data of food distribution. Twiga Foods in Kenya has been a
privacy risks. It is also not clear whether increased leader in this area.
uptake of technology and innovation will be sustained
post-COVID-19. The COVID-19 crisis will likely stimulate faster
changes in AI technology than previously envisaged.
In many cases, AI has had a positive impact. For Governments in LMICs will need to work hard to
example, the Government of Senegal automated part ensure innovators have the best chance to participate
of its COVID-19 response to improve public access in this rapid shift to the fourth industrial revolution.
to healthcare information. The Ministry of Health Technology companies and start-ups can take
partnered with a local lab to create a chatbot that can advantage of the increased attention on the digital
respond to people’s questions on WhatsApp. economy and the potential of AI.
10Overview of AI and its capabilities
11Mapping AI innovations in LMICs
Mapping AI
innovations
in LMICs
As part of this study, our team identified a sample of 450 AI use cases by start-
ups and SMEs in our countries of interest. While over 3,000 separate entities
were identified, we focused on those with a clear impact on sustainable
development.
We then mapped the use cases against the Mobile network operators (MNOs) have not been
following factors: included in the sample due to the focus on start-ups
and SMEs rather than larger companies. However,
• Industry sectors in which AI is being used;
MNOs such as Airtel, Vodafone, MTN and others, are
• Contributions to the SDGs; leaders in the use of AI in many LMICs to enhance
business services and provide a more inclusive service
• AI capabilities being used; and
for more customers.
• Whether a company had received funding and,
where possible, the amount received.
Use cases by country and region
The use cases revealed some interesting findings in AI innovation hotspots in LMICs in Sub-Saharan Africa,
North Africa and South and Southeast Asia:
• India was the most represented country in our • Nigeria and South Africa were the next two most
sample. The country accounted for over 40 per represented nations in the sample with 42 and 38
cent of the sample (180 use cases), indicating a use cases, respectively.
high level of innovation and AI uptake in the
• China was excluded from the study, along with
country. Far more cases were identified in India,
the rest of East Asia, which are emerging as
but were excluded due to limited alignment with
centres of AI innovation and investment. For
development outcomes, apart from general
example, in 2017, China submitted approximately
economic development.
1,300 AI and deep learning-related patents,
compared to 220 by the United States.
12Mapping AI innovations in LMICs
Figure 3
Geographic distribution of AI use cases in our start-up sample
Over 50 11 to 50 5 to 10 1 to 4 AI solutions not included in sample Not researched
Source: GSMA and UrbanEmerge
���
Figure 4
AI use cases by region
Countries with the most
2% AI use cases in our sample
India
6% Nigeria
South Africa
12%
Egypt
49%
48% Kenya
Indonesia
Pakistan
Thailand
33%
32%
Malaysia
Tunisia
Ghana
Uganda
South Asia Sub-Saharan Africa Southeast Asia
North Africa Vietnam
Note: Based on a sample of 450 start-up use cases selected to represent the geographic spread of AI innovations aligned with the SDGs in Africa and
South and Southeast Asia
Source: GSMA and UrbanEmerge
13Mapping AI innovations in LMICs
Use cases by sector verticals
• Business intelligence and analytics had the options,hospital management systems, lifestyle
highest number of use cases as it captures a wide change recommendations and healthy eating
range of business-to-business (B2B) solutions, from habits.
enhanced retail market analysis and predictive
• Food and agriculture, financial services,
decision making to customer service. Customer
education and retail and consumer goods
service chatbots, automated IT consulting, big data
followed these sectors. Food and agriculture
analytics and automated records are some
employs a range of AI-based services, including
examples of AI use cases.
services for identifying and remedying crop
• Healthcare had the second highest number of use diseases, linking producers more effectively to
cases and clearly benefits from AI solutions, buyers and markets and helping farmers maximise
including sophisticated diagnosis and treatment crop yields based on climatic and soil conditions.
