Different companies have different principles and values which define themselves. One core value NILG.AI invests in is team knowledge, both at individual and team levels. We deeply believe that personal growth is a driver for keeping the company thriving.
In this blog post, we go through some of the processes we defined to promote Learning and Knowledge sharing within the team and with the public.
Feel free to integrate it into your company and give us feedback about our methodology and how we can improve it.
Learning Sessions
Our primary way to promote learning and knowledge sharing among team members is through our weekly learning sessions, which already have a fixed schedule, generally on Fridays. Each learning session focuses on a given topic or problem and typically has one person responsible for preparing that session.
In each learning session, the promoter presents the topic while the team, as a whole discusses how to apply it to our current and past projects. So, we have a direct feedback loop from peers to improve ongoing projects. Each client benefits from the overall learning and ideas of all team members.
In order to promote these sessions in a relevant and structured way, there are some key points we take into consideration:
Early planning
At the beginning of each quarter, we plan all sessions for the quarter, where we define the session day, the person responsible for each learning session of the quarter, as well as, the type of session that should be promoted.
In order to promote the learning of the entire team, we prepare the sessions in a rotative way among the group.
Diversity in the type of sessions
As we see it, team learning can beimproved in different ways, whether it is by increasing knowledge on areas the team is not comfortable with, discussing how we would approach a given problem, or simply being specialized in the core areas we work on every day.
Therefore, we saw a need to create different types of learning sessions to improve different aspects we think are important. With this goal in mind, we defined four types of learning sessions:
Use Case: session where we discuss how we would approach a given business problem using AI. These sessions provide the team further comfort and understanding of business aspects of AI challenges.
Relevant topic session: session where a topic or tool relevant for the team (based on the current projects) is presented. These sessions make the team more proficient in their daily activities.
New topic session: session where a tool or technology within an area not spoken yet during the quarter is presented. These sessions make the team members aware of the latest progress in AI.
Lower score topic session: session where a topic in which the team has less knowledge is presented. These sessions take the team out of their comfort zone, promoting learning on topics we haven’t worked on yet, and building a better toolset to handle our client’s requests.
We try to balance the number of sessions of the different types so that all four learning components can be improved.
Diversity of topics discussed
Besides types of session diversification, we also aim to diversify the topics of the sessions so that the learning is not focused on only a small set of topics.
Reporting the main insights from the learning sessions
Besides the session itself, we think it is essential to systematize the learning made. As such, after the session, we report the main insights, discussions, or conclusions taken from that session so we can revise them in the future.
Therefore, at the end of each session, the session promoter fills a reporting template with the details, context, and main conclusions so it can be accessed in the future.
All these aspects are applied to take the most out of our learning sessions, also thinking about the future.
Besides the learning sessions, each team member has four weekly hours available for personal learning, which they are advised to use. This gives each member the opportunity to explore a given tool or topic he thinks is relevant to learn and possibly share with the other team members in a future learning session.
Learning tracking
To have a broad idea of our knowledge of the different topics, we have a learning tracking system in which each team member has a knowledge score associated with the different topics and concepts relevant in the Data Science & AI domains.
This gives us an idea of which areas have a greater margin of progress both at an individual level and as a team.
The final versions of the courses were a result of combining internal knowledge of our team in the different topics. Currently there are 5 courses available, but more content will be coming out soon…
NILG.AI believes that knowledgeis the currency of the 21st century. As such, we continue to invest in knowledge sharing as one of our core values.
Internally, we promote activities like learning sessions, trying to keep them diversified, interesting and relevant to the team, individual learning time, so we can dive a bit deep in areas or topics of interest and learning tracking to have some type of feedback on how the learning is affecting the team’s knowledge.
Externally, we will continue to invest in sharing knowledge through blog posts, our newsletter, and also our online courses.
Due to the value we see in learning, we are planning new initiatives where external people can have a more active participation on the team’s internal learning, so stay tuned for more updates through our social media channels!
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