Top 10 Behaviors of High‐Functioning AI Teams

Top 10 Behaviors of High‑Functioning AI Teams

I didn’t set out to write a long newsletter.

I had a few thoughts about how teams could use AI more effectively. But then I went down the rabbit hole—and realized something much bigger: The physics of how we work is changing.

The center of gravity for team intelligence is moving—from inside our heads to something humans and machines share.

And most people don’t see it yet.

 They’re still treating AI like it’s a faster assistant. But what’s happening is deeper, stranger, and far more profound.

That’s why I wrote this:
To explain what’s really going on—and what high-functioning AI teams are doing differently. Not just to work faster. But to think differently. To collaborate at a higher level.

Introduction – Same Team, New Physics: Imagine your team has spent years mastering the art of walking on solid ground, and overnight, the terrain turns to water. Suddenly, the skills that kept you agile on land need a complete rethink underwater – same body, entirely different physics. This is the kind of transformation AI is bringing to team cognition. We’re shifting from a world where intelligence lives in individual heads to one of distributed cognition, where human minds and AI systems intertwine like swimmers in a synchronized ocean dance. The early evidence is clear: almost every company is dabbling in AI, yet only about 1% feel they’ve truly integrated it into how teams work day-to-day (mckinsey.com). Most teams are still strapping new AI tools onto old processes – effectively trying to swim with a walking mindset – and then wondering why they aren’t winning races.

High-functioning AI-integrated teams, on the other hand, have figured out the new physics of work. They’re not just using AI to speed up tasks; they’re transforming how decisions are made, knowledge is shared, and workflows. Like pioneers adapting to an underwater world, these teams have developed fresh habits, rituals, and mindsets to fully leverage AI as part of their collective intelligence. Below, we present the top 10 behaviors that set these AI-enhanced teams apart. Each behavior is explained with real-world examples to illustrate how professional teams (from tech strategists to operations leads) are evolving their workflows. Dive in and discover how to stop treading water and start swimming ahead in the AI era.

1. Treating AI as a Team Member, Not Just a Tool

High-functioning teams approach AI less like a fancy tool and more like a new teammate. This mindset shift is fundamental – AI isn’t an off-to-the-side gadget; it’s woven into the collaboration fabric. Team members actively “partner” with AI systems in brainstorming, problem-solving, and decision-making as they would with a human colleague. Instead of siloed individual use, they share AI insights, making AI a core part of discussions and project workflowsmedium.commedium.com. This means acknowledging AI’s strengths (pattern recognition, speed, endless creativity) and weaknesses (lack of context or judgment) as you would when onboarding a new team member.

For example, a product design squad might include a generative AI in design reviews – the AI suggests dozens of design variations, and the human designers discuss these suggestions collectively. In practice, leading companies encourage this collaborative ethos: at software firm Front, the CEO openly shares how he uses AI daily and has employees present their AI-driven experiments at all-hands meetingslinkedin.comlinkedin.com. By signaling that AI is part of “us”, leaders set the tone for everyone to treat AI-generated ideas as inputs to vet and build upon, not mystical outputs to distrust or ignore. The result is a team where AI’s contributions are welcomed and evaluated alongside human input, creating a richer pool of ideas and solutions.

2. Establishing Rituals for Human-AI Collaboration

Just as great teams have rituals (daily stand-ups, weekly retrospectives), high-functioning AI teams build new rituals around AI. They know consistently integrating AI into the workflow requires habit, not hope. This could be a daily “AI stand-up”: a quick round where team members share a tip or result from an AI tool they tried since yesterday. Some teams hold weekly demos of the best AI-assisted work, or maintain an internal channel (like #ai-ideas) where anyone can post newfound tool uses. The goal is to normalize AI as a constant companion in work processes, ensuring it’s front-of-mind and evolving through shared learning.

