The goal of this piece is to continue on the topic of the last few articles in AI Impact with implementing AI and beginning the digital transformation in your business.
Some teams may already be further along in this lifecycle.
But for those that aren’t — What steps should you take with AI this year?
We’ve talked about identifying specific problems to solve and the importance of your data. Let’s build out a team.
You’ve Built the Initial Team – What’s Next?
In past articles, we talked about the first team that will handle your initial AI Journey.
The entrepreneur or CEO will be heavily involved, along with another Owner of the project and a Subject Matter Expert in the business area you’re addressing.
You might also add an Analyst as well to being some numbers and data into the team.
Now you’ve got your team, and you might have knocked out a couple of your first projects.
What are some more important team roles you might want to consider for a larger AI Division?
Depending on the size of your company, these teams could get pretty big when you add data scientists, engineers, multiple analysts, testers, machine learning and AI-specific engineers and more…
Right now, you’ve built the team needed to implement an AI project for the first time.
Reminders About Your Current Team
Remember -- You might have great folks on your staff who may not have AI or Data Science backgrounds, but who are willing and eager learners with astute business acumen – this is a great place to start.
And – it’s worth reiterating the importance of building a diverse group of team members with various backgrounds.
AI and its subfields can be complex, and the process will have many iterations, so failure happens and having a good team with different viewpoints to manage that failure is crucial.
This is the group that will be solving real-world business problems for your organization. A diverse, cohesive group is paramount.
Next Roles to Bring In-House
As your team grows and you have more successful executions, you might add some more AI-specific roles in your business.
But just because you may be building a larger budget for AI, that doesn’t mean you’ll be building an AI Super Team right off the bat. Where do you start?
Data Scientist/AI Engineer: A qualified data scientist will help with building your next effective models for bigger projects. Also, this person could be key in your training efforts not only for incoming workers, but also for existing staff. You’ll want to tap into this person’s personnel orbit going forward.
Data Engineer: Finding the right data sources and building that structure, nomenclature, labeling, and filling in holes is crucial. This data is the foundation on which your Scientist builds the models.
Tester: This role is focused on finding the problems with current models.
Machine Learning Engineer: This person tracks and provides evaluation of all models employed in the company and helps the Scientist (and staff) turn the AI into a sellable product. Effectiveness of models is the main goal here.
Now you’ve built your team with real roles and accountabilities. This is a great start on your AI Journey. What are some things to remember?
Final Notes
It is crucial to look for internal employees that can be reskilled and retrained for more AI-specific team roles.
Recruiting is VERY competitive in the marketplace for Data Scientists, Engineers, ML-specific skills, etc. and it will continue to be.
It might be more cost-effective, and good for your staff, if you’re able to repurpose existing workers.
How do you do that? There are a ton of online courses to begin this journey for your staff. Coursera, open.ai, deeplearning.ai, as well as traditional colleges and universities are all offering great intro and intermediate courses to AI, ML, software development, and business operations courses for AI.
Also – STAY LEAN. Always be mindful of what you can outsource vs. keep in-house. You want to build momentum, then build the team.
Stay positive and enjoy the AI journey.