Yes, it's true that Artificial Intelligence is a subfield of Computer Science. But contrary to popular perception, AI is an inclusive and multidisciplinary field that not only welcomes, but requires, people with varied skillsets and professional capabilities. This creates an opportunity for a lot of professionals to pivot to a career in AI with a little bit of upskilling.

• Language experts: The rising scope and applications of Natural Language Processing (NLP) have made this new role prominent in the field. The language expertise of linguists is required while building machine translation tools and conversational AI. Language experts also occupy important positions in teams conducting NLP research.
• Visual designers: In order to derive maximum insights from data, visual storytelling is a powerful tool heavily used in AI projects. Data visualisation lies at the intersection of data science and graphic design. UX design is another area that uses the services of visual designers, requiring design thinking and user-centric design.
• Analyst: In an AI team, this can be the person who bridges the gap between business and data scientists. Analytics Translators don't need deep technical expertise in programming or data modelling. As per estimates by McKinsey Global Institute, the demand for analytics translators may reach 2-4 million by 2026 in the United States alone.
• Marketing professionals: There are two sides of any AI system: the software side and the domain side. While the IT expert understands the nuances of machine learning systems, it is the marketer who will bring a deep understanding of the industry within which the system will operate. The subject matter expertise of these professionals is required to conduct a business diligence of any AI project. 
• Philosophers: Anyone who isn't afraid to ask the tough moral, philosophical and critical questions is fit to be an AI ethicist. This is the person who questions the fundamental issues of fairness and bias in every AI team that is building algorithms that will impact a large number of people. As predictive models begin making important decisions, from hiring decisions to loan decisions, it becomes paramount to ensure that they are built on the right values. Algorithms are prone to mirroring the bias that we as humans have, but given the pace and reach of technology, the implications of algorithmic bias can be exponentially higher than those of human bias. Ethicists promote accountability and responsible use of AI.