The Rise of the Emerligent Economies: How Emerging Economies Will Win The AI Race

Introduction

 

We are living in an unprecedented era of technology revolution which is significantly impacting the daily lives of people. The prominent technologies in this era are Robotics, Big Data, Artificial Intelligence (AI), Cloud computing and the Internet of Things (IoT). Artificial Intelligence and Big Data in particular have the potential to totally transform economies and many of the developed nations like the US, UK and China are leading the charge in the race to become intelligent economies. However this presents the question on what role (if any) will the emerging market economies play in a foreseeable future of Artificial Intelligent systems and Self Learning Machines?

With infrastructural and structural challenges, emerging market economies have lagged behind developed nations in many sectors. Poor management of education systems alongside the issue of brain drain has also caused a talent supply gap. Without access to talent, these developing nations are unable to fully realize the potential of AI in their economies. Other challenges include poor regulatory frameworks and lack of good quality data.

However, there is a class of emerging market economies that will be able to overcome these challenges to adopt the new age technologies like AI, Big Data, IoT to address critical gaps in their societies and leapfrog developed nations in the race to become intelligent economies. These countries are termed the Emerligent Economies.

This report examines the concept of the Emerligent Economies in the context of Africa and presents some recommendations for how these economies can win in the race to become intelligent economies.

Background

 

The internet revolution brought about significant changes to society and the daily lives of its citizens. The internet revolution shaped consumer preferences as consumers depended less on physical systems in favor of digital systems. For example, ecommerce significantly transformed the shopping experience with the dominance of online shopping platforms like Amazon, Ebay, Jumia and many more. The traditional brick and mortar retail stores have either had to evolve or close down.

Furthermore, there is a fusion of the digital spheres and the biological spheres with the development of sensors that are able to track and monitor human vital signs. These sensors are being used extensively in the medical field to improve health outcomes. Consider the example of the smart pill. In 2017, the US Food and Drug Administration department (FDA) approved a smart pill to be used by patients with Schizophrenia and Bipolar disease. Each pill contains an electronic sensor that is activated upon contact with the stomach fluid, sending a message to a patch worn by the patient. The purpose of this smart pill is to track if patients are taking their medication as prescribed and send relevant updates to doctors.

In 2016, the executive chairman of the World Economic forum coined this era as the fourth industrial revolution. An era where we see a fusion of physical , digital and biological technologies. The fourth industrial revolution is ushering in a new intelligent economy that is dominated by interconnected and intelligent machines and self learning machines. There are 5 key new-age technologies that will be prominent in the Intelligent Economy.

 

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Key Technologies

 

Robotics and Advanced Automation

 

Robots range from tiny devices the size of an insect to life-like robots that look and sound like humans. Robots have traditionally been adopted in industrialized manufacturing like car assembly plants where there is a need to minimize errors in a process or system.

In the intelligent economy, robots are set to become even more intelligent. As robots become more agile and aware of their surroundings, they might work safely side by side with people to augment and assist workers, rather than replacing them. Such “cobots” might eventually perceive human movements and automatically and intelligently adjust their movements and routines on the fly through machine learning.

A good example of such a cobot is NAVii. NAVii is an inventory management robot that can move around the aisles of a supermarket unaided to take stock of the products on the shelf and alert the storekeeper if any products are misplaced, mispriced or running low in stock. Robots like NAVii are able to work safely side by side with human agents on the store floor.

 

Big Data and Data Analytics

 

IBM estimates that the world is creating 2.5 quintillion bytes of data every day. That is about 2,500 trillion bytes of data created every single day. Data is being generated from many sources like social media, mobile phones, smart devices, sensors, radio, websites and many more mediums. Within all of these bytes of data, are interesting insights that can be mined to enable more intelligent decision making. Furthermore, there is a field of data analytics called advanced analytics that uses analysis of historical data to better predict future  outcomes. For example, many banks are able to analyze customer transaction history to better predict which customers are more likely to make good prospects for loan products.

 

Cloud Computing

 

Cloud Computing is a system that lets users access a scalable pool of data resources and computing resources on demand through the internet or specialized networks. Cloud Computing has evolved from just decentralized data storages to more complex software as a service (SaaS) and platforms as a service (PaaS) systems. A good example of cloud
computing at work is Google docs or Google sheets. These are essentially office productivity apps that can be accessed from anywhere without having to buy or download the software.

 

Artificial Intelligence and Machine Learning

 

Artificial intelligence is the theory and development of systems that can perform tasks that would otherwise require human intelligence to perform. Machine learning systems are constantly learning from their environments and use new information to improve on their reasoning and responses. Autonomous driving vehicles use Artificial Intelligent systems and machine learning to constantly monitor the environment to drive a car unaided on public roads.

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Internet of Things

 

The Internet of Things (IoT) is the network of devices, cars and home appliances that can connect, interact and exchange data with each other. The internet of things is based on making our homes smarter, vehicles and businesses smarter. The biggest use case for IoT is in the healthcare sector. For example, a bionic pancreas has been developed by researchers to automate the management of blood sugar in patients diagnosed with type 1 diabetes. The bionic pancreas is made up of a glucose sensor and a portable insulin pump that is attached to the layer of fat above a patient’s abdomen. The glucose sensor constantly monitors the blood sugar levels in the patient and when the blood sugar runs low, the sensor sends a message to the insulin pump to administer just the right amount of insulin. This is how many IoT devices work to exchange data in order to make more intelligent decisions on behalf of humans.

