AI Archive - Kauz

AI Phone Assistance for Small and Medium-Sized Businesses: What Matters Beyond the Per-Minute Rate

Elif Cinar — Tue, 04 Aug 2026 10:24:38 +0000

A guide for organizations that want to use AI to enhance their phone service and apply the right criteria in the process.

Telephone service channels have proven to be significantly more stable than many digitalization strategies had anticipated a few years ago. Particularly at municipal utilities, professional associations, chambers of commerce, universities, in the healthcare sector, and among specialized small and medium-sized businesses, the telephone remains the channel through which a significant portion of inquiries requiring clarification are received. At the same time, it is the least automated channel and the one with the highest expectations for reliability.

This is confirmed by the latest figures: According to the 2024 Customer Service Barometer , 78 percent of respondents in Germany used customer service last year, and about 92 percent consider its quality to be a decisive factor in the impression they form of a company. At 89 percent—second only to in-person contact—the telephone continues to enjoy the highest level of trust among all contact channels.

With advances in language AI, telephone-based assistance has quickly evolved from a future prospect to an available tool. At the same time, companies are investing more heavily: According to a CX study by Verint , 66 percent stated that they intend to increase their spending on AI to boost customer satisfaction. The relevant question is therefore no longer whether the channel can be operated with AI support, but rather what criteria an organization uses to select and implement such a system. This article outlines the most important of these criteria.

The market is becoming polarized and creating a gap

The budget standard segment

At one end of the spectrum are streamlined, cost-effective solutions designed for a quick, largely self-sufficient start. They answer calls, provide business hours, answer simple questions, schedule appointments, or take messages. They are practical and efficient for clearly standardized use cases and small organizations. Their limitations become apparent when issues become more complex, require deeper knowledge, involve more sophisticated integrations, or entail heightened data protection and operational requirements.

The Enterprise Segment

At the other end of the spectrum are comprehensive contact center platforms designed for large, integrated service environments with high call volumes, sophisticated routing and agent processes, and dedicated automation teams. For large corporations, they are often the right choice. For many medium-sized and institutional organizations, however, they are too comprehensive, too process-heavy, and require too much integration.

The gap between them

Between these two extremes lies a large portion of small and medium-sized businesses: organizations that handle many recurring but substantively complex phone inquiries and want to set up a professional service channel—without resorting to a purely off-the-shelf solution or a large-scale contact center project. For this group, the usual decision-making frameworks are only of limited use.

Why the price per minute is the least reliable criterion

The price per minute of conversation is the most easily comparable metric on the market, which is why it dominates the discussion. That is precisely where the problem lies: comparability is not the same as informative value. The price per minute obscures the factors that determine actual service quality: the design of the conversations, the quality and verifiability of the stored knowledge, integration with specialized systems, the data protection architecture, and day-to-day operations.

In addition, the per-minute price listed is rarely the total price in practice. Depending on the offering, costs for telephony, language models, data storage, support, compliance options, and development services may be added. A sound decision is therefore not made by comparing individual per-minute rates, but by considering the overall performance over the system’s lifecycle. The relevant question, then, is not “How cheap is a minute of talk time?” but rather “How do we design a phone service that fits our organization and can be operated responsibly?”

New at aiStudio

Your browser does not support this video.

GDPR compliant

Multilingual

Available 24/7

Test Now →

How an AI phone assistant is technically structured

To evaluate this, it helps to take a look at the architecture underlying most systems. At its core, it consists of two levels and a tension between them.

– The telephony and voice layer

It converts spoken language into text in real time (speech-to-text), generates speech from the responses (text-to-speech), and controls the flow of the conversation, including events and interruptions. To create a natural listening experience, good voice quality and low latency are crucial—that is, the time between the end of a statement and the start of the response.

– The AI and Knowledge Level

It determines what the assistant does in terms of content: how it categorizes a request, what knowledge base it uses to respond, when it asks for clarification, and when it hands the request off to a human. This level is configured, tested, and refined over time.

– The conflict of goals between latency and depth

These two levels are linked in a practical way that is often underestimated. A very large language model with a very broad dataset can handle more in terms of content, but tends to respond more slowly—which, unlike in chat, is immediately perceived as an uncomfortable pause on the phone. In many telephone use cases, therefore, a deliberately smaller model with a focused, well-maintained dataset is the better choice. Call quality is not achieved through maximum model size, but through the proper balance of model, knowledge, and response time.

– A natural voice is a requirement

Synthetic voices have now advanced to the point where callers often can hardly tell they’re machine-generated. This is a significant step forward, but it’s a basic requirement—not a quality feature in and of itself. An assistant that sounds natural but miscategorizes inquiries raises expectations it cannot meet. The fact that technical naturalness alone does not build trust is also evident in the numbers: In the 2024 Customer Service Barometer, only 43 percent rate the responses from chatbots and digital assistants as trustworthy. True performance, therefore, is demonstrated through behavior: whether the assistant correctly understands the inquiry, provides reliable rather than merely plausible-sounding responses, asks meaningful follow-up questions when something is unclear, and recognizes when it’s necessary to hand the matter over to a human.

What You Need to Know for Chat and Phone Support

In many organizations, website FAQs, chatbots, and phone hotlines are managed separately—by different teams with varying levels of expertise. This creates extra work and, as callers notice, inconsistent information across channels. The real added value of a platform-based solution therefore lies less in the fact that a system can handle phone calls, and more in the fact that phone and chat draw from the same knowledge base: knowledge is maintained once rather than multiple times, insights from one channel improve the other, and answers remain consistent. A shared knowledge base does not, however, mean identical conversation flows—phone calls follow their own rules for greetings, follow-up questions, confirmations, interruptions, and a style suited to the spoken word, and are specifically designed for the spoken medium.

Integration and Handover to People

Service calls rarely end with just providing information. It is often necessary to retrieve data, create a ticket, schedule an appointment, or continue a process in a specialized application. It is therefore crucial how flexibly a system can be integrated with existing systems such as CRM, ticketing, or specialized applications—including more specialized systems that fall outside the standard framework of common contact center integrations. Equally important is the structured handoff to staff members: A good telephone assistant recognizes their limitations and transfers the call rather than continuing a conversation beyond their area of expertise.

Privacy by Design

Information requiring special protection is regularly discussed over the phone. For regulated service providers, public organizations, and applications in the healthcare sector, data protection is therefore not just a general trust factor, but a specific selection criterion—one that cannot be added later. It must be clarified early on where calls and data are processed, what data flows arise, and whether the AI and knowledge infrastructure can be operated in an EU-hosted, private, or in-house environment when requirements are more stringent. These questions belong at the beginning of a project, not in the final phase.

AI Phone Assistance at a glance

Key decision criteria at a glance

When selecting an AI phone assistant, organisations should focus less on the price per minute and more on how the following factors work together:

Conversation handling: Is the enquiry classified reliably, answered accurately and transferred smoothly when necessary?
Model, knowledge and latency: Are the model size, knowledge base and response time suited to the telephone channel?
Cross-channel knowledge: Do phone and chat draw from one shared knowledge source that only needs to be maintained once?
Quality control: Is there transparency regarding answer sources, testing before changes and the evaluation of real conversations?
Integration: Can existing business systems be connected flexibly, with structured handovers to human employees?
Data protection and operation: Do the hosting and operating models meet the organisation’s requirements, from EU hosting to private cloud or on-premises deployment?
Implementation support: Are conception, setup, testing and ongoing development delivered as dedicated services rather than treated as additional costs within a per-minute pricing model?

