A New Approach - bringing AI into the hands of business

The introduction of artificial intelligence (AI) in the areas of customer service and customer experience offers companies great opportunities - but also brings with it new challenges. While the benefits of AI are obvious, many companies are struggling with uncertainties and unanswered questions that make implementation difficult.

Decision-makers often face similar hurdles:

1. Understanding and maturity of the technology:
   There is often a lack of in-depth knowledge about AI technologies and their practical applications. Many managers do not know which models are actually available, which are suitable for their specific use case and how mature they are.
    
2. Profitability and use case identification:
   When does the use of AI pay off? At what volume does a solution make sense? Identifying and evaluating suitable use cases is often difficult.
    
3. Complexity of the topic:
   AI often appears difficult to understand and technically demanding. There is often a lack of an internal overarching concept with clear guidelines that provide orientation.
    
4. Future-proofing:
   Given the rapid pace of technological developments, there is concern that current solutions will quickly become outdated. Companies need models that are flexible and future-proof.
    
5. Lack of internal skills:
   The implementation and support of AI requires special skills and resources that are not yet available in many organizations.
    
6. Operating costs and flexibility:
   In addition to the initial costs, companies shy away from uncertain ongoing costs for adjustments and optimizations. Companies need solutions that are both flexible and offer cost certainty.
    
7. Legal uncertainties:
   The legal framework, particularly with regard to compliance with the GDPR and the new EU AI Act, creates additional hurdles. It is essential that all compliance requirements are met.
    
8. Variety of providers and models:
   The multitude of AI solutions makes integration and administration difficult. How can different systems be brought together in a meaningful way without increasing the workload?

These uncertainties lead to an area of tension: on the one hand, companies feel a strong pressure to introduce AI, but on the other hand, there is often a lack of clear approaches and strategies.

To overcome these challenges, we believe a new approach is required: a modular, flexible and equally future-proof platform that, in addition to AI and technology, also includes the necessary services for support from the start of the process to implementation and ongoing operation. It combines technological excellence with user-friendly handling and offers companies the opportunity to use AI responsibly and efficiently.

1. Consulting and evaluation:
   Support in the initial phase helps companies to identify suitable use cases independently of AI and carry out initial evaluations. The aim is to decipher the complex landscape of AI solutions.
    
2. Model-agnostic and flexibility:
   The platform should be designed to be model-agnostic, so that different AI models can be integrated quickly and easily, and also replaced or added to in the future, without always having to revise or replace the entire solution. This ensures the necessary adaptability to the high pace of technological innovation.
    
3. Step-by-step entry:
   Instead of launching a large, risky project, such a platform should enable companies to proceed in small steps. This allows companies to gain experience, achieve results quickly and control their investments.
    
4. Modular architecture:
   The platform should include different application areas, such as voice portals, agent assist tools or speech analytics. It can also be expanded with company-specific, specialized AI agents as required and would always remain flexible.
    
5. Centralized management and orchestration:
   Such a platform offers the possibility of managing various AI agents, consolidating their key figures and thus making well-founded decisions. This reduces complexity, improves business and decision intelligence and thus increases quality and efficiency in equal measure.
    
6. Focus on value creation:
   The platform concentrates on the core functionalities that offer the greatest added value. This enables companies to achieve ROI faster and replace solutions that are no longer required more easily.

The platform should make it possible to bring AI directly into the hands of business and operations. Decision-makers can thus concentrate on the requirements and potential of their daily operations instead of dealing with technical details and AI models. The focus is on developing practical and meaningful AI agents with functions that create real added value.

A modular platform should serve as a company's "AI control center" and meet the following requirements:

• Combine domain knowledge and AI expertise: This creates optimal solutions for specific use cases, integrated with internal data and aligned with existing processes.
   
• Best possible human-machine interaction: Sophisticated processes support collaboration between users and AI and lead to the best possible user experience.
   
• Scalability and future-proofing: Easy replacement of AI models and expansion with new functions and AI agents are a prerequisite for this.
   
• Cost control: Calculable operating costs based on the actual outcome enable the best possible profitability.

Companies need to decide how to use AI strategically: Do they focus on long-term prospects, or do they start with tangible, short-term successes at the same time?  A platform like this allows both. It offers companies the opportunity to use AI technologies responsibly, efficiently and future-proof today - and thus secure a clear competitive advantage.