AI Impact on Software Engineering Outsourcing

Artificial Intelligence, or AI, is reshaping businesses. With around 70,000 AI companies worldwide, the shift is likely deepening and sees no sign of slowing down. According to OpenAI CEO Sam Altman, superintelligence may be attained by humans in a few thousand days. In his ideal future, artificial intelligence will enhance human potential and lead to unparalleled wealth and scientific discoveries.

In today’s article, however, we wouldn’t be discussing how AI is reshaping the world but rather how it’s influencing software engineer outsourcing. Does AI simply make up for the human shortage of software development projects? What are the benefits or limitations of AI in outsourcing software development? Which AI-driven developments in software development outsourcing are we likely to witness? This article aims to give answers to these important questions.

What Is Software Engineering Outsourcing?

To truly grasp how AI is affecting software outsourcing, we need to start with its definition.

Software engineering outsourcing involves hiring external experts or teams to manage specific tasks in the software development process, such as system design, coding, testing, and performance optimization. By leveraging specialized skills that may not be available in-house, companies can improve efficiency, reduce costs, and deliver higher-quality software solutions. This strategy is commonly used to scale development efforts or access niche expertise without the need for full-time, internal teams.

We would like to highlight that the terms “software engineering outsourcing” and “software development outsourcing” are often used interchangeably and overlap in meaning, even though the latter often refers to a broader range of tasks involved in creating software. However, as these differences are minor, this article will also use the terms interchangeably.

AI in Outsourcing Software Development: An Overview

Brief AI History Overview

AI began to take shape in the 1950s, even though we can trace its roots to centuries-old debates about machine intelligence and human thoughts. The “father of AI,” Alan Turing, introduced the Turing Test in 1950 to discover whether machines could think. In 1955, John McCarthy coined the term “artificial intelligence”. He is also credited with laying the foundation of AI theory.

After those first bricks were laid, AI has made significant strides. In the 1960s, programs like ELIZA, an early chatbot, emerged, and in the 1970s, basic anthropomorphic robots were developed. The 1980s saw the rise of driverless cars and modern chatbots, while the 1990s were marked by IBM’s Deep Blue defeating a world chess champion. The early 2000s saw the introduction of robots like Honda’s ASIMO and the exponential growth of digital information.

After the 2010s, deep learning and big data have contributed significantly to the advancements of AI. Notable events include IBM Watson’s success in “Jeopardy!” (2011), the rise of virtual assistants like Apple’s Siri (2011), and OpenAI’s GPT-3 (2020).

The evolution of AI shows no sign of slowing down and has significant implications for software development services.

Influence of AI on the Outsourcing Business

According to IBM Global AI Adoption Index 2022, a lack of labor or skilled personnel has led to 1 in 4 businesses adopting AI. Outsourcing software development companies often strive to fill in this gap. However, the rapid development of AI means businesses need to rethink and upgrade their business operations.

Transforming Workforce Dynamics in IT Outsourcing

Those who perform mundane, mundane and low-value tasks are the most affected, as they’re most likely automated with the help of AI. Semi-automated activities might also see a sharp decrease in business volume.

Even software developers are not safe from the impact of AI. With the aid of machine learning (ML), AI can easily generate codes. You only need to tell the AI coding tool what you want the codes to do, and AI will take care of it for you.

Tasks in software development projects have also been automized. For example, AI automatically structures documents, fixes codes to enhance code quality, and provides highly accurate data analytics.

All this information proves that the question isn’t whether you should or shouldn’t use AI, the real question is how you integrate this technology to improve enterprise software solutions dramatically.

Removing Human Dependencies

Humans are no longer the sole knowledge holders. With the help of AI and other technologies, a software outsourcing company can manage and organize knowledge more effectively. Automated systems store, process, and quickly retrieve information, eliminating the reliance on humans. This also reduces the risk of losing information.

Less human dependencies, in this case, is a great opportunity as it allows software engineers to boost productivity, refine their niche expertise, deliver high-value services, and improve the bottom line.

High Customer Expectations

AI has become so ubiquitous that when clients outsource software development, they expect development teams to use the technology to deliver faster results. Not only faster, but clients are also demanding higher-quality products.

This mirrors the growing expectations customers had for robotic process automation (RPA) in business process outsourcing. The automation of repetitive tasks resulted in faster and more effective services. These kinds of expectations push software outsourcing companies to adopt and effectively integrate the technology into their business operations.

Demands for AI-related Skills

McKinsey’s 2022 report shows that 39% of businesses hired software engineers, and 35% hired data engineers for AI-related roles. These numbers suggest significant opportunities for professionals skilled in AI-related fields as businesses increasingly adopt AI.

This shift also demonstrates that many companies are adjusting their workforce and putting more emphasis on talent with AI domain expertise. The growing importance of AI-related skills in all industries is shown in the shift of the job market toward specialized roles that support AI adoption and integration.