Generative artificial intelligence (AI) has made startling progressions, and to successfully integrate this technology into organizations is a question of culture, organizational structure and people development just as much as it is a question of the technology itself, writes George C. Lee, the co-head of the office of applied innovation at Goldman Sachs, in Fast Company.
Lee states that at Goldman Sachs, the human resources department, which fosters the recruitment, training, development, performance management and career advancement of its employees, is referred to as “human capital management.” Likewise, as business leaders develop strategies to incorporate generative AI into their businesses, they will need to develop “machine capital management.”
The companies that are most successful in incorporating generative AI into their businesses should consider these five variables:
Selection: Just like when businesses recruit the best talent to address their opportunities and challenges, they will also need to recruit the best AI models and frameworks to meet their needs.
“Criteria such as diversity, skills alignment or specific experience will be important in model selection, just as they are in our efforts to recruit talented individuals who are looking to make an impact working in close collaboration with their team,” writes Lee.
Development: When businesses invest in the training, development and up-skilling of its employees, those workers are then elevated and your competitiveness as a company improves. Likewise, AI must be trained and developed as well as “systematize[d]...to elevate the ‘skill base’ of these models,” according to Lee.
“While people will remain central to this constant calibration of AI platforms, one can even imagine a well-trained AI system imparting its capabilities on a newer, not-yet-specialized AI system in a way that would more closely resemble human mentorship than the ‘software updates’ of years past,” he writes.
Culture: Companies will need to consider how generative AI fits into their company culture and how they reflect the culture, including rules that govern how AI systems are used, how those systems make decisions, what controls are put in place and how employees interact with the systems over time.
Performance management: “Businesses seek continuous improvement by investing in and elevating the performance of their people. As people gain experience and new skills, their value to their organization grows. Unlike many other technologies that become quickly outdated, these AI models also offer the potential to grow in value as they learn and incorporate more of our institutional wisdom and experience,” writes Lee.
Governance, compliance and risk management: Although AI can be a huge asset to a business, they carry risks, including bias, error and misuse. There should be consistent monitoring and oversight in order to correct these errors, especially for businesses that operate in heavily regulated environments, such as the healthcare and finance industries.
Lee points to one overarching theme that unites all five of these variables, which is the acknowledgment that the role of engineers will change in ways that are both subtle and profound.
Lee says that as these technologies continue to integrate into businesses, organizations should seriously consider how they approach “machine capital management.”
“Moreover, this alignment across the concepts of human and machine capital management may represent an opportunity for us to cooperate and collaborate with machines—creating a common ‘experience’ that better integrates the awesome power of new technologies and the enduring edge created by talented people.”
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