How to Build the Perfect AI Project Team

When it comes to AI projects, the phrase “When in doubt, hire it out” rings true. Of course, that’s easy for me to say since I run an AI company, but hear me out. Software projects, especially automation, can easily slide into one of two, or both, bad outcomes. I want to help you be aware of, and avoid common pitfalls.

The first bad outcome is the project that never ends. There’s always “one more hurdle” to get over. I’ve found most of these projects have the wrong people doing the work. These projects will end 2–3X over budget and will take way too long to complete.

The second bad outcome, and this is the worse of the two, is when automation is built and it doesn’t do what’s intended. Now, you’re left with a bad process that is automated, so you must create a new process to fix the automation’s mistakes. Instead of freeing up your team to do bigger and better things, you’ve freed up your team to babysit an AI.

How do you avoid these outcomes? By building the perfect AI project team. There are two methods for doing that: the first is the done-for-you option, and the second is the done-by-you option.

When it comes to building AI tools, your first option is to hire a product development company. My company, Teammate AI, is an example of this. 

We take care of everything for you. We design, develop, test, deploy, and scale. We create the AI algorithms and maintain them, scale your server capacity, and handle cybersecurity. If you decide you want to turn your newfound AI capability into a new business unit, we even turn it into a SaaS company with you.

Our process is straightforward. We start by coaching leadership teams and finding opportunities, and then we serve as the product development team. Then, if the client wishes, we turn the new AI product into a revenue-generating business unit.

In an AI world, we want to frame the problem so that the problem is solved once-and-for-all. This means that you, the end-user, and the client, never have to touch it again. 

Next, we design the solutions that best fit your business. We talk about what’s possible and our confidence to get the job done on time and within budget. If everyone decides to move forward with the AI project, we can develop the solution in consultation with your subject matter experts and your end-users.

With companies like ours, if you’d like to only gain clarity with coaching and product design, you can do that. You can then hire an implementation team separately. This drastically lowers the cost to an agency, but you’re managing implementation. That is the YOYO method—in other words, “you’re on your own.”

With this method, the budget and product quality are what you make it. Additionally, it does require some key hires, the most important of which is the solution designer. This is your right-hand person and the individual responsible for project success.

A solution designer is an individual who speaks the business language and the technical language. They know what the limits of AI are, they can create and present solutions to your problems, and they know what resources to bring on to accomplish the task at hand. Most importantly, they can give you a ballpark range of the investment required to get the job done.

A solution designer is the person you want to run the entire project, including making hires for talent. At a minimum, they need to hire a project manager, a data cleaner/validator, full-stack application developer(s), Deep Learning/Machine Learning developer(s), QA/Tester(s), and people who can do graphic design/app design.

Which method you choose will depend on your budget, expertise, and what solution you need to be built. Both have their pros and cons, so think carefully before making a decision.

Regardless of what method you choose, your AI project team’s responsibility is to manage the project and deliver the solution. It’s not your responsibility to demand certain project methodologies, deployment strategies, and technical specifications unless you’ve got an IT team working with your AI project team.

There’s a difference between software that works and software that works for your business. You can expect that your subject matter experts will test the solution. This testing will ensure the solution solves your specific use case. 

Let me give you an example.

Let’s pretend that your sales manager or general manager would like to track account executive activity for job performance. Your performance reviews right now are based solely on reaching a quota, but you’d like to track more leading indicators.

Your immediate move is to hire a solution designer, who comes up with a viable solution: store data in a database and use a technology tool called Tableau. The solution is viable, and in fact, it’s relatively common. While it does require developer investment, it’s very doable. You move forward with it.

Top-performing sales reps and underperforming sales reps are now measured by more metrics than just a quota, and your sales manager starts tweaking aspects of your territories, prospecting, and sales funnel to optimize close rates. It’s so freeing to see each sales rep’s activities every week. Tableau is easy to use, and sales management is way easier.

In the long term, this change becomes such a huge component of your performance reviews that your leadership starts looking for ways to track activities across the business. The business, over time, becomes more and more data-driven and predictable, leaving you with a self-managing company.

The example above is just one way that you might be able to make AI work for you. And, coming up with and then implementing this solution is doable using either of the methods I described.

Ready to bring AI into your own business? Consider which processes you want to automate. Then, think through your team’s knowledge base, your budget, and what you want to accomplish. That will help you decide which method is right for you. Once you do, you can pull the trigger and get AI working for you.