AI Can Answer the Question. The Next Challenge Is Helping People Move Forward.

We have become remarkably good at building AI that can answer questions. Ask when an application is due, which documents are required, or how a process works, and it can provide a useful response in seconds. But when the stakes involve someone’s education, financial future, or sense of belonging, an answer is only the beginning. What matters is whether that person can actually move forward.

That’s where agentic AI gets interesting. Rather than treating every interaction as an isolated question, an AI agent can recognize where someone is in a larger journey, identify what needs to happen next, and help them get there. Doing that reliably, however, requires more than a capable language model. It takes purpose-built journeys, connected systems, and human oversight designed into the process from the start.

Consider higher education. A student applies, completes financial aid, registers for courses, meets with an advisor, finds academic support, searches for an internship, prepares to graduate, looks for a job, and eventually becomes an alum. Institutions organize those moments into separate departments and technology systems, but students experience one university. They shouldn’t have to understand its organizational chart to get the help they need.

People Don’t Want Another Interface. They Want to Get Something Done.

In a recent survey of 1,058 currently enrolled U.S. college students, 76% said they expect to resolve most student-service needs, without calling or visiting an office. Yet one in four said their institution doesn’t meet that expectation. When asked what matters most in a modern digital experience, 64% pointed to fast response and resolution, 54% to 24/7 availability, and 50% to being able to complete tasks in one place. Students want faster answers, fewer handoffs, and the ability to complete tasks without navigating multiple systems.

A prospective student who starts a college application but never finishes it. A traditional chatbot might answer questions about application requirements, but the student still has to figure out what’s missing and how to proceed. An agentic system can recognize where the application stalled, identify the next step, and help the student pick up where they left off. If the student says they’ve already uploaded a transcript, the system can check the application record, confirm it’s there, and move past that requirement rather than asking them to complete it again.

This is also where some of the biggest technical challenges remain. Microsoft Research identifies reliability, context retention, and real-world workflow execution among the challenges facing productivity agents. An agent cannot reliably guide someone through a process if it loses context between steps or cannot work across the systems involved.

From Reactive Answers to Proactive Help

The opportunity extends beyond answering questions to recognizing when someone needs support and helping institutions determine the right next step. An advancement team, for example, might recognize signals that an alum is ready to reconnect, volunteer, or make a first gift. But recognizing the signal is only the beginning. The real value comes from understanding which outreach makes sense for that person, at that moment, and what should happen next.

Purpose-built journeys can incorporate established practices for different audiences and goals, giving teams a stronger starting point than generic tools or workflows built from scratch. And the benefit goes beyond saving staff time. Institutions already have enormous amounts of data. What they need is help interpreting those signals and turning them into thoughtful, timely engagement.

The need is clear. In the same student survey, half of the students said they had avoided reaching out to their institution because the process felt too difficult. Another 40% had been asked to repeat information they had already provided, 34% had been transferred without resolution, and 31% couldn’t find what they needed and gave up.

The most useful AI experiences help institutions recognize those points of friction before they become reasons for someone to disengage.

Context Is What Turns an Answer Into Useful Action

We’ve all experienced the frustration of giving the same information to an organization over and over again. Now imagine doing that while trying to register for classes, resolve a financial aid issue, or reconnect with your alma mater. Each disconnected interaction makes the institution feel a little less like an organization that knows you.

Connecting information around the individual, rather than around separate departments, changes that experience. Students, alumni, and donors benefit from more continuous, personalized engagement, while staff gain the context to respond more effectively. When institutions make engagement easier and more personal, they create the conditions for stronger relationships and lasting loyalty.

Students are already signaling that they want digital experiences to work this way. The same student research found that 56% expect systems to feel connected without redirects or repeatedly entering the same information. Half expect digital tools to proactively flag deadlines, requirements, or issues before they become problems.

Delivering that experience requires more than making individual tools smarter. It means connecting the information and workflows that allow institutions to understand where someone is in their journey and respond accordingly.

The Best AI Knows When to Act, When to Protect, and When to Step Aside

The goal of agentic AI should not be maximum autonomy. It should be the right combination of assistance, action, and human judgment, with human approval built into the workflow from the beginning.

As AI becomes capable of taking more action, deciding what it should do independently becomes just as important as expanding what it can do. Routine tasks like identifying incomplete forms or surfacing relevant information may be well suited to automation. But when an AI system prepares outreach or recommends an action that could affect a student’s experience, a person should have the opportunity to approve it before anything reaches the student.

Human oversight shouldn’t be limited to escalating unusual cases or reviewing decisions after they’ve already been made. In high-stakes interactions, approval belongs before the action, not after it. AI can identify signals, recommend next steps, and prepare communications, while staff retain control over what happens next.

This approach also creates space for the judgment and empathy that technology cannot replace. A student navigating an unusual financial situation or struggling academically may need more than an automated recommendation. The technology should help staff recognize those moments and respond with the context they need, rather than removing them from the process.

Responsible implementation also means protecting the sensitive information these experiences depend on. Connecting systems and enabling AI to take action introduces important questions about data access, permissions, security, and accountability. An agent assisting with an application or financial aid process should have access only to the information and actions appropriate to that interaction, with clear boundaries around what it can do.

Microsoft Research’s work on human-in-the-loop agents reinforces the importance of designing human oversight and control into increasingly autonomous systems, including mechanisms that allow people to participate in and redirect an agent’s work. Responsible AI makes better experiences possible by building the trust needed to deliver them.

The Next Generation of AI Is About Outcomes

There’s another shift institutions need to make: changing how they measure whether AI is actually working.

Today, AI performance is often evaluated through activity metrics: conversations handled, questions answered, response times, or inquiries deflected from staff. Those measures can tell us whether a system is being used or reducing workload, but they don’t necessarily tell us whether it helped someone accomplish what they came to do.

For the student who stalled halfway through an application, success means completing that application, rather than simply receiving answers from an AI assistant. For an alum interested in reconnecting, it means taking the next step in that relationship.

The impact of these digital experiences extends well beyond convenience. The student research found that 38% had delayed an important enrollment, financial aid, or academic task because digital tools were difficult to navigate. One in four said those difficulties had made them question whether staying enrolled was worth the hassle. When technology creates that much friction, measuring engagement with the technology itself misses the point. Institutions should be asking whether people are completing the tasks they intended to complete, where they’re getting stuck, and whether the experience is improving over time.

The next generation of AI will be defined by what people can accomplish because of it, and that’s an exciting shift. Whether someone is navigating college, managing their healthcare, or engaging with an organization they care about, the opportunity is to make complicated experiences feel genuinely supportive. Getting there requires journeys designed around real people, connected information, meaningful human oversight, and a commitment to measuring outcomes rather than activity.

The goal isn’t more automation for its own sake. It’s helping people move forward with less friction, greater confidence, and a stronger sense that the organization on the other side actually understands them.