This content is republished with permission from Pandata, a Marketing AI Institute partner.
At Pandata, we hang our hat on educating clients and our community about AI in an approachable and jargon-free manner. This isn’t something that just happened overnight though. It takes a concerted effort to work with our solutions team on knowing their audience and the level of technical detail that is appropriate. This is where the role of the AI Translator can be instrumental in helping to bridge the gap.
To understand the role of the AI Translator, we need to backup and look at the AI team holistically. At a minimum, an AI team is made up of the Data Engineer, Machine Learning Engineer, and Data Scientist. As we move to larger teams, additional roles like AI Product Manager and Machine Learning Researcher (to name a few) can enter the equation. While some of these roles, like the Data Scientist, are extremely adept at collaborating with a client or internal executive team, the AI Translator can add an extra layer of approachability.
The role of AI Translator typically comes with a business background and a working knowledge of AI concepts and processes. Think of them as a go between. They aid the data science team in a myriad of ways. As mentioned above, they can act as a coach, helping members of the data science team to be more relatable to a business audience. They also can help with gaining buy-in for an AI pilot because, as a translator, they can discern what success means for both sides. It is that understanding of the business problem AND how AI can automate or augment a process or business operation that makes for a truly winning combination and the ability to scale AI.
So how can someone become an AI Translator? It takes a combination of education and immersing oneself in the AI team. A curriculum comprised of AI theory and some practice will provide the foundation for the role. A great place to start is AI for Everyone, taught by Andrew Ng—one of AI’s most well-respected Data Scientists. Building off that is our own course, Artificial Intelligence and Data for Beginners, taught by Cal Al-Dhubaib. Also, don’t be afraid to get your hands dirty (so to speak) and take some participatory courses too. Learn a programming language like Python or R, or dip your toe in the water with something like Building AI Powered Chatbots Without Programming.
Lastly, shadow your data science team. Sit in on meetings (lots of them) to gain real-world understanding. Work with the team to author use cases and success stories to learn what an AI project looks like from start to finish. Also, do not be afraid to ask questions and make suggestions based on your business experience. A good translator brings a unique perspective that can really be an asset when brainstorming or selling a solution.
Nicole is the COO and AI Translator at Pandata, a data science firm.