The Secret Behind Generative AI High Performers: Talent Strategy

The Secret Behind Generative AI High Performers: Talent Strategy

A new survey from McKinsey & Company on the use of generative AI in enterprise organizations reveals what many HR leaders already know: talent acquisition, integration, and role-specific upskilling programs are essential for success.

As generative AI continues to disrupt industries and reshape business models, McKinsey’s research sheds light on the critical role of talent development for organizations eager to unlock the full potential of this transformative technology.

The report found a clear distinction between leaders and laggards regarding their talent development strategies for generative AI adoption. Here’s a closer look at what sets high performers apart. 

The 3 Defining Attributes of a Generative AI Leader


McKinsey Generative High Performer traits


#1: A Personalized Approach to Upskilling

Generative AI leaders have tailored learning journeys and talent strategies that put them significantly ahead of laggards. McKinsey’s research found that leaders are twice as likely (43% versus 18%) to have a curated learning journey tailored by role for developing generative AI skills and nurturing technical talent. 


"Generative AI leaders are twice as likely to have a curated learning journey tailored by role"


A similar trend was seen in Correlation One’s 2024 market research study, where 46% of organizations said fully custom training that uses internal datasets and workflows is critical to AI upskilling success. Reflecting on this, targeted upskilling and capability building are hallmarks of the organizations that will successfully navigate the generative AI revolution.

Key Takeaway 

Generative AI upskilling produces the best outcomes when training is rooted in the organizational context in which a professional operates. As a result, training initiatives should be personalized based on:

  • Job function
  • Technology stack
  • Internal workflows
  • Organizational goals and challenges

By providing role-specific training and continuous learning opportunities, forward-thinking companies equip their workforce with the knowledge and expertise required to harness the power of generative AI effectively.

#2: A Concrete Understanding of Workforce Gaps

McKinsey’s survey underscores the strategic foresight of leaders, who are twice as likely (32% versus 15%) to have clearly defined the talent needed in roles and skills to execute their generative AI strategy.


"High performers are twice as likely to have clearly defined roles and required skills"


Strategic talent mapping ensures that organizations:

  • Have the right mix of capabilities and expertise to drive generative AI initiatives forward
  • Minimize technical skill gaps within the workforce
  • Maximize the return on technology investments

In terms of where generative AI skills gaps lie in your organization, Correlation One’s market research study found the following job functions to be most in need of AI-related upskilling:

  • Data and Analytics
  • Information Technology
  • Learning and Development
  • Human Resources 

Key Takeaway 

Generative AI proficiency is desired by employees and executives alike, but a lack of training prevents success. To get started on the right foot:

  • Evaluate workforce data literacy 
  • Outline technical skills gaps 
  • Match upskilling efforts to areas of deficiency 

Remember to meet employees with training that aligns with current proficiency levels and builds technical knowledge.

If you need support, external partners, like Correlation One, provide custom workforce development programs to meet organizational needs.

#3: A Holistic Vision for Talent Management

The popularity of generative AI in the workplace requires a comprehensive people management strategy as it touches every part of the organization. From marketing to HR, the use cases for generative AI applications are numerous and widespread.  

McKinsey’s survey found that generative AI leaders are almost twice as likely (31% versus 16%) to have a talent strategy for effective recruitment, onboarding, and employee integration. 


"Leaders are twice as likely to have a unified talent strategy across the employee lifecycle"


This holistic approach recognizes the importance of attracting top talent and seamlessly incorporating new hires into the organization's culture and workflows.

Key Takeaway 

A comprehensive approach to people management is needed in the era of generative AI. To effectively upskill employees, organizations must develop the workforce collectively instead of piecemeal. Otherwise, newly gained proficiency will remain siloed despite widespread workplace generative AI usage.  

How to Be a Generative AI Leader vs. Laggard

Generative AI is undoubtedly here to stay. In fact, Bloomberg expects the technology’s compound annual growth rate (CAGR) to be 42% in the coming decade. As a result, the difference between a generative AI leader and a laggard will be career-defining.


"Generative AI is set to grow 42% in the next decade"


Here are six actions to take as a generative AI leader:  

  • Advocate for the assessment of data literacy and generative AI skills organization-wide 
  • Work with Human Resources to create custom training programs that address talent gaps
  • Promote a holistic approach to AI upskilling 
  • Partner with external experts for course development
  • Empowers employees to apply generative AI skills to in-house problems and projects

Final Thoughts

As the generative AI revolution continues to unfold, the ability to cultivate and nurture a skilled, adaptable workforce is a key differentiator for organizations eager to stay ahead of the curve. Generative AI leaders can position themselves for long-term success in an increasingly AI-driven business landscape by:

  • Prioritizing talent development
  • Curating personalized learning journeys
  • Implementing robust workforce strategies

Need to take your organization from generative AI Panic to Plan? Get your upskilling implementation plan here.


This article is a guest post by Mark Palmer. He is a Correlation One Program Mentor and the Host of the Data Humanized Podcast. As the former GM of Analytics for TIBCO, he was the head of products and engineering for data science, analytics, MDM, and data products. Palmer was also the CEO of Streambase, named one of the world's most innovative solutions by the World Economic Forum in 2009. Streambase was acquired by TIBCO in 2013. Today, he writes about how data transforms the enterprise on his website, Techno Sapien.



Publish date: June 25, 2024