13 Reasons Why European Tech Companies Greatly rely on Business Process Outsourcing Companies in Africa

In today’s globalized world, outsourcing has become a common business practice, allowing companies to streamline operations, reduce costs, and tap into specialized skills and resources. European tech companies, in particular, have increasingly turned to African countries for outsourcing various aspects of their business processes. This article explores the main reasons why European tech companies outsource work to African countries, with a special focus on Kenya as one of the top destinations for business process outsourcing (BPO) companies in Africa.


In recent years, European tech companies have realized the immense potential and benefits of outsourcing certain aspects of their operations to African countries. These collaborations have opened up new avenues for growth, cost optimization, and access to specialized skills. Among the African countries, Kenya has emerged as a leading destination due to its advanced tech skills and capabilities in handling work like data annotation and digital marketing, making it a preferred choice for business process outsourcing companies in Africa.


One of the primary reasons European tech companies outsource work to African countries is cost-effectiveness. Labour costs in African countries are often lower compared to European counterparts, allowing companies to save significantly on operational expenses. By outsourcing certain tasks, such as data annotation or customer support, to African BPO companies, European tech companies can allocate their resources more efficiently and achieve greater cost efficiencies.

 3. Access to Skilled Workforce

African countries, including Kenya, possess a talented and skilled workforce that excels in various areas related to technology and business processes. These countries have invested in education and training programs, producing a pool of professionals with advanced technical skills. European tech companies benefit from this vast talent pool by outsourcing work to African countries, gaining access to specialized skills in areas such as software development, digital marketing, and data analytics.

 4. Time Zone Advantage

Outsourcing work to African countries provides European tech companies with a time zone advantage. The time difference between Europe and Africa allows for round-the-clock operations. European companies can assign tasks to their African counterparts at the end of their workday, and the work continues overnight in Africa. This ensures increased productivity and faster turnaround times, as work progresses seamlessly across different time zones.

Language Proficiency

English proficiency is widespread in many African countries, including Kenya. This linguistic advantage makes it easier for European tech companies to communicate and collaborate with their African outsourcing partners. Effective communication is crucial for successful outsourcing relationships, and the shared language bridges the communication gap, ensuring smooth project execution and reducing potential misunderstandings.

Cultural Affinity

European and African countries share certain cultural affinities that contribute to successful outsourcing partnerships. The ability to understand each other’s cultures, norms, and work ethics fosters stronger collaboration and synergy. This cultural affinity helps European tech companies to align their business objectives with their African outsourcing partners, leading to enhanced teamwork and improved overall productivity.

 Focus on Core Competencies

Outsourcing non-core tasks allows European tech companies to focus on their core competencies and strategic initiatives. By entrusting certain processes to African BPO companies, they can redirect their resources and expertise towards innovation, research and development, and market expansion. This strategic focus enables companies to stay competitive in the fast-paced tech industry.

 Government Support and Incentives

Several African governments, including Kenya, have recognized the potential of the BPO industry in their economic development and have implemented supportive policies and incentives. These initiatives attract European tech companies by offering tax breaks, infrastructure support, and simplified regulations. Government support ensures a favorable business environment, encouraging European companies to outsource work to African countries.

 Technological Advancements

African countries have made significant technological advancements, especially in the IT sector. This progress has resulted in the establishment of state-of-the-art infrastructure, advanced communication networks, and reliable internet connectivity. European tech companies benefit from these technological advancements when outsourcing work to African countries, as they can leverage the latest tools and technologies for their projects.

Mitigating Seasonal Workload

Outsourcing work to African countries allows European tech companies to handle seasonal workload fluctuations more efficiently. During peak seasons or when additional support is required, outsourcing provides the flexibility to scale up operations quickly. By collaborating with African BPO companies, European tech companies can ensure the timely delivery of projects without compromising on quality or customer satisfaction.

