As artificial intelligence (AI) reshapes the way organizations operate, leaders face a pressing dilemma: how can they design their teams and structures to be both fast enough to innovate and stable enough to scale effectively?
Move too quickly, and they risk inefficiencies, regulatory missteps and operational chaos. Move too slowly, and they fall behind in an AI-driven marketplace where agility is a competitive advantage.
A recent McKinsey study found that over 75 percent of organizations now use AI in at least one business function, with generative AI use rapidly increasing. Yet, most companies still haven’t seen organization-wide, bottom-line impact from AI applications as adoption and scaling practices have lagged behind. Boston Consulting Group’s recent research revealed that only 26 percent of companies have developed the necessary capabilities to move beyond AI proofs of concept and generate tangible value.
Striking the right balance between speed and stability isn’t just an operational challenge – it’s a strategic imperative. Recent research has revealed a critical insight into the relationship between team size and innovation. A comprehensive analysis of over 65 million papers, patents and software products uncovered a clear trend: the larger the team, the lower the likelihood of disruptive breakthroughs. As teams grow in size, innovation tends to become more incremental rather than groundbreaking.
Building on Visier’s comprehensive study of 144 enterprise-sized organizations, which identified a prevailing team size of six to 10 members, our recent research highlights a crucial factor: the way teams are structured plays a pivotal role in addressing this challenge. This research reveals an important pattern: team sizes need to be shaped by business needs, the scope of responsibilities and the complexity of tasks within each domain.
For example, facilities teams are structured around the demands of maintaining office buildings, while technology teams are sized according to task complexity. In areas where tasks are routine or predictable, managers can effectively oversee larger teams with wider spans of control. Conversely, in industries like healthcare – where shift work is prevalent – larger teams are essential for operational efficiency.
Team size matters
This reinforces a key principle: team size should be designed around the desired outcome – the nature of the work to be done. With this in mind, we explore how the speed-stability continuum can help resolve the challenge of driving rapid innovation while maintaining stable structures to assess future risks and challenges.
By considering team design within this framework (Figure 1), organizations can optimize their structures to balance agility and resilience, ensuring they remain both innovative and sustainable in an AI-driven world.
Case Study
A recent organizational network analysis illustrates how team design can optimize the speed-stability continuum based on task demands. The Advanced AI Development Center, home to 137 AI and machine learning experts, operates with 13 specialized teams (Figure 2) ranging from seven to 14 members. By prioritizing small, agile teams, the center fosters rapid innovation, creativity and quick iteration, allowing it to stay ahead of market trends and customer needs. Our research confirms that smaller teams drive new innovations, while larger teams refine and scale existing ideas – a principle the center embraces.
The speed-stability continuum Advanced AI Development Center
This organization effectively balances speed and stability by strategically structuring its teams. As illustrated in the organizational network analysis (Figure 3), small teams – positioned on the network’s periphery – drive innovation and rapid experimentation, while larger teams (blue and gray) at the core focus on scaling and integrating disruptive solutions for long-term growth.
Collaboration between these groups is key. For example, a larger AI governance and security team (dark blue) works closely with smaller innovation teams to develop robust ethical AI frameworks. By embedding these safeguards into the center’s offerings, they reinforce trust, compliance and scalability, ensuring that innovation is both cutting-edge and responsibly deployed.
Network diagram for the Advanced AI Development Center
The size of teams plays a crucial role in enabling executive leaders and organizations to drive innovation in the rapidly evolving AI landscape. Our data-driven analysis of team sizes across job domains provides insights into current spans and layers, drawn from a large dataset of real-time employee records.
The findings suggest that task design and the nature of work significantly influence optimal team size in many organizations. Additionally, our case study highlights the delicate balance between operational stability and agility – both an art and a science – when assembling teams for innovation. As leaders navigate this critical management challenge, they are increasingly turning to strategic actions and tools to take a more disciplined, tailored approach to structuring teams, ensuring a competitive edge in product innovation.
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Taking action
To balance speed with stability in AI-driven organizations, leaders can take the following actions:
- Design teams based on task complexity: Structure teams according to the complexity and predictability of their tasks. Smaller, agile teams should focus on innovation and rapid iteration, while larger teams handle scaling and integration.
- Adopt the speed-stability continuum: Use this framework to determine the optimal mix of agility and resilience. Innovation teams should operate with flexibility, while core teams provide stability and risk management.
- Encourage cross-team collaboration: Facilitate communication between small, fast-moving teams and larger, stability-focused teams to ensure that disruptive ideas can be integrated into scalable solutions.
- Right-size teams for maximum impact: Keep innovation-focused teams small (seven – 14 members) to drive creativity, while ensuring scaling teams have enough capacity to integrate and refine new solutions efficiently.
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