As the line between humans and machines blurs, organizations must equip employees with the skills to harness and implement cutting-edge technology effectively. To explore how businesses can achieve this, Sadie St. Lawrence, Founder & CEO of the Human Machine Collaboration Institute, joined Fivetran’s Kelly Kohlleffel for a discussion on integrating AI while empowering employees with the tools to remain competitive.

Going from orchestrating to conducting in the age of AI

Human collaboration with machines has been evolving for decades, spanning tools like computers, phones, and tablets. According to St. Lawrence, what sets today apart is the unprecedented speed of technological advancement, fueled by the compounding effects of previous platform revolutions — personal computers, the internet, social media, and the cloud. 

This rapid acceleration, exemplified by ChatGPT reaching a million users in just 4 days, is reshaping how humans and machines interact. The pace of innovation is now outstripping our biological ability to adapt, creating a significant challenge in mindset and workflow.

Historically, humans have been the active drivers of technology, directly orchestrating processes. However, AI has shifted the paradigm from doing to asking, requiring individuals to adopt the role of conductor, orchestrating systems rather than executing tasks themselves. This transition demands not only a reevaluation of processes, but also a rethinking of how organizations approach work in an AI-driven world.

Closing the skills gap with strategies for adapting to AI-driven work

As AI reshapes industries, organizations must address the skills gap to remain competitive and adapt to new ways of working. Jobs exposed to AI will require new skills, but many organizations are currently approaching this challenge ineffectively. 

A common misstep is focusing solely on creating policies and guidelines, followed by enabling tools like ChatGPT or Co-pilot. This approach often fails because policies quickly become outdated in the face of rapidly evolving technology, and simply providing tools doesn't foster the necessary mindset shift. 

To truly bridge the skills gap, organizations need a comprehensive strategy that combines clear AI use cases, continuous learning opportunities, and cultural adaptation to integrate AI as a transformative asset rather than just a utility.

Addressing diversity challenges in technology fields

One of the persistent challenges in technology and data science is the lack of awareness about career opportunities. St. Lawrence notes that many individuals enter these fields because they know someone who has already paved the way. Without visible role models and better communication about the accessibility and diversity of these careers, misconceptions persist. For example, stereotypes such as the image of a solitary hacker in a dark room deter many from pursuing technical paths. St. Lawrence emphasizes the importance of debunking these myths and highlighting that success in technology often hinges on strong communication and collaboration skills.

Another challenge lies in the lack of structured support systems. While informal networks and hackathons often help men enter and thrive in technical roles, women frequently face barriers to accessing similar opportunities. St. Lawrence’s organization, Women in Data is the largest global community for women in data and AI and addresses this gap by creating local chapters, offering learning pathways, and organizing events such as datathons. These initiatives provide women with opportunities to test their skills, build networks, and gain the confidence needed to excel in the field.

St. Lawrence’s vision for Women in Data and the broader technology community is grounded in the belief that greater inclusion and diversity will benefit society as a whole. By bringing more voices into the conversation and providing pathways for women to excel in data science, the industry can unlock more innovative solutions and create a future where everyone feels empowered to shape the technology landscape. 

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