17.12.2025 | News The maturity test: AI must prove its value in 2026
2025 was a year of high expectations: new AI models of all sizes came onto the market, reasoning made enormous progress, and generative AI became increasingly available in companies. But it was also a year of disillusionment for some, as it became apparent that not all ideas are viable in practice. In 2026, organizations will therefore approach the topic of AI with much more pragmatism. IntraFind, a specialist in AI and enterprise search, explains what developments it expects.
1. AI agents get to work. Agent-based AI will enter the operational phase in 2026. Agents will be increasingly embedded in workflows, working directly with human colleagues and helping to compensate for the lack of human labor. Control, maintenance, and coordination are crucial, especially when agents have an impact on the outside world, for example through their use in government workflows or critical business processes. Organizations are implementing solutions that create transparency and traceability and offer clear options for intervention. Given the great development potential of complex, autonomous agents, agent systems will continue to shape the AI agenda over the next few years.
2. Companies want to see results. Many companies have now realized that not every use case is economically viable and that some proofs of concept cannot be transferred to productive systems. In 2026, they will therefore focus on real value creation. The need to justify investments is becoming increasingly apparent: those who cannot demonstrate a return on investment will lose their budgets. Companies will initially focus on automating simple and repetitive tasks because this is where the economic benefits are easiest to demonstrate.
3. A hardware push is making AI widely available. 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, especially on-premises for secure enterprise AI. But even if this significantly lowers the technical barriers to entry, the success of AI implementations will continue to depend crucially on realistic use cases, good integration, and sound governance.
4. Multimodal AI replaces classic OCR and enables semantic chunking. Multimodal AI models can understand text, images, document structures, and layout simultaneously. With its ability to "read" documents semantically rather than pixel-based, the model not only recognizes formal boundaries (headings, page breaks, numbering), but also forms chapters based on content coherence. This makes classic OCR (optical character recognition), for example, a marginal technology in decline. Documents that were previously difficult to index, such as PDFs with complex layouts, images with embedded text, tables, or presentations with graphics, can now be fully captured, searched, and correlated. This enables significantly better access to corporate knowledge.
5. Digital clones save specialist knowledge from retirement. Many experienced specialists are approaching retirement. Given the shortage of skilled workers, it will be difficult for companies to fill the vacancies. That is why they are increasingly creating digital clones: AI-based knowledge tools that store the explicit know-how of individuals or teams and make it accessible. This means, for example, that the knowledge of an employee who has been maintaining and servicing special machines for 30 years is accessible via chat.
"In 2026, AI will move from the experimental phase to productivity," explains IntraFind CEO Franz Kögl. "Companies and government agencies will no longer marvel at AI, but will implement it strategically, measure its value, and integrate it securely into their core processes. There will be no next hype, but rather a readjustment. Organizations will ask themselves what really works, where real added value is created, and how they can reliably operate and monitor AI in their daily work."