Figure 5
Distribution of AI use cases by sector in Africa and South and Southeast Asia
142
74
50
37
22 24 10 10 10 9 7 5 5
22 18
�
Environmental
Business
intelligence
and analytics
Healthcare
Food and
agriculture
Financial
services
Retail and
consumer goods
Education
Workforce
and HR
Transportation
and logistics
Humanitarian
Advertising
and media
Smart cities and
government
services
Personal
security
Clean
energy
Industrial and
manufacturing
Note: Based on a sample of 450 start-up use cases selected to represent the geographic spread of AI innovations aligned with the SDGs in Africa and
South and Southeast Asia.
Source: GSMA and UrbanEmerge
Table 1
Sector verticals using AI for social good, by region
Sector North Africa Sub-Saharan Africa South Asia Southeast Asia
Business intelligence and analytics 10 46 66 20
Healthcare 5 19 47 3
Food and agriculture 2 19 22 7
Financial services 1 19 8 9
Retail and consumer goods 5 12 7 2
Education 1 3 20 1
Workforce and human resources 1 5 10 6
Transportation and logistics 2 3 12 1
Advertising and media 1 2 6 1
Environmental 1 5 2 2
Humanitarian 3 5 2
Smart cities and government services 2 6 1
Personal security 4 3
Clean energy 1 4
Industrial and manufacturing 1 3 1
Note: Based on a sample of 450 start-up use cases selected to represent the geographic spread of AI innovations aligned with the SDGs in Africa and
South and Southeast Asia.
Source: GSMA and UrbanEmerge
14Mapping AI innovations in LMICs
When we break down our analysis by region, as healthcare and retail and consumer goods. In South
shown in Table 1, some interesting trends emerge. Asia, healthcare and food and agriculture are the
Business intelligence and analytics is the dominant dominant sectors followed closely by education. In
category across all four regions. The top two Southeast Asia, the top two categories after business
categories in Sub-Saharan Africa are healthcare and intelligence and analytics are financial services and
food and agriculture, while in North Africa they are food and agriculture.
Use cases by SDG
To capture all the areas covered by the 17 SDGs in broad categories, we combined several of them together.
The use of AI is making a significant contribution to several of the SDGs.
Figure 6
Total number of use cases in the sample aligned with the SDGs
Safe and inclusive industrialisation,
SDGs 8, 9 217 fostering innovation, meaningful
employment
SDG 3 90 Access to healthcare
Sustainable agriculture, food security and
livelihoods, poverty alleviation and the
SDGs 1, 2, 13 82 humanitarian setting, all in the context of
climate change
SDG 4 45 Education for all
Sustainable cities and consumption
SDGs 11, 12 43 and production patterns
Access to justice, peaceful and inclusive
SDGs 16, 17 38 societies and enabling partnership
and collaboration
Climate change and the conservation or
SDGs 13, 14, 15 38 responsible use of marine and terrestrial
resources and ecosystems
SDGs 5, 10 20 Gender equality and social inclusion
Provision of safe water and sanitation
SDGs 6, 7 16 and clean and accessible energy
Note: Based on a sample of 450 start-up use cases selected to represent the geographic spread of AI innovations aligned with the SDGs in Africa and
South and Southeast Asia.
Source: GSMA and UrbanEmerge
In Sub-Saharan Africa, sustainable economic growth reduction and climate-smart agriculture. For example,
and employment (SDG 8 and SDG 9) were the top AI solutions can help farmers identify crop diseases,
category, covering a wide range of financial services, understand weather patterns and predict ideal times
as well as general business services that improve to sow and harvest crops to maximise yields and
business intelligence, operations and efficiency. reduce waste, for example, CropIn in India.
There is also a strong focus on food and agriculture
(SDG 1 and SDG 2), particularly in relation to poverty
15Mapping AI innovations in LMICs
The third most prevalent industry that aligned with the (SDG 4). There are also many use cases related to SDG
application of AI is healthcare (SDG 3). There are few 11 and SDG 12, which cover smart-city solutions, smart
educational innovations that rely on AI in Sub-Saharan transportation and road safety and the circular economy.
