The workplace of the future: One AI cockpit for your work

Customer records, orders, documents and email in one working context: How an AI cockpit can reduce application switching and where to measure its value.

Arbeitsplatz der Zukunft: Ein KI-Cockpit für Ihre Arbeit

English edition — originally published in German as Arbeitsplatz der Zukunft: Ein KI-Cockpit für Ihre Arbeit.

A customer request starts in the inbox. The customer number sits in the customer management system, the delivery status in the order system, and the agreement in the document repository. The responsible colleague is absent, according to the calendar. Before anyone can respond, an employee must assemble all these pieces. The actual task is manageable. The route through the applications is not.

The workplace of the future addresses precisely this problem. An intelligent AI work layer brings information, decisions and next steps together in one interface. Management is investing in complete case handling. Value depends on how much searching, copying and coordination it removes.

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Today, work often follows the software

Many workflows are organised around individual applications. Employees remember where information lives, which identifier connects it and which list still needs updating. Every switch requires orientation: Is this the right order? Does this agreement apply? Has someone already replied?

Yet an interruption does not automatically mean lost time. An experimental study of interrupted work published in 2008 found faster task completion alongside greater stress and time pressure. It did not study today's AI cockpits. It cannot establish a general savings percentage for switching between applications. A business case requires measurements of the actual workflow.

The strategic question is which information and actions a person needs to complete the case reliably. Application count is only a starting point.

An AI cockpit for connected work

Imagine a workspace that starts with the request. It presents the relevant order, correspondence, available documents and next possible step. AI establishes the connections, drafts a proposal and identifies what is missing. The person handling the case no longer has to reconstruct its context each time.

Specialist systems can continue operating in the background. Customer master data remains in its authoritative application; orders remain in the order system. The intelligent layer brings together the information needed for each case and mediates permitted actions. This requires dependable connections, appropriate permissions and clear ownership of the underlying data.

A shared interface also means more than a chat window. Comparing several line items, approving a change or recording a clarification each requires a suitable presentation. The workspace should adapt to the case. Voice and text can make it easier to get started; tables, evidence and specific controls make decisions reviewable.

What we are already examining in a prototype

An interface prototype we examined connects tasks, source-based context and prepared actions. It shows who is handling a case, why an action is proposed and where a clarification will return. This provides a concrete design foundation for such a workplace.

The prototype uses demonstration data. Local state changes establish neither actual message delivery nor production connections to external business systems. The next step towards an operational solution is to connect this interaction model to real, controlled interfaces and verifiable results. The examples below describe that target state, rather than a customer installation with measured results.

One request, one continuous working context

Consider an illustrative request to change a delivery date. Today, the message, order and agreement are opened in separate windows. The employee then creates an internal enquiry, a reply draft and a note in the customer record.

In the target workflow, the cockpit identifies the associated order and assembles the permitted information. It shows the source of the current date and flags a missing confirmation. A clarification goes to the responsible person; the case remains visible with a deadline and a return path. Once the clarification arrives, the workspace offers a reply draft.

Before an external action, the person handling the case sees the recipient, content and intended effect. After approval, the cockpit waits for the appropriate result from the destination system. Only that result justifies the corresponding status. A prepared message remains a draft; an accepted sending request is not yet confirmed delivery.

Open interface descriptions also distinguish the model from its tools. A versioned specification for AI tools describes calls to external systems and recommends visible tool use and opportunities for human intervention. It does not replace operational approval rules, but it demonstrates an important distinction: a verbal response and an executed action are different things.

What a single interface must provide

For integrated standard cases, the goal is clear: stay within one working context. Specialist tasks may still require a dedicated application. A visible handover that carries the context with it is preferable to an incomplete recreation of every specialist function.

Likewise, “all information” means information that is available for the case and permitted for the person's role. The cockpit must reveal missing data and make sources accessible. It must not turn a plausible summary into certainty about an unknown order status.

Management should also decide who takes ownership of open cases when a connection fails. Without this operational process, the new interface would simply add another list. Our article on integrating AI into existing systems explores the technical foundation.

Measure efficiency where work is completed

Before the pilot, measure active handling time, waiting time, application switches and necessary corrections. Then compare similar cases handled with and without the cockpit. Faster searching provides little benefit if checking the AI proposal consumes all the time gained.

:::callout{type="info"} Hypothetical planning example: 1,200 cases per month, each requiring three fewer minutes of active handling including review and rework, release 60 hours of capacity. At an assumed CHF 80 per hour, this represents CHF 4,800 of valued time. With CHF 2,000 in additional monthly costs, the calculated remainder is CHF 2,800 before one-off implementation costs. This is neither a measured saving nor an automatic reduction in cash expenditure. :::

The calculation holds only if that volume actually occurs and the released capacity is used. Integration, operations, support and changes belong in the assessment. At smaller volumes, or with unchanged staffing costs, the benefit may instead be shorter turnaround times, a smaller backlog or additional service capacity.

Choose the first case deliberately

Start with a frequent request, one responsible team and a small number of necessary systems. Document the current workflow with the people doing the work. Establish which source is authoritative for each piece of information and which result completes each step.

Build the shared context first. Then add one clearly bounded action, test normal operation and failures, and use the measurements to decide whether to expand. Investment approval should depend on that evidence.

The workplace of the future can be an interface in which people complete connected work while AI organises the links in the background. Its value emerges when a case is completed with less friction and a dependable outcome.

Which of your workflows would make the right starting point? We can help define the first use case and the measures for a credible pilot.

Sources and interpretation

Research reviewed: 26 October 2026. The linked study is older foundational research, not a current productivity measurement. The versioned tool specification establishes technical possibilities and design recommendations. Prototype observations, the target state, the proposed approach and the calculation are distinguished explicitly; no production integrations or customer savings are claimed.