For example, the customer support company Front has an internal ritual of spotlighting employees’ AI use cases at company-wide meetings linkedin.comlinkedin.com. One week a salesperson might show how an AI helped draft a proposal, the next week, an engineer shares how a code assistant fixed a tricky bug. These rituals create a culture of experimentation and knowledge-sharing. Similarly, many organizations have started “prompt jams” or hackathons where cross-functional teams tackle a problem by jointly crafting AI prompts and comparing outcomes. This fun ritual builds prompt literacy (more on that soon) and uncovers creative uses. By institutionalizing such practices, teams move beyond ad-hoc tool use to embedded collaboration workflows. Over time, these rituals reinforce the idea that working with AI is an expected, even celebrated, part of everyone’s role.

3. Externalizing Knowledge with Shared AI Memory

In high-functioning AI teams, knowledge doesn’t live trapped in individual brains or scattered chat threads – it’s logged and shared for collective benefit. One crucial behavior is setting up a “shared memory” for human-AI interactions. Every useful prompt, AI-generated draft, decision rationale, or learned lesson gets captured in a searchable repository so that the team’s extended brain (humans + AI) continuously learns without forgetting. This practice, sometimes called a context repository, prevents the knowledge fragmentation that plagues teams where each person experiments with AI in isolation (linkedin.com). Logging prompts and outcomes centrally (using wikis or tools like prompt databases) allows anyone on the team to leverage past AI queries, avoiding duplicate work and building on each other’s discoveries.

For example, an advisory firm might maintain an internal “AI library”. When an analyst uses an AI assistant to summarize a niche market report, the prompt and the vetted summary are saved to a team knowledge base. Later, another team member tackling a similar market doesn’t start from scratch – they pull from that library, maybe even refining the prompt further. Tech consultancy BCG reportedly bakes such sharing into their workflow, deeply integrating AI into project pipelines rather than one-off uselinkedin.com. The payoff is compounding productivity: the whole team benefits if one person figures out how to get a great result from an AI prompt. In essence, high-performing teams treat information from AI as a communal resource, much like code libraries in software engineering, so the “team mind” gets smarter with each AI interaction.

4. Explicit Context-Steering and Data Grounding

Teams at the top of their AI game never assume “the AI will figure it out.” They actively steer context to the AI, feeding it the right information and constraints for each task. This behavior means carefully selecting and providing the data or background an AI needs to be effective, rather than expecting a one-size-fits-all model to magically know your business. High-functioning teams have well-defined processes to prep context for AI – for instance, curating relevant documents, client history, or project specs and supplying them to the AI when asking for analysis or content generation. This context-grounding prevents the common failure of generic AI output that misses the mark. In practice, these teams often integrate their AI tools with internal databases and knowledge sources so that answers are based on trusted data, not just internet training fluff.

For example, Morgan Stanley’s wealth management team developed an internal GPT assistant that was fed a repository of 100,000 research documents and guidelines, allowing advisors to query AI with firm-specific knowledge at handopenai.comopenai.com. When an advisor asks a question, the AI isn’t drawing from random web info – it’s pulling from Morgan Stanley’s own collective memory. This explicit context provision led to highly relevant answers and fast adoption (over 98% of advisor teams now use the assistant)openai.com. Another case: a marketing team might always attach their brand style guide and recent campaign results when prompting an AI for ad copy ideas, ensuring the suggestions align with their context. The lesson is AI amplifies best when you guide it with the right context – high-performing teams ritualize this, effectively training the AI on the job with each use. By being deliberate about what information the AI sees, they get outputs that are far more accurate and useful for team needs.

5. Human-in-the-Loop Curation and Quality Control

No high-functioning AI team simply takes AI output at face value. A defining behavior is rigorous human-in-the-loop curation: there are always human eyes and judgment checking, testing, and refining what the AI produces. This isn’t a distrust of AI – it’s a disciplined quality assurance process to combine AI’s efficiency with human insight. Teams that excel here establish clear standards: AI drafts must be reviewed and edited before they’re considered complete, and important decisions augmented by AI are double-checked by a person. In fact, studies show that while AI can speed up work, quality gains only materialize when teams implement structured evaluation frameworkslinkedin.com. High performers bake those frameworks into their workflows. For instance, they might require a peer review of any AI-generated client report, or have a checklist to validate facts and data in AI outputs (catching those infamous “hallucinations” before they cause trouble).

