While you are reading this, Artificial Intelligence programs are at work in billions of systems around the world recording songs, directing cars on best traffic routes, automating workflows, responding to emails, preparing financial reports, and getting you to buy something that you never knew you needed. AI in today’s world is all-pervasive. AI is not science fiction. It has become a business disruptor. For businesses to succeed they need to pay attention to big data, machine learning, and artificial intelligence.
Many companies have unique business needs and do not want to buy off-the-shelf AI solutions. In such a context should they have their own in-house teams to customize their AI solutions or partner with a reliable AI company?
The statics for in-house vs outsourcing AI development
Here are a few insights from studies that put front and center why companies are veering towards building AI solutions with external AI companies rather than investing in building their in-house team of AI experts.
A survey conducted by Appen found that 42% of organizations said they spent more than $500,000 annually on in-house AI development.
According to a survey conducted by Deloitte in 2020, 56% of respondents reported that their organizations use both in-house and external resources for AI development, while 23% reported using primarily in-house resources and 21% primarily external resources.
A report by Gartner in 2021 predicted that by 2025, 50% of AI projects will include a hybrid workforce that includes in-house talent, outsourced resources, and AI co-developed with external partners.
A study by the International Association of Outsourcing Professionals (IAOP) found that 86% of companies that outsourced their AI development reported cost savings, while 64% reported access to specialized skills and expertise.
In a survey by MIT Sloan Management Review and Boston Consulting Group, 51% of respondents reported that they had difficulty finding qualified AI talent, while 43% cited a lack of organizational support and understanding as a barrier to developing AI capabilities in-house.
A study by the Everest Group found that the global market for AI outsourcing was valued at $4.5 billion in 2019 and is expected to grow at a compound annual growth rate of 26% through 2024.
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5 Reasons why companies may want to reconsider building AI In-house?
The shift towards outsourcing AI development has been driven by a combination of factors, particularly lack of AI talent. This limited availability of AI talent is the reason why creating AI solutions has remained at around the 60% mark because of the many projects still held up in production. Thishas made organizations consider either a hybrid partnership with outsourced companies or fully outsourcing their AI production. Here are the reasons why many organizations are re-evaluating having an in-house AI team.
Limited availability of AI Talent
Building AI requires a team of experts in machine learning, data science, and software engineering. For example, a healthcare company may want to develop an AI solution to predict patient outcomes, but they may not have a team of data scientists who specialize in building predictive models. In this case, it would be more effective to outsource the project to an AI vendor that has the necessary expertise.
A McKinsey report says that only 10% of data scientists globally have the skills for AI-related work
High cost of creating AI solution
Building AI in-house requires a significant investment in terms of time, money, and resources. For example, a financial institution may want to develop an AI-based fraud detection system, but they may need to purchase expensive hardware and software to support the project. In this case, it may be more cost-effective to outsource the project to an AI vendor that already has the necessary infrastructure in place.
AI Companies provide their business clients with a shorter time to market and its resultant cost benefits.
Lack of scalability and flexibility
Developing AI in-house may work well for small-scale projects, but it can become challenging to scale up the system as the business grows. For example, a retail company may want to create AI-powered chatbot to handle customer inquiries. However, as the number of customers grows, the chatbot may not be able to handle the increased volume of inquiries. In this case, it would be more effective to outsource the project to an AI vendor that can scale the solution to meet the company’s growing needs.
Security and privacy concerns
AI systems require access to vast amounts of data, which can raise concerns about privacy and security. For example, a government agency may want to develop an AI solution to analyze sensitive data related to national security. However, they may not have the necessary expertise to ensure that the data is protected from cyber threats. In this case, it would be more effective to outsource the project to an AI vendor that specializes in data governance and cybersecurity.
The United States Department of Defense (DoD) has outsourced several AI projects to external agencies, including the development of an AI-based drone system to be used in combat and the creation of an AI-driven platform to predict maintenance needs for military aircraft.
AI partners can better provide competitive advantage
Companies that build AI in-house may struggle to keep up with the rapid pace of innovation in the field. For example, a transportation company may want to develop an AI-powered logistics optimization system, but they may not have the resources to keep up with the latest advances in AI technology. In this case, it would be more effective to outsource the project to an AI vendor that is at the forefront of AI research and development.
Examples of companies that outsource their AI development
Many of the leading organizations have outsourced to varying extents their development to third parties. However, outsourcing AI development will benefit smaller companies as they may not have the budget or resources to hire a full-time team of AI developers. By outsourcing, they can access specialized expertise and accelerate creating AI projects.
Here are a few examples to show that outsourcing Ai solutions is the widespread strategy of companies, big and small:
Walmart partnered with Microsoft to develop AI-powered solutions for its stores, including using computer vision to monitor inventory levels and optimize store layouts. Microsoft, in turn, partners with many external AI vendors. For instance, it partnered with C3.ai to develop enterprise-scale AI solutions for industry verticals such as energy, healthcare, and manufacturing. It has also partnered with UiPath, an AI-powered robotic process automation (RPA) platform, to integrate UiPath’s RPA technology into its Power Automate platform, enabling customers to automate repetitive tasks using AI-powered bots.
Adobe partnered with NVIDIA to develop AI-powered creative tools, including using deep learning algorithms to generate realistic images and animations
Uber has partnered with companies like Cognitivescale and Optibus to develop an A platform for route optimization. These solutions use machine learning algorithms to analyze traffic patterns and optimize the routes of Uber drivers.
Allstate insurance carrier in the USA, partnered with H2O.ai to develop AI solutions for insurance underwriting, including using machine learning algorithms to analyze customer data and predict risk.
iTech developed an AI platform for Velie Law’s visas.ai that is the first of its kind for immigration attorneys. Read, Ritzherald’s coverage of the groundbreaking AI platform.
Before choosing an AI partner to develop your AI platform investigate the team’s previous projects and outcomes. Look closely at their portfolio. Ask critical questions to know if the team has the skills in data science and machine learning. Their project management skills are just as important. Ready to find the best AI development team for your company? Get in touch with iTech and ask us the tough questions!
