Process optimization through the targeted use of AI

What is AI process optimization?

AI process optimization describes the targeted improvement of processes with the help of artificial intelligence. Instead of just making processes "leaner" or digitizing individual steps, you use AI to identify patterns in data, support decisions and automate recurring work steps - where classic rules or pure workflow automation reach their limits.

Essentially, the aim is to achieve measurable improvements from the interplay of process knowledge, data and AI models: shorter throughput times, less manual rework, higher quality and more transparency regarding bottlenecks.

It is important to note that AI-supported process optimization is not a "tool project", but a procedure. It starts with a clean process analysis, prioritizes the use cases with the greatest leverage in the organization, quickly implements an initial minimal viable product (MVP) and then improves iteratively - including clear KPIs that demonstrate the effect.

By specifying and prioritizing requirements for the Minimum Viable Product (MVP)

The analysis results in an initial backlog of measures - with requirements for which the use of AI appears to be particularly effective. These requirements are then refined together, checked for feasibility and prioritized according to impact and effort. This results in a clearly defined MVP that can be implemented at an early stage. This enables rapid learning and visible results - with a focus on feasibility, impact and a realistic starting point.

The benefits of process optimization through AI are systematically made measurable

A key success factor in process optimization using AI is the ability to prove the effect - both quantitatively and qualitatively. Right at the start of the project, we define measurable targets together: for example, reduced throughput times, error rates, costs per process or processing volume per day. These KPIs make progress visible and create transparency for stakeholders;

We also record qualitative effects - such as reducing the workload of teams or increasing customer satisfaction. In this way, AI not only makes efficiency tangible, but also verifiable.

From vision to implementation: using AI where it works

Not every process is equally suitable for optimization using AI. Here is a selection of example processes, including the measurable benefits (KPI), for which AI-supported process optimization is particularly relevant:

Document processing 

• Symptom: Information is stuck in PDFs/scans/contracts, is transferred manually - errors and queries accumulate.
• AI approach: Automatically classify documents and extract relevant fields (e.g. from forms/contracts).
• KPI: Processing time per document, capture/extraction accuracy, error rate.

Auditing and accounting

• Symptom: High effort for checking/releasing, many exceptions, media breaks between systems.
• AI approach:  Record invoices in a structured manner, automatically check plausibility, prioritize exceptions and prepare approvals.
• KPI: Invoice-to-approve processing time, number of exceptions per 100 invoices, correction postings.

Customer support (chatbots/email triage)

• Symptom: Mailboxes overflow, tickets are routed incorrectly, response times increase, customers are dissatisfied.
• AI approach: Automatically categorize, prioritize and routerequests to the right place - Chatbot answers standard questions, agents receive suggestions/draft answers.
• KPI: First Response Time, Time to Resolution, First Contact Resolution.

Logistics and planning

• Symptom: Forecasts are unreliable, inventories fluctuate, planning effort is high - short-term changes cause chaos.
• AI approach: Forecast demand/routes/stocks and support planning with recommendations (e.g. "what-if" scenarios).
• KPI: Forecast accuracy, service level, inventory range, out-of-stock/overstock rate, planning effort (hours/week).

Claims handling in the insurance industry

• Symptom: High number of cases, recurring patterns, a lot of manual checking - long processing times, inconsistent decisions.
• AI approach: Automatically pre-check damage, assess risk/complexity, support decision preparation and document checks (human remains in the loop).
• KPI: Processing time per claim, rate of subsequent claims/requests, leakage/fraud indicators, productivity per processing.

Our experience is that wherever processes are rule-based, data-driven and high-volume, process optimization through AI can create significant added value.

Why wibas is the right partner for process optimization through AI

Sustainable AI process optimization goes far beyond technological issues. When AI is used in business processes, familiar procedures, responsibilities and often the self-image of teams change. Such changes are often met with resistance - an aspect that can make the difference between success and failure.
wibas has decades of experience in supporting change processes. As experts in transformation, wibas ensures that optimizations are not only planned but also implemented. Through professional Change Management succeeds in bringing people along, anchoring new ways of working and thus ensuring the long-term effectiveness of AI solutions in the company.