I spend a lot of time very close to data. Too close, probably. I’m the kind of person who will lose an entire afternoon disappearing down a rabbit hole because one metric didn’t quite behave the way I expected it to. I like understanding why things move, not just that they do.
And yet, some of the most uncomfortable moments in my career haven’t come from bad data. They’ve come from hearing good data repeated back to me later, confidently, fluently, and just slightly wrong.
If you work in marketing ops, sales ops, RevOps, or any role that sits close to the numbers, you probably know that feeling. You do the work. You pull the data together. You chase anomalies. You sense where the story really is. Then someone asks you to “just summarize it,” and the insight leaves your hands.
A week later, the story has changed.
The numbers are still there, but the meaning has drifted. The nuance has thinned out. What started as insight has turned into opinion. That gap between what the data says and what people hear is where trust starts to wobble.
The reality of Ops work is that you’re often backstage, making sure the data is flowing, definitions line up, dashboards don’t break, and reports land on time. You’re essential, but unless the organization has a strong data culture, you’re not always in the spotlight when strategy is being discussed or decisions are being made.
AI has changed the dynamics of how data is communicated completely. On the surface, it feels like a gift. You can drop a dataset, a dashboard, or a messy set of notes into a tool and get a clean summary back in seconds. For people who waffle - and I include myself here - that’s incredibly appealing.
The problem is that AI doesn’t just speed things up. It amplifies whatever you give it. If the context is thin, the output is thin. If the framing is biased, the bias gets polished. And when something sounds good, people stop questioning it.
I’ve seen a single line like “engagement dropped after the demo” take on a life of its own. Marketing hears a messaging problem. Sales hears pipeline risk. Finance hears efficiency loss. The same signal turns into three stories. All reasonable, but none aligned.
AI makes that translation gap travel faster.
So at some point, after watching the same story drift one too many times, I realized I needed a habit. Something I could run every time I used AI to help package or explain data. A way to keep the original meaning attached to the story, even when I wasn’t in the room. That’s where the AI Alibi came from.
I call it the AI Alibi for two reasons. An alibi is what gets you out of trouble when things start to unravel. The word alibi also originally meant “elsewhere.” And that’s exactly the problem this is trying to solve. The people closest to the data are often elsewhere when the story is being told.
The AI Alibi is a reusable AI prompt I now run whenever I need a story to travel safely. It slows me down just enough to surface missing context, anticipate how different teams will hear the same signal, and land on language that can be repeated without warping the truth.
The logic is as follows:
Before you frame anything, you slow down enough to actually look at what’s happening.
You summarize the key patterns, flag what feels shaky or incomplete, and ask the question a skeptic would ask the second they see your slide.
This is where you catch the uncomfortable stuff. The outliers that don’t fit. The context that dashboards flatten. The possibility that a drop in engagement has less to do with content and more to do with two buying champions leaving mid-funnel.
More than once, this step has stopped me confidently presenting the wrong story.
Next, you think about how that same signal will land with different teams. They’re all reading the same data through different lenses.
- Marketing looks for performance signals.
- Sales looks for relationship health.
- Finance looks for efficiency and risk.
This step is about anticipating those interpretations and choosing language that creates alignment instead of friction. I’ve learned the hard way that a single word can make another team defensive, even when the underlying data is sound.
When you do this well, the conversation shifts. People recognize their concerns in the story, instead of feeling blamed by it.
Finally, you distill everything into one line that someone else could repeat next week and still get right. I always stress-test it with the following questions:
- Is it clear?
- Is it memorable?
- Does it point to action?
- Could it be misunderstood if it’s taken out of context?
When a line survives that process, it tends to stick. And when it sticks, it travels intact.
I’m sharing the AI Alibi because this is the habit I use myself when I’m moving fast, when the stakes are high, and when I don’t want speed to override clarity. If you want to use the AI Alibi yourself, I’ve shared the exact structure I use as a simple Google Doc. No form fill required, just something you can copy and tweak as needed.
There’s a quick version for everyday moments: campaign updates, weekly readouts, “can you just summarize this?” requests. And there’s a deeper version for board decks, strategy sessions, and decisions that will ripple through multiple teams.
Both follow the same logic:
Spot the signal → Translate for trust → Own the narrative.
If you work close to the data, you already carry responsibility for the story it tells, even when you’re elsewhere. This is how I make sure the story that leaves my hands is the one people keep telling.