Enlight – Embrace your uncertainty

1172 0

Enlight –
Embrace your uncertainty

Enlight is an online platform, which through a self-discovery game, a learning planner and two profiles (private and public) manages to help young people making happier choices about their future, giving them a moment and a space for self-reflection.We believe that by promoting self-awareness and exploration of experiences we can improve the agency people have upon their career choices.

Enlight guides its users through a process of self-understanding in order to expand their range of possibilities, instead of limiting them to one choice. Using data and machine learning Enlight tries to give the best suggestions to its users for deciding their next step.

Enlight’s goal is to prepare the generation Z to deal with the consequences of the future work trends that have already started to emerge. We believe that by encouraging certain behaviours young people will adapt more easily to the future of work.

MA2 – Team 1

Francesca Schiboni Grimaldi
Francesca Ferrari
Yi Wu


Tell us what the concept is about. What are you designing? What are the key benefits?

Enlight is an online platform where young people are guided through a series of activities to improve their self-awareness and their ability to present themselves to the world of work. We want them to view the process of career choice not as reaching an endpoint, but as the definition of a series of possibilities and directions they can take based on their goals, interests, desires and aspirations.

Enlight proposes a self-discovery card game based on different scenarios. The cards cover five different aspects: purpose, personality, passions, skills and beliefs.

Users can review their answers in their private profile, where they can also keep track of their experiences and learn from them.

Using the data collected, the system is able to prepare a learning plan. Users receive one 5-minutes video per day to discover new things to learn, which they can explore better with courses from external platforms. This feature aims to encourage the adoption of a life-long learning approach.

Enlight also matches people with similar paths, to get them inspired by each others’ stories and build a network.

We think that if we help young people learn and reflect more on themselves, they will be able to build their public profile in a way that highlights their value for the job market.

The platform has also a version for recruiters so they can find young talented people that normally don’t stand out in other employment-based platforms.

The key benefits are multiple: on one side Enlight can offer support to young people during a moment of transition normally full of stress, anxiety and insecurity. On the other side, we can prepare these young people to be adaptable and flexible and to be ready for the future of work.


Tell us some of the key findings of your background research and what is the problem you are trying to address.

From our primary research (online survey, face-to-face interviews and consultations with Career Services) we found out how much young people feel stressed and anxious when having to decide what to do after graduation. They are influenced by the fear of failure, feel the pressure from family and society and get disappointed by their first job experiences because their expectations were too high. They often end up comparing themselves with their peers, which leads to insecurity issues.

However, the idea of having to choose one career as soon as possible in order to succeed is quite nonsense today, due to the uncertainty of the future of work. In fact, it is predicted that one in five jobs in the UK will be affected by automation, and the majority of the future jobs don’t exist yet.

Generation Z will be the first generation to deal with the consequences of the changing world of work.

One of the most impactful trends is the gig economy, freelancers are projected to make up 50% of the workforce in the United States by 2020. Moonlighting is another interesting trend: nearly 1 in 4 UK Office workers are moonlighting and 58% of people in the UK think that in the future will be normal to have multiple jobs.

With the advent of automation, workers will need to reinvent themselves continuously and in-the-moment learning will become the modus operandi.

That is why our service proposition tries to rethink the moment of deciding the future career, by transforming it into deciding what will be the immediate next step and learn from every experience.

To achieve this we leverage from our self-discovery process. This is divided into three main steps:
– Self-exploration, for which we based our research on the Myers–Briggs Type Indicator and Holland’s Vocational Types
– Self-efficacy, based on Bandura’s theories
– Self-determination, based on Ryan and Deci’s theories.


Tell us something about your users and key stakeholders involved.

Our main target is young people from 18 to 24, experiencing the transition from studying in college to enter the world of work (so with none or few experiences). We believe that this period of uncertainty is the best moment to intervene, helping young people to embrace that uncertainty, explore multiple possibilities and adapt to emerging working trends. Since studies show that today’s learners will have 8 to 10 jobs by the time they are 38, we believe that our service can be used each time one has to change job. The continuous use of Enlight improves the ability of the machine learning to support the users in their decisions. That said, even if our age target is limited, the retention of the service can last many years.

Enlight has also a version for recruiters and employers, which we developed based on the recruiters’ needs. On Enlight they can easily search for young talented people using keywords and looking for specific skills. The system makes the users’ profiles automatically customised for the different recruiting phases: screening, selection and connection. 

The key stakeholders include:
– Universities and Career Services
– MOOCs partners, who provide the videos for the learning section and can advertise the complete courses
– Departments of Work and Education


Give us an idea of the future scenario where your project will be working. 

Our service proposition aims to be usable from the present time, in order to help the new generations preparing themselves for the future. Changes happen so fast that if we don’t intervene as soon as possible, people may find themselves unable to adapt to future conditions.

We predict that the real impact of our service proposition will be between 2020 and 2030. We anticipate that at this time jobs will be more project-based and each task will require a combination of skills. The job for life will definitely be no more a thing. In this scenario, a system able to present one’s professional identity in a multidimensional way will definitely be very useful. By fully understanding their skills, users will be able to combine them and create new job roles for the market.


Tell us if you have thought of a specific location where to prototype.

Part of the prototypes can be scaled to multiple countries, but we would like to do most of the testing in London, as it’s one of the cities where we can already see some of the effects of the future of work trends (not location-based, freelancing, moonlighting…).

Moreover, London is a multicultural city with a variety of people from different countries and cultures, and the tests’ results can be more differentiated. Working in London also permits us to do long-term testing with people.


Give us an idea of your strategy, your process, your prototyping plan and the next steps.

Our strategy is based on the use of data collected from users to support them with highly personalised suggestions. When working with something so personal as the choice of career, it’s necessary to acknowledge the diversity of the users, each one with different goals, interests and desires. Giving general suggestions can have a limited impact and be misleading. So by improving the knowledge users have about themselves, we can guide them better through the creation of their career path.

In terms of financial viability, there’s good space in the market for our service. Existing employment-oriented services appear to be very profitable, thanks to premium subscriptions. In terms of competitors Enlight finds its space in the market by addressing the specific needs of a more restricted target segment.

Our process relied in particular on the interaction with real people. Our primary research enabled us to create different frameworks that helped us generating our brief. But the most insightful phase was the prototyping and testing. We created five prototypes to test the main components of the service.

1) We tested the general concept at the Skills London 2018 fair, evaluating the desirability of our service with 20 students who are starting to think about their future careers.

2) With an A/B testing on LinkedIn, Facebook and Instagram we better defined how to position our service to attract people’s interest.

3) We hosted a workshop with six RCA students to test the value of the self-discovery process.

4) We got 3 people testing the bite-sized learning experience for one week.

5) We reached 5 recruiters on LinkedIn and we presented them the interfaces of the recruiters’ version, asking for a feedback.

Our next steps plan includes many loops of prototyping/testing/development. Our plan includes:

– Consultation with experts on psychology theories, technologies and platform management
– Test the viability of the service, improving the business model and analyzing different financing models
– Strengthen the processes of technology application (data management, machine learning, platform)
– Involve the main stakeholders (universities, MOOCs partners, Education/Work departments)
– Prototype the interfaces in terms of affordance and usability. Test the functioning of the two-sided model (users/recruiters). Involve programmers for the coding.
– Expand the number of self-discovery cards. Consult with psychologists and research the theories to improve the validity of the cards.
– Improve the branding and communication
– Test the machine learning processes with users to evaluate the quality of the interactions

Thanks to the experimentations already done we can suppose to be able to release a first MVP by the end of February.