The roles of business executives and artificial intelligence (AI) systems will soon become intertwined. Executives who today dream of an AI solution to eliminate their various employee “problems” must tread lightly. A quick reminder, 50% of all leaders are below average. Replacing a poor-performing executive with an adequate AI may make more economic and ethical sense long before replacing frontline workers.
Run a thought-provoking exercise and compare a poorly trained executive’s potential advantages and disadvantages versus a poorly prompted AI system. Let’s examine several leadership domains to illuminate some of the strengths and weaknesses of each to gain insights into how organizations could harness the potential of AI in conjunction with human expertise. While AI systems offer advantages in multiple areas, an executive who barely fogs a mirror could, with the proper incentive, make improvements by focusing on their critical thinking abilities and emotional intelligence.
The thumbsketch analysis below emphasizes that effectiveness and reliability significantly depend on proper training, guidance, ongoing learning, and ethical considerations.
Let’s ponder who has the advantage.
Consistency: Once trained, AI systems can consistently apply the same rules and criteria to make decisions or perform tasks without being influenced by external factors. On the other hand, the consistency of a poorly trained executive may vary, as their decision-making could be influenced by personal biases or inconsistent application of policies.
Data-driven decision-making: Both executives and AI systems can benefit from data-driven decision-making. However, a poorly trained executive may not fully understand how to analyze and interpret data effectively, leading to erroneous conclusions. When properly trained, AI systems can quickly analyze vast amounts of data and make data-driven decisions without succumbing to human biases.
Learning and improvement: AI models can continuously learn and improve through updates and feedback. They can incorporate new information and adapt their responses based on user interactions. Poorly trained executives or managers may not have the same capacity for continuous learning and improvement, potentially leading to stagnant or outdated decision-making practices.
Unconscious biases: Unconscious biases can affect both executives and AI systems. However, AI systems can be trained to identify and mitigate biases by using diverse and representative datasets. While biases can still be present in AI systems if not properly addressed, the advantage lies in the fact that biases can be explicitly identified and addressed in AI. In contrast, unconscious biases may be more difficult for executives to recognize and overcome.
Transparency: Executives can explain their decision-making processes and communicate their intentions and reasoning to stakeholders. AI systems, particularly complex ones like deep learning models, can be difficult to interpret, making it challenging to understand the rationale behind their decisions.
Prejudices: Prejudices can exist in both executives and AI systems. However, AI systems can be designed and trained to minimize prejudices by implementing strict guidelines and ethical considerations. Executives, especially if poorly prompted or trained, may be more susceptible to personal prejudices, which could influence their decision-making.
Critical thinking: Executives with well-developed critical thinking abilities can analyze complex problems, evaluate options, and make sound decisions. AI systems, while capable of processing large amounts of information, lack human-like critical thinking skills.
Adaptability: AI systems can quickly adjust their responses and behavior based on new information or changing circumstances. Executives may struggle with adapting to unforeseen situations if they are poorly trained or lack flexibility in decision-making.
Accountability: Both executives and AI systems should be held accountable for their actions. However, executives can be directly responsible for their decisions and be held accountable through organizational structures, legal frameworks, and public scrutiny. AI systems, on the other hand, may be difficult to hold accountable due to their complex and often opaque decision-making processes.
Performance evaluations and feedback: Both executives and AI systems can benefit from performance evaluations and feedback. However, executives can receive immediate feedback and adapt their decision-making based on real-time information. AI systems require regular monitoring and evaluation to ensure their performance aligns with the desired outcomes.
Vision, Mission, and Values Alignment: Executives can communicate and reinforce these organizational principles, ensuring that decision-making aligns with the strategic direction. Although AI systems can be trained to align with predefined values, they may lack the contextual understanding necessary to fully grasp and embody an organization’s vision and mission.
Employee Communication: Executives can establish personal connections, understand individual needs, and provide guidance and support. AI systems can deliver information and instructions but cannot empathize and build rapport with employees.
Emotional Intelligence: Executives with well-developed emotional intelligence can understand and manage their emotions and those of others, which can positively influence decision-making and relationships. AI systems lack emotions and are not capable of emotional intelligence.
Problem-Solving: Both executives and AI systems can excel in problem-solving. With their critical thinking skills and domain expertise, executives can analyze complex problems and propose innovative solutions. When properly trained and guided, AI systems can also contribute by processing large amounts of data and generating insights humans may overlook. The advantage here depends on the specific problem and the executive and AI system’s capabilities.
This exercise highlights the differences between a poorly trained executive and a poorly prompted AI system varies across domains. AI systems have advantages in consistency, data-driven decision-making, learning and improvement, prejudices, adaptability, and biases. Executives have advantages in transparency, critical thinking, accountability, performance evaluations and feedback, vision, mission, and values alignment, employee communication, emotional intelligence, and problem-solving. Although to be fair, some executives do not perform well in any of these domains.
Unsurprisingly, effectiveness and reliability heavily depend on proper training, guidance, ongoing learning, and ethical considerations for both entities. What is surprising is the lengths some organizations go to shield or even promote below-average leaders. AI will continue to improve and cross the threshold where its unjustifiable confidence in wrong answers (hallucinations) will be less frequent than the hallucinations of ineffective executives.
Some organizations have already crossed that Rubicon.