Digital trends 2026 for AI and software development

2026 will be a decisive year for companies that want to further develop their processes, systems and digital strategies in a targeted manner. The technological possibilities are growing, while at the same time the requirements for stability, traceability and efficiency are increasing. It is precisely in this area of tension that the trends that will have a real impact in the coming years will emerge. Based on our daily project work, intensive market observations and many discussions with companies, we have identified six digital trends that we consider to be particularly relevant for 2026.

Top 4 trends 2026 for digitalization and AI

1. End-to-end process digitalization as a prerequisite for any use of AI
2. Human augmentation as the new productivity standard
3. Make instead of buy through faster and cheaper in-house development
4. A critical look at cloud locations

From 2026, the end-to-end digitalization of business processes will become the decisive success factor for the productive use of artificial intelligence. More and more companies are realizing that AI can only work reliably, scalably and economically if data flows through all process stages in a consistent, structured manner and without media breaks.Manual intermediate steps, isolated Excel solutions and system breaks prevent reliable data flows, slow down processes and make the use of AI virtually impossible. Only fully digitized end-to-end processes create the necessary data quality, transparency and automation capability on which modern AI applications are based. As soon as these prerequisites are met, both process speed and the quality of results can be measurably increased.The trend is clear: end-to-end process digitization is becoming a strategic prerequisite for any sustainable AI initiative.

Core requirements

• Processes visible, documented and automated
• Data consistent, versioned and complete
• Systems integrated instead of isolated
• Responsibilities clearly defined

AI does not replace people. It makes her work effective and helps increase productivity. In 2026, companies will realize that AI only delivers results if people specifically control and test it and incorporate it into their work processes. Productivity is not created by automated processes, but by employees who use AI to make decisions faster, receive clearer information and lose less time on routine tasks.

Concrete effects

• Decisions are made faster and based on up-to-date data
• Results become more consistent
• Specialists are relieved of routine tasks
• Development gains speed through AI-supported code generation
• The pace of work increases significantly because employees achieve results faster with AI

For a long time, “buy” was the standard. In 2026, there are signs of a trend reversal towards “make” and customized software. AI significantly shortens development times, reduces documentation effort and makes integrations easier. This makes custom software not only more realistic, but also more economically attractive than many SaaS products. Companies are increasingly seeing the disadvantages of standardized solutions: limited adaptability, rising licensing costs and dependence on function roadmaps. Custom software, on the other hand, maps processes precisely and grows with requirements.

Key advantages

• Systems adapt to processes, not the other way around
• Companies differentiate themselves through functions that competitors do not have
• No license models and feature surcharges
• Lower running costs through targeted further development
• Code is traceable, maintainable and documented in the long term

Practical example

A customer replaced a SaaS password keeper (annual costs of CHF 10,000) with a customized open source solution. The extension with the AI solution supported the expansion, integrations were faster, and after a few months the solution was cheaper than the previous license costs.

In 2026, companies are looking very closely at where their critical data should be stored. Data protection, regulation and the desire for more control mean that sensitive data is increasingly being kept in Switzerland or Europe. At the same time, practice shows that local data centers cannot cover the technological breadth, speed and scalability of large hyperscalers in all areas. Global platform services, specialized AI services, worldwide availability or highly automated development environments are usually more advanced with international providers, while Swiss data centers score points for direct control, clear responsibilities and local compliance. This results in a realistic hybrid approach. Critical data and core processes remain local or regional. High-performance cloud services are used where they make technical or economic sense.

Typical target architecture

• Swiss data center for sensitive company data
• European platforms for scalable applications
• Global cloud services for specialized technologies

Example from customer projects

TUS operates sensitive data in the Swiss cloud exaSys GmbH. This reduces compliance risks, creates transparency and keeps control within the country. Modern applications remain flexible, only data storage becomes more strategic.

The four trends show where technologies, processes and strategic decisions could develop in the coming years. The decisive factor is which of these topics suit the company’s own starting position, industry and existing systems. Companies that already have their processes well under control can integrate new technologies much more easily and sensibly. Others will recognize through these developments where structural foundations must first be created before further steps are taken.

You should ask yourself these questions in 2026:

• Are our processes clearly defined, documented and digitally mapped?
• Do we have stable, integrated and reliable data?
• Do we know which systems are strategically critical for us?
• Do we have enough in-house expertise to further develop our software in a targeted manner?

This classification will become increasingly important in the coming years. Not to follow every trend, but to consciously decide which developments bring real added value and which are not relevant to our own business model.