Is Your Team In the Loop?

A Simple Guide to AI Collaboration

If you've been hearing about artificial intelligence (AI) and wondering how it fits into your organization, you're not alone. Many business leaders are navigating the same questions: What is AI, really? How should we use it? And most importantly—how do we make sure we have buy-in from the teams that will be using it?

Let's start with a simple analogy. Think of AI as a highly capable assistant that can process information incredibly fast and spot patterns humans might miss. Just like any new team member, AI works best when strategically and thoughtfully integrated thoughtfully into existing workflows

.

Three Ways Organizations Use AI

For those of us who have spent the last decade thinking about AI use cases, from healthcare to government one question always comes up. Will the human be in the loop or will the technology operate autonomously?

Human-in-the-Loop is when people actively oversee and guide AI decisions. Imagine your marketing team using AI to draft email campaigns, but a human always reviews and approves the content before it goes out. This approach keeps humans firmly in control while leveraging AI's ability to process data and suggest options quickly.

Human-out-of-the-Loop is when AI operates independently, making decisions without waiting for human approval each time. Your spam filter is a perfect example—it automatically sorts thousands of emails without asking your permission for each one. This approach makes sense for high-volume, routine tasks where speed matters and the stakes are relatively low.

Most organizations aren't purely one or the other. They use human oversight for critical decisions and automation for routine ones.

I would like to propose a third option

Traditional thinking treats AI implementation as a choice between human control or machine automation. But that's not how modern workplaces actually function. Real work happens through collaboration among people with different expertise, perspectives, and responsibilities.

Team in the Loop recognizes this reality. It's about ensuring that AI doesn't just work with individual employees—it works within your entire team ecosystem, and that everyone who needs to be involved is included and has a foundational knowlege of the tool.

Team in the Loop builds off of human-AI-teaming (HAIT) principles and best practices to include collaboration between muliple humans and (more than likely) multiple AI tools.

Example: my previous company implemented AI systems to handle claims adjudications. In the "human-in-the-loop" mindset, the organization would focus on making sure claims analysts were overseeing AI decisions. That's important, but it's incomplete.

Team in the Loop asks: Who else needs to be part of this? Your quality assurance team should monitor patterns in AI adjudication to catch systematic issues. Your training department needs feedback on where AI struggles so they can update knowledge bases. The customer service team (which may also use AI technology) needs to know there may be a change in the number of inbound calls. Your product team should see what questions customers are asking to identify unmet needs. Your IT security team needs visibility into data handling. Even your customers are part of the loop—they need clear ways to escalate to humans when AI can't help.

Why This Matters for Your Organization

Organizations implement AI without thinking about the team in the loop risk increasing the severity severity an adoption gap.

People feel blindsided by changes to their workflows because they weren't consulted. Important expertise gets overlooked because subject matter experts weren't included in implementation decisions. Problems persist because the people who could fix them don't have visibility into AI performance. Trust erodes because employees feel AI is being done to them rather than with them. Leaving the team out of the loop may result in wasted capital due to decreased ROI and wasted productivity