Dynamic teaming is an operating model where a team assembles around the skills and capacity a piece of work requires right now, then redistributes when that work shifts, with no formal reorg required. It replaces the chain-of-command org chart with a living map of capability, and it asks what the work needs before it asks who owns the box.
I've spent the last five years developing this model with the team at oAT, and the shorthand we use for it is two words. Here's how I define it:
"Dynamic teaming is an operating model that allows teams to go where the work is to move as the work changes. Very simply, it's form and flow." Britt Gage
how We Do: Form and Flow
The framework has two halves, and neither one works without the other.
Form
Form is the pull-together move. You look at the work in front of you this quarter, this launch, this migration, and you ask what skills, what capacity and what technology are required to get it done well. Then you assemble those resources around it. You're not assigning work to people who already sit in fixed boxes. You're building the shape of the team from the shape of the work.
Flow
Work evolves. The team should evolve with it. Flow is the redistribution move: as a work stream matures, some of it becomes repeatable and some of it becomes obsolete, and the people and tools attached to it get released toward whatever needs them next. Knowing where that line sits is the new core competency. Amy Edmondson has argued for years that teaming is better understood as a verb, the ongoing act of coordinating with whoever the situation requires [1].
This is a way of seeing rather than a process you install. Form is how you structure resources for the highest impact available to you today. Flow is how you redistribute them when that answer stops being true.
Traditional Org Design vs. Dynamic Org Design
The clearest way to feel the difference is to compare the question each model opens with.
"the traditional org design asks the question who reports to whom in order to get this work done. Dynamic org design asks the question what does this work require and who can deliver it?" Britt Gage
| Traditional design | Dynamic design | |
|---|---|---|
| Opening question | Who reports to whom? | What does this specific work require, and who can deliver it? |
| Resource logic | Work is routed into the structure that already exists | People, time, attention and AI capacity flow toward the work that needs them |
| What the org chart is | A chain of command | A living map of capability |
| Trigger for change | A reorg, approved from above | The end of a work cycle |
| Unit of planning | The role | The work stream |
Traditional structure forces work into containers built for last year's problems. The inverse approach builds the container after you've looked at the work.
What Dynamic Teaming Looks Like in Practice
It sounds like an organizational nightmare until you watch one cycle run. Here's the basic shape:
- A work stream kicks off. A project, a quarter, a new phase. You define what's actually happening for this period of time.
- You map skills and capacity to that work. Who has the capability, and who has the room. This step replaces assigning whoever happens to own the adjacent function.
- You learn by doing. Midway through, you get reflective and discerning. Which inputs are producing results? What's holding steady with consistency?
- You hand off what's become repeatable. Consistent, well-defined pieces go to specialized inputs: AI, or a specialist with clear roles and responsibilities.
- The cycle ends and the work has changed. You shift to the next phase and repeat. Form, then the flow that follows it.
Nobody waits for permission to restructure, because the structure was never the point. Moving with the work is the point.
Rote Work and Judgment Work
This distinction is fundamental to the model, and AI is what made it urgent. AI is, functionally, a specialist. It's very good at work with clear inputs and clear outputs.
| Rote work | Judgment work |
|---|---|
| Processing and task automation | Judgment calls under ambiguity |
| Pattern recognition | Relationships |
| Summarization | Creative decisions |
| Anything repeatable with consistency | Cross-functional work that makes the rest go |
| Goes to AI or a specialist | Maintaining the AI and checking its output against the outcome you wanted |
The second column stays human. Someone has to notice that a consistent, repeatable process is producing consistent, repeatable results that no longer map to the outcome you're chasing. That's generalist work, and it doesn't get easier as the automated layer gets bigger.
Why Rigidity Is Now the Risk
"The best tools don't determine who wins. It's the best structure." Britt Gage
Organizational fluidity used to be a nice-to-have. AI made it a requirement, because tools, workflows and the definition of a given job are all changing faster than an annual planning cycle can absorb. Rigidity used to look like stability. Today it carries real cost:
- Lag. A structure that can only change at reorg speed is always staffing last quarter's problem.
- Stranded capacity. Skills sit idle inside functional boundaries while the work that needs them waits.
- Misplaced humans. People stay parked on rote work long after a specialist input could own it.
