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.

Facility Management Is Already Experiencing This Shift

Facility management offers a good example because intelligent technologies are already becoming part of how workplaces operate.

AI-assisted building operations can identify patterns and potential issues. Predictive maintenance systems can recommend when equipment may require attention, while occupancy analytics can show how people are using spaces. Smart building technologies can also help organizations respond to changing conditions and use resources more efficiently.

A predictive maintenance system may identify potential equipment problems. The facility professional still must decide what that information means in the context of the building, its operations, and the people using it. Occupancy data can tell you how a workplace is being used. It can't tell you everything you need to know about why.

That's where professional judgment continues to matter. The value of AI depends on what people do with the information it provides.

Leadership Designs Human-Machine Interaction

Leaders play a significant role in determining how people and AI work together, including how free employees are to experiment.

HRCI research found a relationship between organizational encouragement and comfort with AI. Among HR professionals whose organizations strongly encouraged AI use, 70% said they were not intimidated by the technology. That fell to 49% among those who received no organizational encouragement.

I think that's worth paying attention to; the culture an organization creates around AI matters.

Organizations that get this right will also have to accept some risk, and that's a test of trust between leadership and employees. You obviously don't want experimentation to create a serious security, privacy, or reputational problem. But trying to eliminate all risk can eliminate experimentation that helps people discover where AI is useful.

One practical way to address that is to let employees experiment with non-critical past projects whose outcomes are already known. They can compare the results, see where AI performed well, and identify anything different or useful that it surfaced without putting live work at risk.

Organizations can also provide centrally managed sandbox accounts and identify internal champions or power users who can help others learn—particularly those outside the IT department. People who understand the actual work can help colleagues see how AI might fit into it. Then share what people learn. A good experiment that stays on one person's laptop doesn't do much to build organizational capability.

Governance still matters, particularly around sensitive information and the use of public models, but employees should understand where they have room for creativity.

AI Readiness Begins with Human Readiness

AI will evolve, so adaptability must become part of how organizations work. My advice to leaders? Don't wait for someone else to figure it out before you start experimenting.

This is moving beyond previous iterations of workplace technology. There is context that only comes from trying AI within your own organization, looking at the results, giving feedback, and trying again. Case studies are helpful, but they don't substitute for lived experience.

Organizations building that knowledge now will be better prepared to establish proper guardrails while giving people room to learn. Most importantly, they can keep human judgment at the center as AI becomes more capable.

This article first appeared on Connected FM, a blog by IFMA.

Andre Allen, GPHR, is Chief Business Officer at HRCI, where he oversees product development, governance, finance, human resources, legal, compliance, and enterprise services. With more than 30 years of experience in technology and global business, he has led large-scale HR enterprise systems and assessment technologies at organizations including Pearson Performance Solutions, Vangent, and General Dynamics. A former chair of the HRCI Board of Directors, Allen is known for building high-performance teams and driving strategic growth across complex, global organizations.

Related Learning & Resources

AI Adoption in HR Has Moved Faster Than Capability Development. Now What?

HRCI Pro Series: AI in HR

Build Your AI Superpowers

Empowering the Intelligent Workplace: How HR Can Drive AI Adoption with Confidence and Care

HRCI ENGAGE AI + HR Resource Library (free account required)