For decades, our relationship with machines has followed a familiar pattern: We ask, they respond. We click, type, swipe, or speak. Every interaction begins with an act of intent.
That model is beginning to change.
The user-driven world we know won’t be abandoned, but it will sit alongside systems that act in advance, no longer waiting for a prompt to begin. Proactive AI is almost here. Enabled by long-term memory and deeper contextual awareness, these systems will learn from past interactions. Emails will get drafted before we sit down, and AI research assistants will summarize what matters before we even search.
From Response to Anticipation
We’ve seen attempts at proactive assistance before. Launched in 1996, Microsoft’s Clippy was built on a promising idea: software that could anticipate user needs and offer help before being asked. But without the technology to remember past actions or reliably infer user intent, Clippy became widely criticized and was often cited as “the world’s most hated virtual assistant.”
Today, the technology has finally caught up to the ambition, and companies are beginning to explore true proactive intelligence.
So far we’ve seen ChatGPT Pulse, released in September 2025, which researches for users based on past interactions without a user prompt. In December 2025, however, the company declared a “Code Red” and paused some efforts, including ChatGPT Pulse, as it focused on improving the core ChatGPT experience amid intensifying competition.
In December 2025, Google launched a new AI agent called CC, which delivers a daily “Your Day Ahead” briefing to users’ inboxes. Built on Google’s Gemini model, the agent learns about you by connecting with your Gmail, Google Calendar, and Google Drive — no search and no prompt, just information that arrives before you ask for it.
And more proactive tools are coming. In July 2025, leaked documents showed how Meta is training its chatbots to be more proactive, messaging users unprompted to follow up on past conversations. By November 2025, Meta was testing a product that would give Facebook users a personalized, AI-powered morning brief of information relevant to their daily lives.
When AI Decides What Matters
Proactive systems don’t simply save time. They shape attention.
Imagine a leadership team preparing for a board meeting. Before anyone asks, an AI system assembles a briefing. It summarizes market movements, flags competitive threats, and highlights financial risks based on prior meetings and recent performance. By the time the team sits down, the agenda is already shaped. The discussion begins with what the system surfaced first.
Nothing here feels wrong. In fact, it feels efficient. But the system has already made a series of invisible choices about what to emphasize, what to omit, and what counts as “material.”
When the system acts before we do, it implicitly decides what deserves our focus and what doesn’t. Still, it’s important to note this influence exists already in today’s "reactive" systems. A recent study from the London School of Economics and Political Science found that conversational AI systems can influence people’s political opinions with information-packed arguments. In a proactive world, this effect is amplified. We aren't just being influenced by the answers we ask for; we are shaped by the information the system chooses to surface in the first place.
In business contexts, that shift in who sets the agenda carries real consequences. When AI systems summarize, prioritize, or trigger actions autonomously, they are influencing strategy, risk, and decision-making, often without visible signals that this influence is taking place. Leaders need to recognize this influence and decide where proactive systems should inform judgment and where human deliberation must take precedence.
Where Proactive AI Works Best
Certain domains are particularly well-suited to proactive AI. Monitoring and maintenance tasks benefit enormously from systems that can identify problems before they become critical, whether that's IT infrastructure, industrial equipment, or personal health metrics. Nobody wants to be woken up at 3 a.m. to manually restart a server when an AI could handle it automatically.
This intelligence may even move into our physical spaces. In November 2025, researchers at Carnegie Mellon University demonstrated how AI can turn everyday objects into proactive assistants. By combining computer vision with large language models, they created systems that anticipate human needs, such as adjusting furniture or repositioning objects such as a knife before they could do harm. This unobtrusive AI doesn't wait for an explicit command; it infers intent and acts in advance.
Routine coordination tasks are another natural fit for proactive AI. Scheduling meetings, managing email inboxes, tracking deliveries, and maintaining to-do lists are exactly the kind of cognitive overhead that many people would gladly offload to a trustworthy AI assistant. These tasks are important but not particularly creative or strategic, making them ideal candidates for automation. Offloading them to a reliable proactive system can meaningfully improve focus and productivity.
Information curation is another great use of proactive AI. Rather than forcing users to actively search for relevant information, proactive systems can surface news, research, or insights that align with known interests and current projects. Done well, this feels like having a knowledgeable assistant who knows what you need before you ask. The value here is real, but it depends on transparency and the ability to look beyond what’s surfaced.
Trust, Transparency, and the New Design Challenge
As AI becomes more proactive, transparency becomes more critical.
The challenge ahead is making machine reasoning legible without requiring our constant attention. Systems must be able to explain why they acted. A proactive system that reschedules a flight or drafts a high-stakes proposal must be able to surface its logic and its level of uncertainty.
Proactive intelligence can reduce busywork, surface insights, and take meaningful actions on our behalf. But realizing that promise requires earned trust. Systems must show their logic, reveal uncertainty, and, particularly in high-stakes enterprise contexts, ensure the human remains unequivocally in the loop.
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