In a recent LinkedIn post, Michel Lieben 🧠 explores the evolving landscape of artificial intelligence in the workplace, drawing a crucial distinction between sophisticated workflows and true AI agents. As AI adoption accelerates, with predictions suggesting around 80% of companies will embrace them by 2027, Lieben 🧠 emphasizes the need to understand the nuances of this technological shift.
Understanding the Spectrum of AI in Business
Michel Lieben 🧠 begins by highlighting the growing presence of AI, noting that some organizations are even entrusting AI with significant financial responsibilities, such as corporate credit cards. This pervasive disruption is affecting departments across the board. However, the thought leader cautions that many systems perceived as advanced AI agents are, in fact, merely clever implementations of automated workflows.
To illustrate this difference, Lieben 🧠 outlines three scenarios for a common business task: scheduling a quarterly business review with a major client. This practical example serves to demystify the capabilities and limitations of different automation approaches.
Scenario A: Non-Agentic Workflows
In the first scenario, Lieben 🧠 describes a non-agentic workflow where humans remain firmly in control, utilizing tools that execute instructions. Here, a manager initiates the process, checks their calendar and CRM, and then prompts a tool like ChatGPT to draft an email. The human mind drives the initiative and decision-making, with AI serving merely as a content generation assistant.
“Human did all the thinking. AI didn’t take any initiative. It just helped with content.”
As Lieben 🧠 points out, this model relies on human oversight for every step, with the AI acting purely as a tool to execute specific, human-directed tasks.
Scenario B: Agentic Workflows
The second scenario introduces an agentic workflow, which incorporates automated triggers and logic but still lacks true autonomy. Lieben 🧠 explains that a calendar automation might be triggered by a time-based event, such as 90 days since the last review. This automation would then check the manager’s availability and proceed to draft and send an email. While this saves time and offers a semi-autonomous process, Lieben 🧠 cautions about its limitations.
“The workflow is semi-autonomous. It saves time… But won’t adapt to unexpected scenarios (i.e: follow-up, rescheduling)”
According to Lieben 🧠, these workflows, while efficient for routine tasks, struggle to adapt to unforeseen circumstances like rescheduling or the need for follow-ups, requiring human intervention to handle deviations from the norm.
Scenario C: True AI Agents
The most advanced stage, as detailed by Lieben 🧠, is the true AI Agent. This type of AI is defined by its ‘goal-oriented objective,’ such as maintaining client satisfaction and engagement. An AI agent continuously monitors various data sources, including CRMs and past conversations, to inform its actions.
“It coordinates multiple tools. It’s contextual & personalised. It focuses on a ‘bigger goal’ (i.e, client satisfaction) instead of a ‘small task’ (i.e, drafting & sending an email).”
In the context of the business review, an AI agent would not only detect the need for a meeting but also analyze client behavior, such as recent usage drops, and tailor the communication accordingly. It would proactively suggest meeting times, monitor replies, automatically follow up, and even prepare a meeting agenda based on prior interactions. Furthermore, Lieben 🧠 highlights that these agents can identify potential risks, like high churn probability, and alert relevant teams.
Michel Lieben 🧠 concludes by posing a question to his audience about their current AI automation efforts, underscoring the importance of strategic implementation and understanding the distinct capabilities of different AI applications.
📝 About This Content
This article is based on insights shared by Michel Lieben 🧠 on LinkedIn.
📅 Originally posted on October 31, 2025 | View original post on LinkedIn →