If you want to develop a Trusted AI solution that solves a business challenge, you need human input. Or, to be more precise, you need human input from a team of humans.
AI is a team sport, after all. To mitigate risk and bias, every decision you make about an AI solution for your business should involve a team of key players. The number of players and the roles that those players have in your decisions will vary based on the nature and scope of your AI challenge, the size of your organization, and your budget.
Here are the key players you should involve in every AI decision.
Regardless of the AI challenge you are aiming to solve or the size of your organization and budget, there are three people you simply must involve in every AI decision. They are your data scientist, your data engineer, and your stakeholder.
A data scientist analyzes and interprets complex digital data to help your leaders make better decisions. Your data scientist is on your team to help you frame complex problems, to derive insight and meaning by applying advanced statistical techniques to raw data, and to communicate key insights to your leadership. A data scientist may also use advanced algorithms and study design to both develop next-level predictive models, and to extract value from complex data.
Your data engineer develops, constructs, tests, and maintains the systems that you use to collect, store, and analyze data. They create the infrastructure and framework that allows your data scientists to perform their activities in the most efficient way possible and allows your organization to reap the benefits of the data science solution.
Although this person may not be involved in the hands on data science, the stakeholder is nonetheless an essential player. This person brings critical knowledge of the business problems, existing processes, and goals. Through their collaborative involvement, they help ensure the project stays focused and facilitates buy-in from the rest of the organization.
These are the minimum players you need on your AI team. Each player performs a unique function that helps you design and develop trusted AI and ML solutions.
If your organization has the need and budget to support a larger, more sophisticated AI team, then you should include a few other players in addition to the essential roles listed above. Your team should include a data scientist, data engineer, and the stakeholder, plus a few more.
Your AI translator has a business background and a working knowledge of AI concepts and processes. They act as the liaison between your leadership team and your AI team. They bridge the gap between strategy and execution.
It takes both education and a willingness to immerse oneself in the AI project to become an AI translator. Look for courses, like AI For Everyone, that combine AI theory and practical applications to build a steady foundation for this player on your team.
Your data analyst is your “data detective.” They clean, massage, and organize your data to perform statistical data analysis and provide critical reports. This person works closely with the data scientist to build foundations for more complex models.
Larger teams need more direction. Your engagement leader manages your data science team. They have a customer-facing role, maintaining the customer engagement and managing the customer experience. They also act as liaison between your customers and your technical team.
As we like to say at Pandata, “there is no I in data science”. If you want to develop strategic AI solutions, assemble a team of AI specialists, and include that team in your decision making. Or, hire the Pandata team for your next AI project.
Stay up-to-date on the latest in trusted AI and data science by subscribing to our Voices of Trusted AI monthly digest. It’s a once-per-month email that contains helpful trusted AI resources, reputable information and actionable tips straight from data science experts themselves.
Nicole Ponstingle McCaffrey is the COO and AI Translator at Pandata.