AI Agents as Employees: An HR Model for the Digital Workforce

Many companies are considering how AI agents can automate processes, prepare decisions, or take over entire workflows. However, one question is often overlooked: How are these digital employees actually managed? Just like their human colleagues, these systems must be trained, assigned tasks, and develop new skills. Some develop into top performers, while others fail to deliver usable results and must eventually be replaced. This also changes the role of the Chief AI Officer (CAIO). In the future, the CAIO will no longer be responsible solely for models, infrastructure, and governance. Rather, the CAIO will become the HR manager for a new digital workforce. What this means in concrete terms becomes clear throughout the entire „lifecycle“ of an AI agent.

1. The Job Posting

AI agents need a job description: Just like with human employees, the first step begins with a simple question: Where is there a shortage of labor? This means that companies must analyze processes, identify bottlenecks, and then define task profiles for the AI agents.

These systems are particularly well-suited for tasks that involve capturing and analyzing large amounts of unstructured data. This is the case in claims processing, reporting, documentation, and workflow management. Insurance companies, for example, can have agents analyze incoming information or search for similar cases to expedite the claims settlement process. In logistics, on the other hand, agents can optimize warehouse utilization or the supply chain.

Technically speaking, this does not result in simple chatbots, but rather in clearly defined digital roles with access to data sources, APIs, search systems, and line-of-business applications. The agent is, in a sense, given a job description: What tasks does it perform? Which systems is it allowed to use? When does it make decisions on its own? When must a human be involved?

2. The Job Interview

Not every agent is suited for every task: In the future, companies won’t develop every agent themselves. Instead, a market for specialized AI agents will emerge—similar to that for external consultants or specialists. Some agents are generalists; others are true experts. The analogy to recruiting is obvious: A „junior agent“ has only basic skills and requires intensive training. A „senior agent“ possesses industry knowledge, communication skills, or regulatory expertise—but is also significantly more expensive.

That is precisely why companies need to establish a sound selection process up front. What capabilities must the AI agent have? In which areas can it be trained with minimal effort? In which areas is a specialist needed right away? The CAIO thus takes on a task that has traditionally been associated with the HR field: the search for the perfect candidate.

3. Onboarding

AI agents need to be trained: New employees aren’t automatically productive just because they’re given a laptop. The same is true for AI agents. They must be familiarized with company knowledge, processes, policies, and goals. Technically, this onboarding takes place via knowledge databases, retrieval systems, guardrails, and specialized skills. In this way, the agent learns which information is relevant, which decisions are permitted, and how certain processes work.

„If you want to use agents successfully, I have to start,
”to manage them just like a human employee."

Oliver Köth,
Managing Director of Technology
and Innovation DACH,
NTT Data

Collaboration with human colleagues is particularly important in this context. Typically, a hybrid process chain emerges: at the front end, agents analyze data streams and process information; at the back end, other agents automate standard processes; and in between, humans work on complex decisions, negotiations, or customer interactions.

If, for example, a manufacturing company needs to replace a supplier on short notice, an agent can analyze past procurement cases, assess risks in the supply chain, and prioritize possible alternatives. However, the actual negotiations with the new partner and the final decision remain the responsibility of a human.

4. Continuing Education

Good agents are constantly learning: Just like human employees, agents also need ongoing training. There are two forms of learning. The first is the classic „on-the-job training.“ Agents analyze past tasks, evaluate results, and refine their approach. Modern systems store successful solutions, errors, and new insights in what are known as memory architectures or data flywheels. Some models now even automatically consolidate these experiences in the background—much like how humans process experiences while sleeping. The second form relates to external changes. A company must actively feed new regulations, changed compliance requirements, or new market conditions into the agents. For example, a product engineering agent must learn that certain materials are no longer permitted due to new environmental laws. This means that, in the future, companies will need a kind of „agent academy“ that continuously trains, monitors, and certifies their digital employees.

5. The Career Path

From administrative staff to proactive problem-solvers: The difference between average and top-performing employees lies not in speed, but in initiative. This is precisely the kind of development that is also beginning to take place with AI agents. Simple systems merely respond to commands, whereas advanced agents work proactively. They recognize patterns, identify problems, and independently develop suggestions for improvement. This changes the nature of the tasks assigned. Agents are increasingly given specific goals. Instead of „Process Form X,“ the instruction might be „Reduce checkout processing time“ or „Improve inventory availability.“ The agent then independently analyzes data, tests different approaches, and develops optimization proposals.

6. The Termination Meeting

Sometimes agents just aren’t the right fit: Not every agent is truly productive. That’s why companies need clear performance targets and evaluations—even for AI agents. If an agent fails to meet the targets, makes too many errors, or incurs high rework costs, the system must be adjusted, retrained, or shut down. Some agents simply become obsolete when business models or processes change. The advantage of using AI agents is speed. Digital workforces can be scaled up or deactivated within minutes. This gives companies greater flexibility, especially during seasonal peaks or when business volumes fluctuate significantly.

A Leadership Role Instead of an IT Project

With the increasing prevalence of AI agents, a new form of digital workforce is emerging. These systems must be selected, trained, further developed, and evaluated. The focus is increasingly shifting toward issues of organization, control, and leadership. „The technological development of AI is proceeding exponentially. In contrast, companies often adopt new ways of working only gradually, which puts them under pressure. AI agents are transforming not just individual processes, but the entire organization. The CAIO is becoming a new key figure,“ says Oliver Köth, Managing Director of Technology and Innovation for the DACH region at NTT Data. „Anyone who wants to deploy agents successfully must begin to manage them like human employees, including recruitment, training, goal-setting, and performance evaluation.“ (Source: NTT Data)