Your AI strategy: calculated, built, team-ready.

Routine and repetitive tasks (e.g. data analysis, simple bookkeeping, assembly-line work) are increasingly taken over by AI and automation.

Customer service: chatbots and virtual assistants partly replace simple service tasks, while complex customer interactions often remain human.

Production and logistics: robots and AI-controlled systems take over tasks in warehouses and factories.

New professions emerge, e.g. AI trainers, data scientists, AI ethics advisors or specialists in human-machine collaboration.

Creativity and social skills become more important: jobs that require empathy, creativity or strategic thinking (e.g. care, teaching, art, management) are hard to replace.

Hybrid working models: AI is often used as a tool to support people – e.g. doctors in diagnosis or designers in ideation.

Customer service & sales

AI can transform customer service by taking over repetitive tasks while raising the quality of interactions. Chatbots and virtual assistants answer standard enquiries in real time – around the clock and without waiting. That relieves your team and can cut costs by up to 30%. Lead qualification gets even more interesting: AI analyses prospect behaviour (e.g. on the website or in emails) and prioritises the most promising leads. Another big opportunity is personalisation: AI-powered recommendation systems suggest products or services that fit the customer – much like Netflix or Amazon.

Marketing & content: scalable creativity

Content creation is often time-consuming and expensive. AI can generate text, images and even video in seconds – from blog articles and social media posts to ad banners. AI is also particularly valuable for SEO: it analyses search trends, optimises keywords and even suggests structural improvements to texts.

Finance & accounting: precision without manual effort

Accounting is full of repetitive tasks such as invoice checking or data entry. With automated text recognition (OCR) and intelligent data processing, AI can take over up to 80% of the manual work. Fraud detection is even more decisive: AI systems spot suspicious transactions in real time and warn before losses occur.

Production & logistics: predictive instead of reactive

In production and logistics, predictive maintenance is one of the biggest levers: AI analyses machine sensor data and predicts when maintenance is due – before costly downtime occurs. In logistics, AI optimises delivery routes, stock levels and demand forecasts. Even quality control benefits: image processing systems detect defects in real time.

HR & people management: fairer and more efficient

In HR, AI helps evaluate applications faster and more objectively by filtering CVs against relevant criteria. It also supports training by identifying individual learning needs and suggesting tailored content.

IT & development: faster innovation, fewer errors

For development teams AI is a game changer: tools such as GitHub Copilot suggest code or find bugs before they become a problem. AI is also indispensable in cybersecurity, detecting and fending off attacks in real time.

EU AI Regulation (AI Act)

Purpose: regulation of AI systems by risk class (unacceptable, high, limited, minimal risk). Key points: ban on certain AI applications (e.g. social scoring, real-time biometric surveillance in public spaces), strict requirements for high-risk AI, transparency obligations for generative AI, fines of up to €35 million or 7% of global turnover for violations.

General Data Protection Regulation (GDPR)

Relevance: AI systems often process personal data. Key points: lawfulness, transparency and purpose limitation of processing, data subject rights (e.g. access, erasure, objection), data protection impact assessment (DPIA) for high risk, special attention to automated decision-making (Art. 22 GDPR).

Copyright

Current debate: use of copyrighted data for training AI models. EU Copyright Directive: liability and licensing are still to be clarified.

Technical norms and standards

ISO/IEC 42001 (AI management systems): standard for the responsible use of AI in organisations – risk management, transparency, ethics, compliance. IEEE standards (e.g. IEEE 7000 series): ethics, safety, privacy and reliability of AI systems. DIN SPEC 92001-1 (AI lifecycle): guideline for developing and operating AI systems in Germany. Further industry-specific regulations also apply.