 

Innovation in Developing Economies

 

The developed nations like the US, UK are leading the charge in the application of the new-age technologies to solve some of the world’s most prevalent challenges. Many of these challenges cut across both the developing and developed nations alike. However, there are certain challenges that are unique to developing nations alone. For example, the African region carries a disproportionately high share of the global malaria burden. In 2017, the region was home to 92% of malaria cases and 93% of malaria deaths. Many of such challenges can only be solved by the people most motivated to find solutions for them and hence developing nations must work to find solutions to address critical gaps in their
economies. New-age technologies like Artificial Intelligence, IoT, Big Data present great opportunities to use technology to solve some of these challenges. However, the rate of adoption of these new-age technologies in developing nations is lagging significantly behind the industrialized nations. This is due to a number of challenges mentioned below.

 

Key Challenges

 

Slow Regulatory Reforms

 

Whenever there has been great successes in adopting key technologies in African nations, there has always been a willing and motivated regulator to put enabling reforms in place. The research by Morawczynski (2011, 46) brought to light the enormous role played by the Kenyan government in facilitating expansion of mobile network coverage and integration of M-PESA into the financial system. The launch of M-PESA system would have been impossible without collaboration with the telecom’s regulators and the Central Bank of Kenya.
However policy makers in developing nations are usually slower to implement policy and regulatory reforms which are important for businesses and innovation to thrive. There is almost a sense that Innovation follows Regulation in developing nations but in an ideal environment, Regulation should follow Innovation.

Lagging Educational System

 

Artificial Intelligence and Advanced Automation are predicted to disrupt between 400 million to 800 million jobs by 2030. New jobs will be created and some jobs will be lost. One thing that is certain is that more jobs of the future will require digital skills. Furthermore, there is a need for the education system to produce more graduates in the areas of Data Science and machine learning fields. For these reasons, schools and universities need to be agile in rethinking their curriculum to emphasize more on the relevant skills for the future workplace. However developing nations in Africa face a myriad of challenges related to the progress and management of their education systems. Reforms can be slow and curriculum is outdated in certain countries.

 

Scarcity of Talent

 

Globally, there is a scarcity of experienced and capable talent in the IT workforce and this is accentuated more in African Economies where the educational system has its challenges. For example, a typical Data Scientists role advertised by Cousant Connect in Nigeria will attract an average of 7 applications nationwide while a software developer role can attract an average of 100 applications. Without access to great talent, organizations in emerging economies will struggle to lead in the intelligent economy. To be competitive, organizations will have to partner with international organizations or hire expatriates to work with them. Both of these options can be costly many small companies and start-ups may not be able to pursue innovation projects due to lack of access to talent and the cost of hiring expatriates. This presents long term challenges for any economy because startups are the biggest drivers of innovation globally and if they are hampered by cost or access to talent, the economy suffers. the environment to drive a car unaided on public roads.

 

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Data

 

The bedrock of all great intelligent systems is data. In a country like Nigeria with an estimated population of 180 million people and an estimated 150 million mobile phone subscribers, there is a lot of data being generated. The challenge however is that the data is being collected in silos. Organizations for example are collecting data across different software applications, departments, functions, systems and paper records. For any useful insights to be generated, the data has to be brought together in a meaningful way. Such a consolidation process could be very costly and disruptive to an organization’s activities. Furthermore, the quality of the data being collected is also very important for the success of any intelligent system. In many African countries, administrators are dogged by poor documentation, large backlogs of paper records and poor access to, and utilization of, accurate
and accessible data. Data quality issues limit the ability for countries to use evidence based data to make more informed decisions and thereby limits the extent to which intelligent systems can be created.

 

Legacy or Poor IT Infrastructure

 

Infrastructure upgrades are costly to implement and also very disruptive to a business’ activities hence organizations do not necessarily upgrade IT infrastructure in line with every new technology release. However many of the new age technologies require more modern IT infrastructure to work effectively. This presents a financial challenge for any organization looking to start innovation projects around any of the new-age technologies due to the upfront costs to procure or upgrade IT infrastructure.

 

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The Emerligent Economies

 

Regardless of the challenges presented above, certain emerging market economies will overcome these challenges on the journey to becoming intelligent economies. These developing nations are classified as the Emerligent Economies. The Emerligent Economies are developing countries that are likely to have their economies transformed by the adoption of new-age digital technologies like robotics, AI, IoT etc.

Emerligent Economies have very similar environmental characteristics that make them uniquely positioned to adopt new-age technologies to solve their socio-economic challenges. Each of the emerligent economies have in the past been able to find interesting use cases for adopting technology to solve their local challenges.

Take the case of mobile banking. In the developed nations like the US and UK, mobile banking represents a tiny fraction of usage. By contrast in the Philippines more than 4 million people use their mobile phones as virtual wallets — enabling them to buy goods or transfer cash. Furthermore, the MPESA experiment launched early in 2007 in Kenya, which lets people without a bank account use a text message to transfer money to family and friends, withdraw cash and make payments has been the single most important technology that has aided financial inclusion in east African countries.

Such examples show that developing nations are able to leapfrog industrialized nations in their adoption of technology. It is important to note that not every developing nation can be classified as an Emerligent Economy as each nation will record varying levels of success adopting the new-age technologies.

The Emerligent nations in Africa include Kenya, South Africa, Ghana, Algeria, Egypt, Rwanda and Nigeria. These countries share common environmental factors that make them likely to adopt intelligent systems to transform their economies.

 

 

Common Environmental Factors

 

Growing Startup Community

 

Analysis by Crunchbase and TNA Analysis have shown that there was more than $400 million…

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