Enhancing Innovation and Creativity

Diversity and different perspectives foster innovation and creativity. Collaborating with African countries brings fresh ideas and insights to European tech companies, contributing to enhance problem-solving and product development. The unique cultural and intellectual backgrounds of African professionals can spur creativity within European tech companies, leading to the creation of more innovative and competitive products and services.

Quality Standards and Certifications

To maintain their competitiveness and reputation, European tech companies often adhere to stringent quality standards and certifications. African countries, including Kenya, have made significant progress in meeting international quality standards, such as ISO certifications. This alignment in quality standards ensures that European tech companies can confidently outsource work to African countries without compromising on the quality of deliverables.

Data Security and Confidentiality

Ensuring data security and confidentiality is crucial for any outsourcing arrangement. European tech companies recognize the importance of protecting sensitive information and customer data. African countries have made significant strides in establishing robust data protection regulations and implementing secure data management practices. This commitment to data security provides European tech companies with peace of mind when outsourcing work to African countries.

Socioeconomic Impact

Outsourcing work to African countries generates significant socioeconomic impact. It creates employment opportunities and contributes to the economic development of the local communities. By outsourcing work to African BPO companies, European tech companies support sustainable growth and empower individuals with valuable job prospects. This positive social impact strengthens the overall relationship between European and African countries.


European tech companies have compelling reasons to outsource work to African countries, and Kenya stands out as a top destination for business process outsourcing in Africa. The cost-effectiveness, access to a skilled workforce, time zone advantage, language proficiency, cultural affinity, focus on core competencies, government support, technological advancements, quality standards, data security, and socioeconomic impact are key factors driving this trend. By leveraging the strengths and capabilities of African BPO companies, European tech companies can optimize their operations, foster innovation, and achieve sustainable growth in a highly competitive global market.


What is business process outsourcing (BPO)?

Business process outsourcing (BPO) refers to the practice of contracting specific business functions or processes to external service providers.

2. Why do European tech companies outsource work to African countries?

European tech companies outsource work to African countries due to cost-effectiveness, access to a skilled workforce, time zone advantage, language proficiency, cultural affinity, and government support.

3. Why is Kenya considered a top destination for outsourcing in Africa?

Kenya is considered a top destination for outsourcing in Africa due to its advanced tech skills, including data annotation and digital marketing, and supportive government policies.

4. What are the benefits of outsourcing to African countries for European tech companies?

The benefits include cost savings, access to specialized skills, faster turnaround times, improved communication, enhanced innovation, and socioeconomic impact.

5. How does outsourcing work to African countries impact the local communities?

Outsourcing work to African countries creates employment opportunities and contributes to the economic development of the local communities, promoting sustainable growth and empowerment.


Medical Image Annotation: It’s Role in AI Medical Diagnosis

Artificial Intelligence is becoming widespread with better and more functional computer vision-based AI and Machine Learning models.

With more training data, machine learning algorithms enable AI models to learn more variation hence improving prediction results. This improves the accuracy and applicability in the healthcare sector.

In order to make training data useful and relevant, we use accurately annotated medical images to make body ailments or a disease discernible through machine learning. Medical Image annotation involves preparing such data with a justifiable level of accuracy.

What is Medical Image Annotation?

Medical Image Annotation involves tagging of medical image/data e.g. MRI, CT scan, Ultrasound, etc. for machine learning purposes.

Medical Image Annotation plays a crucial role in the healthcare industry. In this blog, we will cover the important role medical image annotation plays in the modern healthcare sector through machine learning and artificial intelligence. We will also discuss the different types of medical images and how they can be annotated to generate different data sets for specific diseases and ailments.

The role played by Medical Image Annotation in AI Medical Diagnostic.

Medical Image Annotation plays a crucial role in identifying the different types of diseases using AI-powered devices, machines, and computer models.

To all intents and purposes, this process provides actual data to enable machine learning algorithms and models to detect diseases when similar images to the training data are brought before the system.

From simple bone fractures to complicated diseases like cancer, accurate medical image annotation can spot disorders on a microscopic level with precise predictions. Below are a number of ailments that can be diagnosed using Medical Imaging Diagnostics.

