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The Growth Power of Artificial Intelligence

Dr. Lobna Karoui
28 Jun 2022

The rise of AI

Since the first century BC, humans have been concerned with creating machines capable of imitating human reasoning. Artificial intelligence (AI) has been defined by Arthur Samuel as the field of study that gives computers the ability to learn without being explicitly programmed. More globally we can define AI as a process of mimicking human intelligence based on the creation and application of algorithms executed in a dynamic computing environment. Its final goal is to enable computers to think and act like human beings. In 1956, John McCarthy and his collaborators organized a conference called the Dartmouth Summer Research Project on Artificial Intelligence, which gave birth to machine learning, deep learning, predictive analytics, and more recently, prescriptive analytics. In 2007,  McCarthy published an important paper titled “What is Artificial Intelligence,” where he clearly answered multiple questions about AI and its precise branches (pattern recognition, ontology, inference, search, etc.). Also, in the last decade, data science has emerged as a new area of study.

The current rise of AI was made possible by four enabling conditions:

  • With the advent of the internet and the development of connected objects, tremendous quantities of data are now available. In 2020, 1.7 MB of data was created each second by every person. In the past two years alone, an astonishing 90% of the world’s data has been created. Every day, 95 million photos and videos are shared on Instagram, 306.4 billion emails are sent, and 5 million Tweets are made1. IDC predicted that the global datasphere will reach 175 zb by 2025.
  • New technologies such as cloud computing have emerged, and we are witnessing an exponential increase in storage capacity and computing power.
  • Society assist to the growing progress in available algorithms developed by researchers. Libraries like TensorFlow (Google) or scikit-learn (Inria: National Institute for Research in Digital Science and Technology), which contain major AI algorithms, are available with no fees. Many communities, like Stack Overflow, are helping AI developers solve problems.
  • The support from industries is growing. Many business sectors have come to understand the importance of AI and are investing massively in this exponential technology.

The importance of data in AI

Data is the new oil, and some big companies like the GAFA (Google, Amazon, Facebook, and Apple) are monetizing it. Today, one of the main challenges faced by business leaders is how to improve productivity and increase profits by using their data assets efficiently.

Then comes the question of what data policy to implement. An efficient data strategy must ensure that good quality data sets are collected and can be used, shared, and moved easily from one system to another. The objective being to make information usable at the right time, in the right place, and by the right person to bring added value to the organization.

AI business applications

Many AI applications have already been deployed in diverse sectors of activities, with great impact on our daily lives as users, consumers, customers, and more. In the following paragraphs, we propose categories of AI usage and propose with concrete examples based on our experience in the AI development area.

Customer first

Customer segmentation

It is a targeted advertising approach. Customer data is used to suggest homogeneous groups for marketing. This classification approach is based on common characteristics, such as demographics (age, geography, urbanization, income, family, job type, etc.) or behaviors (basket size, share of wallet, long-term loyalty). Customer segmentation is popular because it helps you market and sell more effectively. This is because you can develop a better understanding of your customers’ needs and desires. Clustering algorithms are key techniques in building a personalized customer experience. In a Capgemini Research Institute report about customer experience, we found that “66% of consumers want to be made aware when they interact with an AI system.” Being able to implement AI in such processes while  saving the human intelligence part of it is essential.

Weekly churn prediction

Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries. It’s one of the most well-known AI applications in the customer relationship management (CRM) and marketing areas. A company that predicts churn can take proactive action to retain valuable customers and get ahead of the competition. Consumer characteristics and history are used to give a churn score to marketing leaders every week using the cloud.

Real-time chatbot

We’ve all had to deal with a voice server at least once. Behind this technology, you may have a real-time chatbot. It’s a conversational robot that communicates with users in natural language. It is a permanent point of contact for customers, users, or employees. It acts as a virtual assistant and sends them the right information in real time. For the most performant virtual assistant, the benefits are reducing human interaction costs and increasing user satisfaction with immediate and 24/7 responses. Natural language understanding (NLU) algorithms and cloud infrastructure are used here. Not all instant messaging and virtual assistants are based on AI techniques utilizing NLP and NLU. Some of them are mainly rules based.

Intelligent industry

Prescriptive maintenance

With the emergence of the industrial internet of things (IIoT), the field of maintenance is connecting tools, software, and sensors to collect, store, and analyze multiple data sources in one place. Those tools are already unlocking predictive maintenance, where sensors and software predict future failures. However, many maintenance leaders are looking towards a near future based on prescriptive maintenance, where AI machines not only predict failures but also identify solutions. Prescriptive maintenance uses AI with IIoT to make specific recommendations for equipment maintenance. It combines technologies that analyze histories, make assumptions, and test and retest data freely. Complex AI algorithms enable software to automatically identify and learn from trends, recognize data patterns, and apply the best maintenance plan. This AI application, which uses reinforcement learning, helps to reduce maintenance costs.

Real-time anomaly detection

There are three commonly accepted types of anomalies in statistics and data science:

  • Global outliers represent rare events that have likely never happened before.
  • Contextual outliers represent events that fall within a normal range in a global sense but are abnormal in the context of seasonal patterns.
  • Collective outliers represent events that on their own do not fall outside of the standard expected behavior, but when combined represent an anomaly.

Anomalies within a company’s data set can represent opportunities and threats to the business. Real-time detection of anomalies empowers enterprises to make the right decisions to seize revenue opportunities and avoid potential losses. Data from production is used to detect anomalies in a plant in real time thanks to unsupervised learning and a SCADA (Supervisory Control And Data Acquisition) system.

Forecast methods

Business forecasting is the process of using time series data to estimate and predict future developments in areas such as sales, revenue, and demand for resources and inventory. Business forecasting can be divided into two main categories:

  • Demand forecasting: Anticipate demand for inventory, products, service calls, and much more.
  • Growth forecasting: Anticipate revenue growth, expenses, cash flow, and other KPIs.

Time-series algorithms are designed for these categories. These methods are widely used to estimate the evolution of the Covid-19 pandemic.

Many other AI applications developing computer vision and deep learning algorithms are discovering drugs, identifying cancer cells, and used for sorting devices in factories.

Enterprise management

Monthly KPI dashboard

Financial data is used to display important KPIs for top managers every month in a slideshow. An automation system is set to guarantee the quality of the data and the results. The technologies used are ETL (extract, transform, load), Analytics, and Dataviz. In the context of enterprise management, connecting siloed data across the sales, finance, supply chain, and services domains and embedding AI is keyfor better and smarter decisions. Such achievements help large organizations reduce costs, optimize operating performance, and harness the power of data.

Career management

Digitalization entered HR departments several years ago, and AI has logically become part of this evolution. For many entities, it’s today a part of all career management processes.

  • In recruitment, AI significantly reduces delays thanks to intelligent CV sorting. Some HR departments go so far as to carry out a first interview with chatbots.
  • For training, AI allows employees to benefit from an ideal training plan for their skills development.
  • When it comes to internal mobility, today there are solutions for finding the best profile corresponding to a position that needs to be filled within a company.

Among plenty of successful applications in the HR field, AI is bringing productivity gains, procedure reliability, HR processes improvement, and responsiveness in career management.


Artificial intelligence has so far delivered many benefits and is a huge economic growth accelerator. It embraces many sectors of activities and is already impacting our daily lives. It also raises questions about embedding humans in the process, sharing the benefits, being fair, employment, data confidentiality, privacy, violation of ethical values, ​​and trust in results. These concerns need to be addressed through global regulations, certifications of AI models, and more. In a coming blog article, we will address these necessary fundaments around trusted AI.