A vivid example comes from consulting: an MIT-BCG experiment found that consultants using AI produced work faster, but only teams with a review system maintained or improved quality linkedin.com. Some news organizations use AI in media to draft articles, but editors then meticulously fact-check every line against sources before publishing. Likewise, Morgan Stanley’s teams didn’t just deploy an AI assistant – they had advisors and prompt engineers systematically grade the AI’s answers for accuracy, iteratively refining prompts and the model based on those scoresopenai.comopenai.com. This tight feedback loop elevated quality to the point that advisors trust the answers in client meetings. The takeaway: AI can generate 100 ideas or drafts in an instant; high-functioning teams sift and sculpt that raw output into gold. They make human judgment the ultimate editor, ensuring the team’s standards are never lowered by the speed of automation.

6. Prompt Engineering Mastery Across the Team

In an AI-integrated team, the art of communicating with AI – prompt engineering – becomes a core team competency. Top teams don’t leave prompting to chance or a few specialists; they deliberately build prompt literacy throughout the organization. This means training every team member in how to phrase questions or tasks for AI, sharing effective prompts that yield great results, and even creating libraries of prompt templates for common workflowslinkedin.com. High performers treat prompt-crafting like a new language everyone should speak. They might host internal workshops on writing better prompts or encourage a “prompt of the week” spotlight where someone demonstrates a clever query they used. The result is less trial-and-error and more consistent, high-quality output from AI assistants, because the team as a whole knows how to speak the AI’s language.

For example, KPMG launched a “GenAI 101” training for all employees to level-up AI skills, including crafting effective prompts for different scenariosgreatplacetowork.com. Some companies have even created prompt playbooks: if a sales rep needs a first draft of a proposal, there’s a tested prompt format available to guide the AI; if a developer needs code review, they know how to ask the coding assistant properly. A product team at a startup might maintain a shared Google Doc of best prompts for tasks like user research summarization or generating UX text, which everyone contributes to as they discover what works. By demystifying prompt engineering, high-functioning teams ensure AI usage isn’t confined to a couple power-users – it’s a distributed skill. This broad competence both empowers each employee (making AI less frustrating and more fruitful) and improves the team’s overall output as good prompting becomes second nature in their workflow.

7. Async-First Workflow and AI-Optimized Meetings

High-performing teams realize that AI allows work to happen in parallel and asynchronously, so they reinvent their workflows accordingly. Instead of traditional sequences where everyone waits on one person’s output, these teams offload tasks to AI agents off-hours and between meetings, then reconvene to integrate the results. Embracing an “async-first” mentality means if the AI can research, draft, or analyze something overnight, it should – freeing human meeting time for what humans do best (critically evaluating, strategizing, and making decisions). This goes hand-in-hand with AI-aware meeting design: they restructure meetings so that rote updates and data dumps are handled by AI summaries, and the live discussion is reserved for higher-order thinking. The net effect is a significant increase in iteration speed and the number of ideas tested, because work is continuously happening (often aided by AI) even when the team is not all in the same room or time zonelinkedin.com.

For example, a software engineering team might set up an automated pipeline where each evening an AI system runs code quality checks and generates a list of potential bugs or improvements. When the developers start work next morning, they already have a to-do list drafted by the AI, quadrupling how many issues they can consider in a week. In one case, a company found that high-performing groups were testing 4× more ideas per month by using asynchronous AI workflows, compared to teams doing everything in scheduled meetingslinkedin.com. Similarly, a marketing team could use AI to generate 10 variations of an ad copy while they sleep; the team meets the next day only to critique and pick the best – saving the meeting for creative judgment, not blank-slate creation. In practice, many teams now use AI-powered assistants in meetings for note-taking and action items (so no one has to play scribe), or have AI prepare a brief before the meeting (“Here’s what changed in the metrics dashboard this week and why”) so everyone enters the discussion with the same context. By redesigning workflows and meetings to exploit AI’s around-the-clock capabilities, teams create a constant cycle of output and feedback. This continuous rhythm is like having a project progressing 24/7 – humans contributing when insight or decision is needed, AI contributing whenever automated generation or analysis can help.

