AI is becoming part of work across nearly every industry — analyzing information, anticipating problems, optimizing resources, and making decisions faster. As organizations invest in these capabilities, much of the attention has focused on the technology itself.
AI readiness also requires us to think about how people and intelligent systems will work together.
In more than 30 years leading enterprise technology initiatives and organizational transformation, I’ve learned that implementing technology is only part of the work. People must understand how it fits into what they do, and leaders must create conditions for new capabilities to improve performance.
AI Is Redefining the Relationship Between People and Work
Most conversations about AI center on adoption. Organizations are deciding which tools to implement and which functions to automate.
At the same time, AI is changing how employees make decisions, solve problems, and perform their work. Intelligent systems can surface recommendations and identify patterns at a speed and scale we haven’t had before.
Adapting to this shift means considering the interaction between people and technology from the beginning. Giving employees access to AI doesn’t guarantee productive use. People need to understand where it fits into their work and where their own expertise and judgment still need to lead.
Human-Machine Interaction Is the New Workplace Capability
Human-machine interaction extends beyond learning how to use an AI tool. Employees must know when an AI-generated recommendation is useful, when an output needs to be questioned or validated, and when human judgment should carry more weight.
I also think we sometimes lose patience with technology in ways we never would with people.
We don’t expect a new employee to be fully productive on day one. We provide feedback, manage performance, and recognize that it takes time for someone to become effective. Yet when technology doesn’t provide immediate relief after implementation, we can be quick to conclude that it isn’t working.
I’ve seen a version of this conversation around bias in AI. We should scrutinize AI systems for bias. But humans bring biases to decisions, too. When a person isn’t performing effectively, we use feedback and performance management to improve the process. We need some of that same patience with AI-enabled work, with humans continually evaluating the results and adjusting.
That feedback loop is important. It helps organizations understand where technology is working, where it is falling short, and where human expertise still needs to play a larger role. It also develops something an organization can’t simply buy: experience with how AI works in its own environment.