And Carrie On

As a MarComm leader, I want to make sure I am continually investing in tools that allow my team to work better and more efficiently.

Lately that has meant supporting the purchase and deployment of AI tools to help the team do their work. Historically, much of my focus on AI has been around making sure my team is comfortable using AI as part of their work. That is happening, so now I’m adjusting to make sure there is critical thinking about which tools should be deployed.

To do this, here are five questions I think are important for leaders to ask when deciding which AI tools to purchase.

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What problem does this tool solve that our existing tools don’t?

With this question, I’m trying to understand how the tool works and how it aligns with existing tools. I don’t need five tools that do the exact same thing. Additionally, I think sometimes we are eager to jump to a new tool because it offers a cool feature. If we need the feature that’s one thing, but it often feels like we are hopping around tools chasing something that’s shiny and new.

Why this matters? I want to understand the tool and ensure our team has a plan to use it, which helps ensure we are investing wisely.

How does this scale?

Often, a member of a team comes to me with a tool that is great for them. For example, the tool might be ideal for designers. However, I want to know that we’ve explored the possibility of using the tool for the full MarComm team. If it doesn’t scale or can’t scale well, that needs to be factored into the decision about proceeding.

Why this matters? Scaling often involves cost implications, so I want to be sure that we know the scaling cost before we fall in love with using a tool only to find out that scaling is outside of our budget.

What do data and privacy policies look like?

I think it’s important, as leaders, to ask how the data and privacy questions are handled. Understanding how data is collected, stored, and reused is important to determine what can and can’t be put into the tool. Sometimes, this also means we need to walk away from a tool because it won’t work the way we need it to if we don’t put our data into it, and we aren’t comfortable with that.

Why this matters? This is the least fun part of evaluating decisions. However, it’s something the team doesn’t think about as much, so I feel a responsibility to make sure it is fully explored.

How can we customize the tool?

Every institution is unique. I want to understand whether this an out-of-the-box solution, or can I customize the tool in specific ways to better support my use case. Understanding the options (or limitations) helps me gauge how much time our team will have to spend in tweaking outputs before the data can be used in our work.

Why this matters? Time is money, so I want to fully understand where the team’s time will be spent and what tasks they will focus on. This also helps me align tasks with the strengths of team members.

What does success look like?

AI comes with risk, and I want to make sure that we can clearly articulate how this tool helps us achieve our goals. This allows me to understand if the risk is worth it for the level of success the tool can help us achieve. It goes back to whether we have a clear purpose in mind or are just trying the flashiest thing. I want to make sure that if we onboard an AI tool, we have a clear plan and know what our desired outcome is.

Why this matters? Because of my leadership role, I will be on the hook if the tool fails or if the data is compromised. I want us to understand the risk and the potential success to make the right decision for the organization.

What other questions are important?

There are so many questions that can be asked before making decisions about which AI tools to implement. Are there other questions that you find helpful?

If so, please share in the comments section, so we can continue discussing.