Diagnosis of the Brain Disorders

Medical Image Annotation is used to diagnose and identify brain tumors, blood clots, and other neurological diseases. Through a CT Scan or MRI, machine learning models can spot different disorders if properly trained with accurately labeled/annotated images.

Artificial Intelligence in neuroimaging is made possible when brain disorders are well labeled/annotated and fed into machine learning algorithms to enable accurate predictions.

When a model is fully trained and completely adapted to be used in place of a radiologist, it can be able to make better and more accurate Medical Imaging Solutions. This will greatly save time and efforts used by radiologists to make diagnoses.

Liver Problems Diagnosis

Medical experts using medical imaging formats and ultrasounds generally diagnose liver-related issues and complications.

Physicians usually identify, denote, and observe ailments by visually evaluating medical images. Unlike AI and ML models, physicians are prone to biases stemming from their personal experiences.

Detecting Cancer Cells

Using AI-enabled machines to identify different types of cancers plays a big role in early diagnosis hence saving people from life-threatening diseases. When a cancer diagnosis is done late, it takes great effort and time to cure or recover from such an illness.

With the advent of AI models specially trained with accurate medical image annotations, it enables computer models/algorithms to learn from the data hence making correct predictions on the type and stage the cancer is at.

Diagnostic Image Analysis

Diagnostic imaging e.g. MRI, CT Scan, and X-ray scans provide a better visual option to detect diseases and figure out the actual ailment and give the necessary treatment.

Our medical image annotation professionals generate imaging and tag distinct disease symptoms using different annotation methods.

Medical Records Documentation

Medical Image Annotation is also used on a number of medical documents and files through text annotation to make the data discernible to AI and ML models. Medical records data on patients and their health conditions are used to train perception models.

With a professionally managed workforce of experienced annotators, medical records data can be labeled with great accuracy while maintaining the confidentiality of such data.

Below are some of the types of documents that can be annotated using Medical Image Annotation;

  • X-rays
  • CT Scan
  • MRI
  • Ultrasound
  • Medical Records

To build an AI model that makes correct predictions, AI medical diagnostics firms need a wide range of data that’s accurately annotated to train the models.

Impact Outsourcing provides top-of-shelf medical image annotation services at a cost-effective price. With us, your data sets will always be of the highest quality, which is just what is needed to train your perception models.

Whether in the field of healthcare, automotive, agriculture, or autonomous machines, we always got you covered with our world-class data annotation services.


Defining Impact Sourcing

Outsourcing has always been a viable option ever since entrepreneurs found alternative ways that are cost-effective as compared to doing everything internally. In addition, offshoring has been a feature in an entrepreneur’s vocabulary over the last two decades.

What about Impact Sourcing? Simply put, this is the new wave. So far, the best definition for impact sourcing has been; Impact Sourcing is exporting/offshoring digital work to areas and workers that would traditionally never access it.

With this definition in mind, it’s fair to say that impact sourcing simply moves labor from 2nd to 3rd tier locations. For most companies, this is typically the case. However, a thorough definition of Impact Sourcing covers how workers and employers confront the relationships between themselves and the offshored work.

A good example of this is Digital Jobs Africa Initiative, under the Rockefeller Foundation. The foundation has in recent times put emphasis, both in thought and effort, on crafting meaningful strategies for Impact Sourcing. As a result of this undertaking, a robust definition of Impact Sourcing has cropped up:

Impact Sourcing is a socially conscious branch of the Business Process Outsourcing (BPO) and Information Technology Outsourcing industry that purposefully employs people with limited opportunity for sustainable employment, mostly in low-income areas/countries.


Video Annotation in Machine Learning and AI

Video annotation, like image annotation, aids in the recognition of objects by modern machines using computer vision. Detecting moving things or objects in videos and making them identifiable using frame-to-frame. For example, a 60-second video clip with a 30 fps (frames per second) frame rate, has 1800 video frames, which may be treated as 1800 static images. Videos are often treated as data for enabling technological applications to perform real-time analysis for producing accurate results. Video annotated data is required to train AI models designed with deep learning is the significant goal of video annotation. The most frequent uses of video annotation typically include autonomous cars, tracking human activity and posture points for sports analytics, and face expression identification, among others.

