{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Solving the Talent Challenge for Artificial Intelligence","author":[{"@type":"Person","name":"S. Shekhar"},{"@type":"Person","name":"N. Choudhary"},{"@type":"Person","name":"R. Saraiya"},{"@type":"Person","name":"J. Neff"},{"@type":"Person","name":"B. Zolnikov"},{"@type":"Person","name":"D. Almeida Costa"},{"@type":"Person","name":"S. Zulfiqar"},{"@type":"Person","name":"C. Chrysovulou"},{"@type":"Person","name":"P. Manceron"}],"datePublished":"2024","isPartOf":{"@type":"Periodical","name":"ADIPEC"},"identifier":"https://doi.org/10.2118/222820-ms","description":"Abstract\n The digital transformation of the energy sector enhances organizational performance, yet a significant skills gap can hinder an organization's digital aspirations. The industry faces fierce competition for artificial intelligence (AI) skilled professionals, as demand outstrips supply. Concurrently, petrotechnical experts are reassessing their roles due to concerns about skill relevance in the AI era. The achievements of one company's talent development and upskilling initiative illustrate one approach to preparing its workforce for the future.\n Domain expertise is crucial to understand and solve any industry problem. Our talent acquisition strategy emphasizes internal development over external recruitment. We train petrotechnical experts in AI, addressing both challenges effectively. The approach consists of two steps. The first is upskilling petrotechnical experts in our digital division to become data science practitioners, proficient in low-code and no-code AI solutions. The second step is participation in the newly developed domain data scientist program, in which selected experts are equipped with advanced data science and software development skills, followed by hands-on experience on a real-world use case. This intensive 6-month program, managed by technical experts, human resources, and line management, bridges the gap between domain-specific challenges and data science solutions, incorporating machine-learning operations practices.\n The program has yielded significant business outcomes across the industry's three pillars: people, technology, and performance. It has democratized AI expertise within the organization, resulting in a 50% increase in the data science workforce and reduced attrition among petrotechnical experts. This has led to substantial savings in recruitment costs. The program stands as an innovative model for scaling AI competency in the industry. Throughout its various stages, the program has facilitated more than 60 AI-powered innovation projects across the exploration and production life cycle and engaged with more than 300 stakeholders. The program has also fostered collaboration through external learning partnerships, addressing sustainability challenges such as emissions, and providing AI solutions for social issues. Following successful campaign phases, the adoption of data science learning has surged, involving more than 1,300 certified data science practitioners and more than 2,500 employees upgrading their skills. This comprehensive approach demonstrates the program's effectiveness in driving AI innovation, enhancing workforce skills, and achieving sustainable and social impact across the company.\n Since the inception of our AI democratization and upskilling program, our workforce, particularly domain experts, have been motivated to learn and apply data science and AI concepts to business use cases, overcoming previous barriers. AI has transformed from a perceived threat to an opportunity for improvement. This upskilling initiative accelerates AI adoption both internally and externally, promising substantial benefits for the industry.","url":"https://innscience.ca/es/community/literature/22de8743-c740-4ffe-a5d6-4fbac795b3fa"}