How (and Why) to Embrace AI as a Teammate, Not a Technology

AI is increasingly integrated into day-to-day operations, but few organizations have figured out how to make the most of it. It’s often piloted in silos or treated like just another piece of software. When results don’t match the hype, teams blame the technology or move on to the next tool.

The issue, however, usually isn’t technical, it’s conceptual. AI performs differently from traditional platforms. It adapts and improves through use. Its behavior is closer to that of a junior colleague than a finished product, and that’s exactly how organizations need to think about it.

Teams that treat AI as a teammate, not a technology, are seeing greater impact. They’re getting the most out of AI by teaching and guiding it, not merely deploying it. And they’re thinking about it differently: Rather than changing technology, they’re changing how people work with it. Here’s how to shift how your teams use AI.

ASSIGN TWO SEATS FOR AI

One way to reframe AI’s role in the organization is to assume that every team has two open seats—both of which are reserved for AI. The first seat is for automation. This seat can handle repeatable, rules-based tasks that no longer require human effort, such as categorizing support tickets, pulling KPI snapshots, or summarizing notes from a call.

AI is already capable of performing these tasks well, and this frees its teammates for more strategic work. The second seat is for augmentation. In this role, AI supports each team member as a high-functioning assistant would. It helps them move faster, think more clearly, and test assumptions. A strategist might use AI to pressure-test an idea or look for precedent, while a sales lead might ask AI to synthesize a complex client history. Both roles are important.

When AI operates only as automation, your team misses out on its creative potential. When it functions only as a thought partner, your team still carries too much administrative burden. By putting both seats in play, you open up capacity, creating room for better thinking across the board.

WRITE A JOB DESCRIPTION FOR AI

Teams often run into problems when AI is introduced without clarity. If no one defines what it’s responsible for, expectations vary. If the goals aren’t clear, it’s difficult to know whether AI is “working.” AI needs a job description that spells out how it will contribute. What is it expected to own? What tasks fall outside its scope? Where will it interface with people, and what kind of feedback loop is in place?

Consider the case of one growth team that created a formal job description for an AI role tasked with generating proposals. The AI’s responsibilities included speeding up proposal development, improving access to past content, iterating on visuals and messaging, and helping users navigate new ways of working. Success was measured in terms of time to draft, win rate, and volume in addition to using qualitative measures such as proposal quality and teammate satisfaction.

The hardest part, the team found, was defining what “good” looked like. Unlike a human colleague, AI doesn’t set its own goals or flag confusion. It was up to the team to establish those boundaries. Doing so prompted broader questions: Who owns what? Where do we need consistency? What does “done” mean now? That reflection was useful, and sometimes uncomfortable, but it clarified the role of the AI as well as how the team worked together overall.

ESTABLISH TEAM NORMS

AI doesn’t automatically know how to plug in to a team’s workflow, and people don’t always know how, or when, to engage with it. That makes operating norms just as important when AI is involved as it is in all-human teams. At minimum, you need to define how your teams will work with AI. Which processes will include it, and where does the human stay in the loop? How are outputs reviewed, adjusted, and improved over time? Who owns final decisions? It also helps to set expectations around feedback.

AI gets better with context, so team members need to provide input when something’s off and reinforce good results when something’s right. These norms don’t need to be complicated; they just need to exist. Without them, people default to old habits. With them, teams move faster, learn more, and gain clarity on how AI fits in.

RETHINK HOW YOU MEASURE VALUE

Many organizations try to justify AI the same way they’ve justified other technology—by asking how much time or money it saves. When AI is treated as a teammate rather than a technology, the real question is how well it supports the team. The types of measures that matter are the ones that reflect how well the team is using AI, not just how well the AI performs in isolation (Does it reduce bottlenecks or rework? Is it contributing to better decisions, faster cycles, or stronger output?) And over time, these types of measures provide a better lens for evaluating value, especially as the AI learns and grows.

REVISIT THE ROLE OF AI FREQUENTLY

AI tools gain new features, and use cases evolve rapidly, so a job description written 6 months ago for AI might already feel outdated. Make sure you build in regular checkpoints to review how AI is performing and where it could contribute more. Evaluate what’s working, what isn’t, and whether there are any friction points you didn’t anticipate. Regular reassessment will help keep your teams’ expectations realistic and the human–AI roles aligned. It also encourages teams to let go of tasks that no longer require human attention and embrace new ways of working, even if it feels uncomfortable at first.

START BUILDING YOUR AI TEAMMATES TODAY

Don’t just jump into the next AI tool. Take a moment to look inward, as the most effective AI integrations start by understanding the work already in motion and identifying where AI can make an immediate difference. Begin with your existing team. List out everything they’re responsible for, then ask two questions: Which tasks could AI reasonably handle now? Where could AI offer support that would improve how people work?

Even this short exercise can unlock new ways of thinking. It brings clarity to the actual work and reveals where AI can have the greatest impact. Some teams will see quick wins, and others will uncover friction that needs to be worked through. That’s normal. Change isn’t always easy, but it gets easier when everyone knows what’s expected.