McKinsey's research on agile organizations describes something adjacent: a stable backbone paired with dynamic cells that form and dissolve around opportunities [2].
What Changes When You Design Around Work
Adopting this model changes four decisions:
- Who you hire. You hire for range and for the judgment layer, because the narrow, repeatable slices are increasingly covered by specialized inputs.
- How you staff. Assignment runs off capability and availability for a defined cycle.
- How you measure performance. You evaluate contribution to work streams, not adherence to a fixed job description.
- Which technology you choose. Tools get selected for the work in front of you, with the expectation that some of them will be replaced inside a year.
Deloitte's human capital research has tracked the same movement away from job-based structures toward work-based ones across large organizations [3].
Where the Generalist Fits
Once rote work moves to specialized inputs, the remaining human work is disproportionately connective: the judgment, the relationships, the handoffs between functions. That's the work oAT exists to cover. We place embedded generalist operators inside companies across marketing, GTM, sales, business ops, customer success, product, AI workflows, community and chief of staff support [4].
An operator who can form around one work stream this quarter and flow to another next quarter is the practical unit of this model. You'll recognize the need when the connective work is landing on you personally and it's starting to show.
The Stability Objection
Does anything stay fixed?
Yes. Accountability for outcomes, the cadence of your cycles and your standards stay fixed. What moves is the assignment of people and tools to work streams.
Doesn't constant movement exhaust people?
Movement at the end of a defined cycle is predictable. What exhausts teams is ambiguity inside a cycle, which is why step two of the practice matters: skills and capacity get mapped explicitly, in writing, before work starts.
Where does it break down?
It breaks down when leaders form around work but never flow, so a temporary team quietly hardens into a permanent department. Edmondson's work on dynamic teaming makes the same point: the discipline is in the dissolution as much as the assembly [5].
Starting Without a Reorg
You can run this on one work stream without touching a single reporting line.
- Pick one project starting inside the next 30 days.
- List the work it actually requires, then sort each item into rote or judgment.
- Route the rote items to AI or to a specialist, and staff the judgment items by capability and available capacity, ignoring titles.
- At the end of the cycle, hold a 45-minute review: what became repeatable, what stays human, where do these resources go next.
Run that loop three times and you'll have the model. The org chart in the file cabinet can catch up later.
FAQ
What does 'form and flow' mean in dynamic teaming?
Form means pulling together the right people, skills and technology at the right time to do the work happening right now. Flow means redistributing those resources in a meaningful way when the work changes. The pair describes one continuous cycle rather than a one-time restructuring exercise.
How is dynamic teaming different from a traditional org chart?
A traditional org chart is organized around the question of who reports to whom, and work gets routed into whatever structure already exists. Dynamic teaming starts with what a specific piece of work requires and who can deliver it, then assembles resources accordingly. The result is a living map of capability that updates each cycle.
Do you need to do a formal reorg to use dynamic teaming?
No. The model is designed so teams can re-form at the start of a work cycle without waiting for an approved restructure. Reporting lines can stay exactly where they are while staffing decisions are made against work streams.
Why does AI make organizational fluidity more important now?
AI has introduced infrastructure that handles specialty work with clear inputs and outputs: processing, pattern recognition, summarization and task automation. That changes which work humans should own, and it changes it faster than an annual planning cycle can absorb. A structure that can only change once a year will keep staffing against a picture of the work that has already moved.
What is the first step to setting up a dynamically teamed org?
Pick one upcoming project and list the work it genuinely requires. Sort each item into rote work, which has clear inputs and outputs and can go to AI or a specialist, and judgment work, which requires human ownership. Staff the judgment items by skill and available capacity rather than by title.
References
- Edmondson, Amy C. "Teamwork on the Fly." Harvard Business Review, April 2012. https://hbr.org/2012/04/teamwork-on-the-fly-2
- McKinsey & Company. "The journey to an agile organization." https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-journey-to-an-agile-organization
- Deloitte Insights. "Global Human Capital Trends." https://www2.deloitte.com/us/en/insights/focus/human-capital-trends.html
- of All Trades. weofalltrades.com. https://weofalltrades.com
- Edmondson, Amy C. Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. Jossey-Bass, 2012. https://www.wiley.com/en-us/Teaming%3A+How+Organizations+Learn%2C+Innovate%2C+and+Compete+in+the+Knowledge+Economy-p-9780787970932