This article has been contributed by Ronik Patel, Founder & CEO, Weam.ai
“Have you, till now, used AI for something apart from generating content?”. This was my question in one of the recent public speaking opportunities I was a part of. Automation, AI, MCP, trendy words that most have become recent tags of our daily information diet platforms, those were my topics too. At the beginning of the seminar, I asked the question I wanted to ask everyone. It’s not to demean someone; instead, I want to understand how well they have integrated AI into their workflow. And also, the persistence of my follow-up question is justified by this goal.
I let you in on a secret I learned that day. Nobody trusts the technology going past that stage. Why? Well, through recent conversations with my head of marketing, I came to the conclusion, answering; Why don’t we? I asked, “Why do you still reiterate every content you generate with AI and find something wrong every time?”. He says, “Ronik, I don’t want people to feel isolated by seeing our content.” The depth of that question was a bit extensive for me to reach. However, after a few conversations, I understood that it was because he did not want to miss the human element.
The Superfluous Tech
Teams at the agency spent countless hours writing polished prompts to get the response they needed, get work done. Amidst throwing requests at your ultimate assistant who does not need coffee, have you ever thought about strategising for making your workflow smooth?
I believe people are using AI in their workflow more than they want, but not necessarily. The moment an AI solution you have paid for enters the team workflow, chat team dynamics, shared history, and collective knowledge that exists nowhere in your prompt.
But the team needs AI to go through the day as they are drawn towards the endless creative possibilities. We made AI generative, impressive, but fundamentally isolated. Hence, it needs a shared space, a shared functionality for the team, making it collaborative should be one of our primary goals, don’t you think?
Think of an AI platform that is contextually aware of collaborating with a team, dynamically adaptive, and genuinely integrated in a way that reflects how teams think together.
Pratyusha Srivastava
The Context Crisis
Let’s understand the depth of this scenario. I witnessed one Monday morning during our AI week. We wanted to have discussions regarding what we have planned to do in the AI week, and particularly align marketing strategies accordingly. Now there was my HOD of marketing and the team, a new intern we hired, and an AI we usually use to record our meeting for MoM (moments of meeting) purposes. The new hire frantically takes notes, trying to decode these conversations. I just know the AI assistant–It might transcribe every word perfectly, but it’s as lost as the new hire.
Does it lack the capabilities to understand different pitches of voices? I don’t think so, it’s a recording bot which picks context from our meeting and prepares reports that we need. However, what it lacked was even the social awareness to know what it was missing. An AI you use on a daily basis might be good at picking the breadcrumbs that represent months of shared decision-making. It struggles to understand the ecosystem of intentions, relationships, and evolving context that makes teams effective.
Beyond the Prompt Paradigm
“Generate this.” “Analyse that.” “Summarise these.” The whole AI interaction we have designed around is the exchange of information. Real collaboration is more conversational, iterative, and rich with shared understanding that builds over time. It’s not a fundamental flaw, I believe, but a technical approach we can fix.
This brings us to AI agents and MC, which may play an important role in the future of workflows. However, to make a participant understand when you talk about refining your product you consider the whole history of versions of your product commits, API integrations, what worked, what didn’t. Keep every small decision framework and result of those decisions as contextual information for AI. Does that make AI more teamware?
Muskaan Kapoor
Need for The Collaborative Intelligence Workspace
I wonder if the most exciting AI developments are happening between humans. While AI front runners launch features, updates, and models one after another aren’t humans who manage them to fit into their workflow. They understand the solution, figure out their pain points, upskill themselves and then collaborate with the AI to complete the goal of the day.
To intensify the AI and human collaboration, it is important to take into consideration the collective rhythm of how teams work together, too. If we find a way to integrate an AI not to surveil that you are meeting your goals, but to understand the communication channels, wouldn’t it be fascinating? It recognises that when our HOD of marketing asks for “options,” he typically wants three distinct approaches with clear trade-offs.
The AI we perfect through human collaboration generates the right content. It generates relevant content, timed appropriately. That happens when an AI is informed by collective intelligence or trained on expertise by your team.
Solving Productivity Puzzle
Every AI claims to make individuals more productive. Maybe it’s time to take a step back and think about the team and their coherence. Instead of five people prompting with their respective generative AI, getting results, and making decisions, can we have AI that helps teams think together more effectively?
The AI should also understand that a leader, when asking for alternatives, doesn’t just need surface. Educating and understanding the subtext makes the leader and their team worthy of moving forward in this new era of AI revolution. On the other hand, we want a collaborative space integrated with AI that understands the mental mode of the team and its leader.
To take an example, think of engineering specifications that reference actual architectural decisions. We want strategies that are built on previous discussions rather than starting from scratch. Discussions which shape the very fundamentals of AI response and action, leading to fruitful results for the team. When we make that possible, maybe we move past the barrier of AI adoption and augment human-AI collaboration in a better way.
The Unexpected Truth
It’s hard to find the right light in this thick, dense fog of AI noise. A revelation, teamware which even I learned the hard way, is that we are trying to make AI impressive rather than useful. Is it the consequences of our assumption about what AI should be? The question helped me move our solution to move towards making AI easy to adopt and better to collaborate with teams.
I think we don’t want to win because we want t make a better AI that would be adding noise to the chaos we see in the landscape. We want to focus on answering “how AI should work with humans?” instead of building towards “how AI should work for humans?”
We Question, Do we change, We Build!
We built something, but is it worth launching, or are we behind? Should we reiterate? Should we move the goal post further, too? Isn’t that what the true iterative process in the technology space is? These questions that keep every founder awake at night. It sounds easy for people who produce surface-level uses of any AI solution every day.
True reviews that change an AI solution are when a team building towards a common goal uses AI every day to find the best possible iterations. That is what Weam AI is doing right now. Our AI revolution will arrive when we are able to build an AI that actively collaborates with a team to help them achieve quantifiable results.
I definitely think organisations that understand how AI works with a team together by filtering through AI chaos will have an upper hand, compared to those who employ the most sophisticated tool. Here is a practice. A few questions every organisation trying to adopt AI should ask: What would you do if your AI could participate in your team’s collective intelligence rather than just responding to individual requests? How would that change not just your productivity, but your ability to think together, learn together, and create together?
Finding the right way towards the future of work, one needs to find AI that isn’t impressive but collaborates smartly with the team. We are also working towards answering those questions effectively, and perhaps that distinction will make a slight difference.
Rishi Mundra
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