What proactive AI can notice before someone writes a prompt

Proactivity is a spectrum

1. Detect an explicit signal

   Respond to an observable event such as an arriving email, an ended meeting, a changed document, or an approaching date.

2. Maintain state over time

   Track an unresolved reply, assigned action, waiting state, or commitment without requiring a new message to make it important.

3. Predict a likely need

   Infer that preparation may soon be useful, such as assembling background before a meeting. Prediction introduces more ambiguity than event detection.

4. Prepare an action

   Turn the signal and relevant context into a reviewable response, brief, meeting, or document update.

5. Execute under configured authority

   Complete an external action through an explicitly configured workflow with appropriate permissions, monitoring, reversibility, and escalation.

The research frontier is still young

TriggerBench, a 2026 preprint, tests whether language models remember to act when a future trigger appears. Its results describe a trade-off between catching more triggers and producing more false alarms, with weaker performance on implicit or overloaded triggers. A benchmark preprint does not establish workplace productivity.

Another 2026 preprint, ProAct and ProActEval, explores agents that use persistent dialogue history and idle-time computation to prepare for likely needs. The authors also designed the evaluation environment, so independent replication and field evidence remain important.

Initiative can affect how help feels. A preregistered working paper found that unsolicited anticipatory help reduced adoption intentions in some vignette experiments. A small 12-participant wearable study, ProMemAssist, explored assistance timed around working-memory state. These narrow settings make timing and agency worth testing, but they do not establish broad workplace outcomes.

What can be noticed?

These are candidate triggers. They do not prove that action is required. The system still needs context, policy, and a way to express uncertainty.

Six ways proactive assistance fails

1. Missed signal

   The event never arrives, the source is absent, or the language is too implicit to recognize.

2. False alarm

   The system surfaces work that does not need attention and creates enough noise to weaken trust.

3. Wrong context

   The signal is real, but the assembled history is stale, incomplete, or tied to the wrong work.

4. Poor timing

   Help arrives before the required information exists or after the task has already been handled.

5. Excessive interruption

   Individually reasonable suggestions accumulate into a distracting review queue.

6. Inappropriate action

   The system acts beyond its configured authority or takes a consequential step without adequate control.

Questions for evaluating a proactive system

Ask what initiates the work, which systems and states can be observed, how a signal is judged, which context is assembled, and whether the system notifies, prepares, recommends, or executes. Then ask how missed signals, false alarms, delayed sources, corrections, authority, and revocation are handled.

Concrete answers make products easier to compare. They also expose whether “proactive” describes a useful operating model or a vague promise.

A current product example

AUGMTD can initiate work from synchronized provider events, schedules, maintained work state, commitments, and dates. Depending on the path, it may surface an item, prepare an output for review, or deliver an explicitly configured workflow.

This is not evidence that every relevant signal will be caught or that proactive assistance improves every team’s outcomes. Provider delays, source coverage, indexing, configuration, and model limits all matter.

Proactive AI begins before the prompt. Its value is decided through timing, context, precision, authority, and measured effects on the workflow where it runs.