How Is AI Changing Day-to-Day Work in Companies?
AI manifests itself on two levels in day-to-day work. At the individual level, employees use applications for writing, programming, or analyzing. This use has already become commonplace in many companies.
At the process level, the task is more challenging. A business process consists of multiple steps, systems, and responsibilities. If AI handles only one of these, its output must be reliably transferred to the next step. It is precisely at these transition points that data inconsistencies, additional verification efforts, or new bottlenecks often arise.
The effective use of AI therefore begins with a process analysis. Only when the input, desired output, and subsequent processing are clear can one assess whether AI, traditional software logic, or a combination of both is the right solution.
Which processes are suitable for AI?
Tasks in which fixed rule sets quickly become too complex are particularly interesting. AI models can learn from examples which inputs should lead to which results. This also allows for the processing of unstructured content such as texts or documents.
Classifying Tickets and Inquiries
An IT support ticket can be assigned to the appropriate department or person based on its content. AI Flow uses a similar principle to classify incoming inquiries. This requires that categories, responsibilities, and the subsequent processing be clearly defined.
Assigning Knowledge and Preparing Answers
AI can link questions to internal knowledge and provide a preliminary draft response. For this to work reliably, the knowledge sources must be up-to-date, accessible, and unambiguous in terms of subject matter. For sensitive content, a controlled environment is also required, such as a central AI knowledge platform for companies.
Extracting Data from Documents
Relevant information such as amounts, dates, or reference numbers can be extracted from invoices, forms, or other documents. AI Flow Inbox Extract converts such information into a structured format so that operational systems can process it further.
Planning AI Process Automation Correctly
If you speed up a process at only one point, you may simply shift the problem to the next step in the workflow. Henrik Wenck therefore recommends taking a bird’s-eye view of the entire process.
- Identify Bottlenecks
The starting point is the point where a process is currently stalling. There, you should assess whether automation actually saves time or merely creates additional verification work.
- Check Input and Data Quality
An AI model can only work with the information available to it. Missing, inconsistent, or outdated data reduces the reliability of the result.
- Define further processing
Before implementation, it must be determined what happens after the AI step. Can the result be transferred directly to another system? Does it need to be approved? Who takes over a case if important details are missing?
- Set threshold values
Automated decisions require clear quality thresholds. A high confidence level may allow for direct further processing. If the result is uncertain, a human should review and decide.
- Incorporate Traditional Automation
Not every process step requires AI. Clear-cut rules can often be implemented more reliably using traditional software. In practice, the greatest benefit often comes from a combination: AI processes variable content, while deterministic logic controls clearly defined workflows.
Where does AI reach its limits?
AI operates probabilistically. A result that sounds plausible may still be factually incorrect. In long process chains, another effect comes into play: If multiple steps each have a certain probability of error, the deviations can compound over the course of the entire process.
Regulatory requirements must also be clarified before implementation. A functional AI use case is of little value if it processes data in a way that is not permitted within the company. Clear rules and robust AI governance to prevent “shadow AI” are therefore essential components of technical planning.
Responsibility and relationship management remain with humans. This applies to internal decisions as well as to contact with customers. Employees must be able to contextualize results, verify them from a technical standpoint, and assess the consequences of a decision.
What should a good AI training course cover?
An AI training course is helpful when it addresses the participants’ specific day-to-day work. A general introduction can spark interest. For practical application, employees need examples, tasks, and risks drawn from their own processes.
- Process knowledge: Employees must understand how their workflows function, where time is lost, and at which points errors can occur.
- Transferable principles: Individual tools change rapidly. Those who understand the underlying concepts can more easily evaluate new applications.
- Critical evaluation: Good output isn’t necessarily correct. Expertise remains essential for identifying errors, gaps, and inappropriate conclusions.
- Time to apply: After training, teams need space to try out new ways of working. Otherwise, under time pressure, they’ll quickly revert to old routines.
A culture of continuous learning is more effective than a one-time training session. The AI Academy for employees creates a common foundation for this. In addition, key advocates within individual teams can serve as points of contact and share specific use cases.
How can the added value of AI be measured?
AI tools can generate a lot of output in a short amount of time. This activity alone is not yet proof of productivity. The use of AI only becomes meaningful when a company evaluates the process before and after implementation using the same criteria.
- Lead time: How long does the entire process take?
- Quality: How reliable are the results?
- Rework: How often and for how long do employees have to make corrections?
- Usage: How many employees actually use the solution, and how regularly?
These metrics should be tracked continuously. Models, data, and workflows change. A single measurement immediately after implementation is therefore insufficient. When testing artificial intelligence, stability and behavior under changing conditions play a role alongside technical quality.
How should companies get started with AI?
The first step is an honest assessment of the current situation. Companies should identify which AI tools employees are already using, what tasks they are used for, and what problems they are intended to solve. This often reveals more usage than expected.
Next, potential processes are evaluated according to standardized criteria. These include expected benefits, technical feasibility, data availability, testing effort, and potential risks. An AI use case sprint helps structure and prioritize ideas.
An AI assessment or AI audit is suitable for a broader evaluation. Interviews with employees and management reveal where experience already exists and which processes are particularly relevant. This results in a roadmap with short-, medium-, and long-term measures. The AI Compass Health Check provides a structured starting point for this.
Tapping into AI Potential in a Structured Way
Would you like to identify suitable AI use cases, automate processes effectively, and prepare employees for the new way of working? accompio supports you from the initial assessment through the roadmap to technical implementation and empowering your teams.
Common Mistakes in AI Process Automation
A common mistake is an unstructured approach. Companies purchase a tool, offer training, and expect usage to develop on its own afterward. However, this approach lacks shared goals, responsibilities, and measurable criteria.
A binding plan is more helpful. It outlines which processes are to be changed, how employees will be empowered in the long term, and how the impact of the AI being used can be continuously measured. This also includes guidelines for permitted applications, data, and approvals. Since AI solutions evolve rapidly, this plan must be reviewed regularly.
Author: Henrik Wenck, Senior VP, accompio AI
About the Expert
Henrik Wenck has been working in the AI field for more than ten years and serves as Senior VP at accompio AI. He helps companies identify and implement suitable AI use cases and empowers employees to use AI productively.
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