And Carrie On

AI has been on my brain lately. It was a topic of conversation at a PRSA event earlier this month with media partners. My team discusses our individual use cases in staff meetings every week. We all have AI goals. Simply put, it’s become part of the regular conversation each week.

Much of the broad conversation is around time savings. Specifically how the tools can help us work smarter and the value of using AI as a thought partner. Those are good things, but they don’t give a full picture of AI use.

Today’s Partner

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Inputs, Outputs, and Outcomes  

As I’ve spent more time thinking about AI and how it’s being used, I find myself going back to something I learned while earning my APR certification through PRSA. First, here’s a brief primer about inputs, outputs, and outcomes.

Inputs

I think as higher education leaders, we’ve been talking a great deal about inputs. We regularly talk about how through prompt engineering, we get better at our inputs into AI. Here’s a prompt format that I’ve found particularly helpful.

Prompt Format:

Context – What’s key background information for the request?

Role – What role should the AI play? (critic, brainstormer, etc)

Interview – Ask AI to interview you one question at a time to glean other information.

Task – What’s the end result you want to obtain?

Geoff Woods, AI-Driven Leader

Additionally, we talk about developing custom tools to help us generate more consistent, brand-accurate results from AI. Custom GPTs and gems falls into this category.

Outputs

As MarComm leaders, there is also heavy discussion around outputs. I see this discussion falling into two key buckets. First, outcomes provided by AI need to be reviewed with care. Much like a student intern, we would never publish this work without further review. Second, we spend a great deal of time discussing the time savings we see when using AI. For example, I commonly hear how AI helped brainstorm ideas or take a complex piece of data and distill it into reasonable chunks to begin working on.

Outcomes

Higher education leaders are spending much more time focused on the activities around AI and are spending (in my opinion) far less time looking at the outcomes. For example, I hear regularly that teams are saying AI saves them time. What I hear far less often is whether AI helps teams achieve better results. Saving an hour is valuable, but only if the work is equally (or more) effective. We need to understand whether how much AI is helping us be more effective at meeting goals, as opposed to just finishing tasks faster.

Four Metrics that Higher Ed Leaders Should consider:

As I’ve thought about my own AI use and ensuring impact, here are a few metrics I’m looking more intentionally at to assess AI use.

1. Stakeholder Satisfaction

I think it’s important to assess whether the work is meeting the needs of the audience in a meaningful way. One of the biggest critiques of AI is that it can start to feel very cookie-cutter. If partners are feeling that, we’re missing the mark. However, if done really well, AI can help make confusing processes easier to follow, and it can help make information feel customized.

Here are a few questions to help understand if stakeholders are satisfied:

• Are problems solved faster?
• Are stakeholders having a better experience?
• Are fewer follow-ups needed?
• Are outputs more personalized and relevant?
• Are stakeholders more engaged?

2. Staff Capacity

As a profession, we are close on this one. We are getting that AI saves time, but I’m not sure we’re fully grasping how much capacity we can shift toward higher-level work. I’m reminded of a time when I brought an on-demand design solution online to give my graphic designers a break from flyers and posters. I wanted to take that off their plate to give them space for bigger, strategic projects. This is a similar kind of shift.

Here are a few questions to understand the impact on staff time:

• Are employees spending less time creating first drafts and more time refining strategy?
• How many hours spent on administrative tasks have been reallocated to strategic priorities?
• Is more time spent building relationships, analyzing data, or solving complex problems?
• Are we better able to meet individual needs at scale?
• Has the number of pilot projects increased?

3. Quality

This is (to borrow from my mom) where the rubber meets the road. If the work isn’t better, then we need to make a change. It may mean we need to adjust our prompting. Sometimes, the quality lags because we aren’t giving the tool enough information for it to help effectively. This is an easy fix, and looking at quality can help catch this potential problem. Other times, a project may not be well suited for AI. If this is the case, recognizing the quality gap can help teams pivot to a better approach.

Here are a few questions to think about quality:

• Are there fewer requests for edits, changes, and reworks?
• Is the work produced more accurate?
• Are we seeing consistency in the work produced?
• Are outputs aligned with organization goals?
• Are stakeholders commenting about quality? (either improvement or mistakes)

4. Impact

This is the hardest one to evaluate, but it’s important to ensure that AI is helping our teams improve strategically. This is where AI can move from being just another tool to becoming a strategic partner, helping us accomplish things that weren’t getting done or weren’t being done well. Without this review, there is the risk that AI is just a different way to do the same thing, and it isn’t actually moving a strategic needle.

Here are a few ways for leaders to ensure AI use is having an impact:

• What new work is getting done today that wasn’t getting done before AI?
• Are we accomplishing more with the same resources?
• Has AI helped us advance strategic priorities faster?
• Can we connect AI adoption to measurable institutional outcomes?
• Are we creating value that would have been difficult without AI?

Current Conversation

The conversation around AI has been dominated by prompt processes and time savings. These matter but they are only part of the story.

As MarComm leaders, our job is to push past the inputs and outputs to really assess the impact of AI. If we’re not seeing it in our organization, then it’s our responsibility to help our teams move to a more strategic place with AI. If we are seeing the impact, we have to make sure we’re telling that story to leadership.

AI is here, and it’s up to leaders to make sure we’re using it to create meaningful impact.