Before You Buy the Software: Framingham State's CIO on Bringing IT in Early

Your department decides to implement a new piece of software with a grand vision for what it will help you accomplish. However, when and how should IT be involved in this process? For Patrick Laughran, Associate Vice President and Chief Information Officer at Framingham State University in Massachusetts, there's no one-size-fits-all answer.

At Framingham State, Laughran and his team work to advance the institution's strategic initiatives, ranging from a shared roadmap for academic programming administration to, more recently, a new data analytics platform. He spoke with AcOps about what effective partnership requires, how campus units can bring IT into the work, and why decision literacy deserves more attention as institutions integrate AI into daily operations.

Partnerships That Fit the Purpose

When asked how units on campus can most effectively partner with IT, Laughran describes IT partnerships through the lens of musicians who come together to make music. "The number of individuals involved can range from a couple, maybe a duo, to several dozen people depending upon the objective. A relatively small versatile group, for example, may be better suited for improvisation and experimentation whereas a larger group would be much more appropriate for something that's highly choreographed as a concert," he explains.

For academic operations leaders, this requires assessing the scale of the ask. While a quick process fix can be resolved on the fly, a multiyear systems overhaul calls for a different approach to collaboration. Laughran is quick to point out that whatever the configuration, it needs to be "fit for purpose and include a shared sense of responsibility for clearly specified intended outcomes."

Invite, Involve, Incorporate

On a practical level, Laughran offers a simple framework for campus units that want to partner more effectively with IT: "The three I's: invite, involve, and incorporate." The first step carries the most weight: "Invite us in the formative phase of developing strategies early on in the process. And also when you're trying to work through problems. We can help jointly define the desired outcomes and determine the root causes before settling on a solution."

Part of the early-stage collaboration also focuses on making timing and resource allocation decisions. Laughran points out that "we all have more great ideas than we can ever execute against simultaneously. So we need to make decisions about resource allocation." These conversations help weigh money, time, available expertise, and the opportunity cost of one project against another.

Framingham State now asks a consistent set of questions to evaluate project proposals including:

1. What work is most urgent?
2. What's feasible to do right now?
3. What should we do first in order to make it better for subsequent projects?

Laughran notes that this intentional planning pays dividends past a single budget cycle: "We were able to do an even better job this fiscal year to not only plan out how we're going to allocate our finite resources for the current fiscal year, but we now have a three-year plan, which is subject to change and we'll make adjustments."

A Systems Approach to Academic Programming Administration

One of Framingham State's most significant academic operations efforts came directly from the strategic planning process. Specifically, the institution wanted to bring academic programming administration, including the catalog, academic scheduling, faculty workload management, and syllabi, under one umbrella. Laughran explains why that framing mattered: "The benefit of looking at those things holistically, under the broader umbrella of academic programming, was that we were able to take a systems design approach to implementing solutions in a sequence and along a timeline that were mutually reinforcing."

Rather than adopt separate point solutions for each area, the university set out to build a shared foundation. IT's contribution started with the roadmap itself: "We [IT] work together with them [academic affairs] on developing that roadmap. ... we were involved at the very front end of not only identifying what are the unmet and anticipated needs that could be bundled together into a roadmap, but also helping to put together the request for proposal and to evaluate the solutions that are available."

As a result of the collaboration, the institution implemented Coursedog and established a base for "more operational efficiencies, more transparency, and combining information from multiple sources." Framingham State is now building on this work with a data analytics platform focused on "data access, use and the infrastructure around managing that data so that it's trustworthy."

Both efforts serve strategic goals of transparency, consolidated information, and consistency (not uniformity). Laughran explains the larger aim:

"The overriding objective here is to have a more unified user experience because we're just as interested in providing a better total digital experience for people who work here as the students who come here."

Decision Literacy Starts Before the Decision

Like many institutions, Framingham State has access to plenty of data. The harder task, in Laughran's view, is turning that data into the right decision at the right moment. He sees a common pattern where the pressure to decide quickly "tends to override the preconditions for what I think is even more important, which is precision and timeliness. Precision being providing the trustworthy data. And analysis when it's needed and for who's been given the decision-making authority."

The answer lies in groundwork. When institutions invest early in data governance, data accuracy, and data quality, the right inputs already exist when speed matters. That groundwork extends to data workflows. Laughran explains that institutions need to ensure "that those data assets are made available when they're needed, to who they're needed, by when they need them."

AI agents add a new layer to the question, and Laughran expects this will drive an even greater need to understand how a decision is made. He breaks it down: "In some instances, it may be appropriate to delegate that to an agent and have it automated for a very basic transaction. But anything with financial consequences, hiring consequences, etc., we need to build up the decision literacy within our organizations to be able to discern, well, what should be delegated? What can't be delegated? And what is decision support?"

Think in Capabilities, Not Technologies

For leaders who approach a new system or a data literacy effort, Laughran recommends a shift in perspective:

"Think in terms of capabilities rather than what a technology does and how it works. Looking at technological capability then allows us to not only focus on what it is that we want an information system to provide, but also allows us to step back and think through, well, what about the organizational capability?"

Framingham State put that thinking to work when it considered an investment in a data lake warehouse. Laughran explains the reasoning: "If we're going to integrate all of these data into one system where it can be assembled for analysis from all these different sources, that requires a greater depth and breadth of data governance than we had in place. What does that mean organizationally? Well, that organizationally means putting in place practices that build the capability."

That conclusion shaped the proposal itself, which included hiring a director of data governance to lead the effort. Laughran outlines why this progression is critical. While many see the need for a new technology, he notes that "it's not an appliance that you can just plug in," but rather requires the right people and capabilities to make it successful.

Laughran believes building this underlying infrastructure will only become more important. He explains: "I think decision literacy is going to be something that is going to warrant more attention as we find ways to integrate AI into our workforce." The academic operations teams best positioned for that shift will likely be the ones that already know who decides what, which data they can trust, and when to bring IT into the room.