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.

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.

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.

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.

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.

“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.

Read more in this blog post:

• Which influencing factors you should consider when investing in AI
• Which prerequisites enable a sustainably positive AI ROI
• How AI ROI can be measured and optimized in practice
• How KPIs and a personal exchange of experiences contribute to AI-first companies

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.

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.

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.

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.

9 Auswahlkriterien für ein CompanyGPT 

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.