A phone bot is a project, not a switch

These criteria lead to a practical conclusion: A reliable phone assistant for complex inquiries cannot be created simply by a well-worded prompt and an active phone number. Among other things, call objectives, typical pitfalls, escalation and transfer procedures, data access, and tone of voice must be designed—and all of this must be tested before regular operation begins and continuously refined thereafter. This calls for an approach that combines the platform with ongoing support and views day-to-day operations as a standalone quality service. Availability, reducing the workload on employees, consistent service across all channels, and controlled refinement are the key metrics by which value is measured.

How to make this happen: the Phonebot from Kauz.ai

This is exactly where the Kauz.ai Phonebot comes in. It is designed for discerning small and medium-sized businesses and institutional organizations—that is, for the space between affordable standard phone assistants and enterprise call center suites, where the criteria described above take center stage.

The starting point is deliberately not an isolated telephony product, but rather the shared AI assistant platform: Phone bots are created, configured, and further developed in aiStudio and share the same knowledge base as chatbots. This allows the phone bot to answer specific inquiries just like the website chat, while its conversational flow is specifically designed for the voice channel. For quality control, the platform incorporates tools proven in chatbot operations—knowledge management, transparency regarding the source of responses, tests and test reports prior to changes, insight into real conversations, and, for particularly sensitive topics, expert-reviewed answers drawn from topic catalogs.

At the telephony level, Kauz.ai relies on an EU-hosted voice and telephony infrastructure; the AI and knowledge layer can be integrated with EU hosting or, for more demanding requirements, with private cloud or on-premises options . Existing systems—such as those for data retrieval, ticketing, CRM, or specialized applications—can be integrated via flexible programming interfaces, as can the handoff to employees. The platform is complemented by support for design, setup, testing, and further development—depending on the project, either as managed operations or under shared responsibility with internal teams and partners.

Phonebots for Your Customer Experience

For small and medium-sized businesses, AI-powered phone assistance is no longer a question of “if,” but of “how.” This “how” determines far more than just a single metric. Whether a phone assistant adds value depends on the interplay of several factors: the ability to control call behavior; a shared knowledge base for chat and phone interactions that allows for independent call handling; the ability to integrate into existing systems; a data protection architecture designed from the outset; and operations designed to deliver consistent quality performance.

Anyone who uses these points as a benchmark shifts their perspective: away from a tool that you activate, toward a service channel that you design. For organizations whose issues are complex and whose reputation hinges on every single call, this is the real opportunity. They can tailor the phone—often their most direct and most frequently used line of communication with customers and citizens—so that accessibility, reliability, and consistency are not left to chance. This is precisely the difference between a number answered by an AI voice and a phone service that fits the organization.

Smart AI Solutions for Small and Medium-Sized Businesses

AI Workspaces for Your Specialist Teams

Give your employees access to all relevant AI tools through one central workspace and actively manage the use of AI within your company.

Learn more

Cross-Channel AI Automations

With more than 80 integrations, templates and 500 actions, you can quickly and easily automate a wide range of business processes.

Learn more

Actively Shape Your AI Communication

Whether it is an internal or external chatbot or your phonebot, aiStudio allows you to actively shape response behaviour, review ongoing conversations and initiate further actions.

Learn more

Der Beitrag AI Phone Assistance for Small and Medium-Sized Businesses: What Matters Beyond the Per-Minute Rate erschien zuerst auf Kauz.

Data-Sovereign AI in Practice—Recommendations for Small and Medium-Sized Businesses

Elif Cinar — Mon, 03 Aug 2026 10:57:38 +0000

At the very least since the politically motivated decision not to make Anthropic’s latest model, Fable, available in Europe for the time being, Europe’s technological dependence on U.S. providers has shifted from a much-discussed theoretical threat to a concrete reality. The ensuing debate has been followed by numerous political initiatives and commitments to greater technological sovereignty. However, small and medium-sized enterprises don’t have to wait for this to happen: They can already align their AI investments today in a way that ensures they remain future-proof even amid changing market and regulatory conditions.

In this article, we examine which criteria—when selecting an AI platform—determine whether data sovereignty will be preserved in the long term.

1. Technology-agnostic AI platform

A technology-agnostic AI platform is characterized by several features. Support for and flexible use of various LLMs form the foundation for a future-proof AI strategy. This is because not only is the performance of these models evolving rapidly—their optimal areas of application are also constantly changing. A model that is the best choice for a specific task today may be replaced tomorrow by a more powerful or cost-effective alternative.

With us, you not only gain access to all leading LLMs. With KauzSelection, we automatically recommend the optimal model for each task—delivering maximum quality at the best price.

Learn more

Open APIs and standardized interfaces are equally crucial. They enable seamless integration into existing enterprise systems and lay the foundation for an interoperable AI landscape. New technologies and standards—such as the Model Context Protocol (MCP) for the standardized connection of AI agents to enterprise systems—can thus be easily integrated. This keeps the platform open to future innovations, reduces proprietary dependencies, and protects existing investments.

2. Data Sovereignty Through Flexible Operating Models

Companies vary significantly in terms of their data protection, compliance, and IT infrastructure requirements. While a traditional cloud environment may be sufficient for some use cases, the location and operator of the cloud infrastructure also play a decisive role in choosing the operating model. Cloud offerings from major international providers such as Microsoft Azure, AWS, or Google Cloud may be suitable for many scenarios, while regulated industries in particular are increasingly turning to European cloud providers such as Open Telekom Cloud, Hetzner, or OVHcloud to meet stricter requirements for data sovereignty and compliance. For particularly sensitive use cases—such as in the financial or healthcare sectors—a private cloud or on-premises solution may be required, in which confidential information is processed within the organization’s own control boundaries. A sovereign AI platform should therefore support various hosting options and give companies the freedom to choose the operating model that best suits their needs—and to flexibly adapt or switch it as needed.

3. AI must not be a black box

Who uses AI in the company? What is the knowledge base behind the results? What are the actual costs of using AI? Where can it be improved? And can AI results be reviewed and, if necessary, adjusted manually?

As AI usage increases, it becomes difficult to maintain an overview of content and organizational structure. Different AI tools, decentralized licenses, and unclear responsibilities make it difficult to use AI securely and cost-effectively. At the same time, the risk of “shadow AI” increases when employees resort to unauthorized applications, with all the associated data protection implications. A in-house CompanyGPT as a centralized, approved work environment is therefore one of the most effective measures for curbing unauthorized AI use and preventing data leaks.

A central AI platform also provides transparency and control: It consolidates various AI applications into a unified working environment, manages access through role-based and permission-based models, and simplifies license management. At Kauz.ai, this transparency extends beyond day-to-day operations: AI chatbots can also be specifically controlled in terms of content and continuously optimized. Dialogues are documented and can be reviewed and improved as part of human-in-the-loop and feedback processes. This ensures that not only the usage but also the quality of the AI applications remains traceable at all times.

We combine governance, cost control, and quality management into a single interface, making AI transparent and manageable for IT, business units, and management alike. This transforms AI into a professionally operated enterprise application.

Book a consultation Try aiWorkplace free for 7 days

Try it for free or speak directly with our AI experts.

4. Maximum Cost Control

The costs of professional AI deployment are difficult to calculate: token prices fluctuate constantly, and usage varies depending on the task, frequency of use, and language model. Systematic cost monitoring thus becomes a crucial management tool for assessing which use cases are worthwhile and which employees actually need which licenses.

To this end, Kauz.ai offers a cost dashboard that transparently displays the consumption of AI points—a unit that makes the costs of different models comparable—by time period and use case. In addition, KauzSelection optimizes costs by automatically assigning each request to the appropriate LLM: Expensive top-tier models are used only where they are truly needed.

