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 biggest challenges posed by the use of AI?
- In retrospect, what can be chalked up to AI hype and what to sustainable AI development?
- 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: Willingness to invest in AI vs. ROI
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. At the same time, we take a differentiated view of the ROI debate surrounding AI: Amortization is primarily a question of the time horizon. It depends largely on the prioritized use cases, the organization’s learning curve and, last but not least, the corporate culture.
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.
Even if the ROI debate is real, by 2026 at the latest it will be clear that there is no way around AI.
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2. 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 by far the most popular entry-level 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 are also a key measure to curb unauthorized AI use in 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.
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3. 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.
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4. 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.
5. 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.
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6. Multifaceted topic: AI use between cost savings, effort and innovation
While companies are currently using AI primarily to automate processes, increase efficiency and reduce costs, a change in perspective is emerging as AI maturity increases. Cost benefits remain a key driver, while at the same time there is a growing awareness that the use of AI itself is associated with rising costs. It is not the technology itself that is becoming more expensive, but its growing complexity, scaling and use – which is why ongoing use case considerations remain essential.
The balance of power between efficiency orientation and innovation focus is also expected to shift further in 2026. AI will increasingly be seen less as a pure productivity tool and more as a strategic enabler that redefines value creation and enables sustainable transformation.
7. Engineering focal points: AI memory, AI agent orchestration, new OCR models and data quality
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.
Data quality remains a key factor across all these key topics: it is increasingly evolving from a detailed operational topic to a strategic scaling lever that is firmly anchored in architecture, governance structures and clear responsibilities. 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.
Our practical tip: The Kauz.ai partner ecosystem combines technological, industry, specialist and systemic expertise and can support your company throughout the entire AI lifecycle.