8. Building Trust through Transparency and Checks

Trust is the oil that makes a human-AI team function smoothly, and high-performing teams know trust doesn’t come for free – it’s built through transparency and clear checks. One key behavior is making the workflow around AI outputs visible and accountable: team members document where an AI was used, who prompted it, how the result was verified, and whether it’s been approvedlinkedin.com. This provenance tracking means anyone reviewing a piece of work knows its history (no more mystery “who wrote this?” when an AI is involved). It prevents the distrust that can happen when people aren’t sure if something was vetted or just came out of a black box. High-functioning teams also set boundaries on AI autonomy, deciding which decisions AI can make vs. what requires human sign-off, bringing clarity that further reinforces trust. Essentially, they create a safety net for AI use: everyone knows the guidelines, and there’s a system to catch errors or escalations, so the team can confidently embrace AI without fear of unseen risks.

For instance, a finance team might stipulate that if an AI generates an analysis report, it must include footnotes linking to source data, and a human analyst must sign off those findings before they influence any investment decision. By doing so, colleagues trust the AI-augmented reports because they can see the evidence and the human audit trail. This aligns with research: teams that lack such criteria and transparency often waste over 30% more time second-guessing AI outputslinkedin.com. High performers avoid that pitfall. Another real-world example is in customer support – some companies have AI draft responses to support tickets, but they always label AI-drafted text for the human support rep and require a quick review click. Over time, as reps see the AI’s suggestions and validate them, their trust builds and the review becomes faster. At Morgan Stanley, achieving near-universal AI assistant adoption (98% of teams) was credited in part to rigorous compliance checks and making the AI’s limitations and review processes explicit, so advisors felt comfortable relying on itopenai.comopenai.com. The lesson: transparency and guardrails don’t slow a team down – they speed up adoption by ensuring everyone can trust the augmentations in their workflow. High-functioning teams take the time to build this trust infrastructure, knowing it pays off in both confidence and effectiveness.

9. Redefining Roles to Leverage Human-AI Complementarity

In teams that thrive with AI, roles and workflows are constantly redefined to best exploit what humans and AI each do well. Rather than each person doing end-to-end tasks as before, work is redistributed: routine or computation-heavy parts handled by AI, and judgment-intensive or creative parts led by humans. This reallocation of responsibilities is a key behavior – it requires regularly assessing who (or what) is the best entity to do this part of the job? – and then updating job descriptions or daily duties accordingly. High-functioning teams are fluid and ego-free about this. A data analyst might become more of an “AI curator,” focusing on selecting data and verifying AI analysis rather than crunching numbers manually. A project manager might spend less time compiling status updates (if an AI can do it) and more time on strategy and risk mitigation ,which AI can’t handle. Everyone shifts gradually into roles that maximize uniquely human strengths like intuition, empathy, and critical thinking, while trusting AI to cover the grind and the scale.

Consider a sales team at a large firm: they introduced an AI tool to qualify leads and draft initial outreach emails. The savvy salespeople didn’t fear for their jobs – instead, they adapted their role to focus on the human elements of selling. The AI now researches prospects and writes personalized intro emails, freeing the reps to spend time on live calls and relationship-building, which are their competitive advantages. The sales team essentially made the AI their prospecting specialist, changing how they allocate their time. In another case, a hospital’s radiology department integrated an AI that pre-scans medical images for likely issues. The radiologists now start their day with AI-marked areas of interest, and their role has shifted toward deeper diagnosis and patient consultation rather than scanning every image pixel by pixel. These examples show a pattern: high-functioning teams regularly rebalance workloads between AI and humans as new AI capabilities emerge. They ask, “How can we let the AI handle more of X, so we can spend more time on Y?” and adjust roles accordingly. This behavior keeps the team adaptable and focused on high-value work, rather than everyone clinging to old task boundaries. Over time, it leads to a powerful synergy: the AI takes on the drudgery and data deluge; the humans drive insight, innovation, and connection.