In this blog, we will understand about video annotations, how it works, features that make annotating frames easier, uses of video annotations and the best video annotation labeling platform to choose.

What is Video Annotation?

The process of analyzing, marking or tagging and labeling video data is called video annotation. The practice of correctly identifying or labeling video footage is known as video annotation. It is performed in order to prepare it as a dataset for machine learning (ML) and deep learning (DL) models to be trained on. In simple terms, human annotators examine the video and tag or label the data as per predefined categories to compile training data for machine learning models.

How Video Annotation Works

Annotators use multiple tools and approaches in video annotation that are essential to do annotation. The video annotation procedure is lengthy often due to the requirement of annotation. A video can have up to 60 frames per second, which implies that annotating video takes much longer time than annotating images and necessitates the use of more complex or advanced data annotation tools. There are multiple ways to annotate videos.

Also Read: Why Data Annotation is Important for Machine Learning and AI?

1. Single Frame: In this method, the annotator divides the video into thousands of pictures, and then performs annotations one by one. Annotators can sometimes accomplish the task with the use of a copy annotation frame-to-frame capability. This procedure is quite time-consuming. However, in other instances, when the movement of objects in the frames under consideration is less dynamic, this may be a preferable alternative.

2. Streaming Video: In this method, the annotator analyzes a stream of video frames using specific features of the data annotation tool. This method is more viable and allows the annotator to mark things as they move in and out of the frame, allowing machines to learn more effectively. As the data annotation tool market expands and vendors extend the capabilities of their tooling platforms, this process becomes more accurate and frequent.

Types of Video Annotations

There are different annotation methods. The most commonly used methods are 2D bounding boxes3D cuboidslandmarkspolylines, and polygons.

  • 2D Bounding Boxes: In this method, we use rectangular boxes for object identification, labeling, and categorization. These boxes are manually drawn around objects of interest in motion across several frames. For an accurate depiction of the item and its movement in each frame, the box should be as close to every edge of the object as feasible and labeled appropriately for classes and characteristics.
  • 3D Bounding Boxes: For a more realistic 3D depiction of an item and how it interacts with its environment, the 3D bounding box method is used as it indicates the length, breadth, and estimated depth of an object in motion. This method is most efficient for detecting common to specific classes of objects.
  • Polygons: When 2D or 3D bounding boxes are insufficient to correctly depict an object in motion or its form, Polygon method is frequently employed. It typically necessitates the labeler’s high level of accuracy. Annotators must create lines by placing dots around the outer border of the item they want to annotate with precision.
  • Landmark or Key-point: By generating dots throughout the image and linking these dots to build a skeleton of the item of interest across each frame, key-point and landmark annotation are widely used to identify tiniest of objects, postures and shapes.
  • Lines and Splines: While lines and splines are most commonly utilized to teach robots to recognize lanes and borders, notably in the autonomous driving sector. The annotators simply draw lines between locations that the AI program must recognize across frames.

Use of Video Annotations

Apart from identifying and recognizing objects, which can also be done using image annotation, video annotation is used in building the training data set for visual perception-based AI models. For computer vision object localization, localizing the objects in the video represents another use of video annotation. In reality, a video has numerous objects, and localization aids in discovering the primary item in the image, which is the thing that is most apparent and concentrated in the frame. Object localization’s primary goal is to anticipate the object in an image and its bounds.

Another important goal of video annotation is to train the computer vision-based, AI, or machine learning models to follow human movements and predict postures. This is most commonly used in sports fields to track athletes’ activities during contests and sporting events, allowing robots and automated machines to learn human postures. Another application of video annotation is to capture the item of interest frame by frame and make it machine-readable. The moving items appear on the screen and are tagged with a specific tool for exact recognition utilizing machine learning techniques to train AI models based on visual perception.