Why most business should outsource their AI & Machine Learning Development Work?
Artificial intelligence (AI) solutions are increasingly becoming important to businesses as an advanced tool to help extract valuable insights out of the vast amount of data they collected, as well as allowing organizations to boost employees’ productivity.
In recent years, many companies have started to realize the benefits of applying AI/Machine Learning and Data Science into their operation, especially for a smarter way of making business decisions.
AI-powered solutions offers many advantages to business such as automation of numerous time-consuming and repetitive tasks, providing insights to support business decision making for better outcomes as well as many others.
However, together with the tremendous benefits are the difficulties implementing the AI/Machine Learning strategies – especially considering many organizations still do not have a thorough understanding of how to acquire AI and data science capabilities into their operation. 
In particular, one of the most often encountered questions faced by organizations is whether to build their own in-house AI team or outsource their AI/Machine Learning projects to an external Machine Learning consulting firm.
Depending on unique project and business requirements, it could be best for companies to employ the services provided by the consulting firm to get their AI/Machine Learning applications created - by the Machine Learning engineers team who are experienced and have already successfully developing similar AI projects.
Nevertheless, it’s still important for business organizations to realize whether it’s necessary for them to establish their own data science team rather than outsourcing.
Understanding how much your business should invest by either hiring an in-house team of AI engineers and data scientists or outsourcing is critical to determine the success of your organization’s AI strategy.
Before deciding to start hiring in-house AI engineers and data scientist, let’s consider some of the key factors for an AI project as follow:
- Does your organization have the data for the development of AI project? Without proper data preparation and cleansing, (most the time business organizations do not have the sufficient data required) which is crucial for AI project development. If your organization rarely collect and/or store customer and other transnational data, then it will take a while to get started and see any fruitful results of your AI project development.
- How many AI/Machine Learning projects are going to be developed in your organization? Will there be more than one? If your business only has only one specific problem that needs to be solved by using AI then outsourcing would be a far better option.
- Additionally, business organizations need to consider if the AI solution will be adopted and regularly put into use and responded by end-users. Without the need for continuous improvement and fine-tuning, then companies might not need an in-house team of Machine Learning engineers and data scientists to frequently re-train and fine-tune their machine learning models.
In-house AI solutions development department:
After considering all of the above factors, businesses should have a good idea of whether to go with outsourcing or in-house development. 
Again, even if your organization decides to go with the in-house development route, it should be noted that it might take a while to see results from the AI project due to numerous reasons, as follow:
- As stated in our previous blog posts, due to the shortage of skilled IT professionals, it has now become very difficult to hire qualified developers for your project, especially when it comes to skilled data scientists and machine learning engineers in the local market. The overall hiring process may take months or longer than expected.
- Furthermore, as the AI project requires the involvement and contributions from various department, it’s worth considering how the AI team should be strategically placed inside your organization in order to ensure that the team can work well alongside other departments (such as IT, Product, and/or Business Intelligence teams), so each team can bring to the table the expected work required. Ideally, the in-house AI team should be placed in a function to successfully build product and continuously improve upon the feedback loop from the rest of the business.
- For a successful AI/Machine Learning project, it’s crucial to have business data that is of quality (in order to create and train Machine Learning model) which organizations would not find it to be readily available easily. More importantly, if the in-house team does not have experience working with big data analytics, then the outcome would not turn out to be good as expected.
- Most importantly, there’s no guarantee that the AI project developed in-house will be successful.
The overall result will heavily depend on the availability of business data as well as the competency of the in-house team.
Thus, if your organization can manage to address all of the above-mentioned issues, then in-house AI development can prove to be the right choice.
It will be a matter of keeping the team up-to-date with all the latest updates and changes in the Artificial Intelligence (AI) development landscape, as well as ensuring the required business data is readily available and meet the requirements of the project.
Outsourcing AI/Machine Learning project
After considering all the key factors for in-house AI development, businesses may find in-house development is not the best way forward to accomplish the endeavor of “building in-house AI team” is not possible. This could be due to one or many of the above-listed reasons, mainly the time taken could be too long and too costly. And if that’s the case, companies can definitely consider the outsourcing option.
