Putting the power of generative AI to work in a skills-based organization: Three urgent questions
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If you’ve felt overwhelmed by recent advancements in artificial intelligence, you’re not alone. AI adoption is moving with unprecedented, even revolutionary speed — impacting how we work and how we think about work. As tempting as it might be to take a wait-and-see approach, skills-based organizations must get out ahead on AI to stay competitive.
Generative AI is already transforming skills-based workplaces by enhancing human capabilities. For example, we wrote the first draft of this article with help from GenAI! Of course, we still needed the human touch to edit and refine it. To harness its full potential, organizations and leaders should be thinking about how to embrace AI-driven tools, adapt their workforce strategies, and prioritize employee upskilling and reskilling to thrive in an AI-powered future of work.
This intersection of AI and the skills-based workplace will be a theme of this year’s annual Mercer breakfast at Davos: “Tapping AI’s Power to Optimize Our Working World.” Oliver Wyman’s Chief Knowledge Officer, Ana Kreacic along with myself will lead a panel discussion to explore essential questions about how generative AI is reshaping workforce management strategies. Ana and I will be joined by CEO of Exponential View Azeem Azhar, Microsoft Americas President Deb Cupp, Standard Chartered Bank CHRO Tanuj Kapilashrami, and Dan Vahat, Founder and CEO of Huma Therapeutics. Oliver Wyman Forum CEO John Romeo will also share highlights from the new Generative AI Report.
In the meantime, here are three urgent questions we think every leader should consider.
Why is generative AI so transformative?
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Massive memory and pattern recognitionGenerative AI can connect distant concepts, draw inferences and recognize patterns in a vast amount of data. This capability allows it to provide insights and solutions that may not be immediately apparent to humans — and can elevate and speed our creative and analytical processes.
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Low/no code requirementsUnlike traditional AI, which often requires extensive coding skills, generative AI can be harnessed with minimal coding expertise. This democratizes access to AI-driven solutions, making them accessible to a broader range of professionals.
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Absence of logicGenerative AI makes predictions based on extensive training data rather than rigid logic. This approach enables it to adapt and learn from data, potentially offering innovative solutions beyond the constraints of traditional logic-based systems.
How will generative AI work best in partnership with humans?
The true strength of generative AI is in augmenting human work rather than replacing it with technology. For leaders, putting humans and AI into partnership means determining where AI can substitute for repetitive work; where it can enhance human creativity, critical thinking and empathy; and where it can create entirely new work opportunities and demand new skills.
In our book Reinventing Jobs: A 4-Step Approach for Applying Automation to Work, John Boudreau and I talked about how work can be classified into four distinct outcomes.
Here is how those might play out with the various generations of AI from simple rules-based systems to generative AI:
- Error elimination: In high-stakes situations like airline piloting, predictive rules-based systems and machine learning can help eliminate errors, ensuring safety and minimizing potential negative consequences.
- Minimizing variance: For transaction-processing work, the aforementioned technologies can help maintain performance in the narrow zones required to ensure optimal impact.
- Improving productivity: For work like sales, generative AI can augment human performance and productivity, directly contributing to organizational value.
- Achieving breakthroughs: In creative fields like data science and writing where even small improvements in performance can lead to significant gains in value, generative AI can augment decision making and lead to breakthroughs not forseen.
As we look to AI to help us achieve these differentiated outcomes, it is essential that leaders understand the work and when they should or should not rely on various AI technologies — while carefully consider the role AI should have in either substituting, augmenting or transforming the work of human employees.
What guardrails should we be setting for generative AI?
Many technologists, ethicists and futurists are approaching AI with cautious enthusiasm — warning that even as we embrace the benefits of generative AI, we must also create new guardrails for its integration into the workforce.
Here are some to consider:
- Establish a work model that includes AI: Create a work operating model that analyzes work and responsibly applies emerging AI and automation.
- Create an AI-friendly talent model: Develop a talent model that ensures a pipeline of skills while progressively applying more AI.
- Develop future skills with AI in mind: Promote upskilling and reskilling of the workforce to adapt to AI advancements and adapt roles and skills to account for human-AI collaboration.
- Evolve your mindset and culture: Foster a culture of perpetual reinvention to take full advantage of AI's democratization of knowledge and creativity. Educate yourself and your team to minimize fear and disruption.
Integrating generative AI in skills-based organizations is both exciting and challenging. As we enter uncharted waters in automation, organizations must take the time to plan, adapt, embrace and integrate AI into their workforce management strategies. It is the only way to stay competitive and ensure a prosperous future where we can harness AI’s potential to augment human expertise.