Africa, although this may be due to the lack of high-
quality and digitised data, combined with challenging In Southeast Asia, beyond sustainable economic
internet infrastructure. growth and employment (SDG 8 and SDG 9), there
is significant alignment with poverty reduction and
In North Africa, apart from sustainable economic food security (SDG 1 and SDG 2), followed by a
growth and employment (SDG 8 and SDG 9), handful of use cases that contribute to sustainable
healthcare (SDG 3) is a clear leader. This is followed cities, environmental protection and enabling justice,
by a handful of examples that align with food security, collaboration and partnership. The Gringgo Indonesia
education, gender and inclusion and sustainable cities. Foundation, which has developed an image recognition
tool to improve plastic recycling rates, reduce ocean
Beyond business-related services, which are captured plastic pollution and improve waste management in
under SDG 8 and SDG 9, South Asia has a particularly under-resourced communities, is a good example of an
strong focus on healthcare (SDG 3) and education environmental services application of AI.
Use cases by AI capability
Segmenting use cases by AI capabilities is challenging how they fit together, including within various sub-
given that there are significant variations in the categories. Figure 7 shows the categories with the
definitions and descriptions of AI processes and greatest overall distribution of AI capabilities.
Figure 7
AI capabilities within the sample of use capabilities
278
215
106
64 63
14
7 2
18
Deep learning Decision Computer Virtual Natural AI-optimised Robotic Speech Biometrics
platforms management vision agents language hardware process generation
processing automation
Note: Based on a sample of 450 start-up use cases selected to represent the geographic spread of AI innovations aligned with the SDGs in Africa and
South and Southeast Asia.
Source: GSMA and UrbanEmerge
16Mapping AI innovations in LMICs
Deep learning platforms are the most commonly used, speech generation. There were very few examples
followed by AI decision management processes. Both of biometrics in the sample, but a notable one is
are used by start-ups seeking to gain insights from big M-PESA’s secure personal identification process
data and gain a greater edge in meeting the needs of implemented by Vodafone. This allows persons with
customers and responding to customer feedback disabilities to use their voice as a biometric password,
more effectively. facilitating greater access to financial services and
more autonomy in how they use the service.
Computer vision is the third most used capability.
Linked to deep learning, this is often involved in image It should also be noted that several use cases employ
recognition processes in diagnostics, such as in more than one AI process. Figure 8 shows that over
healthcare or agriculture. It is also important for tools half the use cases employed two or three AI processes
that use a mobile phone to identify products or read and a small minority used four or five. These are often
text for persons with visual impairment. Virtual agents used in combination, for example, computer vision
are also prevalent, such as chatbots that are used for a may be used for image processing combined with
wide range of applications, from customer service to a deep learning platform to diagnose specific health
advice on health-related issues. Examples include the issues. An example of this application is Skinzy in India
Foreign, Commonwealth and Development Office’s a healthcare start-up that aims to provide affordable
(FCDO) Frontier Technology Livestreaming (FTL) pilot skincare solutions to people in rural and peri-urban
for reproductive healthcare and Springster by the India, where specialised dermatologists and doctors
organisation Girl Effect. are scarce. AI-based machine learning and image
processing algorithms help users detect skin diseases
Natural language processing is another important and connect them to a dermatologist to receive care
AI use case in the sample. Only a handful of examples quickly.
used AI-optimised hardware, RPA and
Figure 8
Number of AI processes used per solution (nominal value)
214
180
48
6 2
One Two Three Four Five
Source: GSMA and UrbanEmerge
Our mapping analysis also provided insights into and virtual agents, are also used consistently across
the use of AI by sector, as shown in Figure 9. Both other industry verticals, suggesting a high degree of
healthcare and food and agriculture make extensive application in a range of sectors.
use of computer vision, deep learning and decision
management. Virtual agents are used in the greatest Smart cities and government services use AI-
proportion for business intelligence and analytics, optimised hardware the most, particularly for
healthcare and financial services, to provide an Internet of Things (IoT) and interconnected devices,
interface between users and the solution. However, data and hardware.
