By Richard Sonnenblick
The value of large language models (LLMs), particularly those that don’t hallucinate and possess early-stage reasoning capabilities, can’t be overstated. However, the growing sophistication and utility of LLMs and AI applications have led to a replacement of humans in certain job functions. In 2024, AI-related layoffs picked up across industries, providing what many speculated to be a window into what an AI-supported future might look like. Among the high-profile cutbacks was Klarna’s choice to cut thousands of sales and marketing team members in favor of custom-designed AI chatbots (“Why Is This C.E.O. Bragging About Replacing Humans with AI?” New York Times, February 2, 2025). Of course, some organizations are taking a more measured approach. For example, rather than cut jobs, IKEA trained more than 8,000 employees on AI and, in doing so, unlocked $1.4 billion in revenue (“How IKEAs Use of AI Resulted in a $1.4 Billion Revenue Uplift with No Layoffs,” 74 Digital).
Regardless of an organization’s approach, the end goal is always the same: enabling teams to do more with less. It’s a task at which AI tools can excel—but to attain their intended outcomes, leaders must first understand the technology’s limitations.
RECOGNIZING THE LIMITATIONS OF AI
While AI’s capabilities are undeniably powerful, leaders must understand that AI is not an organizational cure-all. Many current AI models, especially those based on machine learning and deep learning, are impressive in pattern recognition, automation, and optimization. However, these technologies still fall short when replicating the full scope of human qualities, such as empathy and problem solving.
AI is not adept at understanding the nuances of complex organizational dynamics, including team collaboration, or the subtle, sometimes unpredictable nature of human interaction. It cannot truly understand the cultural and ethical implications of business decisions, nor can it lead with the vision, adaptability, and insight humans bring. Furthermore, while AI can be incredibly accurate in predicting outcomes based on data, it cannot think on its feet in the way humans do, which includes drawing from past experiences to resolve novel problems.
Many AI models still lack the innate human qualities that drive innovation and support organizational dynamics. Take the developers that companies are replacing with AI models. Those employees aren’t just putting their hands on a keyboard to create code; they also collaborate with other developers, product managers, and testers. They work with customers, they solve complex problems using their knowledge of development to guide solutions, and they make decisions about whether to employ a library or build a few functions from scratch based on the unwritten practices of their team. Those are all tasks beyond the scope of today’s LLMs, at least without extensive prompting from a seasoned developer who knows precisely what the LLM needs to know.
These limitations should not be seen as weaknesses but rather as an essential part of the conversation around an emerging AI strategy. By recognizing where AI excels and where it falls short, organizations can create a balanced approach to maximize the value of human employees and AI.
OPTIMIZING RESOURCE ALLOCATION AND STREAMLINING PROCESSES
The most successful AI implementations complement, rather than replace, the human workforce. AI systems can analyze vast amounts of data, identifying patterns and insights humans might miss. By leveraging these capabilities in day-to-day activities, organizations can help their teams make more informed decisions and offload time-consuming, repetitive work to AI. For example, in marketing, AI can analyze customer preferences and behavior to craft personalized email and social marketing campaigns, letting employees focus on strategy setting and critical initiatives to ensure the company’s brand resonates with its target audience.
Similarly, AI can expedite testing and iteration processes in product development and design. It can simulate various product scenarios, analyze results, and suggest improvements, allowing human designers to focus on the creative and intuitive aspects of innovation. This collaborative approach enables employees to leverage AI’s data processing and optimization strengths while contributing their expertise in areas requiring empathy, intuition, and an understanding of human needs.
One of the most powerful ways to leverage AI is using it to identify operational inefficiencies and streamline business processes. AI can analyze workflows, pinpoint bottlenecks, and suggest optimization strategies that may not be immediately obvious to human managers. For instance, in supply chain management, AI can predict inventory needs, optimize shipping routes, and automate order processing, which can significantly reduce waste and improve overall efficiency. This strategic use of AI helps organizations operate in ways that are leaner, more agile, and more responsive to market demands—without sacrificing critical human elements.
Implementing AI in this way doesn’t just enhance efficiency; it also levels up the workforce and increases job satisfaction. By using AI to identify and address inefficiencies, organizations can implement a more sustainable business model and make the most of their existing workforce, ensuring that employees are utilized in roles that maximize their strengths.
When humans and AI work together effectively, results exceed what a person or an AI could singularly accomplish. Employees are empowered to be more productive and engaged while AI optimizes processes, provides data-driven insights, and identifies opportunities for improvement.
This collaborative model encourages innovation and contributes to a more resilient and adaptable workforce. While employees focus on higher-level initiatives that require creativity and emotional intelligence, AI handles data analysis and repetitive tasks. Together, they can forge dynamic, future-ready organizations poised for sustained growth.
Organizations must rethink how they approach AI, shifting from a mindset of replacement to one of augmentation. By recognizing AI as a tool that complements and enhances human capabilities, businesses can drive greater efficiency and employee satisfaction—ultimately gaining a competitive advantage that allows them to adapt in an ever-changing marketplace. Rather than fearing AI’s potential to disrupt the workforce, companies should embrace it as a powerful ally.
VIEWING AI AS A COLLABORATIVE CATALYST TO AUGMENT HUMAN TALENT
The notion of automating tasks traditionally performed by skilled staff can seem appealing, as AI appears temptingly capable of performing a wide variety of tasks more efficiently and accurately than humans. However, this view can miss AI’s larger and more powerful opportunity, serving not as a substitute, but as an effective tool to enhance human capabilities and drive overall organizational efficiency.
Richard Sonnenblick, Ph.D., is chief data scientist at Planview and holds years of experience working with some of the largest pharmaceutical and life sciences companies in the world. Through this in-depth study and application, he has successfully formulated insightful prioritization and portfolio review processes, scoring systems, and financial valuation and forecasting methods for enhancing both product forecasting and portfolio analysis.