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When should companies implement AI projects internally and when should they outsource?

Artificial intelligence (AI) will soon be a standard technology in companies. According to McKinsey, in 2023, 55% of all companies already use AI in at least one area of their business – a figure that has remained stable for years [1]. How strategically adept they are in doing so will be a key factor in competing for customers in the future. For this reason, the discussion as to whether they should set up an internal AI team is gaining momentum in executive boards and management.

In this article, we outline the pros and cons of in-house AI teams versus outsourcing AI development. As a provider of AI solutions, we are not entirely objective, but we have good reasons to recommend outsourcing in certain cases. Which ones? Read for yourself.  

The first idea: build up AI development within the organization

The idea of employing your own AI developers is an appealing one: in the future, artificial intelligence will probably be used in (almost) every business and every area of the organization. The developers gradually deepen their expertise and the costs for such a team are offset over time compared to regularly hiring external service providers.

Another apparent advantage of internal AI teams is that they can develop applications tailored to the individual needs of the company. If companies procure AI applications, the range of customization options is limited. However, this would be the only realistic alternative for most of them.This is because complete custom development by external AI experts is too expensive for many companies. 

On a closer look, however, neither of the supposed main advantages of in-house AI development are convincing.

The benefits of outsourcing

Anyone who has tried to expand their development team in the past knows how difficult it is to find IT specialists. Finding AI engineers is even more difficult. The search takes a long time and is very costly. Building an internal AI team can quickly become a bottleneck that hampers the economically important progress in implementing the technology.

But even if one or two AI engineers have come aboard: The field of AI development is broad and in order to deal with data sets and algorithms in such a way that a company can exploit the potential of the technology, developers must bring domain knowledge to the table. The use of AI to automate processes in logistics differs from the use of AI for automated document review. 

The more use cases are to be optimized with AI, the less an internal team can contribute this domain knowledge. Experts from AI providers who specialize in particular fields of application are different: They don’t have to spend a long time familiarizing themselves with the topic.

The idea that AI outsourcing is inherently more expensive than in-house AI development is a naïve argument.Due to their experience, experts generally need less time to deliver better results. For individual projects, companies can benefit from the knowledge of top experts that they cannot afford to hire on a permanent basis. In addition, excellent tools are already available on the market for many use cases, which providers can customize for their clients.

It is true that this means businesses have to compromise on the individuality of the solution. However, the benefits resulting from the rapid integration into the work process far outweigh the disadvantages. Internal teams would have to spend weeks or months learning the ropes in order to reach the level of expertise of specialized service providers. During this time, not only personnel costs are incurred, but also training costs as well as costs for hardware and software that internal developers need if they want to develop AI services. After deployment, there are also ongoing costs for maintaining the data model of the AI application.

Outsourcing not only provides companies with a functional solution quickly, but also saves them various running costs. Service providers continuously update their AI application. With the SaaS model, customers benefit from the improvements at no additional cost. Fully automatically. In the event of problems with the software, companies have competent contacts who respond promptly and know the technology in detail – better than any internal IT support could manage to do.

It depends, but often it does not

Outsourcing AI development is not a perfect solution. Companies have to make compromises, but in comparison to in-house development, the advantages outweigh the disadvantages in most cases.

If companies do not yet know the extent to which they would like to integrate artificial intelligence into their processes and business model in the future, they should first have individual use cases implemented by external service providers. Setting up an in-house team would involve too many risks.

Decision-makers should invest sufficient time in the selection of the AI service provider and clarify important questions in advance to ensure that the procured AI application contributes to the company’s goals in the best possible way:

  • Has the provider already implemented similar use cases?
  • Does it have industry experience? 
  • What is the company’s reputation like?
  • Is it possible to view references?
  • What customer service does the service provider provide?
  • Does the provider cater to the privacy and confidentiality needs?

Careful due diligence lays the foundation for a successful collaboration. 

Even if the choice is made for AI outsourcing, we recommend building up internal AI expertise – in IT and the specialist departments. This helps companies to better assess the quality of the service provider and develop new ideas for optimizing products and processes with the help of AI. 

References

  1. McKinsey Global Survey on Al, 1,684 participants at all levels of the organization, April 11—21, 2023, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year#steady

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