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A procurement manager enthusiastically presents the company's latest investment to the board of directors—a revolutionary AI-powered invoice processing tool. "It extracts data from any PDF with 95% accuracy!" he says proudly. The demonstration is impressive. Six months later, employees are still manually transferring data into the ERP system. They're chasing approvals via email and tracking statuses in Excel spreadsheets. The AI has remained just an isolated tool, a point solution in a sea of manual processes.
In the current market frenzy of "AI-washing," almost every piece of software has suddenly become "AI-powered." But what does this actually mean? Mostly, it refers to chatbots, simple predictive models, or document recognition components. None of these are bad in themselves. The problem starts when these point solutions are sold as if they represent a complete digital transformation, as if AI alone could solve every process automation challenge.
Think about it: what's the value if AI perfectly categorizes incoming emails, but someone still has to manually forward them? What's the point if AI recognizes the data on an invoice but can't automatically check it against the purchase order, enter it into the system, and initiate the approval process?
The reality is that AI is just one tool among many. It's an important one, but it's not enough on its own. It’s like putting a Formula 1 engine in a horse-drawn carriage—no matter how powerful the engine is, without the rest of the car, it’s not going anywhere.
A service task is any automated step in a process that performs a specific function without human intervention. The key word here is automated—but not necessarily AI-based.
If-then logic: Simple conditional branches. If the amount is greater than €10,000, it requires CEO approval. It may not sound innovative, but most business processes are full of such decision points.
OCR (Optical Character Recognition): This technology has been with us for decades. It can extract text from scanned or photographed documents. It doesn't "understand" what it sees; it only recognizes characters and converts them into digital text.
RPA (Robotic Process Automation): RPA executes the same steps on a user interface that a human would. It clicks, copies, pastes, and fills out forms. It's useful where there's no API or when legacy systems don't support modern integrations.
API Calls: An API (Application Programming Interface) allows different systems to communicate with each other. When you need to check if a purchase order exists in the ERP or update inventory records, you use API calls.
Webhooks: When a specific event occurs, a new process is automatically triggered. This event-driven architecture enables processes to run truly automatically, without human intervention.
AI Agent: This is where intelligence comes in. An AI agent doesn't just see characters; it understands them. It knows that one string of text is a supplier's name, another is a tax number, and another is a total amount. It can interpret information in context and make decisions.
The figure below helps to understand in which cases it is worth using rule-based systems and in which cases it is worth using AI to perform the task:
To keep this from being purely theoretical, let's look at a simplified process. Let's follow an invoice from its arrival to its payment:
Do you see the complexity? Without even diving into the intricacies of automation, it took eleven different service tasks and seven different technologies to build a seamless process. If any single point operated in isolation, the entire process would break down.
The example above is built by connecting micro-processes. A micro-process is a well-defined process designed to achieve a specific business goal, constructed from smaller, reusable components. Think of it like a LEGO construction—the basic bricks (service tasks) are always the same at a technological level, but they can be combined in infinite ways. The advantage is a scalable system that can be used across multiple departments. It also allows for gradual process improvement, elevating the level of automation step by step.
The key is that this is not a single leap but a gradual evolution. If you still have paper-based processes, you must first digitize them before you can begin to automate. Once everything is digital, you can start connecting systems and adding more intelligent, automated elements.
The benefit of gradual automation is that far fewer errors creep into the system than if an organization aims for full automation from day one. Furthermore, this step-by-step approach gives employees time to adapt to new ways of working. Read our article on change management.
When you use separate AI tools, each works within its own database. The invoice processing AI knows about invoices, and the contract management AI knows about contracts, but they don't talk to each other. Therefore, the system doesn't know that the invoice that just arrived belongs to last year's framework agreement, which has special pricing.
Okay, the AI has extracted the data. Now someone has to copy and paste it into the ERP. Then, someone else needs to send an email notification. Every one of these "bridges" takes time and introduces the potential for error.
The AI sees the invoice, but it doesn't know that there was a quality complaint regarding this supplier last week. It doesn't know that the project it belongs to has been canceled. It doesn't see the full picture, only its own narrow domain.
An isolated AI tool creates a bottleneck not only within a single process but across the entire organization. The invoice processing system solves a problem for the finance department, but what happens when the HR department wants to automatically process incoming resumes, or the legal department wants to handle contracts? These purpose-built tools cannot be repurposed for other departments.
The company is thus forced to implement a new, standalone point solution for every new process it wants to automate. The result is an expensive, hard-to-maintain, and opaque mix of systems—not a scalable strategy, but merely a pile of isolated solutions.
In contrast, when service tasks are connected in an integrated process:
No Data Loss: All information flows through the entire process. What the OCR extracts is used by the AI; what the AI categorizes is verified by an API call, and so on.
No Waiting: One step triggers the next. There's no "I'll get to it tomorrow" or "let's wait for Mr. Smith to get back from vacation."
Full Transparency: Every step is logged. We know where each invoice is, how long it's been there, and what the next step is.
At Fluenta One, we believe in this exact micro-process-based, service-task-centric approach, which we offer through workflows complemented by user tasks. We don't sell AI; we sell intelligent process automation, where AI is just one component among many. This doesn't mean we underestimate the role of AI. On the contrary: we know exactly where and how it can add the most value. But we also know that it is not enough on its own.
The AI revolution is real. But it's not about solving every problem with AI. It's about intelligently integrating AI with other technologies to create real, working, value-generating processes.
Don't fall for someone who tells you that a single AI tool will solve all your problems. But don't dismiss automation because it seems too complex either. With the right approach—thinking in micro-processes and intelligently combining service tasks—true digital transformation is achievable.
Curious where your company stands? Fill out our free automation assessment and find out in 5 minutes which of your processes are ready for automation! You'll receive personalized recommendations for your next steps.
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