The licensing model is also designed to be flexible: Instead of rigid flat rates per user—which often force unnecessary upgrades—the pricing packages include a shared AI point allowance. If additional points are needed, they can be purchased on an ad hoc basis. This makes AI costs predictable, and companies pay only for what they actually use.

5. Think About Compliance from the Very Beginning

With the EU AI Act, the GDPR, and regulations such as DORA, the regulatory framework for AI use is becoming increasingly concrete. In particular, the traceability of AI decisions is evolving from an option to a requirement: The AI Act mandates comprehensive logging for certain AI systems to ensure traceability and human oversight.

A future-proof AI platform should not address compliance only after the fact, but should incorporate it from the ground up. That’s why our architecture is designed from the outset to meet the regulatory requirements of the EU AI Act and the GDPR—from the hosting infrastructure and data processing to the contractual framework. A finely granular role- and permission-based system ensures that only authorized individuals can review and approve AI decisions: a key component of the required human oversight.

6. Independent of external specialists

Data sovereignty has both a technical and a human dimension. Those who rely on external developers or consultants for every adjustment to their AI applications are simply trading dependence on the technology provider for dependence on the service provider. No-code platforms solve this problem by empowering business departments to independently create, maintain, and scale AI chatbots, AI agents, and AI workflows—without any IT support.

aiWorkplace and aiStudio, designed for developing AI chatbots, are consistently tailored for business users: content management, control of response behavior, and dialogue analysis are handled through a user-friendly content management system. Pre-built templates and over 500 actions in the aiWorkflow Builder also enable a quick start with multi-step AI process chains. This ensures that knowledge about the company’s own AI applications remains within the organization—and with it, control.

Data sovereignty is a matter of choice

Data sovereignty is achieved through specific architectural and selection decisions: a technology-agnostic platform with flexible model selection, open standards such as MCP, selectable operating models—including on-premises options—full transparency regarding usage, quality, and costs, integrated compliance, and the empowerment of internal business units. Companies that consistently apply these criteria when selecting a platform future-proof their AI investments—regardless of how the market and regulations evolve. The best time to set the course is now.

Book a consultation Try aiWorkplace free for 7 days

Try it for free or speak directly with our AI experts.

AI Impulses for AI-First Companies

Checklist: AI Maturity Levels

Evaluate your company’s AI maturity level across eight dimensions and receive concrete recommendations for the next stages of development.

Download now

Evaluating AI Smartly: ROI Measurement

Discover in this article how to evaluate AI use cases and prioritise them using concrete performance metrics.

Learn more

AI in First-Level IT Support

Discover how our customer Bardusch uses an AI chatbot in internal IT support to simplify and automate ticket handling.

Learn more

Der Beitrag Data-Sovereign AI in Practice—Recommendations for Small and Medium-Sized Businesses erschien zuerst auf Kauz.

Kauz Selection: The Right AI Model for Every Task

Elif Cinar — Mon, 22 Jun 2026 22:29:33 +0000

The generative AI market is undergoing constant transformation. New models are released every month, existing models are continuously being improved, and companies are faced with a growing range of technologies to choose from. At the same time, cost pressures are mounting. Modern frontier models are becoming more powerful, but are often significantly more expensive than their predecessors. In addition, token consumption for many applications has risen considerably: While simple chat queries require only a few hundred tokens, deep research tasks, multi-stage AI workflows, or the creation of extensive documents can increase resource requirements many times over. This creates a new tension for companies between achieving the highest possible quality of results and ensuring cost-effective use.

The Kauz Selection was developed specifically to meet this challenge.

The Right Model for Every Task

The Kauz Selection is an intelligent routing system within aiWorkplace that automatically selects the optimal model for each request. Instead of requiring users to manually choose between GPT, Claude, Gemini, or other models, Kauz-Selection analyzes the requirements of a task and decides within milliseconds which model offers the best combination of quality, speed, and cost.

Users no longer have to worry about selecting a model themselves. They simply enter their query as usual, and the appropriate model is selected automatically in the background.

For example, a translation or a short email can be processed by a cost-effective model, while complex contract analyses or demanding research tasks are automatically routed to more powerful reasoning models.

Counteracting Hidden Cost Spikes

Since 2024, the costs of leading frontier models have increased significantly. While GPT-4o started at $2.50 per million input tokens and $10 per million output tokens, prices for GPT-5.5 are now $5 for input tokens and $30 for output tokens. This means that input costs have doubled and output costs have even tripled. However, resource consumption per task is rising even more sharply. A typical chat with GPT-4o used to process around 700 tokens. Modern reasoning queries already require about 5,000 tokens. Agent-based tasks operate on an entirely different scale: the automated creation of a presentation or a moderately complex coding task can consume several hundred thousand tokens. The actual cost explosion is therefore driven not only by higher model prices but, above all, by the increasing complexity of modern AI applications.

However, most business inquiries do not require the full capabilities of a Frontier model. A summary, a translation, or an internal email can often be processed with smaller models while maintaining comparable quality. The situation is different when it comes to contract analysis, multi-step research, or complex reasoning tasks.

The key question, therefore, is no longer: Which model is the best? But rather: Which model is the best choice for this specific task?

Quality First—Cost Optimization as a Result

However, the development of the Kauz Selection was not focused on reducing costs, but rather on ensuring the highest possible quality of responses. The goal was to select, for each task, the model that achieves the desired quality with the least possible use of resources.

To test the performance of the Kauz-Selection, it was evaluated using 105 test questions from ten different application areas and five difficulty levels. The results show that intelligent model selection can deliver nearly the same quality as leading premium models.

Claude Opus 4.6 achieved an average quality score of 4.68 points. GPT-5.4 scored 4.65 points. The Kauz Selection scored 4.64 points, placing it at virtually the same quality level.

The main difference was in the costs. While Claude Opus 4.6 consumed an average of 27.09 AI points per test case and GPT-5.4 consumed 5.18 AI points, Kauz-Selection required only 1.91 AI points per case.

The result: costs up to 90 percent lower compared to a strategy that consistently relies on top-tier models for all tasks, without compromising on quality.

These results have since been updated using Opus 4.8 and GPT-5.5. The results remain virtually unchanged.

LLM orchestration is the future

The Kauz Selection is therefore more than just another AI feature. It forms the foundation for the sustainable and cost-effective use of AI within a company. After all, the more AI applications are integrated into business processes and the more complex agent-based workflows become, the more important it is to intelligently balance quality, speed, and costs.

Try it now in aiWorkplace for free

AI Features in aiWorkplace

Discover all the features that turn a central AI workplace for companies into a productive work environment.

Discover now

Our Customers’ Success Stories

See how other companies use AI and accelerate your own AI transformation with proven best practices.

Learn more

Your Knowledge Advantage: Kauz.ai AI Tutorials

Use our tutorials to learn how our AI solutions are applied in practice and how simple AI implementation can be.

Watch now

Der Beitrag Kauz Selection: The Right AI Model for Every Task erschien zuerst auf Kauz.

Goodbye tool zoo: how AI really works in marketing & sales

Elif Cinar — Fri, 15 May 2026 11:54:21 +0000

In many companies, marketing and sales are among the pioneers in the use of AI solutions. The communication- and data-intensive work in these areas opens up numerous use cases in which the added value of AI quickly becomes visible and immediately usable.

At the same time, many companies are facing a new challenge: instead of scalable AI processes, isolated individual solutions and specialized tool landscapes are often created that make integration, collaboration and sustainable scaling more difficult.

Although many teams are experimenting intensively with generative AI, there is often still a large gap between the first pilot projects and real productivity in day-to-day business. A lack of integration into existing processes, unclear responsibilities, data protection requirements and a lack of governance often prevent AI from reaching its full potential.

