How can we ensure that our data and analytics teams have the skills and resources they need to implement AI effectively?

Please forgive my "glowing" endorsement below. Honestly, I don't get anything from O'Reilly learning as a service in sharing this. I've been paying for their online service since the mid-2000's and I as long as my work requires me to be on the cutting edge of technology, I will be using their service.

Their content is top-tier and their ever expanding catalog of material improves daily.

If you or your team need to get up to speed on any technology but in particular the technology at the center of the generative AI wave that is sweeping the world, O'Reilly has what you need.

From the fundamental technologies and mathematics of Machine Learning, Deep Learning, leading to Large Language Models, and generative AI, they have the content to support any learning path.

I would recommend you invest in the service for the team.

But none of this means much if people don't get the experience they need by actually using the technology. If you don't have a "lab" or safe environment today, invest in one. Create an environment and a context to safely perform experiments. Developing "intuition" about this technology is critical and that only comes with applied learning. Iterate to success.

Etablish a Community of Practice to share ideas and curate knowledge. As much as posible, foster a culture that values "growth mindset" and critical thinking skills.

Get comfortable "eating the learning curve", because the pace of change will continue at an exponential rate.

I believe it's crucial to have a multidisciplinary team of data scientists. The traditional approach is no longer sufficient because techniques are evolving rapidly. Clients demand the latest models and solutions, and different industries have different regulations, especially around data privacy.

It's important for the team to be informed about these requirements from the start to avoid having to backtrack. This calls for a high level of synergy and communication within the team.

Another important aspect is the ethical use of AI. All team members, from the most junior to the most senior, need to understand the ethical implications of their work. This is especially true given the potential liability issues that can arise from data bias.

The key to ensuring that data analytics teams are equipped to implement AI effectively lies in embracing change and fostering continuous learning. AI is a disruptive technology, and the pace at which new technologies are emerging is accelerating. This means that the team must not only understand and apply these new tools but also continuously learn and adapt to the evolving landscape.

The rapid innovation cycle can create anxiety within the team, especially when it comes to identifying the right skills and tools to focus on. There's also the risk of automation, which can raise concerns about job security. However, by understanding the level of automation and applying AI skills, team members can enhance their value. The ability to adapt quickly to the changing needs of the environment is a crucial skill for team members to develop.