Why CIOs should think like HR leaders to onboard agentic AI

Agentic AI tools are being hired into businesses faster than we can fathom. They’re already helping shape workflows, influencing decisions, and generally assisting with the day-to-day running of a business. But without the right training, oversight, and integration, they risk becoming more of a liability to a business than a competitive advantage.

Unlike generative AI tools, agentic AI tools are designed to act independently, by identifying tasks, making decisions, and taking actions without direct instruction. They’re built to think and act, not just respond: becoming actors in the workplace, participating in workflows, integrating across functions, and shaping outcomes.

Built for a specific outcome

Most of the tools we see today are built for purpose or a specific outcome and are generally more like a bot that front-ends a workflow or business process. Today, we are largely at the task agentic worker level for automation, where we have repeatable tasks that are completed within very specific guardrails and there is very little gray area. Where this is gray area, there is a human in the loop that is making the ultimate decision or determination.

Traditionally, the role of a Chief Information Officer (CIO) is to manage and implement a company’s information technology systems and strategies. As overseer of all IT operations, from hardware and software to data and IT infrastructure, the job is to ensure technology supports and drives business goals.

Managing agentic AI tools

Today, managing agentic AI tools, CIOs are beginning to find themselves in less familiar territory. To manage these new digital entities responsibly and effectively, CIOs must step into a role that looks increasingly like that of an HR director, using onboarding and training, setting expectations for roles and responsibilities, and continuously evaluating their performance, just like you would with a new employee.

This might sound surprising, but if CIOs don’t adopt this mindset shift soon, they risk introducing not just inefficiency, but reputational and security risks across their business.

Agentic AI Is Your New Co-Worker

When a human employee joins a company, they’re given training, clear expectations of their role, access to the right systems, and, crucially, guardrails. We should not be treating our new agentic AI co-workers any differently.

Many organizations believe that they can implement these tools quickly, with minimal oversight or structure – indeed many of the agentic AI vendors are marketing their technology with exactly these promises.

But agentic AI tools are dynamic: they evolve over time, learn from the data they interact with, and can behave in ways developers can’t fully anticipate. CIOs must therefore take the lead in designing structured, thoughtful onboarding processes for these tools, as well as ongoing reviews and feedback loops.

This includes defining their scope of responsibility, controlling what data and systems they can access, and outlining what constitutes successful performance. Without them, agentic AI tools can quickly create confusion, make flawed decisions, or even introduce bias or compliance risks.

Think Metrics, Not Magic

When a human employee joins a company, they’re given training, clear expectations of their role, access to the right systems, and, crucially, guardrails. We should be treating our new agentic AI co-workers exactly the same.

Like any other employee, agentic AI tools need clear success criteria, so CIOs should ask themselves: Is the AI doing what I expect it to do? And is it doing it well?

It’s critical that CIOs establish the right metrics to measure the performance of agentic AI tools. Primarily, CIOs must understand how agentic AI is improving workforce functionality by setting KPIs for tangible metrics, for example, speed and accuracy of execution, cost saved, and alignment with business goals. But performance should also be viewed through the lens of efficacy of outcomes: what are we willing to pay to achieve a specific outcome and what margin of error is acceptable?

This should not be a one-time thing. Annual performance reviews are inadequate for these new colleagues. Metrics need to be tracked and evaluated continuously because agentic AI can drift. As these tools learn and adapt, their behavior may change in subtle ways. Without regular check-ins, CIOs could find themselves managing a tool that no longer does the job well, or worse, one that has learned to manipulate its objectives or produce skewed results.

Ultimately, CIOs must understand not just how well an agentic AI tool is performing, but also whether it remains within regulatory boundaries. Similarly, just like humans, can that tool be corrupted to produce biased results? In many sectors, from finance to healthcare, a human must still make the final call. Agentic AI can assist, but cannot replace that responsibility.

Apply the Same Growth Mindset You Would to Employees

As CIOs assess and evaluate the performance of agentic AI tools, they should also consider mapping how they will expand their use across the business. As new employees are not expected to immediately take over every function of the business, you should start with small deployments of agentic AI and expand over time. Just like you coach and develop co-workers to take on new responsibilities, there should be a considered and phased testing and roll out of the appropriate tools for different jobs.

It’s about building pathways for responsible expansion. Consider how the AI tool could take on increasingly complex tasks, under increasing scrutiny, with increasing access to sensitive data and systems, but only when it’s earned that trust through performance.

Similarly to how you might bring in a separate employee with a different skill set, you should also consider bringing in different tools based on different models that might be better suited to your desired task. As you search hard to find the right person to fit the team, so too you should find the right AI to support that function.

That level of careful progression doesn’t slow down innovation. It makes it sustainable.

Be Cautious About Permissions

With generative AI tools, concerns are largely centered around data leakage. But, with agentic AI, the more pressing issue is access. These tools rely on deep integration across systems in order to deliver value, but too much access creates unnecessary risk.

Just as they do for human employees, CIOs must apply a principle of least privilege to agentic AI, granting access only to the systems and data required for the task. Excessive user privileges contribute to more than half of security incidents for nearly a third (32%) of organizations.

Permissions are often given for a specific use case and never revoked, leading to unchecked access that can be exploited, or cause unintended consequences. Agentic AI adoption offers a clear opportunity for organizations to reevaluate legacy access structures and introduce zero trust access controls that are fit for purpose.

Agentic AI Governance Is Workforce Management

Ultimately, what we’re seeing is the evolution of CIO responsibilities. Where once the role was focused on infrastructure, systems, and uptime, it’s now becoming one of HR / IT hybridity, and managing intelligent, autonomous systems that function like staff.

One thing is clear: agentic AI is coming. And if anything, it will soon be as embedded in business operations as cloud services or productivity tools. Agentic AI tools offer extraordinary potential, but only if they’re managed with the same discipline, foresight, and empathy we apply to human teams.

Today, CIOs have a unique opportunity to shape how these tools are brought into the business, not just as technology, but as contributors to outcomes, and innovation.

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