Also, for a lighthouse project or one-off thing, it’s definitely a better option to outsource as it helps companies to have quick access to a large pool of tech talents who are already experienced and familiar with various AI projects before.
A reliable outsourcing partner will be able to assist your organizations with the development by providing reliable Machine Learning & BI consulting services and custom solution implementation, while at the same time understand the importance of adoption, and challenges in transitioning into a data-powered company. Outsourcing, when done right, will provide quality results quickly, speed up the critical time-to-market.
One of the advantages of outsourcing over in-house AI development is the experience of the external machine learning consultants allows for a better and smoother development process.
Organizations do not have to bear the cost of hiring and training or learning about the products which will take a while and become very costly. The added value provided to business can be a lot more valuable.
Yet, similar to any other offshore software development outsourcing agreement, there will be potential issues that may arise and block the development, including missing milestones, deadlines, inexperienced developers, scope creep leading to higher budget, etc. thus, business organizations should conduct due diligence before finalizing on their AI outsourcing partner.
It’s also worth considering the incremental approach and also to explore the various cooperation models and other partnership agreements.
FACTORS CONSIDER WHEN CHOOSING AI SOLUTION DEVELOPMENT PROVIDER
There are a number of factors that business need to consider, thoroughly, in order to choose the right AI solution provide meeting their business requirements, as follow:
Proven AI engineering experience
First of all, it’s crucial that the machine learning development firm your company hires have the needed experience in developing AI projects, in particular the ones with specific industry requirements that are similar to your one.
There are various machine learning use cases that solve a specific problem in a niche industry.
An AI solution for the logistics industry will be different compared to an e-commerce one.
Hence, it’s important that you conduct due diligence before finalizing on the outsourcing vendor of your choices.
Generally speaking, companies should study the portfolio and examine past AI projects completed by outsourcing vendors in order to select the right technology partner for their project.
Expertise in AI/Machine Learning technologies
AI development covers a wide range of tools, technology, and development practices that should be selected depending on the nature of the project requirements.
For example, if your organizations are looking to build an image recognition system, you will probably need to have machine learning engineers with experience in computer visions on the team.
When examining potential AI solution development providers, customers companies need to ask about the expertise their vendor can offer in regards to programming languages and frameworks, such as: Python, R, TensorFlow, Caffe, Torch, etc.
Scalability requirements
It’s important to select the firm with adequate capacities and resources to take on your AI project. This factor plays an important role in ensuring the potential growth of your AI-powered operation will be achieved and maximized.
When scaling up, your AI project will require a bigger development capacity, thus, you need to take into consideration the outsourcing vendor’s ability to meet your project scalability requirements in the future.
Choose the right cooperation model
Last but not least, it’s essential that you pick the right outsourcing engagement model that is appropriate for your project requirements as this will have a direct impact on the overall project budget and ROI (return on investment) of your organization’s AI initiatives.
The most popular engagement models to choose from are: Fixed cost, time and material, and dedicated team.
Read more about how to choose the right outsourcing engagement model.
AI projects may require a lot of research and development, together with changes and scalability in the future stage. The dedicated team might prove to be the best suitable choice (especially for AI project with evolving requirements), offering flexibility and scalability in the later stages of the project
Conclusion:
To conclude, business organizations should carefully consider both approaches in order to effectively implement their AI strategy. Moreover, the decision should be based on the nature of their AI project and the data available, as well as their overall long-term corporate strategy.
Side-notes:
TP&P Technology is a leading software development company in Vietnam helping business organizations transitioning into the AI era, empowering data with custom made solutions, build AI teams as well as Machine Learning and BI Consulting services.
TP&P Technology specializes in Predictive Analytics, Computer Vision, and Big Data Analytics.
Contact TP&P Technology today to talk to our Solutions Specialist about your business Machine Learning and Data Science needs.
