AQ, or adaptability quotient, is the first ever holistic metric of adaptability in the workplace. We measure adaptability across three core dimensions (ACE):
Ability (your adaptability skills), your Character (the innate aspects of Self that determine the ways in which you may approach adapting), and Environment (how your environment can help or hinder your adaption). Together with twelve sub-dimensions, such as Grit, Resilience, Mindset, and Learning Drive, we can give an accurate picture of where you are in terms of your AQ journey.
Continual improvement in coaching value. Algorithms built to get better with every interaction, ensuring more impact with every conversation.
Aida is super scalable, always on hand and unlike humans she can communicate with multiple users all at the same time.
With the rise of advanced sensor technologies we are building an API integration engine. Enabling multiple data source inputs, expanding our assessment accuracy and coaching impact.
Improve decision making, problem solving and solution diversity, unlocking new opportunities for innovation. Track your teams AQ training progress with actionable insights.
Aida lives where you do. She’s available on multiple platforms and channels of communication, making on-boarding super easy and super-fast.
Aida learns if you are part of a team, connects silos and helps you build team profiles for group interactions. Delivering high performance teams.
Aida loves partnerships, she’s motivated to provide exclusive access to innovative products and services, proven to strengthen adaptability and performance.
A catalyst to employee engagement and expanding organisational ‘change-readiness’ through personal support and tailored experiences for each user.
To build our AQ index, we analyzed hundreds of scientific and industry studies in depth.
We found that: Adaptivity positively influences career adaptability, which in turn positively influences adapting responses and adaptation results (Rudolph et al., 2017). By adopting a problem-solving approach to work goals, employees become efficacious and are ready to confront change when it arises. This is in line with what has been called an incremental approach to assimilate change (Armenakis & Bedeian, 1999), whereby successful resolution of challenges produces mastery experiences (Bandura, 1986) that help employees deal with change.
We make sure our measurements capture your adaptability using 5 principles: valid, reliable, robust, unique and distinct, as well as advanced statistics methods: factor and cluster analysis, plus multilevel modeling.
Our AQ Change Index shows how likely the average employee is to adapt to circumstances, challenges, and change in the workplace.
We designed this index to reflect the most important dimensions of our ACE model, using a unique combination of Ability, Character, and the Environment.
While our AQ Change Index is heavily based on the science of human behaviour and cognition, we are continually researching and collecting data to optimize our index with the goal of predicting how effectively employees will adapt.
We offer our preliminary AQ Re-skill Index to give you an indication of the degree to which an average employee can improve existing skills or learn new skills.
Our data collection and predictive machine learning models are still in the early stages of development. Therefore, the composition of the AQ Re-skill Index will likely change as we gain learning data and refine our understanding.
Currently, the higher the AQ Re-skill Index, the more likely employees are to learn new skills and the faster they will learn them. Our early insights show that employees who proactively move beyond their comfort zone, use diverse perspectives when searching for solutions, and overall are more confident, will master new skills quicker.
Please note: We never share an individual’s assessment data with their organisation directly. We aggregate and anonymise the data to build the workforce dashboard.
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AQai is a registered trademark of Adaptai Ltd. Registered in England and Wales No. 11715483