ConsultingServices.aiAI Consulting for SMEsUse Cases & Business Value
Each use case describes a real everyday problem in IT & organizations, the suitable AI solution, and the measurable result. We focus on "quick wins" that typically pay off within 2 to 6 months (ROI 150-500%).
AI in Your Industry
Here you can see which AI scenarios offer the highest leverage in your industry.
Challenge: Material ordering, scheduling, and documentation steal productive hours on-site and in the office.
Challenge: Hours of preliminary review of standard documents and time-consuming client onboarding.
Challenge: Siloed knowledge among long-term employees and inefficient processes in B2B sales.
Challenge: Constantly occupied phone lines and high administrative effort from front desk staff.
High call center/support volume, 80% repeat questions. Overload during peak times leads to dissatisfaction (NPS drops).
Multilingual AI Chatbot & Voice Assistant handles first-level inquiries 24/7 and integrates into existing systems (Zendesk/Salesforce).
ROI: 150-300% p.a. | Implementation: 2-3 months (Quick Win).
Up to ~120,000 tickets p.a., 25-30 min. processing time per case and only 40% self-service rate tie up enormous support resources.
AI Service Desk Agents & Chatbots take over automatic categorization, resolution of L1 inquiries, and intelligent ticket routing.
ROI: 100-200% p.a. | Implementation: Pilot in 3-4 months.
Long release cycles (avg. 6-8 weeks), high manual effort in code reviews, and error-prone change management.
Developer copilots for code generation, automated test case creation, and AI-supported risk assessment of changes.
Fewer outages, faster time-to-market, and higher code quality.
Rising cost pressure with increasing cloud/on-prem complexity. Often lacks transparency over inefficient resource usage.
AI analyzes costs in real-time (anomaly detection), makes rightsizing recommendations, and provides forecasts for cloud budgets.
Ensures IT cost control with increased scalability.
Historically grown system landscapes (>200 systems) and 70% of data in silos block real AI innovations.
Building a scalable lakehouse architecture as a "single source of truth" for centralized analytics and AI.
Massive reduction in manual data preparation. Pilot in 6-9 months.
Customer data is spread across CRM, support ticketing, and billing. Lacks a consolidated view of customer value.
Harmonization and integration of distributed data into a central 360-degree data model for predictive analytics & personalization.
Significant increase in cross-sell and up-sell rates.
Sales and marketing work in silos, leads on the website drop off, scatter losses instead of hyper-personalization.
AI conversion agent on the web presence plus generative AI for creating tailored landing pages & mailings.
ROI: 200-500% p.a. | Better lead quality from the first contact.
High and inefficient energy consumption in data centers and networks (e.g., cooling 24/7 at full load).
AI-based energy optimization and smart cooling dynamically adjust infrastructure to actual load.
Contribution to CO2 reduction and achievement of sustainability goals.
Time-consuming, manual invoice verification. Risk of double payments or incorrect accounting in finance.
Optical Character Recognition (OCR) + AI extracts invoice data and automatically compares it with ERP orders.
ROI: 150-300% p.a. | Implementation: 3-6 months.
Customers cancel unexpectedly. Sales & account management only realize it when the cancellation occurs.
Algorithms analyze usage data, support tickets, and CRM history to proactively signal churn risk.
ROI: 200-400% p.a. | Increase in customer lifetime value.
Too high OPEX and inefficiency during disruptions. Often 45-60 min. mean time to repair (MTTR) and 60% false positives in alerts.
AI analyzes network telemetry in real-time for anomaly detection, alert reduction, and automatic root cause analysis (AIOps).
Significant reduction in MTTR and drastic decrease in false positives.
Unexpected machine or network failures block operations, maintenance only reacts (break-fix).
AI models analyze sensor/telemetry history to predict wear and failures in critical components.
Cost savings and extension of asset lifespan.
Employees spend over an hour daily searching for documents. Onboarding new talent takes months.
Internal company AI, connected to SharePoint, Confluence, and intranet, answers questions including source references.
Massive reduction in inquiries to senior employees.
Massive time investment for active sourcing, CV screening, and scheduling. "Time to hire" is too long.
Candidate matching & chatbots for CV uploads. Automated scheduling and initial interview guidance.
ROI: 120-250% p.a. | Significant reduction in time-to-hire.
Complex, multi-stage processes (research, data entry, reporting) tie up highly qualified labor.
Coupling LLMs with tools (APIs). Agents independently complete complex workflows instead of just generating text.
Freeing up work time for strategic core tasks.
Unstructured field data (photos, sketches, voice messages) force tedious, error-prone manual documentation.
AI interprets acoustic & visual data (e.g., damages) and translates them error-free into structured ERP/CRM tickets.
Real-time evaluation and avoidance of costly data errors.
Work Examples
This is how a use case idea becomes a solid work result that your team can use directly.
Entry questions, qualification, fallback, and forwarding in a clean process.
ResultQuestions are transferred into categories, answer modules, and responsibilities.
ResultBefore/After, key figures, risks, and a clear recommendation for the next step.
What needs to be maintained after go-live and who is responsible for what.
Transparency
Such values often seem like marketing exaggeration. In fact, they are due to the structure of AI projects: The cloud infrastructure costs little, while the freed-up work time ("time savings") creates massive value.
Important: In practice, it is almost never about layoffs. The team now spends its time on problems that create real service value for customers (level 2/3), instead of ticket ping-pong.
The results are model calculations based on typical SME starting situations.
The building blocks from which use cases are implemented.
Packages & PricesTransparent explanation — with concrete deliverables.
FAQThe most common questions before an inquiry.
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