The success of artificial intelligence (AI) initiatives relies not only on data science and machine learning but also on a harmonious blend of various backgrounds and expertise. In this article, we will explore a range of key roles, including non-technical positions, that contribute to the success of AI projects.
Mikhail Yarmaliuk, CTO of Lomray Software, emphasizes the importance of collaboration between IT and domain experts in any AI endeavor. IT experts understand the machine learning toolkit, such as selecting algorithms to solve specific problems and optimizing them for accuracy. Domain experts bring domain-specific knowledge, including data sources’ quality and the relevance of machine learning algorithm recommendations. This highlights the significance of teamwork in AI success.
Keith Collins, EVP and CIO at SAS, advocates for diversity in AI teams, stating that a variety of disciplines is the key to success in AI. He identifies four core disciplines essential for AI teams:
Business experts: Understanding critical business processes and real-world applications.
Analytics experts: Proficiency in machine learning, statistics, forecasting, and optimization techniques.
Data experts: Knowledge of data sources, quality, and security measures.
AI architects: Transforming analytics into practical solutions.
These core disciplines can originate from diverse backgrounds, including music, chemistry, and physics. Such disciplines foster critical thinking skills and complex problem-solving abilities.
Diverse AI teams offer numerous benefits, from mitigating AI bias to solving complex business challenges. Jeff McGehee, a senior data scientist, emphasizes that diversity of opinions and professional backgrounds enrich AI projects by providing fresh perspectives and innovative solutions. However, building diverse teams requires proactive efforts in recruiting and hiring.
Now, let’s explore various roles that can be valuable to an AI team:
Domain Experts (Subject Matter Experts): Domain experts offer in-depth knowledge specific to the AI system’s operation. They provide critical insights and optimize AI system performance for their respective domains.
Data Scientists: These professionals determine what AI systems can do, training them to identify and use patterns, drawing from backgrounds like statistics, engineering, and psychology.
Data Engineers: Responsible for translating data scientists’ ideas into functional AI code and ensuring its seamless interaction with users and devices.
Product Designers: They contribute the “why” and roadmap for desired AI outcomes, using backgrounds in art, design, and engineering.
AI Ethicists and Sociologists: These roles focus on the societal and ethical impacts of AI, ensuring fair treatment of underrepresented groups.
Lawyers: Legal experts are crucial to navigate the evolving landscape of laws governing algorithmic decision-making and AI usage.
Moreover, considering your specific AI goals and use cases, your team may require additional roles. For instance:
Executives and Strategists: These leaders identify opportunities and risks related to AI, examining its impact on the business model and considering data privacy and human-machine interactions.
IT Leaders: IT teams play a vital role in ensuring data privacy, security, and efficient data storage and retrieval, leveraging AI tools like chatbots.
Human Resources Leaders: HR can enhance efficiency with AI tools and assess AI’s impact on the organization.
Marketing and Sales Leaders: Domain expertise in areas like sales and marketing can contribute to revenue generation by leveraging AI.
Operations Pros: Operations and DevOps professionals bring their expertise to automate processes, improve data collection, and explore off-the-shelf AI models and datasets.
Diverse backgrounds and personalities within your AI team ensure a comprehensive approach to problem-solving, addressing all crucial details, and identifying innovative solutions. Building a well-rounded AI team is essential to AI success.
