Intent Intelligence:
The Metric That Matters Most
BY D'ARCY TOFFOLO
The Why
Through answer engines and generative search, AI is changing how audiences discover, evaluate, and act on information. As it does, the metrics leaders have leaned on for a decade are losing their grip. Accenture finds 54% of people usually get everything they need from the AI summary, and two-thirds now use AI search weekly. Measuring "did we get the click?" tells you nothing about whether the story that reached your audience was even yours.
The click, the impression, and the shared dashboard were built for a world where the customer journey was visible and traceable. That clear line of sight is fading. In 2027, organizations must respond by going back to the basic question: What, specifically, are we trying to change? The answer becomes the top rung of a new customized framework for the customer journey.
How
For a century, marketers have pictured the customer journey as a funnel. It’s wide at the top and narrow at the bottom; prospects pour in and filter down to the buyers. Standard frameworks measure it by counting how many people reach each stage, but AI is draining these metrics of meaning. This moment calls for a new shape: The Influence Ladder.
54% of people say they usually find everything they need in the AI summary; two-thirds now use AI for search weekly. (Accenture)
Picture influence as five rungs. You set your sights on the top rung — the change you want to create — and climb toward it, letting that goal define every measure below.
Delivered → Seen → Noticed → Remembered → Acted On
- Delivered. Your message landed where it was meant to: the page, the feed, the inbox.
- Seen. It actually rendered on a real person's screen or other channel.
- Noticed. It won at least a genuine sliver of attention.
- Remembered. It left a trace that outlasts the moment.
- Acted On. It changed what someone did through a purchase, a choice, a recommendation, a shift in behavior.
Nearly one in three marketers already name measuring effectiveness across channels as their single biggest challenge. (BCG)
Most organizations measure the bottom two rungs and stop there. In the old funnel, that was enough: A click stood in for interest, so what happened at the bottom let you infer what was happening at the top. AI breaks that shortcut. The middle of the journey now happens inside answer engines you can't see, so guessing no longer works. You have to measure the top of the ladder directly.
That's the job of a customized measurement framework. Think of it as a system for defining, capturing, and valuing outcomes — a system shaped by an organization’s mission and business model, as well as the decisions it needs to make. The framework comes down to four parts:
- Outcome definition. Choose three to five real-world changes that count as success, such as behavior change, policy movement, enrollment, donor retention, trust recovery, etc.
- Signal selection. Set obtainable indicators that credibly move with those outcomes, including the qualitative and non-digital signals most standard frameworks throw away.
- Value weighting. Agree on what each outcome is worth relative to the others. This step is especially helpful if budgets tighten.
- Decision cadence. Establish rhythm of reviewing the data and decision rules in advance — what gets scaled, paused, or reallocated, and when. A framework with no decision trigger is just a report.
Organizations that engage in public affairs, advocacy, and regulated industry sectors like healthcare already have deep experience in this. In advocacy campaigns, behavior-change communications, and public sector programs, a click can no longer be the proxy for "did anything actually change?" The organizations that have had to prove behavior change, not just reach, are the ones with a head start on where every marketer is heading.
Leading marketers are twice as likely as their peers to embed AI into the core of their measurement approach. (BCG)
Trust Factor
New measurement creates an advantage only if people trust it enough to act. Trading the metrics everyone shares for a customized framework means giving up a quiet reassurance — a number the whole industry recognizes — and asking leaders to steer real decisions by attention scores and simple tests instead. That trust runs two ways: Teams have to report what's true, not just what flatters the work. And leaders have to act on an honest number even when it disappoints, rather than retreating to vanity metrics. Build that trust, and you climb the ladder faster.
What's Next
Before a campaign, decide which rung it's really for — to be noticed, remembered, or acted on. That choice shapes both the work and what you measure. Use attention data, quick brand surveys, and simple tests to measure the top rungs. Then add those measures to the dashboard you already run.
Each rung calls for a different measure, and the higher ones are both more valuable and harder to discern:
- Delivered and Seen. The ads and emails you send still track easily, but in AI search, your content can reach someone with no click and no impression to count. New measures must be added to determine whether your brand actually shows up in this new AI landscape. There’s a young, crowded cohort of AI-visibility or generative engine optimization tools to try.
- Noticed. Here you measure attention: whether people actually looked, and for how long. New standards from the Interactive Advertising Bureau (IAB) in collaboration with the Media Rating Council (MRC) make attention something you can track with more confidence.
- Remembered. Here you measure what stuck. Short surveys can tell you whether people remember your brand after seeing your work, and a rise in people searching for you by name is another sign it stayed with them.
- Acted On. Here you measure what people did. The cleanest way is a simple test: Show the work to one group, hold it back from another, and compare what each group does.