In this blog post, we show you how AI can be used in marketing and sales to create sustainable value. You will receive a compact overview of:

Relevant AI use cases in marketing & sales
Centralized AI workstations instead of fragmented tool landscapes
Technological and organizational requirements for scalable AI processes
Automated workflows and AI agents in everyday business life
Governance, data protection and secure AI use in a business context

The most important AI use cases in marketing & sales

Content creation & campaign production

AI is changing the way marketing teams create content and develop campaigns. Content can be produced faster, personalized and scaled across channels. AI models support research, structuring, variant creation and target group-specific communication in particular.

However, original ideas, creative concepts and a clear brand voice remain decisive differentiating factors. AI does not replace creative work, but acts as a powerful assistant that relieves teams, accelerates processes and creates more space for strategic and creative tasks.

Access to different AI models opens up additional scope for optimizing results in a targeted manner depending on the application, style and task.

Personalization & customer experience

AI enables companies to address customers more individually along the entire customer journey. Content and offers can be dynamically adapted based on behavior, interests and interactions.

A relevant and authentic approach remains the key success factor. AI supports teams in efficiently scaling personalized experiences, identifying needs more quickly and creating consistent customer experiences across channels.

Offer & sales automation

AI can make administrative and preparatory sales tasks significantly more efficient. Offers can be prepared automatically based on existing customer data, follow-up emails can be personalized and CRM entries from meetings or sales calls can be documented directly.

At the same time, sales opportunities can be identified more quickly, leads can be prioritized more specifically and sales activities can be implemented more efficiently. This noticeably reduces manual effort in day-to-day sales and gives sales teams more time for qualified customer meetings.

Market Intelligence

AI-supported market monitoring helps marketing and sales teams to analyze market movements, competitors and target groups faster and more data-based. In contrast to traditional alert systems, modern AI systems recognize correlations, automatically prioritize relevant developments and translate information directly into concrete insights.

Company news, product launches, job advertisements and competitor activities can be continuously monitored, analyzed and prepared for specific teams. AI agents automate the research effort and create a more sound basis for decision-making for campaign planning, sales and business development.

Central AI workstation for marketing & sales teams

But how can these and other company-specific use cases be scaled within individual teams and across departmental boundaries?

A crucial step is the establishment of a central AI platform that provides employees with structured access to relevant AI applications. Depending on whether tasks are mapped via simple prompts or deeply integrated AI workflows, suitable admin and governance structures are required to efficiently control usage, access rights and quality standards.

A central AI workstation creates a shared working environment for marketing, sales and other areas of the company.

Approved prompts, AI assistants, automations and agent-based workflows can be used in a standardized way and further developed across the organization. At the same time, data protection, compliance and brand guidelines can be secured centrally.

As a result, AI is evolving from an isolated productivity solution for individual employees to a scalable infrastructure for collaborative value creation. Companies that establish appropriate structures at an early stage create the basis for rolling out use cases more quickly, making knowledge available more efficiently and integrating AI into operational processes in the long term.

Try aiWorkplace for free

MCP for cross-system integration options

The AI maturity level of marketing and sales teams increases in particular when AI tools are deeply integrated into the existing data and system landscape. However, this is precisely where challenges still exist: With the Model Context Protocol (MCP), a common standard is currently being established through which AI models can access external systems and data sources. Many software providers are still at an early stage when it comes to implementing the corresponding interfaces, which often makes consistent and scalable AI workflows more difficult. But here, too, the following applies: those who rely on a central AI platform at an early stage ensure the sustainability of their AI investments and enable company-wide scaling.

Learn everything you need to know about MCP (Model Context Protocol) in our tutorial.

Watch the tutorial now

AI security: data control

If you want to make widespread use of AI with your company’s own data, you need clear security and governance structures. This involves both secure access to documents and the question of where and how data is processed, for example in European data centers, within isolated company environments or without use for external model training.

Marketing and sales teams in particular work with sensitive information such as customer data, offers and sales documents on a daily basis. AI systems must therefore adopt existing authorization concepts and ensure that employees can only access approved content.

Transparency is crucial for increasing AI automation: companies must be able to understand which data flows into AI-supported processes, which systems are connected and where human approval remains necessary. Only if AI remains secure, traceable and controllable can it be integrated into company processes in a productive and scalable manner.

GDPR, EU AI Act & IT Security Compliance

When introducing AI in a company, relevant security, data protection, and governance requirements should be considered at an early stage. Ideally, these requirements are already covered centrally through the AI platform in use.

AI Security at Kauz.ai

This might also interest you

Checklist: AI Maturity Levels

Get a structured overview of the different AI maturity levels in companies and learn which steps are essential for successfully continuing the journey toward becoming an AI-first company.

Download now

60 AI Use Cases at a Glance

Discover the many ways AI can be used across the entire value chain — from marketing and sales to HR, customer service, operations, and knowledge management.

Download now

Practical Insights: Success Stories

Learn from our customers how they use our AI solutions to make their companies more efficient and unlock new potential for growth, innovation, and collaboration.

Learn more

Der Beitrag Goodbye tool zoo: how AI really works in marketing & sales erschien zuerst auf Kauz.

AI in companies: A review and outlook based on 7 AI theses

Elif Cinar — Mon, 12 Jan 2026 14:29:25 +0000

Three years of generative AI in corporate practice: this phase was characterized by initial scepticism, occasional euphoria, numerous pilot projects and a steadily growing dynamic in almost all areas of the company. We are experiencing a time of profound change. It is becoming increasingly clear that companies that had the courage early on to systematically test and expand the use of artificial intelligence are now creating measurable advantages for themselves – be it through efficiency gains, new scope for action or at least the ability to remain economically stable during one of the longest periods of recession. AI will remain the dominant field of activity for many companies in 2026, but will be the subject of increasingly controversial debate. Above all, the requirements are increasing: Pure demos and error-prone agents are no longer enough. Users and companies expect reliable, productive results.

Based on leading studies on the use of AI in companies and supplemented by our own practical experience at Kauz.ai, we dedicate this blog post to the following questions:

How willing are companies to invest in AI?
How can the AI maturity level of a company be determined?
What are the key areas of application for AI?
What are the biggest challenges posed by the use of AI?
What are the key recommendations for action to become an AI-first company?
Which AI topics will keep us busy in 2026 and beyond?

1. expectation management: from PoC to roll-out

According to KPMG around 80% of the German companies surveyed plan to further expand their AI investments – half of them by up to 40%. This is despite the fact that several studies, including one by the MIT and the Boston Consulting Groupshow that so far only around 5% of companies that have invested in AI have been able to achieve a clearly measurable ROI.

We are also seeing steadily growing interest in our AI platform for companies. We take a differentiated view of the ROI debate surrounding AI: At the same time, we take a differentiated view of the ROI debate surrounding AI: Amortization is primarily a question of time horizon. The first AI-first companies did not do everything right from the start. Rather, they started working with AI early on, gaining experience and gradually expanding its use. In this way, they were able to achieve measurable effects – and also realize no less relevant indirect productivity gains.

By 2026 at the latest, it will be clear that there is no way around AI. The phase of PoCs will be replaced by stringent, scalable roll-outs.

Unser Praxistipp: In unserem E-Book zu KI-Use-Cases finden Sie eine kompakte Übersicht, wie unterschiedliche Unternehmensbereiche durch KI transformiert werden können.

Jetzt downloaden

2. European data sovereignty

European data sovereignty is increasingly becoming a key decision-making factor for companies that use AI. With the growing spread of data-intensive AI applications, the dependency on sensitive company, customer and personal data is increasing – and with it the risk of losing control over their processing, storage and further use. European data sovereignty ensures that data is used in accordance with European values, legal requirements and transparency requirements and that companies retain full sovereignty over their data at all times.

