2024 and 2025 were considered the beginning of the agent era – 2026 will be its operational phase.
Agents are increasingly being embedded in workflows and working alongside human employees. In view of demographic change and the shortage of skilled workers, AI agents are seen as an important solution to compensate for the lack of manpower.
However, with operational deployment, the following challenges come to the fore:
Control, maintenance, and coordination are crucial, especially when agents have an impact on the outside world, for example, when they are used in government workflows or critical business processes. Solutions are needed that create transparency and traceability and offer clear options for intervention - a core aspect for trust and acceptance within the company. AI thus becomes something that must be guided and managed – not just used. This is central to achieving AI Act compliance.
Since there is still enormous potential for development in the maturity of complex autonomous agents, we would agree with renowned AI expert Andrej Karpathy in saying that this is not the year of agents, but rather the decade of agents.
The AI hype has led to widespread experimentation – now comes the phase of economic evaluation.
Many companies are realizing that:
This phenomenon - often described as "Through of disillusionment" (according to Gartner) - will shape the year ahead. 2026 will therefore be a year of focusing on real value creation:
The need to justify investments is becoming increasingly clear: those who cannot prove their return on investment will lose their budgets.
Our recommendation: Companies should initially focus on automating simple and repetitive tasks. This is where the ROI is most quickly visible.
This is aptly summed up by author Joanna Maciejewska: "I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes." In a business context, this means that when AI takes over simple, repetitive tasks, employees can focus on more complex, value-adding tasks.
With the increasing production capacity of major hardware manufacturers, significantly more computing power will be available on the market in 2026 than in previous years. In particular, the increasing delivery of powerful GPUs will make it easier and more cost-effective for companies to run AI applications – both in the cloud and on-premises.
This improved availability will accelerate the introduction of AI in many areas. At the same time, it will become clear that more hardware alone does not guarantee successful AI projects. With growing use, the first cases will also arise in which solutions fall short of expectations in practice.
The trend makes it clear: technical barriers to entry are falling significantly, but business success continues to depend on realistic use cases, good integration, and sound governance.
Traditional enterprise search is being augmented by systems that not only find documents, but also understand content and derive suggestions for action from it. Users also expect answers and support for their next decision.
This includes functions such as:
This changes day-to-day work: instead of reading through documents, you get a comprehensible, context-related summary of the relevant content that fits directly into the work process. Enterprise search thus becomes a decision-making aid.
Another trend is that AI systems are becoming increasingly tailored to users and roles. They understand who the user is, what role they have, what projects they are working on, and which colleagues they interact with most.
They take into account:
This type of proactive, personalized support, such as automatic reminders of upcoming appointments and the provision of relevant background information, will further increase productivity in daily work.
Generic AI models are no longer sufficient for practical applications.
In healthcare, government agencies, financial processes, and mechanical engineering, for example, there are strict requirements, terms, processes, and data formats.
Organizations expect precise solutions for their reality, not just generic text processing:
With multimodal models that understand text, images, document structures, and layout simultaneously, classic OCR (optical character recognition) is becoming a marginal technology.
These models "read" documents semantically, not pixel-based.
This means that pure text recognition as we know it will become largely obsolete, as AI models will be able to understand the context and content of images, videos, and audio in combination with text much more comprehensively.
This opens up completely new possibilities for enterprise search: documents that were previously difficult to index (e.g., scanned PDFs with complex layouts, images with embedded text, presentations with graphics) can now be fully captured, searched, and correlated. This leads to a much deeper understanding of corporate knowledge and even more precise searches.
A fascinating trend that will emerge in light of demographic developments in Germany is the "digital clone." As experts retire, finding suitable replacements is becoming increasingly difficult.
This is why digital clones are being created: AI-based knowledge bots that store the explicit knowledge of individuals or teams and make it accessible.
Example: An employee who has been maintaining special machines for 30 years → his knowledge is made accessible via chat.
And another development beyond AI, that appears on our office screens:
A look at China shows very clearly where the global robotics market is heading. China is now the world's largest producer and user of autonomous robot systems. With over two million units installed and the first fully automated "dark factories" – production facilities that operate entirely without human presence – a new era of industrial automation is already emerging there. This development will also reach other regions in the coming years.
2026 will be the year in which companies and public authorities learn not only to experiment with AI, but also to implement it strategically, measure its value, and integrate it securely into their core processes. The flood of available hardware will further accelerate adoption.
For us as an enterprise search provider, this means further developing our solutions to meet these new requirements: supporting intelligent agents, enabling the leap from "finding" to "acting," processing multimodal data, and always ensuring the highest standards in terms of security, governance, and explainability. The future of work is being redefined by AI – and we want to help shape this change.
