Case study — Covering the gap between companies and AI professionals
AiNOVA is a recruitment startup project looking into offering career counselling and talent matching for Artificial Intelligence (AI) Talent — professionals working in the field of AI — and companies recruiting those professionals.
Timeline: 2 weeks (Part-Time)| Role: UX/UI Designer| Client: AiNOVA Startup Project
BRIEFING
I was contacted by Alban Aufray — the Project Lead of AiNOVA — to help him develop a demo of his platform to kick-start the roll out of the start-up. At that time, he was preparing a grant application in order for AiNOVA take steps towards becoming a reality.
The project was at its humble beginnings, still very much at the research stage, business modelling and market validation. Therefore, I saw an opportunity to use my experience as an entrepreneur in order to help Alban bring focus and definition to exactly what he needed. Alban already has a lot (a lot!) of experience as an HR for extremely large companies. We both felt that our past experiences as entrepreneur and corporate leader respectively made us perfect match.
The goal was create a minimum viable product that would convey the idea and business of AiNOVA. So the challenge that was brought to the table was laid down as the following:
How Might We build a digital bridge between companies searching for AI professionals and the professionals themselves?
🔍 BACKGROUND RESEARCH
Knowing what we know, what we need to know and what we don’t know
The first workshop me and Alban had together proved to be of real value to both of us. From my side, the main concern was to pick his brain as soon as possible in order to be at the same knowledge level as him. To do so, we started building the CSDi Matrix — a tool that enabled us to map out our certainties, assumptions and doubts.
For the record, the CSDi Matrix is my favourite go-to starting tool. It really helps me wrapping my head around the challenge and understanding where to start.
To Alban, this exercise put him in a mindset that forced him to face his findings up to that point and the ideas that he still needed to investigate. As the image below shows, there were quite several post-its in under the Assumptions and Doubts categories.
What do we need to know?
At this initial phase, my task was to help Alban understand what his assumptions were and how we could validate them. What we realised was that from a business point of view, he had a very good grasp of their needs but from the AI professionals — and, effectively, the users of the platform he was proposing to build — there was still a lot to be investigated. From that bunch, I highlight the following:
- What are the pain points in the search of jobs in AI?
- How relevant is the company culture for these professionals?
- What are the motivations of these professionals when applying for jobs?
- What are the current solutions that they have at hand to get their jobs done?
🔥 BURNING QUESTIONS
Based on our first workshop, it was clear that we had to go out there and investigate some more about the AI talent, as Alban called that specific segment. He had been particularly active in that regard by the time we met, thus he naturally took over that department.
In addition, my proposal, in order to for his project to gain a stronger information backup, was to develop an online survey that would allow him to get some quantitative data regarding the people that he’d come across with.
FOCUS & METHODOLOGY
The focus of this survey was to be placed on the AI Talent. We were interested in exploring more that side of the AiNOVA users and to better understand their motivations and pains. Hence, it should be seen as an exploratory questionnaire that is intended to know AiNOVA talent and to frame potential solutions for their needs.
The methodology for the drafting of this questionnaire was based on the Lean Survey Canvas, a simple to use tool that helped framing the questionnaire.The questions of the questionnaire were based on the following burning questions that we had prior to this project. These were as well incorporated in the script of the interview that Alban had been using for the past weeks and he took charge of further investigating these parts of the project.
- How much do talents know about themselves?
- Do talents care in knowing about themselves?
- How much time do talents dedicate themselves to self-development?
- What channels do talents use to search for jobs?
- Which industries are more in demand of AI talent?
- Are talents ready to foster a more personal relationship with the recruiter?
- What makes talent happy in a company?
- What are the AI Talent career motivations?
- What kind of company culture do AI Talent dream about?
VALUE PROPOSITION CANVAS
Upon conducting interviews as well as running an online survey, we were able to better define the value that AiNOVA provides to the market. Therefore, we advanced to use the Value Proposition Canvas to define the pains and gains associated with the jobs that AI professionals are trying to get done and define how AiNOVA could create gains and relief the pains.
🤓 PERSONA DESIGN
Together with Alban, we’ve identified three different personas who differentiate themselves based on their level of experience in the field. This part is mainly built on what AiNOVA had already developed, nonetheless, a good starting point to know who we are designing the platform to.
- The Junior/Leader of Self: young professionals with less that 3 years of experience that are eager to find a job and need to determine which kind of first learning experiences will suite them best.
- The Senior/Technical Leader: technical experts/ managers with up to 10 years of experience who are often dissatisfied with their current employers
due to the company culture, their line manager or insufficient growth opportunities, etc. They are an attractive target for recruiters but at the same time focused on their careers, which leads to this segment of AI talent being in need of career counselling and advice to help them select the right company. - The Guru/Organisation Leader: professionals with more than 10 years’
working experience and extensive knowledge and understanding of the technical aspects, persons aspiring to achieve greater purpose (e.g. deep expertise in the field or securing a top management position). They are much more selective in their choice seeking for cultural fit, relying on referrals.