A clear change in perspective is emerging for 2026: data sovereignty will no longer be defined primarily by the physical location of data centers in Europe, but increasingly by the actual control exercised by European providers. The decisive factors are ownership, operating and governance models, access rights and independence from non-European jurisdictions. For high-risk sectors in particular – such as finance, healthcare, industry, public administration or critical infrastructures – this aspect is becoming a mandatory prerequisite for the legally compliant and trustworthy use of AI. However, medium-sized companies that are expanding their AI processes are also increasingly recognizing and appreciating the strategic and psychological added value of European data sovereignty.

Testen Sie kostenfrei unsere KI-Plattform aiStudio und entdecken Sie die vielfältigen Einsatzmöglichkeiten von KI in Unternehmen – in einer sicheren, kontrollierten Umgebung mit klaren Governance-Strukturen.

Jetzt aiStudio testen

3. CompanyGPT as a starting point: from individual productivity to process efficiency

2025 has shown very clearly how companies implement a holistic AI strategy in practice: In addition to the introduction of chatbots, the establishment of CompanyGPTs was by far the most popular entry project – be it to make company knowledge more searchable or to provide a secure CompanyGPT that uses sensitive company data to optimize text-based workflows. CompanyGPTs have also become a key measure to curb unauthorized AI usage within the company. According to Bitkom around 10% of all companies in Germany are already affected by so-called shadow AI – and the trend is rising.

It also became clear that generative AI only creates real added value in the corporate context if the internal data quality is high. In addition, faster access to information alone is not enough – only advanced AI workflows enable real process changes and create scope for innovation and measurable business impact.

This pattern will continue in 2026: Companies that start with AI will first introduce CompanyGPTs and gradually roll them out based on well-curated knowledge bases. Building on this, the use of AI workflows will increase significantly and contribute significantly to the design of AI-optimized business processes contribute.

Unser Praxistipp: Testen Sie kostenfrei unseren aiWorkplace - ein sicheres und funktionsstarkes CompanyGPT für mittelständische Unternehmen.

Jetzt aiWorkplace testen

4. Use of AI as a lever for increasing sales through innovation

While companies are currently using AI primarily to automate processes and thus increase efficiency, a change in perspective is emerging as the level of AI maturity increases.

For 2026, the balance of power is expected to shift further away from a pure focus on efficiency in favor of an innovation-driven focus on increasing sales. AI is increasingly seen less as a pure productivity tool and more as a strategic enabler that redefines value creation and enables sustainable transformation.

5. technical focus: integrations and AI workflows

Generative AI unfolds its full potential, especially across systems. In companies with advanced AI deployment, the focus is therefore shifting to technical issues relating to the integrability of databases and their use in multi-stage, automated workflows.

With the Model Context Protocol (MCP) the AI community has agreed on an open standard to make it easier for AI models to communicate uniformly and securely with external systems, data sources and tools. Software manufacturers are responsible for implementing the necessary interfaces. In 2025, however, many providers have reacted more slowly than companies with a high level of AI maturity require.

For 2026, we expect significantly better framework conditions for the integration of AI models into existing system landscapes – and thus the basis for a simpler and broader expansion of diverse AI workflows.

Unser Praxistipp: In unserem E-Book zu KI-Workflows finden Sie eine strukturierte Übersicht über einfache wie auch komplexere Use Cases, die sich schrittweise erproben, gezielt ausbauen und sinnvoll miteinander kombinieren lassen.

Jetzt downloaden

6. Engineering focal points: AI memory, AI agent orchestration and new OCR models

In order to gain further performance as an AI organization, central development areas must be expanded in a targeted manner. To enable AI systems to learn across contexts and remember individual usage patterns, memory functionalities must not only be further improved, but also designed to be secure, controlled and governance-compliant. The orchestration of AI agents also needs to be significantly improved. Instead of isolated models, agent systems based on the division of labor that autonomously plan, coordinate and harmonize tasks are coming to the fore. Their control also requires the development of clearly defined roles, interaction logics, control mechanisms and transparent decision-making and escalation paths. The rapid progress powerful OCR models acts as another important enabler: it enables agents to reliably access previously unstructured documents and information sources, thereby significantly expanding their scope of action.

In 2026, the establishment of professional engineering structures will therefore come to the fore. They are necessary in order to reliably orchestrate complex AI ecosystems, design them cost-efficiently and enable truly interdisciplinary interaction between IT, business and specialist departments – as the basis for secure, scalable and productive AI use.

7 Strategic partnerships: The enabler for AI-first companies

The successful use of generative AI in companies is not determined by the technology alone, but to a large extent by the strength of the partner ecosystem. As AI maturity increases, complex issues come to the fore: access to specialized technological expertise, the technically sensible design of AI applications, support for data integration and requirements for security, compliance and the scalable orchestration of complex AI workflows.

Strategic partnerships enable companies to access modern technologies more quickly, use industry-specific best practices and develop AI solutions embedded in existing system landscapes rather than in isolation. At the same time, specialized partners ensure the reliable and sustainable use of AI in day-to-day business through content templates, governance structures, security concepts and regulatory expertise.

For 2026, we therefore expect strategic AI partnerships and industry-internal exchange to become even more important: They quickly pave proven paths from initial use cases to measurable business impact and structural change in company processes.

Unser Praxistipp: Das Kauz.ai Partnerökosystem verbindet Technologie-, Branchen-, Fach- und systemische Expertise und kann Ihr Unternehmen entlang des gesamten KI-Lebenszyklus unterstützen.

Jetzt KI-Partner kennenlernen

Sources:

Accenture (2025): Sovereign AI

BCG (2025): The Widening AI Value Gap

Ernst & Young (2025): AI Barometer

KPMG (2025): AI in the German economy

McKInsey (2025): Global AI survey

MIT (2025): State of AI in business

Der Beitrag AI in companies: A review and outlook based on 7 AI theses erschien zuerst auf Kauz.

AI avatars: Innovations in user interfaces

Lukas — Fri, 24 Oct 2025 09:33:21 +0000

The design of AI user interfaces will play a central role in the upcoming fall release. Even though the use of AI avatars is still in its infancy, we assume that they will increasingly replace the classic chatbot window. This is why the early integration of leading avatar technologies was an integral part of our product roadmap. Sign up now for the fall release to experience the new AI user interfaces.

Artificial intelligence promotes technology acceptance

Why has artificial intelligence established itself so rapidly as a productivity tool in the last two years? Because users immediately recognize the added value of a CompanyGPT or an AI chatbot with excellent language comprehension and integrated workflow functionality – and benefit from it directly. But that’s just the beginning: some communication-intensive use cases, such as in customer service and sales, are becoming self-evident, but AI will also make internal, complex processes more automated and easier to design.

Multimodal AI expands application scenarios

User acceptance of new technologies has always been the decisive factor for successful implementation projects – and a well thought-out user interface plays a central role in this. However, generative AI opens up completely new design possibilities: Thanks to its multimodality, it can understand and process writing, spoken language and images.

Communication with systems is therefore becoming more natural, more personal and more diverse. AI avatars can create closeness, build trust and reduce the distance between humans and machines. Precisely because users increasingly expect dialog-oriented experiences, such avatars will become an integral part of modern user interfaces in the future.

However, AI avatars also offer exciting new opportunities to combine the digital and physical sales, service and marketing experience. Innovative trade fair organizers, retailers and hotel chains are already experimenting with using AI avatars to speed up and simplify their processes at the point of sale.

Experience AI avatars live in the fall release!

In our fall release, we will show you how AI avatars can look and be used in your company.

Der Beitrag AI avatars: Innovations in user interfaces erschien zuerst auf Kauz.

