For any business that’s concerned about managing risk with AI: You are not alone. Just 37% of businesses report that their risk management practices are at a maturity level of a 3 out of 4 (or 4 out of 4), according to McKinsey industry research. In other words, nearly two-thirds of businesses are still a maturity level of 1 or 2 out of 4 when it comes to risk management.
At the same time, prioritizing risk management is worth the investment. McKinsey research confirms that businesses with higher AI maturity scores have invested proportionately more in responsible AI practices.
Risk management maturity is particularly important for agentic AI, a newer form of AI that’s just starting to gain traction in the business world. Unlike other forms of AI, agentic AI can think and act on its own, with minimal human guidance and oversight.
With this great power, of course, comes great responsibility. Businesses need to put in place governance structures, frameworks and guardrails to manage the risks associated with agentic AI. Let’s explore four of the most important risk mitigation measures that every business should be implementing when investing in agentic AI:
Don’t allow lift-and-shift
Agentic AI has the potential to dramatically streamline and simplify workflows—significantly, in ways that require modifying and even wholly reinventing these workflows. Unfortunately, human teams that are accustomed to existing workflows are often resistant to change. Their instinct is to do what’s known as lift-and-shift, where the technology solution (in this case, agentic AI) is layered on top of the legacy workflows, enabling the legacy workflows to stay largely intact.
Read more: 5 Strategies For Getting a Strong ROI with Agentic AI
Lift-and-shift represents a major threat to finding success with agentic AI. Lift-and-shift enables businesses to keep the steps of a workflow in a suboptimal order, to impose rules and restraints on AI that affect its ability to think and act optimally, to prevent AI from accessing and analyzing data effectively, and to block AI’s ability to readily scale. That’s why it’s so important to draw a hard line in the sand on lift-and-shift.
Keep humans in the loop
As powerful and self-sufficient as agentic AI is, it’s not a technology that should be implemented and left on its own. To be sure, agentic AI can think and act on its own, but it still requires iterative training, where human teams need to essentially teach AI how to do its job effectively and for the optimal benefit of the business. Even after agentic AI is relatively self-sufficient, agentic AI shouldn’t be left alone. Human teams should be reviewing agentic AI’s performance and outputs, then providing feedback directly to AI on how well it’s doing. Ultimately, agentic AI is still a machine; it lacks the ingenuity, creativity, and contextual analytical capabilities that are hallmarks of human intelligence.
Read more: 4 Changes Organizations Must Make To Enable Agentic AI
That’s why businesses need to prioritize keeping humans in the loop, no matter where and how agentic AI is being deployed. Businesses should put in place mechanisms and workflows to ensure human teams are periodically reviewing agentic AI’s work. Significantly, these mechanisms need to reinforce to human teams that AI is the assistant—and they are the leader.
Resolve resistance to organizational change
When humans are resistant to the organizational change that accompanies agentic AI, businesses may not feel that every instance is worth the battle. That’s a bad instinct. Because agentic AI requires the organization to adopt and accept significant change, the success of agentic AI can quickly be derailed by human resistance to this change. Moreover, the resistance isn’t always just due to general discomfort with change; it also can be caused by genuine fear of job losses.
Thus, businesses cannot ignore resistance to change any more than they can deem it an unimportant or overblown issue. Ultimately, businesses need to meet employees where they’re at: If employees are fearful of job impacts, the business needs to offer reassurances and explanations. If employees don’t understand how to make use of the technology, the business needs to offer training and support.
Don’t cheap out on talent
Agentic AI isn’t ready for business applications out of the box. It requires custom development work as a precursor to unlocking value for the specific business being served. For example, because agentic AI requires training on what the business considers to be appropriate vs. ineffective actions, there is no way to implement an off-the-shelf solution that will have this training already completed. While most businesses initially invest in the human teams necessary to develop the models that power agentic AI, they often don’t devote proper resources to deploy, maintain, and scale these tools. This decision can be incredibly consequential for the business, leading to technology that is underutilized and/or used incorrectly, in ways that expose the organization to avoidable risks.
Read more: 5 Reasons Organizations Are Replacing GenAI With Agentic AI
Fortunately, there’s no defense for cheaping out on talent and exposing agentic AI to the risks associated with underqualified teams managing it. If a business cannot afford to employ a team of machine learning engineers in house, a highly effective alternative is to turn to implementation partners, who offer unrivaled combinations of skills, expertise, and experience—qualities that cannot be matched by even the best in-house teams.
Final thoughts
Like any technology initiative, investing in agentic AI carries risks. The key is to understand these risks and to develop proactive, comprehensive AI strategies for mitigating and preventing them. Among the most important risk mitigation strategies are holding the line against the phenomenon of lift-and-shift when migrating workflows to AI, ensuring human teams provide adequate oversight of AI, identifying and working through resistance to organizational change, and investing in the right human talent to chart agentic AI’s future.
To learn more about identifying and addressing the risks associated with agentic AI, please reach out to the agentic AI experts at Fullcast. We’ve helped countless organizations to reset their thinking and their approach to agentic AI, so they can extract maximum value from this technology at minimal risk.
