When AI knows your intent before you state it

To date, most people experience artificial intelligence as a reactive tool. You ask a question, formulate a task or set a goal. The system answers, drafts something or carries out a clearly defined assignment.

That way of working is changing. AI systems are increasingly connected to calendars, emails, project platforms, meeting minutes, key figures and internal knowledge. The more context a system receives, the less it has to be told explicitly about what is going on. It recognises patterns, repetitions and deviations.

An agent can then notice, for example, that a project has been stuck at the same point for several weeks. It sees that a decision keeps being postponed, that a metric is drifting from target or that certain customer enquiries are clustering. From these observations it can infer which next step is probably needed.

From answer to inferred intent

The decisive leap is not better phrasing. It is that the system no longer merely reacts to an assignment but identifies possible goals itself. It assembles information, produces a risk report, prepares a decision or proposes an action before anyone has explicitly asked for it.

That can relieve companies. Recurring problems become visible earlier. Information does not have to be gathered from scratch every time. Leaders get signals before a problem escalates.

At the same time the balance of power shifts. A system that infers from your behaviour what you probably want does not merely describe your intent. It can also shape it. The very choice of what it highlights, which option it shows first and which action it prepares steers the decision.

When the assistant serves two interests

It becomes particularly critical when the same provider controls both the assistant and the platform through which products, services or recommendations are brokered. A system can then help you and pursue the provider's commercial interests at the same time.

For the user this conflict of interest often stays invisible. The recommendation feels personal because it is based on your own context. It can nevertheless be shaped by rankings, business models or preset objectives.

The decisive question is not only: what does the AI recognise? But: in whose interest does it then act?

What companies must clarify now

Before proactive agents are deployed, companies have to settle four points.

Which data and knowledge may the agent access? By which rules does it identify a relevant intent or a need to act? Which steps may it prepare itself and which may it actually execute? Who reviews, corrects and takes responsibility for its conclusions?

Without these rules, convenience is quickly mistaken for control. The system seems helpful because it anticipates work. At the same time it becomes ever harder to trace how an observation turned into an action.

Where I come in

I therefore do not aim for maximum autonomy but for a controlled link between knowledge, processes and decisions. The AI may identify connections, prepare information and propose next steps. The knowledge base, the rules and the approvals belong inside the company.

That way an inferred intent does not become an automatic assignment. The organisation keeps the option to review, change or reject the proposal. Responsibility stays where the consequences are carried.

An AI that recognises your intent can serve you. It can also make your intent exploitable for others. The difference lies in the infrastructure and in the control.

This is the first of three articles on AI agents in the enterprise. The second is about agents that start working without an assignment. The third about why agents stay blind without an organisational memory.