Smart evaluation of AI: ROI measurements and investment decisions

Angelika Pfeifer — Fri, 11 Jul 2025 12:38:23 +0000

“Just start with AI” is certainly a good credo at the beginning of the AI journey. However, if you want to go beyond prototype applications, have achieved initial AI successes and want to scale them, you should also take a strategic approach to large-scale AI implementation.

What do companies expect from the use of AI?

An ROI example from customer service

The use of an AI chatbot in customer service can potentially realize all these expected benefits, as the following quantitative example illustrates: A medium-sized company with 100 employees has a service team with 5 FTEs. An AI chatbot is to be implemented for the automation of service requests, which:

answer simple customer inquiries independently
makes the internal knowledge database more searchable for service employees

The costs incurred amount to approx. 20,000 euros per year in the first year and approx. 17,000 euros in subsequent years. Employee costs in service without automation amount to approx. 300,000 euros per year. If 30% of service requests are automated, the company can save 1.5 FTEs or deploy them elsewhere and thus save 90,000 euros per year in service. This means that the AI implementation pays for itself in the first year.

An ROI example from internal knowledge management

The medium-sized company from the previous example also relies on AI for internal knowledge management in order to make processes more efficient. Although the return on investment (ROI) cannot be calculated as precisely as in customer service, a rough, quantitative estimate is still possible.

If all 100 employees save 10 minutes a day – at a very conservative estimate – by using an AI chatbot or CompanyGPT, for example through quick access to guidelines, processes, instructions or reusable text modules, this results in an annual productivity gain of around 3,600 hours. At an internal hourly rate of 40 euros, this corresponds to a value of around 145,000 euros, which can be used for other tasks.

At 15,000 per year, the costs for a second AI chatbot are already significantly lower than the initial implementation.

Consider AI scaling from the outset

The prioritization of use cases, meaningful ROI evaluations and the technical setup or use of a suitable AI platform are crucial for AI success. Companies that use different solutions for their AI automation not only pay more, but also need significantly more time to realign their processes. Kauz.ai’s aiSuite, on the other hand, provides you with best-in-class AI tools for data, chatbot and agent management on a single platform. This is constantly being expanded with new value-adding AI methods and simplifies and expands the possible uses of artificial intelligence in companies.

Creating the right set-up for all AI maturity levels

Companies that take artificial intelligence seriously as an economic growth driver often start with pilot projects and gradually expand them. By introducing central responsibility for AI – in the form of an AI manager, for example – interdisciplinary project groups can develop into sustainable professionalization. In this way, initial training courses in individual departments develop into a company-wide, everyday use of AI tools. The following overview of AI maturity levels helps to keep the possible organization of the future in mind, even at the beginning of the AI journey.

Download AI maturity levels as PDF now

Take qualitative assessment criteria seriously

The introduction of artificial intelligence (AI) requires openness and a willingness to explore at every stage of organizational maturity. The exchange of experience between AI users within companies plays a central role in this – it promotes creativity, the ability to learn and continuous development in dealing with new technologies. A platform provider that not only provides new AI functions, but also communicates them transparently and regularly, makes a decisive contribution to establishing a continuous learning culture. In times of rapid technological developments, this ability to learn is a key competitive advantage.

AI transformation: ROI and other success factors

In this webinar, AI experts will provide insights into successful AI transformations. Learn more about AI use cases, their evaluation as well as organizational and technological setups for a successful AI implementation.

Watch the webinar now

Westfalenhallen Group of Companies

Find out how the trade fair provider was able to automate recurring requests for different trade fair events.

Read now

ias Group

Find out how the integration of generative AI improves the health portal and optimizes the use of information by customers.

Read now

Warendorf district

Find out how the Warendorf district administration was able to improve the customer experience by making an AI chatbot available 24/7 in its service portal.

Read now

Der Beitrag Smart evaluation of AI: ROI measurements and investment decisions erschien zuerst auf Kauz.

How CompanyGPTs are transforming medium-sized companies

Angelika Pfeifer — Fri, 20 Jun 2025 09:14:36 +0000

Während große Konzerne bereits eine KI-Adoptionsrate von rund 40% verzeichnen, liegt dieser Wert im europäischen Mittelstand deutlich niedriger: Lediglich 10 bis 20% der Unternehmen haben bislang relevante Implementierungen und Fortschritte im Bereich der Künstlichen Intelligenz erzielt.

In diesem Artikel erfahren Sie:

Wie mittelständische Unternehmen den Anschluss an den technologischen Fortschritt halten
Wie ein CompanyGPT den Schnellstart in Produktivitätssteigerungen ermöglicht.
Welche Ausbaustufen ein CompanyGPT für KI-Automatisierungen bieten sollte.
Worauf Sie bei der Auswahl eines CompanyGPTs achten sollten.

Ein CompanyGPT gehört zum neuen Arbeitsstandard

Mit dem Durchbruch von ChatGPT, Perplexity & Co. als neue Generation von Produktivitätstools lag für alle Unternehmen eine Schlussfolgerung nahe: Ein unternehmensinternes CompanyGPT, angewendet auf Geschäftsdokumente, wird Arbeitsprozesse sowie die Entwicklung von Produkten und Services grundlegend verändern.

Schnell kamen erste Lösungen auf den Markt – doch damit auch neue Herausforderungen. Die Vielzahl an KI-Tools und Beratungsangeboten führt zunehmend zu Überforderung und einer Angst, unter Marktdruck die falschen Entscheidungen zu treffen. Denn ein CompanyGPT ist ein zentrales, aber nur eines von vielen zukünftigen Produktivitätstools. Erst in Verbindung mit durchdachten KI-Workflows entsteht daraus ein wirklich transformativer Beitrag zur modernen Arbeitswelt.

aiWorkplace: Die KI-Schaltstelle für Mitarbeitende

Daher haben wir unsere aiSuite, die auf die Entwicklung und Verwaltung von KI-Chatbots und KI-Agenten ausgerichtet ist, um den aiWorkplace erweitert. Dabei handelt es sich um eine CompanyGPT-Anwendung, mit der sich die individuelle Arbeit mit Unternehmensinformationen und KI-Workflows zusammenführen lässt.

https://kauz.ai/wp-content/uploads/2023/11/Video-aiWorkplace.mp4

Das zeichnet den aiWorkplace aus:

Sichere KI

Datenschutz ist längst keine lästige Regulierung mehr, sondern ein großes Anliegen aller Unternehmen. Das Thema reicht von Hosting-Optionen, also Unternehmensinformationen vertraulich zu halten bis hin zu Möglichkeiten KI-Halluzinationen zu erkennen und zu verhindern. Kauz.ai bietet hier drei Optionen: von der EU-Cloud, Private Cloud bis hin zur On-Premise Installation. Mit dem aiWorkplace ermöglichen Unternehmen ihren Mitarbeitern Zugriff auf ausgewählte und idealerweise kuratierte Teile ihrer Unternehmensdaten, sowie zentral verwaltete, aber individuell freigeschaltete KI-Agenten.

Systemübergreifendes Arbeiten

Mitarbeiter können auf einer vertrauten, benutzerfreundlichen KI-Oberfläche unternehmensspezifische Inhalte erstellen und KI-Agenten für weitere Prozessautomatisierung rund um effizientes Dokumentenmanagement systemübergreifend nutzen.

