Editorial
Agentic AI is here. Seventy-nine percent of the 300 organizations PwC surveyed are already using AI agents, and over half of those companies have already achieved “broad adoption” or “full adoption [of agentic AI] throughout the company.”
Agents create new opportunities for increased productivity and efficiency, but agentic AI’s unique abilities mean that organizations will need to rethink how they approach cybersecurity, data security and human-AI collaboration. Below are three tips leaders can use to implement agentic AI securely and efficiently, making it easier to realize the full promise of the technology.
1. Keep a Human in the Loop — But Don’t Micromanage
To understand the capabilities of agentic AI relative to other AI tools, it’s helpful to compare their abilities to those of human employees.
An AI assistant is like an intern; it completes basic tasks and analysis via direct prompting from a human employee, who closely supervises its work and gives it direct and highly detailed instructions. An AI agent, on the other hand, is more like an entry-level employee. While its work still requires close supervision, it can act with a much greater degree of autonomy, and does not require direct, detailed prompting to perform every action. This means that agents require a different quality and level of supervision, much in the same way that interns and junior employees require different levels of supervision and management.
Imagine, for example, the different ways that you might approach collaboration with AI assistants and AI agents in the field of project management. While an AI assistant might need to be told when and how to create a project plan for a specific project, an AI agent with proper instructions would already know how to do this and would be able to accomplish the task without being told when and how to do so. One is like a project management intern; the other is more like a project management coordinator. Both have different applications, limitations and abilities.
To mitigate risks and improve efficiency, it’s important to keep a human in the loop. At the same time, it’s important not to micromanage agents, since their ability to act without overbearing instructions is exactly what makes them valuable.
2. Optimize Data to Improve Collaboration, Security and Efficiency
One of the greatest risks to overall AI efficacy and security is the quality of the data that powers generative AI. This is true of all generative AI technology, but the stakes are particularly high with agentic AI due to its autonomy and decision-making power.
When AI assistants like Microsoft 365 Copilot have access to confidential information, they can accidentally share that confidential information with the human user, opening the organization up to serious legal, financial and reputational consequences. That same risk exists with generative AI, but the risk is heightened due to the agent’s heightened decision-making authority and autonomy. When there’s no human in the loop to regulate the flow of overshared information, the potential consequences of oversharing are much greater.
This poses a serious threat not just to the security of the organization, but also to the whole idea of agent and human collaboration. If agents are in an environment where they can't be trusted to handle sensitive information in a secure way, they can’t automate tedious manual work or perform valuable tasks. They become less of a value-add and more of a liability.
This is one of the reasons why, according to IBM, organizations with greater AI maturity (including optimized and securely classified data) reported a 15% higher human-agent satisfaction score, indicating a much more positive and productive human-agent relationship.
3. Scale Agents With AI Governance
Microsoft’s Work Trend Index lays out a three-step process to help companies understand how to scale agentic AI efficiently. Each step of this process is defined by a different level of human-AI collaboration, culminating in a fully mature AI environment where autonomous agents work both alongside and independently from human employees.
At step one in this process, agentic AI is not yet implemented, and employees are using assistants like Microsoft 365 Copilot or Google Gemini. At this point, when agentic AI has not yet arrived, leaders need to make sure they’re laying the groundwork for agentic success by identifying proper use cases, securing and optimizing data, and developing a realistic roadmap for AI success.
Once the scaffolding for agentic AI is properly implemented, AI agents will be ready to join teams as “digital colleagues,” working alongside human employees with specific direction. This is step two of the three-step process. At this point, it’s important to test the use cases for agents that you’ve already outlined and help your workforce get adjusted to working with AI agents.
Finally, at step three, you can implement AI agents at scale, resulting in what Microsoft calls a “human-led, agent-operated" environment. Here, humans oversee large teams of agents that operate under their supervision and perform tasks independent of prompting.
It’s important for leaders to understand that, in order to get to step three, their organizations need a whole new set of trust and control mechanisms in place — essentially, an entirely new discipline and series of frameworks called AI governance. This includes guarding against accidental disclosures, malicious threats and aligning implementation strategies with business priorities, among many other changes.
Agentic AI has the power to completely transform the way we do business, but more work is needed to help it realize that potential and work side-by-side with human employees. By optimizing data and developing strong data and AI governance frameworks, leaders can get help their organizations realize the full promise of agentic AI.
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The future of AI is exciting, and it’s almost within our grasp.
Editor's Note: Read more about AI governance, AI colleagues and human-AI collaboration:
- AI Governance: A Roadmap for Success — In this final article in the AI governance series, you'll learn how to tie together all of the elements into an actionable AI governance roadmap.
- Will Your Next Hire Be an AI Agent? — Autonomous AI agents are making their way into every corner of the workplace. A look at where we are and where we're headed.
- AI Isn't Ready to Make Unsupervised Business Decisions — AI isn't ready to fly solo on business decision making. Keep humans in the loop and set up these guardrails before setting it loose.
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