Doron Sitbon is the Founder and CEO of Dot Compliance, a provider of AI-powered quality and compliance solutions for the life sciences.
Most organizations still think of AI as a tool. You install it, configure it and expect it to behave exactly as instructed from day one. When it doesn’t, your team may get frustrated, which minimizes adoption before it ever really starts.
That frustration usually comes from a mismatch in expectations rather than a flaw in the technology. But we don’t expect a new employee to master every process on their very first day. We give them time to learn, we introduce them to the team and we adjust our expectations as they grow into the role. AI agents deserve the same treatment.
Reframing AI as a new hire rather than a piece of software changes the perspective. A new hire is someone you onboard, give feedback to and expect to improve over time. That change in mindset alone can reduce the frustration that so often hinders AI adoption inside organizations.
Addressing The Underlying Fear
One of the biggest obstacles stems from emotion. People are afraid an AI agent might do their job better. That fear is worth acknowledging rather than dismissing. Employees also worry about what it means to work alongside something that never gets tired and can seemingly do more with less.
The way an organization frames that reality matters. Communicated poorly, an AI agent looks like a threat sent to replace people. Communicated well, it becomes clear that this is a resource that takes the low-value, repetitive work off someone’s plate.
Framing the rollout like a new employee’s introduction puts change management in a context people already understand. It also keeps communication concrete, focused on which duties the agent will take on, what will change and what guardrails are in place. Centering the work rather than the technology is what can help drive adoption.
Additionally, it helps to frame the value of agents around what employees are likely to gain. For example, its adaptability means an agent can adjust its tone, pace and even language for each person it works with.
Why First Impressions Still Matter
Nobody gets a second chance to make a good first impression, and that applies to AI agents as well. The first few days an agent is live shape how much trust it earns going forward. If those early interactions are smooth and genuinely useful, people start to give the technology the benefit of the doubt, the same way they would a promising new colleague still finding their footing.
That also means organizations should give AI agents a grace period. Mistakes early on shouldn’t be treated as proof the technology doesn’t work. They should be treated the way we’d treat a new hire’s early missteps, as part of the learning curve.
This grace period should be a little shorter than what you would give a human in the same role. Depending on the initial feedback, though, you can extend it if the response is neutral or negative.
Equip The Managers, Not Just The Agents
Managing an AI agent is its own skill, and most managers have never done it before. Just as new managers often don’t know where to start with a human direct report, they won’t automatically know how to direct, evaluate or give feedback to an AI agent. Training shouldn’t stop at teaching employees to use the tool. It needs to extend to teaching managers how to actually manage it.
Employees also need a basic understanding of how to communicate with it effectively, what to ask, how to give feedback and when to loop in a human instead. That accountability is important. Someone has to instruct, evaluate and answer for how an agent performs, and as more organizations bring agents into the workforce, that responsibility will likely need its own formal structure, like non-human resources.
There’s also an opportunity here for managers. When an AI agent performs well, that reflects directly on whoever trained it and supervised it. Being able to say you successfully deployed and managed AI agents is becoming the kind of experience professionals will be expected to have on their résumés.
Getting Started
Rolling out an AI agent as a new hire benefits from a clear process rather than a soft launch:
• Start by defining the agent’s role clearly, what it will and won’t do and which team it supports.
• Pick a small number of high-value use cases to start with rather than trying to cover everything at once.
• Write a short style guide so the agent’s tone stays consistent, whether that’s warm, direct or somewhere in between.
• Build clear escalation paths for anything that needs a human decision-maker.
• Run a small pilot before a full rollout, and use that pilot to refine how the agent responds and where it hands off.
• Perform ongoing performance evaluations to ensure agents don’t drift over time.
• Communicate the launch clearly, so people know exactly what to try first and what to expect.
Treated this way, onboarding an AI agent looks a lot like onboarding a new employee because, in every way that matters, it is one.
Where We Go From Here
Treating AI as a new hire rather than a tool reflects how these digital coworkers operate. They perform best when properly onboarded, guided and given room to grow over time.
In regulated environments, this means maintaining ongoing regulatory updates and enforcing separate trust levels for each activity, with a clear line between deterministic tasks and the AI’s own judgment calls. Some of this will come from a vendor or platform, and some will come from internal governance, audit processes and security review. Defining these boundaries requires the right combination of platform, policy and people, including experts who understand both AI and system architecture to advise on configuring, training and assigning responsibility.
Establishing this level of technical governance is how you turn this vision into reality.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?