KI-Kostenkontrolle

Der Einsatz von CompanyGPT und erweiterten KI-Workflows steigert nachweislich die Produktivität und sollte allen Wissensmitarbeitenden zugänglich gemacht werden. Damit einher gehen jedoch auch Kosten, die aktiv gesteuert werden müssen – das betrifft Lizenzen und den gezielten Einsatz von Large Language Modellen. Im aiWorkplace erhalten Mitarbeitende Zugang zu verschiedenen KI-Modellen, bei On-Premise Installationen auch auf Open-Source-Varianten. Über die integrierte Kostenkonsole können Unternehmen die Nutzung und Intensität der KI-Anwendungen transparent nachverfolgen und steuern.

KI-Skalierbarkeit

Der aiWorkplace kann sowohl als eigenständiges Modul als auch als Teil der aiSuite lizenziert werden. Je nach KI-Reifegrad des Unternehmens können so schrittweise weitere Funktionalitäten freigeschaltet werden, ohne die Mitarbeitenden mit einer Vielzahl unterschiedlicher Tools zu überfordern. KI-Skalierbarkeit zeigt sich auch auf Kostenebene: Der Bezug weiterer Lizenzen über eine zentrale Plattform ist kosteneffizienter als der Bezug über mehrere Plattformen.

Regulatorik

Mit der Einführung des aiWorkplace ist die Einhaltung aller notwendigen Regularien, wie sie beispielsweise im EU AI Act definiert sind, gewährleistet. Diese werden zudem laufend aktualisiert. Beachten Sie bitte, dass bei vielen anderen Plattformen regulatorische Prozesse oft wiederholt durchgeführt werden müssen, wenn sich Änderungen ergeben.

Ausblick: Kollaborationsfeatures

Der Funktionsumfang des aiWorkplace wächst kontinuierlich. Uns ist bewusst, dass eine erfolgreiche KI-Adoption sehr stark vom Austausch von Erfahrungen unter Kolleginnen und Kollegen lebt. Deshalb werden wir in den kommenden Monaten die zwischenmenschliche Zusammenarbeit im aiWorkplace durch innovative Kolllaborationsfeatures gezielt stärken und weiter ausbauen.

Test aiWorkplace now

9 Auswahlkriterien für ein CompanyGPT

Holistic AI platform

Hosting & license model

User-friendliness

Availability of further productivity tools

Integration capability in third-party systems

Availability of different LLMs

Flexible security levels for handling sensitive data

Simple implementation of multi-agent systems

Powerful new AI automations

Organisationstransformation durch ein CompanyGPT

Die Einführung eines skalierbaren CompanyGPT markiert einen entscheidenden Schritt in Richtung einer KI-gestützten Organisation. Bereits durch die Bereitstellung eines internen GPTs, das auf Unternehmensdaten zugreift und individuelle Arbeitsbereiche unterstützt, kann ein spürbarer Produktivitätsschub entstehen. Kommen dann noch spezialisierte KI-Assistenten hinzu, wird Künstliche Intelligenz zu einem aktiven Bestandteil der Unternehmenskultur.

AI workplace of the future

In this e-book, you will learn how organizations are changing at a process and cultural level through the use of AI.

Download now

aiSuite Spring Release

Our release webinar will give you a practical insight into the use of aiSuite and aiWorkplace.

View now

AI transformation of companies

This expert talk will give you an overview of the organizational transformation stages that result from the use of artificial intelligence.

View now

Der Beitrag How CompanyGPTs are transforming medium-sized companies erschien zuerst auf Kauz.

From dialog-based AI chatbots to action-oriented AI agents

Angelika Pfeifer — Fri, 23 May 2025 13:08:25 +0000

As an experienced company in the development of chatbots, the breakthrough of generative AI was a real game changer for us. The implementation of dialog-based AI applications has become much easier and therefore faster – the effort on the customer side has been significantly reduced. The flexible and personalized response behavior of AI chatbots increases the user experience and acceptance, allowing entire business processes to be optimized and redesigned. With Agentic AI, we are now on the cusp of the next evolutionary stage: the transition from dialog-based chatbots to action-oriented, autonomous chatbots. AI agents.

In this article you will learn:

How AI chatbots can be developed into capable AI agents
What technical requirements are necessary for this
How companies sustainably manage the economic use of AI
What to pay particular attention to when introducing and managing AI agents

From chatbot to digital assistant

At the latest with the triumph of ChatGPT, AI-supported chatbots have established themselves as a new, intuitive interface – both for external customer interactions and for internal applications. Initially, they facilitate access to information. In further expansion stages, they take on active roles in workflows, for example in:

Appointments
Complaints handling
Product recommendations
Onboarding processes

The next logical consequence is the linking of entire process chains: for example, leads can be automatically reported to sales teams, incomplete data records can be added to the CRM or individualized offers can be generated – all through the interaction of AI agents, API interfaces and integrated tooling. This new form of Work organization with intelligent, active AI components will fundamentally change the corporate world in the coming years.

Agent management: When AI instances grow

As the company’s AI expertise grows, the number of agents used in parallel also increases. A complex ecosystem of digital assistants quickly emerges that needs to be managed in a structured way. Platforms such as our aiSuite provide the necessary infrastructure:

Processes can be modeled and transferred to agents
Data sources and third-party tools can be connected centrally
Active agents can be monitored in real time and flexibly adapted‘

The result is a controllable, scalable agent landscape that can be dynamically developed in line with the individual requirements of each company.

Technological requirements for AI agent systems

The building blocks for maximally automated processes have long been technologically available – but their integration varies considerably depending on the use case. Simple processes can be handled by a single agent. More complex requirements, on the other hand, benefit from a multi-agent approach in which specialized agents work closely together.

This is where orchestration comes into play: a central instance – such as a higher-level orchestrator agent – controls the distribution of tasks, monitors processes and coordinates decisions. This creates an intelligent, networked agent system that can operate stably and efficiently even when requirements grow.

Business impact: The economic use of AI agents

However, every automated request – whether from customers or employees – generates costs. The challenge therefore lies in the economic balance between performance, scalability and budget.

A technology-agnostic implementation in the aiStudio allows flexible access to different Large Language Models (LLMs) – depending on the requirements:

Type of task
Accuracy and relevance of the answer
Availability of domain-specific knowledge
Requirements for security and hosting factors
Cost-benefit ratio

This modular architecture allows our customers to choose the optimum solution for every application – both technically and economically.

People as a success factor: a pragmatic recommendation for action

Despite all the technological advances, one thing is certain: the key to success lies in meaningful collaboration between man and machine.

Our customers regularly report positive transformation experiences. Employees are relieved, repetitive tasks are automated – and free space is created for creative, value-adding activities.

AI success definitely needs more than technology:

Project managers must take over data maintenance, agent training and dialog analyses
Internal initiators are needed to drive forward further developments
The pace of change must match the corporate culture

In companies that follow this path, the development of AI expertise is increasingly seen as a strategic competitive advantage and acted upon accordingly.

How AI agents and multi-agent systems

are changing the world of work

This e-book provides you with a compact overview of the use of AI agents and their future expansion stages. You can use the accompanying AI checklist to determine the maturity level of your organization for the use of agents.

Download e-book now

Der Beitrag From dialog-based AI chatbots to action-oriented AI agents erschien zuerst auf Kauz.

AI chatbot providers: A market overview for decision makers

Angelika Pfeifer — Tue, 29 Apr 2025 13:32:46 +0000

Artificial intelligence has arrived in the world of work and in society: According to Deloitte At 56%, a fundamentally positive attitude towards the potential uses of AI prevails. Many employees and customers increasingly expect AI chatbots and agents to make work and communication easier. However, due to the rapidly growing market offering and rapid technological change, it remains a challenge for companies to make the right strategic decision and at the same time get started quickly and easily with the first AI solutions in order to create initial successes with artificial intelligence and the basis for its scaling.

In the following article, we present ten selection criteria for selected providers of chatbots and AI agents, which we also apply to a selection of long-established chatbot and AI companies. You can also deepen your functional understanding of chatbot platforms with our Function & Feature Overview.

