Benefits & Restraints of Implementing Agentic AI

With AI innovation rapidly improving, it’s more important than ever that your company has the right talent in place to ensure sustained effectiveness. One increasingly powerful capability is taking over – agentic AI. This advanced form of AI handles more than your typical chatbot. It operates autonomously, setting its own goals, making its own decisions, and executing its own workflows. The best part? It only improves as it learns dynamically inside your company’s operating system. As innovation becomes imperative, companies are increasingly turning to this capability.

Below, we outline what sets agentic AI apart from other types of AI, as well as the benefits and constraints that come with implementing agentic AI. 

How is Agentic AI Different?

You’ve probably heard of the traditional AI capabilities, like generative or predictive, whose value lies in outputs that stem from boxed-in responses to clearly instructed inputs. These types of AI excel at forecasting, reporting, and creating recommendations, while a human worker takes those outputs to inform decision-making and execution.  

Agentic AI, on the other hand, learns within a company’s operating system until it can execute workflows autonomously, creating value beyond predefined inputs. This type of AI differs from the rest in its ability to go past creating things like reports and forecasts, and instead plans, executes, monitors, and adapts toward defined goals.  

Benefits & Capabilities of Implementing Agentic AI 

Companies across all different industries are adopting AI agents at a rapidly growing pace, with leading software vendors, like Microsoft, Salesforce, and SAP, having already begun large-scale efforts to implement them directly into their software platforms. A multitude of benefits are realized by the enablement of agentic AI-powered capabilities within a business:  

Autonomous Execution

Agentic AI enables autonomous execution by planning, coordinating, monitoring, and adapting workflows independently. Over time, the AI agents learn patterns and improve execution by comparing outcomes, adjusting strategies, and recommending process improvements.  

In procurement, for example, the AI agents detect supplier disruption continuously, evaluating alternate vendors, checking prices and inventory, drafting purchase orders, and updating ERP automatically. Compared to traditional workflows where a human manually reviews supplier risk reports and contacts suppliers, the agents automate the process entirely, allowing those same workers to instead focus their time on more important, less repetition-oriented tasks.  

Enhanced Efficiency

Going back to how agentic AI differs from traditional AI, its ability to operate with minimal human intervention gives it one of its greatest benefits: enhanced operational efficiency. Agentic AI enables a reduction in cycle times, improved resource utilization, and scaled operations without a proportionate increase in workforce, transforming end-to-end processes. Instead of replacing human employees, the agents shift their role from performing routine work to supervising the agents and managing exceptions, leaving more time for higher-priority tasks.  

One organization that has seen these benefits of efficiency is The Estee Lauder Companies. They implemented ConsumerIQ, an agentic AI solution built with Microsoft Copilot Studio, that centralized and streamlined consumer data to enable instant access to actionable insights. Over time, the agent reduced the time required for marketers to gather data from hours to seconds, preventing duplicated research while shortening cycle times.  

Smart Decision-Making

Agentic AI’s ability to make smart decisions is one of its hallmarks. By evaluating information, applying business rules, weighing tradeoffs, and determining the best course of action, agents make and reassess decisions without requiring human intervention.  

An example of these capabilities can be seen in improved store inventory management. Say a national retailer is using an agentic AI system, with the agents enabling continuous monitoring of sales velocity, inventory, weather, local events, and lead times. By determining stores at risk of stockouts, for example, the agent can decide the correct course of action, executing the appropriate actions automatically. The agents would therefore improve speed, consistency, and responsiveness compared to traditional store inventory management, where decisions are made manually on a periodic basis that can leave stores vulnerable to demand volatility.  

Restraints & Risks of Implementing Agentic AI 

In addition to all the benefits that come with implementing agentic AI, companies must also consider the various high-stakes over-reach, governance, and change resistance issues that exist. Before implementation, different restraints and risks must be considered:  

Governance & Guardrails

Agentic AI’s effectiveness depends highly on strong governance and clear, predefined guardrails. Due to its capability to go beyond recommendations and take action, companies need to establish policies before integration that define what decisions the agents can make independently, when human approval is required, and how an agent’s actions are monitored. These guardrails prevent the agents from executing conflicting actions and allow for continuous updates to implementation conditions. Effective governance includes role-based permissions, decision thresholds, audit trails, continuous monitoring, and human-in-the-loop (HITL) checkpoints.  

Johnson & Johnson is a great example of a company that has maintained emphasis on AI governance as they expand their use of AI across business functions. They established an enterprise AI governance framework, including cross-functional oversight, human review, risk assessments, and regulatory compliance policies that allow for scaled adoption that maintains trust. By doing so, Johnson & Johnson ensures that the autonomous AI agents operate within their clearly defined business and ethical guardrails under human oversight, reducing organizational risk as the agent accelerates workflows.  

Over-Reach & Over-Reliance

Organizations must avoid extending agentic AI’s authority beyond the cage of well-suited decision-making it should stay in. When a business allows AI agents to handle complex, ambiguous, or client-sensitive decisions – decisions that should require human judgement – that’s called over-reach. Similarly, over-reliance occurs when human employees get to a point with an autonomous system of agents where they simply accept the output without sufficient oversight. Both over-reach and over-reliance can lead to hard-to-identify errors that affect operations and clients. To prevent these issues, companies must define clear decision boundaries for the agents to stay in, requiring human intervention when that boundary is crossed.  

As an early adopter of agentic AI, Klarna took on an aggressive mindset toward AI-first customer support. They gave the agents permission to handle millions of customer conversations, significantly reducing resolution times. However, they found that the agents struggled with emotion in sensitive conversations with customers. In prioritizing automation, they had completely taken the sympathy out of customer service, being forced to cut back on the AI’s reach and reintroduce human representatives.  

Adoption Anxiety

Perhaps the biggest barrier in implementing agentic AI, adoption anxiety pertains to employee concerns over adoption’s impact on their role, responsibility, and authority within the business. Due to the agents’ autonomy in execution, employees may fear their own job or reduced trust in their abilities if correct change management practices are not taken. Organizations can resolve this adoption anxiety by positioning agentic AI as a tool that enhances employees’ performance rather than replacing them, inviting individuals known as change champions to advocate for the AI integration and settle concerns. Successful change management highlights top-down communication of how roles will change, demonstrating the AI’s value in removing repetitive workflows, building trust through transparency and proper AI literacy training.  

Say a company is experiencing employee skepticism as they seek to integrate agentic AI capabilities throughout operations. Rather than strictly focusing on the AI technology itself, the company could turn to a human-centered AI-readiness rollout that includes AI literacy training, with a gradual introduction of AI assistants into operations before they evolve into independent agents. This would increase employee confidence in integration by allowing them to get an idea of how the agents actually enhance human expertise instead of replacing it, getting employees to a point where they are on board with broader implementation.  

Looking Ahead  

As agentic AI innovation continues to grow in ability, so does the need for skilled workers around that innovation that gives it its value. Talented AI specialists assist in diminishing potential risks of agentic AI implementation, like over-reach, over-reliance, and adoption anxiety. Organizations utilizing the capability need to begin AI readiness, starting with hiring the right implementation team. 

The best results for AI integration include autonomous execution of workflows, enhanced efficiency, and smart decision-making, but they only exist when proper governance and guardrails are implemented, with change management best practices and a limit on the AI’s reach.  

Need help sourcing talent to help leverage the latest AI advancements? Click here. 

Contributions from Spencer Simco