Workera’s vision – by the person who started it
For Workera CEO and co-founder, Kian Katanforoosh, the world of skills intelligence offers endless possibilities for how we identify, shape and leverage both our innate talents and skills we learn throughout our lives. Some of them we may not even be aware of yet.
So how exactly could skills intelligence help steer our careers? Kian uses a simple example of a young student to explain what could be possible: “Using AI, we’re able to understand the granular connections between skills thanks to both the semantic understanding of skills and the ongoing measurements of many people taking tests and exams.
“Now imagine if a child at school scores 7 out of 10 correct answers in a mathematics exam: instead of grading the student’s competency as “7 out of 10”, AI-powered skills intelligence enables us to tell them, for example, that math has 180 skills and we estimate that the student currently has 100 of those skills – and thus lacks the remaining 80. This puts them in the top 20th of students in their grade and an overall proficiency score of 205 out of 300. With every new question the student answers and every new learning activity they perform, we’re able to update the skills predictions. Using AI, we’ll soon be able to accurately predict thousands of skills in minutes.”
A mission inspired by family
Kian was born in France to Iranian parents who’d independently fled the increasing violence and destabilization of the revolution in Iran in the middle of their studies. In doing so, they’d each been forced to leave behind promising and potentially rewarding careers. Kian’s father was pursuing a scientific degree back in Iran – but now, in France, he and Kian’s mother had to navigate a new country, a new language, and the unfortunate reality of unequal pathways to success.
“My father wanted to pursue a scientific career, but he gave up any future career when he left Iran and ended up selling clothes in France to make a living,” Kian tells. His experience of growing up with parents who had to prove their skills against a new standard in Western Europe instilled in Kian the value of mentorship: enabling and empowering people to achieve their potential, regardless of their background or skill set.
The age of data and AI makes it possible to isolate people’s skills and knowledge – whoever they are, wherever they are – to connect data and information and effectively map out careers. In a world where not everyone gets the chance to expand their key skills, solutions like Workera offer everyone an advantage to achieve more.
The importance of mentorship
Mentorship is scarce. Students at top universities often have access to direct mentorship through their professors and peers. They know what skills they need to develop and they know how their skill development compares to others. But most people, especially professionals working in large corporations, don’t benefit from the same guidance. Moving to the US – first to study and later teach AI and machine learning at Stanford University in California – helped inform Kian’s perspective on mentorship and its critical role in professional development. Using the example of the student and the mathematics test, what does the reality of mentorship translate to?
“Mentorship starts with an assessment,” Kian explains. “You need to understand someone before you can help them. Once you understand where they are today, you need to visualize the future state. What do you want to achieve? Where do you want to be in ‘X’ years? The job of the mentor is to map this potential, using experience and data to create that projection. A good mentor will even help the student fine-tune their goals. ”
Thus, the young student is able to isolate the skills they’re missing and be directed towards the learning and mentorship opportunities to help them develop their missing skills by targeting those specific competencies they’re missing, from what we will know about the skills that the majority of people possess.
Scalable mentorship – without the need for a physical mentor – is core to the Workera mission, which all begins with an assessment of an employee’s existing skills. Kian likens Workera’s process to the way he approaches mentoring students in his Stanford Deep Learning class. He asks them broad questions first, and then gets more specific to deeply understand their technical capabilities. Finally, he makes a recommendation based on what the student wants to achieve.
How Workera reimagines learning
Kian recognized there was enough content online to sustain people’s learning. He also saw firsthand that most education programs were one-size-fits all – leading to low engagement as employees reported that programs were too easy, too hard, or not relevant to their goals.
He saw an opportunity to leverage the increasing presence of AI and machine learning in everyday society by designing a platform that tailors learning to the learner, offering them bespoke mentorship based on the granular data gathered from both their responses and the responses of others within the Workera software.
“The Workera assessment is precise. Most learning and talent platforms use implicit signals such as course completion or keywords on resumes to determine someone’s capabilities. But that doesn’t say much about what the learner can do with their skills,” says Kian. “Instead, Workera measures skills explicitly and provides granular feedback to the learner at multiple levels of cognition spanning theory and practice.”
For users, skills assessments precisely measure what a person can and can’t do with their current skills, identifying their strengths and areas for improvement in a certain domain. By serving these results back to them, they’re able to become more self-aware and make better career decisions, using Workera’s platform to connect with the right learning content and pathways.
In business application, Workera allows leaders to assess the current skills of their teams – but also map what the future of these skills could look like. Armed with this knowledge, companies are empowered to apply learning and development in the most appropriate and efficient ways, which supports learners individually and streamlines working practices into the future.
