AI in practice: how applications make complex technology accessible

In a demo, AI always looks perfect. In practice, it often falls short. Systems crash under heavy load, interfaces confuse users, databases respond too slowly. The difference between success and failure often lies in the application layer. That is why this layer is becoming increasingly important as AI moves out of the experimental phase and into real-world use.

The application layer: where technology meets people

In day-to-day use, software applications form the bridge between complex technologies like AI and practical application. Behind every software application is an entire ecosystem of algorithms, databases, and data flows.

These applications consist of multiple components. The frontend is what people see and interact with, ranging from simple dashboards to full-featured mobile apps. The backend does the heavy lifting. Databases store and retrieve large volumes of data, APIs enable different applications to communicate with each other, and cloud environments scale rapidly when demand suddenly increases.

Software is becoming increasingly important

In every sector, the importance of software continues to grow. Companies in manufacturing often struggle with this shift. For years, their competitive advantage lay in mechanical precision, smart materials, and production techniques. Now they suddenly need to think like tech companies.

This transformation is visible everywhere, and AI plays an increasingly central role. A few examples from industry and beyond:

• Shipping companies are no longer focused solely on engines and physical components. They are transforming into complete digital ecosystems. Sensors collect real-time data, AI predicts maintenance needs, and routes are automatically optimized to save fuel.
• Automotive manufacturers face competition from companies like Tesla, which are essentially software companies. Where traditional brands focused on engines and bodywork, Tesla emphasizes self-driving algorithms, over-the-air updates, and intelligent energy management.
• Industries that have long worked with machines can no longer ignore software. ASML, for example, develops systems that remotely monitor and adjust chip manufacturing machines. Algorithms detect deviations in exposure and correct them automatically, without technicians having to enter the cleanroom.

Outside manufacturing, similar transitions are taking place. Banks are shifting from physical branches to software platforms such as mobile apps. Where a teller once helped customers at the counter, AI will increasingly handle credit checks, detect fraud, and provide personalized financial advice through mobile applications.

Interface 1.0 is getting smarter

The move toward software and AI also requires better user interaction. Many AI applications still rely on fairly basic interfaces. A text field to enter a question, a send button, and a response that appears. Sometimes there are filters or suggestions, but complexity rarely goes much further.

This type of interface has its advantages. It is clear and intuitive, and users immediately understand how it works. But it also has limitations. You need to know exactly what to ask. If the system gives an incorrect answer, it is difficult to understand why. For more complex tasks, users often need to ask multiple questions and manually piece the answers together.

In many cases, that works just fine. An internal chatbot that answers frequently asked questions about HR procedures can perform perfectly well with a simple interface. But as AI systems become more capable and take on more tasks, the need for more advanced ways of working with them increases.

AI integrated everywhere

Development is moving in several directions. Voice control is becoming increasingly reliable. Instead of typing, users can simply speak to their system. This is useful for people on the move or those who need their hands for other tasks.

Even more interesting is the rise of proactive AI. Instead of waiting for the user to ask a question, the system makes suggestions on its own. An email program may suggest scheduling a follow-up meeting after an initial conversation. A dashboard may automatically display a warning when data patterns indicate potential issues.

The biggest change is likely integration. AI functionality is being embedded into software people already use. Microsoft does this with Copilot in Office applications. In Excel, AI helps with formulas. In Word, it assists with writing. In PowerPoint, it supports presentation creation. Users do not need a separate tool. The AI is available directly where they already work.

For enterprise software, this means AI is added to ERP, CRM, and other systems. Accountants, for example, can use AI to review bookings without leaving their familiar software environment.

Better interfaces also introduce new challenges. Who is allowed to use which AI features? Can the system access confidential customer data? Is it allowed to retrieve information from the internet, or should it be limited to internal documents?

Access to sensitive data

These questions become increasingly important as AI gains more capabilities and access to sensitive data. A chatbot that only answers questions based on public information is relatively harmless. That is very different from a system that has access to all customer records and can automatically generate proposals. Such use cases require strong access controls.

Identity and Access Management, or IAM, is therefore becoming indispensable in AI implementations. Different users are granted different permissions. This differentiation must be reflected in the interface. Certain buttons or features are visible or hidden depending on who is logged in.

Privacy is closely related. Is it acceptable to send company data to external parties? Systems like ChatGPT and Google Gemini run in the cloud environments of those providers. For many use cases, that is perfectly fine. For sensitive information, however, organizations may want everything to remain on their own servers. In those cases, local AI models that can be installed and managed internally are the better choice.

Success factors for AI applications

A strong application layer has several essential characteristics:

• Usability comes first. An interface can be technically flawless, but if people cannot work with it, it will not be adopted. This means keeping things simple where possible and providing clear feedback about what the system does and why.
• Scalability must be right from day one. Software often works well with ten users. But what happens when a thousand people log in at the same time? Databases must handle large volumes of data, APIs must remain responsive, and the user experience must not degrade under heavy load.
• Reliability is non-negotiable. Users need to trust that the system works when they need it. That requires robust architecture, solid monitoring, and fast recovery in case of issues.
• Compliance is becoming increasingly important. The AI Act, privacy regulations, and other rules impose requirements on how AI systems operate and what information they may use. These requirements must be enforced through the interface, for example by restricting certain features, anonymizing data, or tracking exactly who accessed which information and when.

Toward intelligent collaboration

The AI application layer is evolving from simple question-and-answer systems into intelligent assistants that proactively support users and integrate seamlessly into existing workflows. This evolution is driven by better technology, but also by growing experience with what works and what does not in practice.

For organizations, this creates opportunities to work more efficiently and make better decisions. At the same time, it requires careful planning. Which processes can be improved with AI? How do you ensure the technology aligns with how people actually work? And how do you maintain control over systems that are becoming increasingly autonomous?

Successful AI adoption does not depend on the most powerful algorithms, but on how well that intelligence is integrated into the systems people actually use.