7 Agentic AI and chatbot providers in comparison

Selection Criterion	Kauz.ai	Cogningy	Parloa	moinAI	Rasa	Userlike	Microsoft Copilot
Use Cases	Customer service Knowledge management AI Workplace	Customer service	Customer service	Marketing, Sales & Customer Service	Marketing, Sales & Customer Service	Customer Service	AI workplace
LLM-agnostic platform	LLM selection options	LLM selection options	LLM selection options	LLM selection options	LLM selection options	GPT Technology	GPT Technology
Data Security & GDPR compliance	EU data hosting Cloud or on-premise Private LLMs Confidential computing	Certifications Cloud or on-premise	Certifications Data encryption	EU data hosting Opt-in for personal data	On-premise and private cloud deployment	No details available	Microsoft’s security and compliance mechanisms
Dialog and process templates	Chatbot templates Free dialog control via agent templates Fixed dialog control via click flows	Flow templates Graphical conversation design tool	No public disclosures	Reusable dialog components	Chatbot templates Dialog patterns	Dialog workflows	Dialog design and process templates
CompanyGPT	Suitable for internal knowledge management and personal AI workplace	❌	❌	❌	❌	❌	Central area of application
Control & Management	Analysis dashboards LLM orchestration Chunking and crawling configurations	LLM orchestration Explainable AI	Evaluation tools	Access only to central knowledge database	Response variations & conditions Contextual response rephraser	Central knowledge database Message center	Context management Copilot Control System
Multi-Channel	Chat Live-Chat Voice input	Telephony Chat Voice input	Telephony Chat	Chat Live-Chat	Chat Messaging apps	Chat Messaging apps	Microsoft 365 apps
Usability	No-code platform User interface for business users	Targeted at technical users	User interface for business users	Intuitive Interfaces	For developers	Business User Console	Contextual support in Microsoft 365 applications
Innovation maturity	Rapid expansion into a comprehensive AI agent platform	AI agents in the contact center	Use of agentive AI in customer service	First AI agents	Focus on GPT technology	Focus on chatbots & multichannel integrations	Deep integration into the MS365 ecosystem
Pricing	Starting at €499 per month for company-wide access, or from €20 per user per month	Subscription or usage-based pricing starting at €2,500	Individual	Starting at €475 per month	Individual	Starting at €90 per month	From €30 per user per month

What you should definitely look out for when selecting an AI chatbot

Scalability of your AI applications

Chatbot integrations with high response quality have been accelerated by up to 90% thanks to large language models. Generative AI has become a true game changer for communication automation. For companies, this means they should think beyond isolated chatbot use cases. The deployment of AI will expand across all areas of the organization, and the number of specialized applications will grow rapidly. Therefore, it is advisable to rely on an AI platform from the very first projects—such as the implementation of chatbots—in order to scale AI usage effectively.

User autonomy

The vast future potential of artificial intelligence lies, among other things, in enabling employees to become more efficient in their workflows through AI tools—and to perform tasks independently that would otherwise require assistance. Natural language interfaces eliminate onboarding time and are set to fundamentally transform workplace culture. When choosing your AI chatbot provider, ensure that your employees can control the content quality of the AI application themselves. This allows them to actively shape the transformation process driven by AI deployment.

AI Chatbots 2.0

AI chatbots are the user interface of the future—whether in customer service, internal knowledge management, or individual work. With data integration and the inclusion of AI agents for process automation, AI chatbots are becoming increasingly powerful. Therefore, when choosing a provider, be sure to consider customization capabilities, even if you're starting with simple use cases.

Data Sovereignty

When it comes to service and support, the regional proximity of technology providers has always been an important decision factor. With the rise of AI, regionality has become even more critical. GDPR-compliant data processing within the EU and the highest possible assurance that company data will not be used for AI training are top priorities for many organizations—but they can also lead to higher costs. Carefully evaluate the security measures of chatbot providers and whether they offer scalable options for enhanced data protection.

AI guide: 60 use cases at a glance

Get a comprehensive overview of the company-wide application possibilities of AI chatbots and AI agents in our free e-book.

Download e-book now

Outlook for the future: Use of Agentic AI

Find out from our AI experts which AI architectures are necessary and possible for agent systems.

Download e-book now

AI webinar library

We regularly host webinars with AI experts and AI practitioners. Take the opportunity to gain exciting practical insights.

Watch AI webinars now

Der Beitrag AI chatbot providers: A market overview for decision makers erschien zuerst auf Kauz.

AI Archive - Kauz

AI Phone Assistance for Small and Medium-Sized Businesses: What Matters Beyond the Per-Minute Rate

The market is becoming polarized and creating a gap

The budget standard segment

The Enterprise Segment

The gap between them

Why the price per minute is the least reliable criterion

How an AI phone assistant is technically structured

– The telephony and voice layer

– The AI and Knowledge Level

– The conflict of goals between latency and depth

– A natural voice is a requirement

What You Need to Know for Chat and Phone Support

Integration and Handover to People

Privacy by Design

Key decision criteria at a glance

A phone bot is a project, not a switch

How to make this happen: the Phonebot from Kauz.ai

Phonebots for Your Customer Experience

Smart AI Solutions for Small and Medium-Sized Businesses

AI Workspaces for Your Specialist Teams

Cross-Channel AI Automations

Actively Shape Your AI Communication

Data-Sovereign AI in Practice—Recommendations for Small and Medium-Sized Businesses

1. Technology-agnostic AI platform

MCP – The new standard for AI integrations

2. Data Sovereignty Through Flexible Operating Models

3. AI must not be a black box

4. Maximum Cost Control

5. Think About Compliance from the Very Beginning

6. Independent of external specialists

Data sovereignty is a matter of choice

AI Impulses for AI-First Companies

Checklist: AI Maturity Levels

Evaluating AI Smartly: ROI Measurement

AI in First-Level IT Support

Kauz Selection: The Right AI Model for Every Task

The Right Model for Every Task

Counteracting Hidden Cost Spikes

Quality First—Cost Optimization as a Result

LLM orchestration is the future

AI Features in aiWorkplace

Our Customers’ Success Stories

Your Knowledge Advantage: Kauz.ai AI Tutorials

Goodbye tool zoo: how AI really works in marketing & sales

The most important AI use cases in marketing & sales

Content creation & campaign production

Personalization & customer experience

Offer & sales automation

Market Intelligence

Central AI workstation for marketing & sales teams

MCP for cross-system integration options

AI security: data control

GDPR, EU AI Act & IT Security Compliance

This might also interest you

Checklist: AI Maturity Levels

60 AI Use Cases at a Glance

Practical Insights: Success Stories

AI in companies: A review and outlook based on 7 AI theses

1. expectation management: from PoC to roll-out

2. European data sovereignty

3. CompanyGPT as a starting point: from individual productivity to process efficiency

4. Use of AI as a lever for increasing sales through innovation

5. technical focus: integrations and AI workflows

6. Engineering focal points: AI memory, AI agent orchestration and new OCR models

7 Strategic partnerships: The enabler for AI-first companies

Sources:

AI avatars: Innovations in user interfaces

Artificial intelligence promotes technology acceptance

Multimodal AI expands application scenarios

Experience AI avatars live in the fall release!

Smart evaluation of AI: ROI measurements and investment decisions

What do companies expect from the use of AI?

An ROI example from customer service

An ROI example from internal knowledge management

Consider AI scaling from the outset

Creating the right set-up for all AI maturity levels

Take qualitative assessment criteria seriously

AI transformation: ROI and other success factors

How CompanyGPTs are transforming medium-sized companies