many AI processes, such as NLP, computer vision
17Mapping AI innovations in LMICs
Figure 9
Use of AI by industry vertical
Business intelligence and analytics
Healthcare
Food and agriculture
Financial services
Retail and consumer goods
Education
Workforce and human resources
Transportation and logistics
Environmental
Humanitarian
Advertising and media
Personal security
Smart cities and government services
Clean energy
Industrial and manufacturing
0 50 100 150 200 250
Virtual agents Natural language processing Computer vision Speech generation Deep learning platforms
AI-optimised hardware Decision management Biometrics Robotic process automation
Source: GSMA and UrbanEmerge
18Mapping AI innovations in LMICs
The use of AI capabilities by region generally aligns generation more than those in other regions. Use
with the geographic distribution of use cases, which of AI decision management tools also seems to be
suggests that no AI process is used to a greater disproportionately higher in Sub-Saharan Africa than
or lesser extent in a particular region (Figure 10). in South Asia.
However, start-ups in South Asia use RPA and speech
Figure 10
Use of AI by region
Deep learning platforms
Decision management
Computer vision
Virtual agents
Natural language processing
AI-optimised hardware
Robotic process automation
Speech generation
Biometrics
0 20 40 60 80 100 120 140 160
South Asia Sub-Saharan Africa Southeast Asia North Africa
Note: Based on a sample of 450 start-up use cases selected to represent the geographic spread of AI innovations aligned with the SDGs in Africa and
South and Southeast Asia.
Source: GSMA and UrbanEmerge
19Mapping AI innovations in LMICs
Funding for AI use cases
Mapping AI use cases also allowed us to analyse Our analysis of start-up funding from venture capital
funding to AI start-ups in Africa and South and partners, accelerators, foundations and international
Southeast Asia. In our indicative sample, most of donors revealed that India was the clear leader. Kenya,
the funding is directed at financial services, followed Malaysia, Thailand and South Africa were next in terms
closely by food and agriculture, healthcare and of funding volume, followed by Tunisia, Nigeria and
business intelligence and analytics. It is worth noting Indonesia. However, these results are only indicative as
that this is a high-level analysis based on limited, the availability of funding data is extremely limited.
publicly available data.
Many of the use cases have benefited from funding
Some notable start-ups that have received large from multiple partners. For example, CropIn in India
amounts of funding in the food and agriculture sector has received a grant from the Bill & Melinda Gates
are Twiga in Kenya ($30 million), CropIn in India ($13.5 Foundation and a mix of venture capital partners.
million) and Dahmakan in Malaysia ($27 million). In Dahmakan in Malaysia has received funding from Y
the financial services sector, some notable funding Combinator and a mix of venture capital firms.
recipients are OneFi in Nigeria ($6 million), SME Corner Technology companies like Google are also involved in
in India ($50 million) and Happy in India ($17 million). financing innovative start-ups in LMICs. For example,
Instadeep in Tunisia has received $7 million in funding
from Google Launchpad.
Examples of AI use cases across the SDGs
Apollo Agriculture, Kenya Mindspark, India
Apollo Agriculture4 helps farmers in emerging Mindspark,5 built by Educational Initiatives (EI),
markets improve yields and efficiency and is an adaptive learning software that improves
maximise profits. Its solution uses agronomic ML, educational outcomes. The solution progressively
remote sensing and mobile phones to deliver introduces more challenging concepts using
financing, farm products and customised advice text, video, games and interactive tutorials that
to smallholder farmers. Apollo uses ML algorithms students can access on multiple devices. At £35
on satellite data, soil data, farmer behaviour and ($45) for one student licence, the initiative is cost
crop yield models to deliver customised packages effective. Based on a robust evaluation of the
of seeds and fertiliser, as well as advice on how to effectiveness of the software, Mindspark is now
grow better crops. Apollo bundles many services being scaled across India. RISE is also researching
a farmer is likely to need: financing, farm inputs, how Mindspark could be integrated into schools
advice, insurance and market access. most effectively. A randomised study of 619
government school students in Delhi by MIT
found that students in grades one to eight made
significant progress in mathematics and Hindi
after using the Mindspark software.
