Let me start with a prediction. Going into 2027, the word I expect to hear more than any other in AI conversations is intelligence. We're going to see it in product names, in department names, in job titles, and in the way organizations describe how they're using more and more of their data.
Some of that is already happening. In March 2025, Salesforce launched a new product called Marketing Intelligence, separate from the older Marketing Cloud Intelligence it already sold. In higher ed, Ithaca College created a new Executive Director for Institutional Intelligence role this year and promoted Rob Snyder into it. When Dave Weil, Ithaca's SVP for Strategic Services and Initiatives, announced the position, he described institutional intelligence as bringing analytics, institutional research, data governance and institutional AI together to help the college make better decisions. As more of these show up, the word will catch on in even more places, and that's how it becomes a buzzword.
Buzzwords usually get dismissed, and I think this one deserves a closer look before it does, because there's something real underneath it.
In 2025, I hosted a webinar with Brad Leupen, CEO of Entrinsik, and the Entrinsik team. That conversation was the first time I could really visualize where analytics was headed with AI. By late summer and early fall of that year, I was starting to see the power of it in practice, and as the use cases kept evolving, what surprised me most was how easy it had become. With a moderate level of prompting skill and an understanding of how to build a Claude artifact (or Canvas in ChatGPT and Gemini), you can build these views yourself.
A year later, I'm living in it.
What the new dashboards produce is a deeper layer of data, and that deeper layer is what creates a new level of intelligence. Building one takes real thought and real prompting, and that same skill is what lets you start asking it questions the moment it's built. You end up seeing the small things that basic analytics would never surface.
I've seen this firsthand on a campus dashboard. Most enrollment teams can tell you how many people came to an open house, and many can tell you how many open house attendees applied afterward. This dashboard showed, right away, how many of those attendees had already been on campus for a tour, came back for the open house, and then applied.
That's a small segment, and seeing its actions and timeline clearly is what lets a team reach it with richer, more aligned communication that actually engages and informs those students. The same kind of insight can feed predictive models for propensity to enroll or for aid strategy, and that work is starting now as both firms and tech vendors pivot to AI to support those models.
The tour data, the open house data and the application data were all already sitting in the same CRM. What kept those points apart were report-building tools rigid enough that someone had to piece them together by hand. Reports were static and controlled by whoever built them, and if you had a question beyond what was on the page, you asked that person and waited days or sometimes weeks for an answer.
With AI connected to the CRM, the tools can see all of the data at once. You can ask a deeper question and get an answer immediately, and then ask the next one. Organizations are adding layers of data and context at a pace that wasn't possible even a year ago, and that's the intelligence the buzzword is pointing at.
To show what this can look like, I rebuilt my own dashboard for a fictional institution, Awesome University, using demo data. It started with one narrow job and grew in layers, from website performance to email to campaigns to inquiries tied to the pipeline. Some sources are connected directly and others still come in as exports, which is the honest middle state most institutions are in right now.
The part that excites me most is what I've called the holy grail for enrollment marketing and admissions: seeing the pipeline through the marketing campaigns that fed it. You can see which channels drove the most conversions, and you can see some return on the campaign dollars you spent. In the demo, 44 prospects signed up for the open house through an Instagram campaign and 16 of them applied, and right beside that you can see how paid search, email and every other source compared.
Demo data for a fictional institution. Not client results.
When intelligence starts showing up everywhere next year, the more useful question is what it would take to build more of it on your own campus or in your own department. What data do you already have that you could be leveraging better? And what data don't you have yet that you'd need to build your own intelligence layer?