Your team is struggling with varying AI expertise. How can you ensure project progress isn't hindered?
Dive into the AI conundrum: How do you rally your team's varied tech savvy to keep projects on track? Share your strategies for seamless collaboration.
Your team is struggling with varying AI expertise. How can you ensure project progress isn't hindered?
Dive into the AI conundrum: How do you rally your team's varied tech savvy to keep projects on track? Share your strategies for seamless collaboration.
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While the team is learning and developing their skills, it maybe worthwhile to partner with a vendor to give the project team the initial push to ensure project progress. In the meantime, a comprehensive training plan needs to be in placed. This is to avoid reliance to a vendor permanently. The training plan should also include peer learning, ensuring your team member who is most experienced in AI is also challenged and their continuing developing too.
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UTILIZE TEAM STRENGTHS AND PROVIDE TRAINING To manage varying AI expertise, assign roles based on each team member's strengths, in my opinion. Let those with advanced skills handle complex tasks while others contribute where their knowledge fits best. This keeps the project moving without overwhelming anyone. Additionally, offer targeted training and peer mentoring to help team members upskill. By fostering a learning environment and aligning tasks with expertise, you can ensure steady project progress without delays.
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I like to first foster an environment of learning and collaboration. Pairing more experienced members with those still developing helps bridge the skill gap. I also focus on breaking down complex AI concepts into digestible pieces so everyone is aligned on the project goals and methods. It’s crucial to assign roles that play to each team member’s strengths, so I’ll ensure that the more technical tasks are handled by those with deep AI expertise, while others focus on complementary areas like project management or user experience. Ongoing training keeps the whole team advancing together without stalling the project. The right team dynamics and teamwork is what is often needed for success in the AI space.
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I pair less experienced team members with those who have more technical expertise. This not only fosters collaboration but also creates a natural learning environment where knowledge transfer happens organically. Regular check-ins and feedback sessions ensure that everyone feels supported and can voice their concerns or suggestions. By recognizing and respecting the different levels of technical understanding within the team, I can provide the right support to each member. This approach helps maintain momentum and ensures that the entire team is aligned and engaged.
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There's two kinds of AI expertise (at least): one is the technical know-how on how to build AI systems, which is getting easier by the day. Companies are making it easier and easier to build AI systems requiring fewer of the previously scarce data scientists, ML engineers, and others to make AI solutions happen On the other hand, there's another kind of expertise: the people who know how to solve problems with AI, to know what steps to follow in AI projects (using CPMAI methodology, for example), and know which patterns of AI are best suited for which problems. This is still fairly scarce. These aren't technology issues but people and process challenges. Organizations need to level-up and learn how to get AI right to get AI value.
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