4 Apollo Agriculture is funded by Shell Foundation and FCDO.
5 Mindspark is an EdTech Hub and FCDO-funded grantee.
20Mapping AI innovations in LMICs
Gringgo Indonesia Foundation,
Eyedentify, India
Indonesia
Gringgo is creating an image recognition tool to Eyedentify is an end-to-end automotive IoT
help informal waste collectors and independent solutions company that strives to alleviate the
waste management companies in Indonesia safety and security concerns of vehicle occupants,
increase recycling rates and better integrate drivers and fleet owners. The system alerts drivers
with city sanitation crews.6 By improving and to be attentive and drive safely. Eyedentify’s
expanding community trash collection, the start- solution has three main components: vehicle
up is addressing Indonesia’s plastic waste problem tracking (GPS), camera and edge video analytics
and reducing ocean plastic pollution. With 50,000 platform, which identifies and monitors the driver
kilometres of coastline and low public awareness of based on vehicle location and speed combined
waste management, much of Indonesia’s trash ends with edge-based video analytics. It incorporates
up in the ocean. To add to the complexity, waste all AI-related processes, including convolutional
workers often have irregular routes and schedules, neural networks (CNNs), GPU optimisations,
leaving many parts of the country unserved. feature point extractions and a trained 128-D
Workers also do not always have the knowledge and feature vector embedded for analysis.
expertise to accurately identify what can be recycled
and what recycled items are worth. Together, these
factors have had a devastating impact on recycling Twiga Foods, Kenya
rates and the livelihoods of waste workers.
Twiga Foods7 is a mobile-based supply platform
for Africa’s retail outlets, kiosks and market stalls. It's
Synapsica, India cashless business-to-business (B2B) supply platform
enables urban grocers to order produce from
Synapsica is a HealthTech and teleradiology smallholder farmers across Kenya and have it
firm that provides AI-enabled automation of delivered at competitive prices. This eliminates the
diagnostic radiology workflow and reporting. The inefficiencies of sourcing perishable foods daily while
company uses NLP, computer vision and deep also guaranteeing farmers consistent income and
learning software products to assist with diagnosis timely payments. Twiga has been sourcing from over
in x-ray, CT and MRI scans. ML automation 17,000 producers and delivering three times
supports workflow management at diagnostic a week on average to over 8,000 retailers.
centres, improving the efficiency of stakeholder
coordination, disease diagnosis and report Twiga’s digital platform and logistics network links
production. Synapsica’s goal is to make reporting retailers with farmers and food manufacturers,
more efficient in terms of quality, time and cost. providing a convenient and reliable alternative to the
current inefficient and expensive farm factory-to-
market processes. To optimise delivery for large
Digi Smart Solutions, Tunisia numbers of customers, Twiga maps its vendors using
a Geographic Information System (GIS) and its AI-
Digi Smart Solutions provides smart water enabled distribution platform to see who is ordering,
management solutions using IoT and AI, offering an where they are located, the road conditions and how
intelligent system for managing and controlling water to plan deliveries most efficiently.
consumption in real time. By collecting data from
multiple devices, sensors and systems, it can provide
actionable reports for management and decision-
making processes. By managing and saving water
resources, Digi Smart Solutions is helping to meet
future water demands, deal with urgent challenges
and forge a pathway to sustainability and resilience in
arid climates.
6 To reduce plastic waste in Indonesia, one startup turns to AI”,
Javerbaum, M. (10 July 2019), “To AI Google.org.
