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Using generative AI in the company - tools & practice

Using generative AI in the company - Tools & practice - All about methods, tools and application scenarios for AI, automation & data-driven efficiency increase in the company.

Introduction

Using generative AI in the company - tools & practice is at the heart of digital transformation. Today, companies are increasingly using artificial intelligence and automation to make processes more efficient, use data better and develop new business models.

What is Generative AI in the company - tools & practice?

Using Generative AI in Business - Tools & Practice describes methods and technologies that enable machines to perform tasks independently, recognize patterns, make predictions or process language. The aim is to supplement or automate human work.

Relevance in the corporate context

Using generative AI in the company - tools & practice enables potential savings, quality improvements and accelerated processes. Used correctly, it strengthens competitiveness and creates scope for value-adding activities.

Typical challenges

  • Lack of data quality & data access
  • Unclear objectives or missing use cases
  • Technology complexity & tool diversity
  • Acceptance problems & ethical issues

Practical example

A service provider implemented generative ki in the company - tools & praxis to automate invoice verification and document processing. The result: faster processes, a lower error rate and more time for customer service.

Our consulting approach

  1. Use case identification & target image definition
  2. Data analysis & technology selection
  3. Proof of concept & MVP development
  4. Scaling & change management
  5. Governance & performance measurement

Conclusion

Using generative AI in the company - tools & practice is not hype, but a strategic lever for digital efficiency. Clear goals, suitable tools and a step-by-step approach are crucial.

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FAQ

What are the benefits of using generative ki in the company - tools & practice in the company?

Efficiency, scalability, new services, better decisions - data-based & automated.

How do I get started with generative ki in my company - tools & practice?

With a targeted use case, data analysis and a proof of concept for technical feasibility.

What are the risks?

Data problems, lack of know-how, ethical challenges, poor integration into processes.

Which tools are used?

Depending on the objective: AI platforms, RPA software, process mining tools, GPT models, OCR systems and much more.

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