Imagine Peter, a procurement manager, walking into his office on Monday morning. Two different realities could await him:
In the first version, an advanced AI tool is available on his computer. Peter opens the program, enters his questions, manually uploads supplier data, and the AI creates useful analytics for him. This is valuable, but Peter has to initiate every step—the AI only works when Peter "picks it up" and instructs it.
In the second version, an AI agent is already working when Peter arrives. It has automatically processed the invoices that arrived over the weekend, identified issues requiring attention, and has already initiated routine processes. Peter only needs to make strategic decisions, while the AI handles everything else autonomously—embedded in the corporate workflow.
These two scenarios illustrate one of today's key strategic decisions for companies: should AI be applied merely as a tool or as an active workforce member?
Using AI as a tool is like using an extremely advanced hammer. No matter how sophisticated this hammer is, it won't drive a nail by itself—it must be picked up and used each time.
Catherine, a corporate lawyer, uses an AI-based contract analysis tool. It can impressively identify risky clauses and make suggestions. But Catherine must manually upload each new contract, request the analysis, and then make decisions by interpreting the results. The AI is helpful, but it passively waits for her commands.
Characteristics of AI as a Tool:
In contrast, applying AI as a worker is like putting the hammer in the hands of a robot beside an assembly line. The AI agent is embedded in the organization's workflow, independently monitoring events and intervening when necessary—without human initiation.
Balazs, a financial department manager at a company that has successfully integrated AI agents into the invoice processing workflow. The agents continuously monitor incoming invoices, automatically reconcile them with orders, identify discrepancies, and only request human intervention when they cannot resolve something. The system operates 24/7, not waiting for instructions from Balazs or his team.
Characteristics of AI as a Worker:
The difference is not merely theoretical. Workflow-integrated AI agents result in dramatic efficiency improvements in three key ways:
When AI is merely a tool, human capacity remains a bottleneck. Even the most advanced AI tool provides no benefit if the user doesn't have time to "pick it up."
At a Hungarian logistics company, procurement specialists had to run supplier risk analyses weekly using an AI tool. Often there wasn't time for this amid daily tasks, so risks went undetected. After switching to a workflow-based AI agent that continuously monitors suppliers and automatically alerts, the risk detection rate increased from 27% to 94%.
AI as a tool is only active when a human uses it, while workflow-integrated AI agents can work 24 hours a day, seven days a week.
A Budapest-based financial institution used an AI tool for fraud detection, but only during business hours when analysts ran the checks. After implementing an AI agent integrated into the transaction process, the number of fraud attempts detected on weekends increased by 340%, as the automated system worked even when human analysts weren't on duty.
AI as a tool increases human productivity. AI as a worker transforms human roles, allowing professionals to focus only on high-value decisions.
At a Hungarian manufacturing company, the introduction of AI tools increased procurement specialists' productivity by 22%. When they switched to workflow-based AI agents, procurement specialists could focus on 78% more strategic projects because routine tasks were handled by the AI agents.
Implementing workflow-based AI agents is not a leap, but a gradual transition. Most companies go through three developmental stages:
1. Automation Islands: Initially, AI agents operate only in isolated, low-risk areas, such as routine data entry or simple categorization tasks. These already provide autonomous operation, but their impact is still limited.
2. Connecting Process Chains: In the next step, AI agents manage complete processes, from supplier registration through risk assessment to contract preparation. Significant efficiency gains are already experienced at this stage.
3. Adaptive Ecosystem: The most advanced level, where AI agents not only execute but also learn and adapt. The agents collaborate, optimize processes, and proactively suggest improvements.
Most Hungarian companies are currently positioned between the first and second phases, and the workflow-based approach can help them reach the third level faster.
Most of today's AI solutions still follow the tool paradigm—they offer advanced features, but humans must use them every time. Fluenta One takes a fundamentally different approach. We integrate our AI agents directly into workflows, enabling them to act autonomously.
Fluenta One's AI agents are not merely passive tools, but active participants in procurement processes:
The question of using AI as a tool or as a worker is really about what level of value creation we expect from technology. AI as a tool is valuable but limited—like a hammer that's only useful when someone picks it up. AI as a worker, embedded in workflows, acts independently, eliminating bottlenecks and freeing human talent for strategic tasks.
The most successful companies don't simply provide AI tools to their staff; they integrate workflow-based AI agents into their operations. The Fluenta One platform makes exactly this possible: making AI not just smarter, but also more autonomous, transforming it into a true digital workforce.
As technology evolves, the question is no longer "should we use AI," but "how do we integrate AI agents into our workflows?" The answer to this question fundamentally determines companies' competitiveness in the next decade.