7 Twiga Foods is a GSMA and FCDO-funded grantee.
21Mapping AI innovations in LMICs
GSMA AI initiatives
Telco-X Collider Lab Global AI Challenge
Telco-X Collider Lab is part of the GSMA’s on-going The GSMA Global AI Challenge is a GSMA initiative
AI partnership with the University of California, open to members to solve challenging and complex
Berkeley and the Sutardja Center for Innovation and real-world business problems by accessing the best
Entrepreneurship. Launched in September 2020, AI talent and state-of-the-art machine learning
the lab is open to GSMA members from around the techniques.
world, who, as participant sponsors, will be matched
In partnership with The Alan Turing Institute and the
with UC Berkeley student teams. Bridging the gap
UK National Institute for Data Science and Artificial
between academia and industry, the lab offers an
Intelligence, the Global AI Challenge brings together
advanced project course that will create new
stakeholders from the mobile industry and data
applications for AI in telecom operations,
scientists from various backgrounds to explore new
new opportunities for different verticals and ML
approaches and expand current efforts in AI.
applications in the 5G era. The course builds on
the spring 2020 course, ‘Innovating 5G Mobile The Global AI Challenge is a week-long hackathon
Networks with AI’, and employs a mix of online and followed by a longer-term collaborative research
in-person formats, which are interactive, dynamic project. During the hackathon, dedicated teams of
and collaborative. PhDs and researchers work closely with
participating companies to explore what does and
GSMA members can leverage the course as an does not work and identify new ways of solving
open-ended project, and continual and close complex problems in the telecommunications
interaction with UC Berkeley will maximise its industry.
potential. The first half of the course is devoted to
The Global AI challenge is open to all organisations
generating a story and low-tech demo for a real-
across the mobile industry. More information about
world project, while the second half is an agile sprint
the Global AI Challenge, how to participate and
that culminates in the demonstration of working
previous projects, can be found here
re.
project code.
Telco-X Collider Lab commences in January 2021.
More information on the course and how to apply
can be found herere.
22Mapping AI innovations in LMICs
23Understanding AI business models in LMICs
Understanding
AI business
models in LMICs
Given that AI use is still nascent in LMICs, most businesses are still learning
how best to monetise their solutions and attract users. While attention has
been focused primarily on the effect of AI on consumers and businesses,
there has been a shift towards understanding how existing technology
business models can be transformed to account for the more complex
aspects of AI.
To fully grasp the opportunities, limitations and designing a business model is the Strategyzer Business
outlook of AI business models for a company’s growth, Model Canvas developed by Alexander Osterwalder.8
and the wider economic and societal implications of However, this study used a more concise assessment
AI, technology start-ups must conduct a substantive that looked at three factors: consumer segments,
analysis that evaluates critical business factors at monetisation and outlook. The following sections
the intersection of financials, technology and future present the findings, observations and insights
growth. One common approach to evaluating and gathered from our interviews and case studies.
Monetisation
Monetisation is a critical component of any business companies. To better understand the business
model. The results of the use case mapping, along models, we have divided AI companies into two
with existing industry analysis and interviews, show groups:
that the majority of AI companies globally remain AI-
• AI-enabled companies: AI is used to enhance their
enabled, although there are a growing number of AI-
offerings; and
first companies. This distinction determines the core
competency and unique value proposition of all AI • AI-first companies: AI is the core product.
8 The Business Model Canvas.
24Understanding AI business models in LMICs
AI-enabled companies
The revenue streams of most AI-enabled companies business to sell products and services or help
resemble traditional software companies, such as acquire customers.
subscriptions, commissions, revenue sharing and a
• Hardware fees: Companies may charge for the
project-based approach. These companies often strive
purchase of related hardware while software and
for multiple revenue streams to make the most of their
subsequent updates are free.
digital platform and to reduce the risk of one revenue
stream becoming unviable. Besides acquiring a diverse • Consulting fees: Companies can charge for
range of clients and customers, multiple revenue consulting services that may be supported by AI-
streams can be generated through a combination of powered tools.
the following approaches:
• Set-up and maintenance fees: Companies may be
able to provide a service that is essentially free
• Software-as-a-Service (SaaS): This is a delivery
apart from an initial set-up cost and on-going
and licensing model in which software is accessed
maintenance.
on the web via a subscription rather than installed
on local computers. SaaS typically incorporates a • Advertising revenue: Rather than charging a fee
freemium model9 with tiered levels of pricing based to users or customers, revenue is generated from
on the features and benefits offered. advertising.
