Machines excel at collaborating, but human teamwork is far more complex. Poor team performance can lead to frustration, high turnover, delays, and costly interventions. What if you could predict team performance using established scientific principles? With our assessment tool, Team-composer, you can estimate the likelihood of successful collaboration even before the team begins its assignment.
Team assignments with a mission impossible lead to frustration, turnover, and disappointing results. Identify risks in advance.
Complex projects are delayed by suboptimal team dynamics. Accelerate innovation through predictive insights into team performance.
Staff turnover disrupts the continuity and performance of multidisciplinary teams. Map team chemistry in advance.
Team composer measures a team’s collective intelligence and predicts team performance. In a world increasingly shaped by AI, team intelligence is vital for innovation, productivity, and cohesion, especially as work becomes increasingly project-based and dynamic.
We understand that the key to a high-performing team lies not only in talent but also in the synergy that comes from the right combination of skills and personalities. This team synergy enhances motivation, performance, and engagement.
Team-composer helps you create teams where members reinforce one another. Each team is carefully assembled, matching individual strengths and characteristics to create mutual complementarity and robust Collective Intelligence.
The success of a team depends in part on the team assignment. With Team-composer, you can identify the risks of a team assignment in advance. This allows you to make timely changes so that the team can start the assignment under the right conditions.
Collective Intelligence is a kind of wisdom and knowledge that comes from a group.
Collectively intelligent teams form a kind of intelligence that simply cannot exist at the individual level.
Many organizations continue to rely on intuition, traditional selection methods, or ad hoc compositions when forming teams. This frequently results in inefficiency, skills mismatches, and diminished team synergy.
Our tool assembles teams by matching the collective of team members to the team assignment. Our approach combines predictive psychological insights, AI-driven matching, and data analytics to objectively and transparently assemble teams.
By combining the right professionals, we assemble teams that are an optimal fit for the assignment and each other from day one.
Diversity is not only socially desirable, but it also endures when it contributes to team success. For example, innovativeness depends in part on the degree of diversity in a team. Therefore, we match multiple aspects of diversity to the assignment’s nature.
Inclusiveness is highly desirable and provides functional benefits. Given the scarcity of labor in the market, it is essential for everyone to participate. Our team composition tool enables everyone to contribute to team cohesion and helps avoid human bias in team formation.
With team composer, you make sure everyone can participate and team members can reinforce each other.
Many organizations still rely on intuition, traditional selection methods, or ad hoc compositions when forming teams. This often leads to inefficiency, skills mismatch, and lower team synergy.
Our tool assembles teams by matching the collective of team members to the team assignment. Our approach combines predictive psychological insights, AI-driven matching, and data analytics to objectively and transparently assemble teams.
By combining the right professionals, we assemble teams that are an optimal fit for the assignment and each other from day one.
More and more organizations are moving away from the classic silo-based hierarchy and organizing work into (multidisciplinary) teams. Not because it sounds more “modern,” but because it helps to learn faster, collaborate smarter, and innovate more agile. That’s what growth demands.
Curious to hear stories about teams as growth accelerators? Listen to our Podcasts or join the discussion on LinkedIn group MindsUnited ➚.
For nearly a century, assessments have been used to predict individual work performance. Meanwhile, collective intelligence is a scientifically based and proven concept that can predict team performance.
Collective Intelligence is the shared ability of a group of people to solve problems, make decisions, and deliver superior results together. To achieve Collective Intelligence (C.I.), the team needs specific combinations of psychological traits, depending on the task’s context and objectives.
Good results require team chemistry. In psychology, we call team chemistry Collective Intelligence.
Team members must complement and reinforce each other to be maximally effective. Team members in collectively intelligent teams reinforce each other’s problem-solving abilities, leading to higher team performance.
In general, two factors determine Collective Intelligence: the nature of the task and the combination of the team members. To achieve Collective Intelligence, the team needs specific combinations of psychological traits. These combinations depend on the task’s context and objectives.
Schedule a consulting session with demo today and discover
the world’s first application for forming
Collective Intelligent Teams.
This fact sheet provides an overview of the most commonly used (psychological) selection methods, both classical and modern. The figures are based on meta-analyses and dominant scientific literature.
| Method | Predictive validity (r) | Typical reliability |
|---|---|---|
| Cognitive ability (GMA test) | .51 | High (.85-.95) |
| Work test | .54 | High (inter-rater ≥.70) |
| Structured interview | .51 | Medium-high (.60-.75) |
| Unstructured interview | .18-.38 | Low-medium (.40-.55) |
| Integrity test | .41 | High (α ≥.80) |
| Conscientiousness (Big Five) | .31 | Medium-high (α ~.75-.85) |
| Job knowledge test | .48 | High (≥.80) |
| Years of service | .18 | Not applicable |
| Video/asynchronous interview (incl. AI) | .30-.40 | Good at structuring; algorithmically variable |
| Machine learning / algorithmic models | .20-.50 | Depends on dataset; generalizability limited |
| Serious games / game-based work samples | .35-.50 | High on objective metrics |
| Social media screening | .00-.20 | Low and variable |
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