The Next Phase of AI is About Execution, Not Answers
Can AI actually get things done? People use AI every day to answer questions and surface trends, but the truth is, this type of use doesn’t solve real efficiency obstacles.
Our Chief Product Officer, Scott Rakestraw, recently published a piece on Unite.ai that digs into this exact challenge. In it, Scott discusses why AI that only provides answers falls short, and why the next phase of AI adoption centers on execution, not just insight.
Here’s what Scott had to say about where AI is heading in 2026, and why higher education institutions are perfectly positioned to lead this shift.
The gap between insight and action
Since AI’s inception, organizations have primarily treated it as a tool for generating insight. Chatbots can answer questions, dashboards can uncover trends, and copilots summarize instantly. While these tools deliver real value, many organizations fail to materially change outcomes.
As Scott puts it: AI that only focuses on answering questions rarely solves the operational bottlenecks teams face every day.
According to the McKinsey Survey on the State of AI, nearly nine in ten organizations now report using AI in at least one business function. However, very few say those efforts have translated into meaningful, enterprise-wide impact.
Similarly, a 2025 analysis of GenAI deployments found that 95% of enterprise implementations have produced no measurable financial impact, largely because teams never embedded AI outputs into real workflows. The issue isn’t access to intelligence, but rather the ability to operationalize at scale.
In practice, most AI systems don’t execute. They identify opportunities, but ultimately, humans must decide how and when to act — usually across fragmented systems and under tight timelines with limited resources. In many cases, AI increases awareness but doesn’t increase throughput. The next phase of AI adoption is shifting toward AI that can act.
What makes execution different
Scott describes “AI that acts” as systems that move approved actions across workflows — triaging requests, routing tasks, drafting follow-ups, and escalating exceptions when needed. But this doesn’t replace human judgment. Humans still define outcomes and approvals while AI handles repetitive work. Oversight remains built-in through review processes and governance structures.
This approach addresses the biggest barriers to adoption: transparency, accountability, and control concerns that Pew Research Center identifies as critical trust factors.
Why higher education leads the way
Scott points to higher education as the perfect proving ground. Students expect instant support throughout their journey. Alumni want ongoing engagement. Yet advancement teams must scale relationships with shrinking resources, all while managing sensitive data under strict governance.
Knowing who needs outreach isn’t enough. The challenge is acting at the right moment, consistently, across the entire lifecycle. AI that acts turns engagement signals into automated follow-ups, freeing staff for complex, high-value conversations.
If AI can execute responsibly here — with personal data, complex lifecycles, and strict governance — it creates a blueprint for other high-stakes sectors.
Governance enables action
Scott emphasizes that hesitation makes sense, but weak governance frameworks are what actually limit AI’s value. Nearly half of organizations cite inadequate governance as their primary barrier, while those investing in responsible AI practices scale impact faster.
Moving from recommendations to execution requires clear guardrails: defined approval paths, auditability, escalation rules, and privacy controls. Organizations that succeed build these into their design from day one.
As Scott notes, governance doesn’t slow AI down, it enables confident action.
What readiness looks like
In 2026, Scott predicts AI maturity will be measured by execution capability, not adoption rates. AI-ready institutions share three characteristics: clear outcome targets, governance frameworks with built-in controls, and unified data that allows AI to act.
The institutions leading this shift design for responsible action from the start, enabling teams to accomplish more without losing the human touch that matters most.
We’re building for this future
At Gravyty, we’re seeing institutions move beyond AI that just recommends, toward AI that executes safely, responsibly, and at scale.
Want to learn more about how AI that understands education? Learn more about Gravyty’s purpose-built AI for higher education.