• Transaction fees: These are fees charged as • Selling data: Companies selling data to third
a percentage of sales for processing a user parties is also a potential revenue stream. Many AI
transaction, such as a payment processing companies whose solutions and services are based
company. Fees can be incurred as a percentage of on processing customer data have data volume at
sales or a flat fee per sale. the centre of their monetisation strategy, rather
than just charging for a finished product or service.
• Commissions and referral fees: These are
However, privacy and ethical considerations are
usually commission and referral fees received by
important to maintain customer confidence.
companies when they partner with a third-party
AI-first companies
Whereas the quality of a AI-enabled company is AI-as-a-Service platform as its unique value
defined by the functionality of the end solution, the proposition because the solution is the service.
quality of AI-first companies is much more open to Accordingly, the value of the solutions and the pricing
subjective benchmarking as the solution or output is of the technology are implicitly connected.
the data interpretation itself, such as the accuracy of
speech recognition and language processing. While AI business models are still trial and error and
will continue to evolve, AI-first companies resemble
The threshold for quality can vary widely among more of a service than traditional software, especially
customers, who commonly, but unrealistically, expect with their back-end human support for maintenance
that the AI should be a panacea. Moreover, the data and custom inputs for clients.
interpretation and training also depends on the quality,
complexity and volume of data the client provides to Given that the AI-first sector is in early stages of
the company, which often require more customer development, there is still a lag in technology
education and persuasion and customised investors' understanding and expectations of the
on-boarding to improve the integrity and quality of business models. According to Forbes,10 “One of the
the inputs. As a result, the sales cycles of AI-first major drivers of the slow rate of adoption is the
companies are often slower and optimal outputs may disconnect between venture capital norms and the
be delayed given the initial learning curve. AI-first realities of building an AI-first company. Specifically, AI
companies often have an companies require
9 A freemium business model offers a basic service for free. Additional premium functions or services are available for a fee.
10 AI C Chha n g e tthhe W
Caan C Woor l d … I f O
Onnl y IInnv e s t o r s C
Caat c h U
Upp, Forbes.
25Understanding AI business models in LMICs
three to six times greater upfront investment as the predilections of most venture capital funds that
compared to traditional SaaS companies but in return typically look for a seven-year time horizon.”
have three to six times market opportunity. Therefore,
the time horizon to realise meaningful commercial Therefore, while displaying distinctive characteristics
traction is much longer; typically between five to that can be qualified as a new category of business,
six years and a minimum of $10 million investment combining a traditional SaaS company with a service
to progress from concept to working prototype business, it is still critical for AI-first companies to focus
and approximately 10 years to get to meaningful on building a sustainable, long-term foundation.
commercial traction. This timeline does not align with
Consumer segments
AI companies primarily serve businesses (B2B), process is different and requires more investment in
consumers (B2C) or both, depending on the product marketing and promotion.
and industry. Each customer segment has its own
advantages and disadvantages. For example, B2B For AI-enabled companies, B2C is more common than
businesses tend to have longer sales cycles and more in AI-first companies. Due to the higher upfront
relationship management but offer greater upfront investment in AI resources, AI-first companies are
revenue, which many AI-start-ups prefer. While the focused primarily on B2B customers, which provide
B2C sales cycle is shorter, the customer acquisition better opportunities for initial cash flow.