For this project, we decided to focus on The Junior because it not only represents the biggest part of the pie in the world of AI professionals but also is likely facing more issues at the moment in nailing the best job. Hence, meet Joana, to whom we’re design AiNOVA.
📍USER JOURNEY MAPPING
Together, we created the User Journey Mapping of Joana that showcased her process to search and apply for jobs as an AI professional. The goal of this activity was the define the design opportunities that existed and which AiNOVA could leverage on.
During Joana’s journey, we were able to pinpoint so many different several low points, indicating that Joana might be facing several issues. So it was with this exercise at hand that we were able to further discuss how AiNOVA could help, what features should the platform have and as well imagine the interaction that Joana could possible have with it.
✏️ CONCEPTUALISATION
Finally, with so much discussion and better understanding of the market needs, we moved on to the conceptualisation of AiNOVA. The first thing we did was to lay exactly how AiNOVA was going to position itself.
💼 BENCHMARKING ANALYSIS
I searched for other companies which were doing something similar to what AiNOVA is aiming at. At a first glance, from a competitive benchmarking point of view, I was specially interested in analysing exactly how these platforms worked and how they looked like. My analysis was divided in:
- Visuals and Aesthetics
- Business Strengths and Weakness
- Learnings and Inspiration
🎯 DEFINITION
USE CASE SCENARIOS
Listing all the possibilities and list of actions that Joana can take within the platform was the next step. The table below showcases the main scenarios that this project is looking into.
SITE MAPPING
What we had been discussing was that the platform would probably have two different sides to it — one allocated to the businesses & recruiters; another one allocated to the AI talents seeking jobs. For this project, of course, we were only looking over the latter.
Important was to make it clear how you could navigate through the website, and adding a direct question — “Need Top Talent” — right on the top bar is a way of directing recruiters to their own side of the platform.
USER FLOW
Finally, a basic flow was established. Our focus was her first interactions with a chatbot, accessing the platform and her possibility to schedule calls with experts, mentors and HR counselling — some of the main features of AiNOVA.
🎨 VISUAL BRANDING
MOODBOARD
Before I jumped into creating the demo of AiNOVA, I put a moodboard together in order for me to get some inspiration for the aesthetics and image of the platform. I wanted to gather terms such as Artificial Intelligence, Coding and Business, blend them in all together in order to get a visual platform that was related with its operational field but also gave the credibility of a recruiting agency.
STYLE TILE
In the end, I settled on the Style Tile below. I usually like to use two main colours which are complementary to one another.
- I went on with shades of purple since it is very catchy, business oriented and somewhat like a colour that we can see in the lines of programming and coding activities.
- The same with its complementary colour, shades of lime, which would give the final touch of modernism. This is a colour that is being used for the main calls for actions in the platform.
🏗️ PROTOTYPING
By the time I had arrived to the prototyping phase, we had little time. So for better or worse, I jumped into making my prototype already a mid-high fidelity — since all the features and planning had already been though-through by Alban and the ultimate goal was to show a quick demo of AiNOVA in order to be able to get the grant.
✨ RESULTS
Here are the final results of the demo that we created together.
The outcomes of this project were:
- Assumption Validation: Alban was able to validate a series of assumptions that existed prior and solidified his understanding of the AI talent and their needs & wants.
- Robust Value Proposition: Several design opportunities were identified, thus, making the concept of AiNOVA more robust and strengthening its business model and correspondent value proposition.
- Basic User Flow: we define the basic user flow and what sort of interactions users would have within the platform
- Demo: finally, a demo that could be shown and used for future grant opportunities.
🔑 LEARNINGS
This project had such a massive learning curve regarding the work of an UX/UI Designer. It can be extensive to the extent that a good portion of one’s time goes directly to research and conceptualisation, which was mainly the task I had at hand here. Thus, my biggest learnings:
- My past experience as an entrepreneur came into play heavily because most of my time was spent on conceptualisation, finding out the needs and wants of the user, user journey mapping and persona designing
- As designer, I like to keep things steady and with a flow but once someone hires you to pull of a project which is already mid way, you have to adapt and admit that certain aspects are already predefined. Alban knew what he wanted so I tried to guide him through his thought process in order to close down decisions
- Time Constraints are a thing — I was brought in for a limited amount of time (and budget) thus there was only so much I could do. When time isn’t much, you have to choose better how much time you’re going to spend in certain tasks and define what is the minimum that everyone is going to be happy about.
