Business and technology leaders often emphasize the transformative potential of artificial intelligence (AI) across various industries. While this view is largely accurate, it is important to consider how AI is applied and the trust employees must have in its capabilities. AI systems need to be assigned the right tasks, and users must trust the results they produce. However, AI is frequently deployed across businesses without sufficient consideration, often because overconfident leadership assigns tasks for which the technology is not designed. This can lead to employees struggling with new roles in managing AI tools, as trust in AI is built through experience over time. When AI delivers results that are logical and helpful, users tend to become more confident in its use. Conversely, mistakes can quickly erode this confidence, leading to employees bypassing AI or ceasing its use altogether. If leadership ignores employee concerns about improving AI's effectiveness, its potential benefits can be undermined. AI excels in areas where it can process probabilities and handle complex or unclear information. It is particularly effective in tasks involving large volumes of documents, where it can extract, classify, and summarize information that would otherwise take humans significant time to review. AI can also support executives in decision-making by analyzing various data points and suggesting options. However, the final decisions are typically made by humans, as AI is not always the best choice for decisions with high stakes. AI can also contribute to personalization, making products more relevant to individual users. This is an area where probabilistic approaches can be effective, as long as the content is sufficiently tailored to create a sense of personalization. Assigning AI the wrong tasks can lead to significant issues. AI-generated answers may appear confident but can be incorrect. If these errors trigger actions without validation, they can propagate quickly through workflows. This is particularly problematic in areas like contract management, where small mistakes can have far-reaching consequences. AI should be managed similarly to a highly capable intern, requiring guidance and oversight from experienced colleagues. Its access to sensitive information should be limited and granted only as it demonstrates the ability to make sound decisions. In software development, AI can generate code quickly, but the quality may not match that of human developers. Managing large volumes of AI-generated code can be challenging, with costs increasing due to factors like token consumption and consumption-based pricing, as reported by Gartner. AI should be used to assist in design rather than control execution. Allowing AI to reason deeply within the implementation layer can lead to significant issues if mistakes occur. A more sound approach is to use AI to work with business ideas and rules, enabling easier checks and understanding of its suggestions. Domain-specific languages and model-driven development can help separate AI's role from execution, allowing developers to validate models before they are transformed into software. This abstraction simplifies complex systems and prevents small errors from becoming major problems. As AI becomes more prevalent, the role of developers is evolving. They will continue to write code but will increasingly oversee systems, check outputs, and catch mistakes. This shift provides opportunities for learning new skills and working at higher levels of system architecture. Ford has successfully integrated experienced engineers to train younger colleagues and improve AI and automation tools. This approach has led to better results, with experienced engineers acting as internal auditors, identifying potential issues before they become major problems. Combining AI's ability to recognize patterns with human expertise creates a more powerful tool. This synergy is crucial for AI's effectiveness, rather than relying solely on AI or human input. Enterprises should carefully consider the deployment of AI, avoiding the pressure to use it everywhere simply because competitors are doing so. Leaders should ask critical questions about the roles AI is assigned, whether its outputs can be checked and corrected, and the consequences of potential errors. These considerations influence how much autonomy AI should be given. Using AI for specific, practical tasks can demonstrate its value to employees, helping them understand its capabilities and limitations. This approach can build trust over time, as employees see the system working effectively and understand what it can and cannot do. The key to successful AI integration is creating a setup where humans and machines each handle tasks they are best suited for. AI is effective in processing large volumes of information and identifying patterns, while humans excel at understanding the bigger picture, identifying mistakes, and taking responsibility for actions.