The right skills for an effective AI team
Jeanne-Louise Viljoen, Data Engineer at PBT Group
In my previous article, I discussed the importance of using data specialists in artificial intelligence (AI) projects. The technology on its own means very little when it operates in isolation. Data specialists play a crucial role in transforming the raw data into something that can benefit the AI model’s transformation process. In this follow up piece, I examine how companies can identify the right skills needed as they assemble an effective AI team.
A well-structured AI team should consist of a mix of professionals who provide various skill sets to ensure the implementation of a successful AI project. The team needs to consist of individuals whose expertise includes the following:
- Data engineering
- Data analytics
- Data science
- Software development
- Testing
- Domain-specific knowledge and insights
- Project management
- Ethics and compliance specialisation
Of course, this might seem like a complex undertaking, especially if the organisation is only in the initial stages of becoming a data-driven business. This is where skills mapping plays a significant role when it comes to the typical AI project life cycle.
Understanding the problem and the scope
The first step entails identifying the problem the business is trying to solve with AI. This could even show that AI might not be the right solution for solving it. Team members with domain expertise are important here.
From here, companies need to understand the scope of the problem. They should explore relevant use cases and identify the business value and impact of the project. Knowing the success criteria and the expected outcomes will significantly aid in defining how the performance of the AI solution will be measured and evaluated. For this to work effectively, the AI team needs to have a combination of data science and analytics, domain expertise, and project management skills.
Getting to the data
Two important steps in the AI project process entail data acquisition and data exploration. The former is about confirming access to the right sources required for the model. This is where it is important to examine the data policies around those sources.
Being able to explore, understand, and review the data in the selected sources becomes a vital enabler of the project. The team must be able to confirm that all the sources are what is needed for development. The accuracy of the source is critically important when the team starts building models from them. For these steps, skills such as data science, data analytics, and project management are important.
Modelling work
Once the data is in place, the AI team can start developing and building the model. Data wrangling is crucial for the preparation of the data for these training models with the process to be repeated quite often.
The outcomes of the model must be evaluated from an ethical perspective to prevent inaccurate, biased results. Testing and reviews are therefore essential before deployment. Project managers will need to plan, monitor, and adapt tasks throughout the AI project to ensure the deliverable of the project on time and within budget.
Teams who are skilled in data science, analytics and engineering, testing, evaluation, ethics, and compliance specialisation, as well as project management, will effectively navigate the modelling component.
Putting it into action
Deploying the model and having it reviewed by an internal third party can be incredibly beneficial. This will highlight any additional testing that must still be done. In this step, it is important to get feedback from users, customers, and stakeholders to understand their expectations. For this to work, AI teams must contain data science, data engineering, software development, and project management specialists.
Once all this is done, it is time to release the model. It is important to monitor the quality, accuracy, metrics, ethical measures, and data drifting of the model. Here, project management domain expertise, ethics, and compliance specialisation will be crucial.
A last step in the AI project life cycle is the continuous integration and delivery pipeline. This requires teams to use the feedback from stakeholders, users, and customers to implement enhancements. Team roles will encompass a mix of expertise from the previous steps.
When putting together a team with the right combination of skills, it is good to remember that the relationship between technological improvement and human ingenuity works hand in hand. The joint efforts of diverse professionals within AI teams are essential for navigating complex challenges, driving innovation, and delivering impactful solutions in a world where AI continues to transform industries and reshape our lives.
