For the last two years, the industry has been flooded with “AI powered” tools. Every product claims to be using AI in one way or another. And in a way, they are. But what we call AI today is mostly a very smart autocomplete. You type something, it answers. You give it text, it summarizes. You ask for ideas, it throws them at you.

Useful, yes. Transformative, not fully.

My guess is that the shift will happen when AI stops responding like a search engine and starts thinking like a teammate. When it understands context, priorities, ownership, company structure, ongoing projects, and the environment you operate in. When AI starts connecting the dots in a way that feels human.

And that’s where contextual AI comes into the picture.

Most people don’t realize it yet, but the expectations toward AI have already changed. Users want more depth. They want accuracy. They want something that keeps track of their world without constant repetition. But as expectations rise, the quality of prompts actually drops, creating a widening gap between what users want and what AI can reliably do.

I remember the first time I came across ChatGPT. The CEO of a small Startup I was consulting for shared a poem generated by AI that was supposed to reflect my personality. I thought ‘Great, but what am I supposed to do with this?’ This was in October 2023. Now, I expect AI to know and deliver exactly what I want and how I want it.

Did my prompting get better? Most likely. Are my expectations unreasonable? Definitely.

AI can’t yet do what I would want it to do, at least not fully. Not because it’s not smart enough, but because it hasn’t evolved in being fully contextual.

That shift is already happening quietly in the background. You can see it in how people talk to ChatGPT or Claude. They expect these models to remember things, to understand their role, to interpret their tasks, to recall previous conversations, and to make sense of complex instructions with half the information.

Inside the workspace, Large Language Models (LLMs) were not designed for that level of memory or reasoning across different apps, tools, and workflows. People started assuming that LLMs have this invisible intelligence layer, when in reality the models are operating almost blind. Most of the context lives outside of them, scattered across documents, tasks, messages, notes, meetings, and human memory.

Users feel disappointed when generic AI cannot behave like a knowledgeable colleague. You can work around this by manually feeding it context, but this means spending almost as much time preparing the input as you would spend solving the problem yourself. It becomes a different workflow, but not a more productive one.

The frustration doesn’t come from the model’s limitations but rather from how we use AI.

The real opportunity is to build the system around the AI. A system that gathers, organizes, and structures the information your team uses daily, so the AI always has the background needed to give meaningful, accurate, and relevant output.

In the SaaS sphere, I’m pretty convinced Contextual AI will be the next big thing because it moves the burden away from the user. Instead of you explaining the same things again and again, the environment does the heavy lifting.

The AI understands your team’s structure, your responsibilities, your projects, your documentation, your meetings, your deadlines. It knows who owns what. It knows how work flows across the company. It knows what you worked on last week. It sees the gaps, the stalled tasks, the missing clarity. And because it knows all of this, it becomes proactive instead of reactive.

Imagine asking for a project status and getting an answer based on actual tasks, meeting notes, blockers, progress signals, responsibilities, and deadlines. Not based on a random guess.

Imagine onboarding a new employee and the AI automatically generating a personalized path with the right documents, the right team members, and the right responsibilities, without you having to build everything manually.

This is the leap. Contextual AI is about a system that understands the entire environment that produces the answer. It removes the chaos, so teams can work with clarity instead of constant catch-up.

I believe contextual AI will be the next big thing because it brings AI down to earth. It makes it practical. It makes it reliable. It makes it useful in a way that actually impacts daily work. And once people experience an AI that acts like it knows their world, everything else will feel outdated.

We’ve already entered a phase where AI has become part of the workflow, not an optional tool sitting next to it. And the companies that build the right foundation today will define the next decade of workplace software.

This is what I’m betting on with Loopwell. A system that gives AI the context it needs to behave like a teammate, not a chat box. A system that turns scattered information into clarity, and clarity into momentum.

Contextual AI is coming, and once people feel the difference, there will be no going back.