Figure 11
Customer segments of different AI business models
AI business models are relatively Greater upfront sales volume
aligned with standard business B2B Longer sales cycle
models in the IT/software industry. Company as customer
In terms of monetisation, the
majority of AI companies use some Shorter sales cycle
type of SaaS model with freemium B2C Requires large customer base
subscriptions and offsetting of End user as customer
additional products/services. For
many B2B2C companies,
Healthcare
monetisation commonly includes B2M
commissions and transaction fees Retirement
on products sold. Business to many:
Blend of B2B and B2C
Network effects
B2B2C Powerful moats; data
Many e-commerce and
aggregator platforms
Faster sales processes
B2C2B Influence-driven marketing
Bottom-up approach:
Company employees as end
user to drive mass adoption
across the company
Source: GSMA and UrbanEmerge
26Understanding AI business models in LMICs
Outlook
AI companies, particularly in LMICs, must anticipate opportunities and prepare for risks in a rapidly evolving
industry. Business models must withstand the impact of these risks and reach scale with new markets and
customers. Specifically, resilient AI business models must consider the two following factors:
A dynamic regulatory environment
The industry must address a variety of questions from AI guidelines were in place or created value rather
users and customers about how they use their data. than harm, differed significantly between countries.
Our research revealed that many companies in LMICs Countries with a robust start-up scene and active
are being asked questions such as: investor community, such as India, already have more
regulatory frameworks in place. In less-developed AI
• Who will own the IP on my data? markets, such as Myanmar, interviewees indicated
they could converse with the government on how such
• Will my data be sold without my authorisation?
regulations should take shape.
• How will my data be collected? What degree of
anonymity do you provide? Another consideration for AI companies is the
monetisation of data itself, given the magnitude of
• How is my data stored? What are your security
data needed for algorithmic computations. Many AI
measures to prevent and deal with hacking?
companies whose solutions and services are based
With respect to regulations, a number of AI companies on processing customer data have data volume at the
interviewed indicated that bureaucratic red tape, centre of their monetisation strategy. While many AI
varying levels of government readiness for AI-related companies sell data to third parties, whether as raw
policymaking and wariness of AI technology create user information databases or packaged reports and
measurable delays in product launches, approvals analytics, ownership of client and customer data is a
and expansion. In highly regulated industries, such as major concern.
financial services and healthcare, the extent to which
Industry trends and outlook
AI-related companies in LMICs should anticipate the blockchain. IoT is proving to be a transformative
following AI technology and business model trends, technology for start-ups that use connectivity to
which will shape their outlook: streamline resource-consuming functions and
responsibilities.
• More advancement in data analytics, from
• More careful assessment of company operations
diagnostic and predictive to prescriptive. This will
and sustainability, in both LMICs and high-income
be a game changer in terms of enhancing business
countries (HICs). In terms of investment, there
operations and processes.
has been much hype surrounding AI and machine
• Significant advances in AI research. Applied AI learning, leading to saturation in funding and less
innovation is still a burgeoning space for exploration, than favourable results for many bandwagon
and implementation and research should be aligned investors.
with sustainability and social innovation for effective
• FinTech, HealthTech, EdTech, AgriTech, InsurTech,
deployment, from the lab to the market.
GovTech and ClimateTech are all growing sectors for
• AI will continue to be shaped by and paired with AI innovation and deployment in LMICs, especially for
other frontier technologies, such as IoT and COVID-19 and post-COVID-19 solutions.
27Understanding AI business models in LMICs Qlue Indonesia Problem Government services face huge challenges with effectively and efficiently, including flooding, traffic monitoring, data collection and service management, congestion, crime and waste management. Fieldwork especially in cities like Jakarta. They need better can be inefficient and costly, with poor reporting due to solutions to monitor and manage social problems the manual collection of data. Solution Qlue is a platform that provides a comprehensive better decisions. Consumers also use the Qlue app solution for government services. Features include to report issues, such as potholes and crime, and visual ID, facial recognition and vehicle recognition, can communicate with police and other government and it uses predictive data analytics to provide responders depending on the issue. reporting for the government to act and make Business model Qlue is based on a B2B SaaS model. Customers pay for the solution, a one-time fee is charged along for subscriptions and are charged per account or with maintenance fees. Hardware is also a one-time engine/function. For governments that cannot pay purchase. Citizens can use the app for free. Impact and traction Qlue was established in 2014 in Jakarta, working side flood points by 94 per cent, improved government by side with the city government to implement the first performance by 61.4 per cent and increased public smart city concept in Indonesia. Between 2014 and trust in the government by 47 per cent. 2020, the province and its citizens reduced potential 28
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