INBOX INSIGHTS: Finding AI Wins, AI Use Case Formats
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Finding Your First AI Win
Last week, a member of our community shared something that hit home: “My CMO is pushing us to implement AI, but with our workload and deadlines, finding time to experiment feels impossible. What if we pick the wrong place to start?”
That tension - between the pressure to innovate and the reality of running a marketing department - is something I’m hearing from leaders across our community. Let’s talk about how to find the right starting point that actually makes your life easier, not harder.
What “Easy” Really Means in Marketing AI
One of our clients, a content marketing manager at a B2B tech company, made a discovery that changed her team’s approach. She realized that their first AI project didn’t need to be customer-facing content or complex campaign automation. Instead, they started with something they could confidently evaluate: internal briefing documents.
“We were spending hours creating briefs for our creative team,” she explained. “We knew exactly what good briefs looked like, could easily spot any issues, and had plenty of past examples. It was perfect for testing AI.”
Finding Gold in the Mundane
Another community member, a digital marketing director, shared an approach that revealed surprising opportunities. His team tracked their “productivity thieves”—those marketing tasks that felt like they were always getting in the way of strategic work:
Let’s look at how one team turned these insights into action using our 5P Framework.
A Real Marketing Team’s First AI Win
A marketing team at a mid-sized SaaS company was buried in monthly reporting. Every month, they spent days pulling data from multiple platforms and crafting it into meaningful narratives for stakeholders. Here’s how they approached their first AI implementation:
Purpose
They needed to reduce report creation time without sacrificing insight quality. The goal wasn’t to automate the entire process, but to speed up the first draft while maintaining their analytical rigor.
People
The marketing analysts on the team were:
Process
They started small—using AI to help with just the executive summary section. They created a simple prompt template:
“Analyze the following marketing metrics for [Month Year]:
[pasted metrics]
Create an executive summary that:
Use a professional but conversational tone.”
Platform
They chose a tool that:
Performance
The results after one month:
Red Flags: When a Task Isn’t Ready for AI
Through our community’s experiences, we have identified warning signs that a task might not be the right starting point:
Your Next Step: The Marketing Task Audit
For the next 24 hours, notice which marketing tasks make you think:
“This basic work is keeping me from strategic thinking.”
“I do this repetitive task every week/month.”
“I know exactly what success looks like here.”
“Any mistakes would be easy to catch and fix.”
These are your potential first AI projects—tasks where you can build confidence while reclaiming time for strategic work.
Making the Case to Leadership
Once you’ve identified your starting point, frame it in terms of concrete business impact:
“We’re starting with our monthly reporting process because:
Remember, you’re not just looking for an easy win - you’re building a foundation for strategic AI implementation while solving real problems in your marketing workflow.
What repetitive marketing task is consuming too much of your team’s time? Let’s discuss whether it might be your perfect starting point. I’d love to hear about your experience and what you’ve learned in the process.
Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.
- Katie Robbert, CEO
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Binge Watch and Listen
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss deconstructing AI use cases. You will learn how to cut through the hype and understand how to truly use AI to solve real problems. You’ll discover a practical framework to break down complex AI initiatives into manageable steps. This episode will show you how to avoid common pitfalls and ensure your AI projects deliver measurable results. Watch this episode to gain actionable insights and start making AI work for you today!
Last time on So What? The Marketing Analytics and Insights Livestream, we covered using generative AI reasoning models to build a skills matrix assessment. Catch the episode replay here!
This week on So What? we’ll be deconstructing an AI use case live. Are you following our YouTube channel? If not, click/tap here to follow us!
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Data Diaries: Interesting Data We Found
In this week’s Data Diaries, let’s talk about the ingredients for a good use case. To be clear, a use case isn’t the same as a case study - a case study tells you what happened, whereas a use case tells you what’s going to happen if it’s built correctly.
There’s no shortage of use case templates, but one of the easiest to wrap our heads around is the humble recipe, the same one you see in cookbooks, cooking shows, etc. Why? Because most people understand what a recipe is, what it does, and how to use it.
A recipe has a lot in common with the Trust Insights 5P Framework. There’s a purpose - a reason we’re making the recipe. There’s a performance measure - the finished dish. (Even if it looks like it should be on Nailed It) And then there’s the people - who’s cooking and their skills, what they’re cooking with (platform), and the recipe they’re following (process).
When we think about AI use cases, if we approach them with this framework, it’s much easier to understand how to put them together, how to package them so that people understand what we’re trying to express. Let’s say our AI use cases is to improve the content we post on LinkedIn. We could have some long-winded, HBR-style brain dump, or we could make something like this:
Improving LinkedIn Content With AI
Purpose: As a content marketer, I want to create more engaging content on LinkedIn so that my posts are seen by more people.
Performance: LinkedIn posts that exceed 500 views per post.
People: I’m the one doing the work, but I want to focus on the last 8-10 people who engaged with my LinkedIn content.
Platform: For me, Google Gemini 2 Flash Thinking.
Process: Here’s the basic recipe.
Ingredients
Appliances
Directions
This recipe takes what could be a very long-winded explanation and boils it down to something that we can easily understand, a clear use case for generative AI that has a defined purpose, a clear outcome that can be measured, and how to accomplish it.
When you’re presenting use cases to managers, peers, subordinates, or partners, consider something as simple as a recipe format. Many people understand it intuitively, and can follow along, if not execute it entirely themselves.
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