10. Continuous Learning and Adaptation (AI Fluency as a Mindset)

Finally, the most effective AI-enhanced teams foster a culture of continuous learning and adaptation. They treat AI integration not as a one-off project but as an ongoing journey – much like agile software teams treat each sprint as a chance to improve, these teams regularly reflect on how they’re using AI and seek to improve. Concretely, they stay up-to-date with rapid AI advances, encourage ongoing training, and evolve their processes as new tools or features become available. It’s common to see these teams hosting monthly “AI update” sessions where someone presents a new capability (a model that can handle images or a new prompt technique) and the team brainstorms potential uses. They might rotate an “AI champion” role that keeps eyes on industry trends and pilots new tools. Crucially, they also encourage safe-to-fail experimentation: team members are empowered to try new AI-driven approaches and share lessons, even if it doesn’t always work out. This relentless learning mindset ensures the team doesn’t stagnate in leveraging AI. In a field evolving this fast, yesterday’s best practice may be today’s old news, so high-functioning teams are always in beta – refining prompts, updating workflows, and educating themselves.

For example, professional services giant KPMG (and many of its peers) launched firm-wide AI learning programs, signaling that everyone from junior analysts to senior partners should upskill in AI greatplacetowork.com. Tech companies often run internal blogs or Slack updates about “AI hacks” discovered by employees, ensuring knowledge spreads laterally. One operations team in a manufacturing company started an “AI lab hour” on Friday afternoons, an informal time to play with new AI APIs or automate a manual task, which often led to process improvements that became permanent. The strategic benefit of this continuous learning is agility: when new opportunities or challenges arise (like a competitor deploying a powerful AI tool), these teams can adapt faster because they’ve built change muscle. They won’t be caught flat-footed relying on last year’s AI tricks. Instead, they continuously disrupt themselves now before someone else disrupts them. In the long run, this makes the difference between teams that ride the wave of AI-driven change and those that are swept aside by it.

My Disruptive Take – Evolve Your Team’s “Cognitive Playbook”

The message is clear: the game has changed, and so must your playbook. The behaviors outlined above aren’t just tips; they form an adaptive strategy for thriving in an AI-shaped world. Think back to our opening metaphor of moving from land to water – the teams that flourish are willing to relearn how to swim. They let go of rigid old habits and embrace new rituals, re-engineer their workflows, and even their mindset about what “work” means. The unifying theme is a shift from internalized cognition (each person for themselves) to distributed cognition (the team brain extends into machines and back). High-functioning teams realize that the collective intelligence of a human-AI hybrid can far exceed the sum of its parts – but only if we fundamentally reorient how we collaborate, communicate, and trust in this hybrid environment.

Adopting these ten behaviors is challenging; it requires leadership and cultural buy-in, not just individual effort. Yet the alternative is stark. One McKinsey report warned that leaders must “advance boldly today to avoid becoming uncompetitive tomorrow”mckinsey.com. The same applies to teams: sticking to old ways while others reinvent will leave you at a serious disadvantage. The opportunity here isn’t merely about doing things faster – it’s about doing things differently to achieve what wasn’t possible before. Your team’s capacity for creativity, problem-solving, and speed can be radically amplified, but it demands an evolution.

So ask yourself and your colleagues: Which of these new behaviors will we start practicing this week? You could begin by instituting a simple AI ritual, or by carving out time to build a shared prompt repository. Perhaps you tackle a project in parallel with an AI partner and a human reviewer to see how it feels. The important thing is to start adapting your team dynamics now, not later. In the age of AI disruption, the “physics” of business is shifting fast. The teams that treat this moment as an invitation to innovate on how they work – to rewrite their cognitive playbook – will lead their industries into the future. It’s time to evolve and make AI an integral part of your team’s DNA, so you’re not just surviving the disruption, but truly owning it. The water is rising; the teams who learn to swim will stay afloat and chart the next course forward.

Disruption Now is launching a new cohort to train with the best AI and integrated team practices. To be part of the first cohort, join the waiting list here.

Keep disrupting my friends,

Rob Richardson, CEO of Disruption Now & Chief Curator of MidwestCon

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