Collaborating with AI: What It Takes to Work Together

There’s no shortage of opinions about artificial intelligence these days. Depending on who you ask, AI is either the future of productivity or the end of human relevance. But beyond the noise, one thing is becoming clear: AI will be part of how we work.

The real question is how.

Will AI quietly run in the background? Will it take over tasks? Will it replace people? These are the wrong starting points. A more useful perspective, especially for companies operating in complex, regulated environments, is this: What would it take to work with AI, not just around it?

At Collaboration Now, we explore how companies work together. And AI, like any new team member, only adds value if it's integrated thoughtfully, with structure, clarity, and responsibility.

It’s not magic. It’s collaboration.

Most AI systems today rely heavily on inputs: structured data, accessible content, and well-defined tasks. Without these, even the most sophisticated model can’t do much. And yet, this is precisely where most organizations fall short.

Information is spread across PDFs, emails, legacy systems, and internal silos. Roles and responsibilities aren’t always well defined. Policies exist, but enforcement is manual or inconsistent. These are not conditions where any kind of automation, let alone collaboration, can thrive.

If we want to make AI a participant in real work, we have to stop treating it like a magic trick. It’s not a black box that can replace humans. It’s a system that can collaborate, but only if we let it do so within a clear, structured environment.

Structure is a requirement, not a bonus.

AI is particularly sensitive to a lack of clarity. Where humans guess what someone wants, fill in gaps, or ask clarifying questions, AI needs parameters. It needs to know what type of data it’s working with, what task it’s expected to perform, and what it is and isn’t allowed to access.

This means that before AI can become a valuable collaborator, organizations need to get their house in order.

• Creating systems that produce and manage structured, accessible data
• Defining responsibility for each step in a workflow, including both human and machine roles
• Ensuring compliance mechanisms are built into the process, not tacked on afterward

These are not small tasks. But they’re the exact prerequisites we already face when working across company boundaries. In many ways, working with AI is like working with another business: it demands standards, governance, and accountability.

Evolving from Assistance to Collaboration.

In industries like real estate and finance, AI already shows promise in well-scoped tasks: summarizing documents, extracting key terms, and generating first drafts. But in most cases, these are isolated utilities and are not deeply embedded in the collaborative fabric of a transaction.

To move from augmentation to real integration, AI must become part of the workflow. That doesn’t mean it acts autonomously or replaces decision-makers. It means the system is designed in such a way that AI can:

• Perform defined sub-tasks
• Interact with relevant data
• Return outputs in a way that’s traceable and auditable
• Be monitored and reviewed, just like any human contribution

This doesn’t make the system perfect. But it makes it usable. And it opens the door to scaling workflows without increasing headcount, while maintaining transparency.

Responsibility stays with the humans.

One of the more uncomfortable truths about AI is that it often delivers output with confidence, but without accountability. That’s a problem, especially in regulated sectors, where responsibility cannot be delegated to a model.

This is why AI should not be treated as a decision-maker. It can assist, propose, and summarize, but the final responsibility must remain with a human. Not just for legal reasons, but because trust and judgment still matter in most real-world business contexts.

Building systems where this is clear, with AI’s role defined and constrained, is what turns automation into actual collaboration.

Designing for AI is designing for clarity.

Adding AI to a messy system won’t make the system smarter. It will only make the mess faster. The opportunity, then, is to treat AI as a forcing function: a reason to clarify data, roles, permissions, and processes. And that’s something every company should want, even without AI.

When we design systems that are ready for collaboration, whether with humans or machines, we create more resilient, scalable, and compliant operations. We reduce friction. We enable handoffs. We make work more transparent.

So while the hype continues to focus on what AI might do instead of humans, we’re more interested in what it takes to work together.

AI doesn’t have to replace anyone. But it will demand that we collaborate better, both with it and with each other.

Written by Michael Wiedemann, Co-Founder of Gridwork and Brixel. If you’re building in this space, let’s talk. Collaboration is no longer optional — it’s the unfair advantage.