Machine Learning Outsourcing: Statistics, Pros, Cons, Advice

Rising Market Trend

AI technology is indeed a rising market trend for operational transformation. The advancement and effectiveness of AI are demonstrated through a series of breakthrough applications across different industries including gaming, entertainment, fintech, and sales, etc. Because of this excitement about AI and its related subfields, users now tend to choose and use new software that integrates artificial intelligence, rather than being loyal to traditional applications with old technologies.

Some individuals come to AI solutions due to their ability to enhance productivity and streamline processes, while others are simply curious explorers interested in the capabilities of this modern technology. Whatever the users’ purpose in approaching AI-based apps is, they both contribute to the growing interest and adoption of AI in the current landscape.

As types of software products are born to serve the needs of target audiences, integrating AI into business operations and new applications is considered one of the urgent and necessary tasks to attract and retain users, further creating a significant competitive advantage.

AI Talent Shortage

Similar to the lack of software engineers in previous years, AI talent is also in a noticeable shortage.

The strong interest of users in AI and machine learning in recent years has motivated more and more businesses to deploy this advanced technology, creating a fiercely competitive market.

As the contributions of developers and data scientists are the foundation for the success of future AI products, companies large and small are fighting over talent across the globe. However, the fast-paced advancements in AI have posed many challenges for educational institutions to produce graduates with the necessary skills, limiting the number of students who meet the requirements to graduate each year. Rapid AI adoption needs, combined with high demands for technical expertise, have led to a situation where demand exceeds supply, exacerbating the shortage.

This talent gap is one reason why many companies choose to outsource their ML projects instead of hiring a development team internally.

Lack of In-house ML Developers

With the situation in which the speed of adopting AI technology outpaces the current supply of trained ML experts, business owners around the world are competing for the same developer pool, creating a more competitive market than ever.

Like other job seekers, highly skilled AI experts often prioritize working in large enterprises with better benefits, which has caused great difficulties for startups in the same industry in attracting talent. In addition, complicated recruitment processes, which include countless different stages such as creating job descriptions, screening applicants, and conducting interviews, also contribute to the difficulty in hiring and managing in-house resources.

All of the above challenges create great motivation for the development of machine learning outsourcing models. With large available talent pools, customers using services from third parties can quickly gather dream AI development team without worrying about hiring hassles or developer shortages. The shorter the time you spend to form a team of experienced professionals, the shorter the time to market and success.

Project-based Needs Without Long-term Commitment

You cannot hire a talented person without a long-term commitment to their working environment and benefits. However, what if you only need to hire machine learning developers in the short term? In such a situation, outsourcing machine learning projects to third-party providers is one of the best solutions to consider.

Typically, a software development company offers three different hiring strategies, including dedicated software teams, IT staff augmentation, and project-based. While a dedicated team model can meet both short-term and long-term needs based on the agreement between the two parties, staff augmentation and project-based approaches are especially suitable for customers with temporary or project-specific ML requirements.

Reducing long-term obligations and ongoing employees’ benefits, such specialized methods provide clients with the flexibility to pivot or change strategies as needed. When the assigned outsourcing machine learning tasks are completed, software development contracts are immediately terminated until the next signing, if any.

Needs for Advanced AI Tools and Related Technologies

It would be a waste to invest in AI tools for just one project. However, they are indispensable tools for the success of AI products/services. Machine learning outsourcing comes into play in this situation and helps customers access advanced technology without investment costs.

To serve the professional purpose of providing machine learning services to customers in need, software development companies often invest heavily in cutting-edge technologies and software. Focusing on investing in new technologies is not only a must for such third-party organizations to serve client project requirements well but also plays a role as part of their business model to attract customers, continuously innovate, and maintain a competitive advantage.

In addition to providing support tools, outsourcing vendors with experience working with technological advancements are able to provide technical advice related to how to apply advanced AI tools to optimize the highest efficiency of the project.

Priorities for Core Competency Focus

There is no denying the importance of integrating artificial intelligence into existing products/services. However, it is often not the sole priority to sustain a business, as maintaining and improving overall operational efficiency can be more immediately impactful for a company’s bottom line. If you are overloaded with other critical business processes but still want to integrate new technologies at the same time, outsourcing your machine learning project can be an ideal choice.

By letting experts do the experts’ things, your in-house team can concentrate on core competencies and strategic initiatives without being diverted by complex machine-learning projects. Developers from software development companies with experience implementing AI models in many projects are able to protect your organization’s sensitive data, implement data analytics, and bring successful outcomes independently.