How Should Leaders Use AI?

When using AI becomes the goal, teams begin by searching for use cases. Work may increase without changing customer value or business outcomes.

First define which problem should improve, for whom, and in what way. Is the goal to save time, reduce missed issues, or support new choices? Different purposes require different data and systems.

Responsibility must also be clear. Who reviews AI recommendations, who can stop the system, and who explains an error? Defining these roles before adoption is the foundation of trustworthy use.

Artificial intelligence is the broad field of using machines for tasks people associate with intelligence, such as working with language, recognizing images, making predictions, or supporting plans.

Machine learning is one way to build AI. Instead of writing every rule manually, a system learns patterns and relationships from data for prediction or classification.

Deep learning is a method within machine learning that uses neural networks with many layers. Leaders do not need to memorize every term. They need enough understanding to discuss which method, if any, fits the business problem.

AI is useful for organizing large amounts of information, finding similar cases, and generating multiple options quickly. It can also help people notice patterns they might otherwise miss.

People define what a good outcome means, interpret context, and consider effects on others. They take responsibility for final choices, including circumstances that are not captured in data and values the company intends to protect.

The division is not fixed. Low-impact and reversible work may support more automation, while decisions affecting rights, safety, or trust require stronger human review and explanation.

First, review value. If the company cannot state what becomes better for customers or employees, adoption should not be rushed. Second, review data: how it was collected, whether its use is permitted, and whether it can remain current.

Third, review impact. Consider the harm of an error and whether the outcome can be reversed. Fourth, review explanation. Can the result and its limits be communicated to users and affected people?

Comparing all four on one page prevents decisions based on potential benefit alone. Leadership must judge not only technical accuracy but alignment with the business promise and organizational responsibility.

Begin with one bounded task and a defined group of users. Record current time, errors, and burden, then compare them with the AI-supported process. Review decision quality and confidence as well as speed.

During the test, retain inputs, outputs, and human corrections so unexpected results or bias can be traced. NIST's AI risk management approach similarly organizes continuous work around governing, mapping, measuring, and managing risk.

Define stopping conditions as well as success. Pause when errors exceed an agreed level, results cannot be explained, or workload increases, then return to the original purpose.

RBI, supported by BOEL, uses robotics and AI to assist drug discovery research. Its advanced technology alone made it difficult for researchers, investors, and society to understand the value beyond technical performance.

The company was therefore reframed not simply as a research automation provider, but as an organization creating an environment where technology supports repetitive work and researchers can focus on creative thinking.

The case shows why AI goals should not be limited to reducing people. When a company can explain how people and technology divide their roles and what future they create together, technology becomes value society can understand.

AI can generate many options and predictions. But companies and people must decide which future they want and which impacts they are willing to take responsibility for.

BOEL begins with the experience and business meaning to be created, rather than searching for uses from the technology. We then separate the judgment that remains human from the processing delegated to machines.

With clear criteria, AI does not weaken corporate intent; it supports execution. Management in the AI era needs the ability to choose the right question as much as the ability to produce answers quickly.