While various companies around the globe have started replacing certain human-held positions with artificial intelligence (AI), there are still many positions that will need human expertise for quite some time.
One of them is the “AI team”. It would have been wonderful if AI was able to automate the complete management of its own data transformation and sourcing, modelling and ethical evaluations. This innovation may become a reality in the future; however, currently AI is still completely dependent on human intervention in AI projects. And the need for human intervention will remain.
AI projects are sparked and motivated with the goal to solve a problem. Whether that problem is cost, delivery, sales, automation… the list goes on, AI can be utilised to bring solutions through its outcomes.
The collection, transformation and modelling of data are the main driving forces behind AI. Without it, there can be no beneficial utilisation of information to empower businesses, institutions and societies.
Data models are unable to self-create, self-correct, or even monitor their own actions in relation to their ethical obligations.
Raw data, currently, cannot arrange itself into a meaningful model. Data models are unable to self-create, self-correct, or even monitor their own actions in relation to their ethical obligations. A group of individuals with various skills and expertise are needed to collaborate, run and manage AI projects.
Other dependencies that need human intervention during AI projects are:
- Data management and design
- Domain expertise
- Ethical implications of data usage
- Algorithm applications
- Project lifecycle
AI team skills
A well-structured AI team would be assigned to address the above dependencies. Such a pool of professionals should provide various skills sets to assure the implementation of a successful AI project. The team needs to be made up of and include the following expertise:
- Data engineering
- Data analytics
- Data science
- Software development
- Testing
- Domain-specific knowledge and insights
- Project management
- Ethics and compliance specialisation
The skills required in the AI team can be mapped to each step in the AI project lifecycle, as outlined in the infographic that appears at the bottom of this article.
Step 1: Problem identification
Description: What problem is the business trying to solve with AI and is AI the right solution for solving it?
AI team skilled roles: Domain expertise.
Step 2: Scope
Description: Explore relevant use cases. Look at the business value and impact of the project. Identify the success criteria and the expected outcomes. Define how the performance of the AI solution will be measured and evaluated.
AI team skilled roles: Data science and analytics, domain expertise and project management.
Step 3: Data acquisition
Description: Confirm access is granted to the correct sources required for the model. Look at data policies around those sources.
AI team skilled roles: Data science, data analytics and project management.
Step 4: Data exploration
Description: Explore, understand and review the data in the selected sources. Confirm that all the sources are what is needed for development. Go back to other potential sources that may have been excluded, if need be, to enrich the data and ensure a good outcome. Source accuracy is of high importance when building models from them.AI team skilled roles: Data science, data analytics and project management.
Step 5: Develop, build, model
Description: Data wrangling is crucial for the preparation of the data for training models. This process will be repeated quite often. Visualisation of the model results can provide better understanding of the data quality and outcomes, which offer insights into tweaking and enhancing the model or its data. The outcomes need to be evaluated from an ethical perspective to prevent inaccurate, biased results. Testing and reviews are vital 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. Task blockers need to be highlighted early on, so as to mitigate the upcoming issues with better time constraints.
AI team skilled roles: Data science, analytics and engineering, testing, evaluation, ethics and compliance specialisation, and project management.
Step 6: Deploy
Description: Model review by third-party, and more unit testing to be done. Sign-off of the deployed model. Gather feedback from users, customers and stakeholders to understand their expectations.
AI team skilled roles: Data science, data engineering, software development and project management.
Step 7: Release
Description: Monitor the quality, accuracy, metrics, ethical measures such as bias findings, and data drifting of the model.
AI team skilled roles: Project management, domain expertise, ethics and compliance specialisation.
Step 8: Continuous integration and delivery pipeline
Description: Use the feedback from stakeholders, users and customers to implement enhancements. From the monitored data gathered, the model may need to be improved or corrected.
AI team skilled roles: The roles will include those specified in steps five, six and seven. With the implementation of the enhancements, these steps will be repeated, as indicated in the visual diagram.
In summary, the relationship between technological improvement and human ingenuity work 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.
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