The Next Phase of AI is About Execution, Not Answers

Since its inception, AI has been treated primarily as a tool for generating insight. Chatbots answer questions. Dashboards surface trends. Copilots summarize faster than any human could. These tools deliver real value, but for many organizations, they fail to materially change outcomes. After years of pilots and proofs of concept, a clear pattern has emerged: AI that has only focused on answering questions rarely solves the operational bottlenecks teams face everyday.

This isn’t anecdotal. According to the recent McKinsey Survey on the State of AI, nearly nine in ten organizations now report using AI in at least one business function, yet 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 AI outputs were never embedded into real workflows. The gap isn’t access to intelligence, it’s the ability to operationalize at scale.

In practice, most AI systems stop short of execution. They identify opportunities, but leave humans to decide how and when to act, usually across fragmented systems and under tight and limited teams and timelines. In many cases, AI increases awareness but doesn’t increase throughput. That’s why the next phase of AI adoption is shifting towards AI that acts.

From AI that answers to AI that acts

AI that acts represents a move away from passive intelligence toward systems designed to move work forward.

Instead of stopping at recommendations, agentic AI moves approved actions across workflows:  triaging requests, routing tasks, drafting follow-ups, nudging stakeholders, updating systems, and escalating exceptions when human judgement is required. Importantly, execution-focused AI doesn’t replace human judgement. It reduces the friction between insight and follow-through: humans define outcomes, approvals, and escalation paths; AI handles the busywork that slows teams down; and oversight is built in through review, audit trails, and governance.

This human-first approach is essential for trust. Research from Pew Research Center’s studies on AI trust consistently shows that concerns about transparency, accountability, and misuse remain top barriers to adoption. AI that acts responsibly addresses those concerns by making action visible, explainable, and controllable.

Reaching the inflection point

Several factors are contributing to push organizations beyond AI that answers.

• First, teams are being asked to do more with less. Workforce constraints are no longer temporary; they are structural. At the same time, expectations for speed and consistency continue to rise across every industry.
• Second, foundational AI models are becoming increasingly accessible. As a result, differentiation is shifting away from model selection and toward orchestration – how AI is integrated into day-to-day work. As Harvard Business Review has noted in its coverage, real value emerges when AI is embedded into processes, not layered on top of them.
• Finally, the cost of inaction is growing. When insights sit idle or follow-up falls through the cracks, the downstream impact compounds. In many environments, delayed execution matters as much as incorrect execution.

In this context, AI that merely informs is no longer sufficient. Organizations need systems that can execute routine work safely and consistently, reducing friction rather than adding to it.

Higher education as a real-world test case

Higher education offers one of the clearest examples of why this shift is necessary. Engagement across the higher education lifecycle has fundamentally changed. Students expect instant, consistent support from their first inquiry through graduation. Alumni look for ongoing value, not sporadic outreach. Advancement teams are expected to deliver greater impact and build long-term relationships at scale, even as staffing and budgets continue to tighten.

At the same time, engagement signals arrive continuously: applications submitted, milestones reached, events attended, gifts made. Turning those signals into timely, coordinated action still relies heavily on manual work across disconnected systems.

Higher education leaders increasingly view AI as essential to scaling engagement and student support, while remaining cautious about governance and data readiness. Similarly, other analyses of edtech and enrollment trends highlights growing interest in AI-driven lifecycle engagement, alongside frustration with fragmented systems that slow execution. In this environment, AI that only surfaces recommendations quickly reaches its limits. Knowing who needs outreach is useful, but knowing the right moment to deliver that outreach for maximum impact is much more difficult.

AI that acts helps bridge that disconnect by turning signals into next-best actions and automating routine follow-ups across the lifecycle. Staff remain focused on empathy, judgement, and complex conversations, while AI ensures engagement happens consistently and on time.

Higher education is especially revealing because outcomes depend on trust and human connection. If AI can act responsibly in the higher education environment, across complex lifecycles and in a space that deals with personal student data and information all while keeping governance intact, it offers a blueprint for other high-stakes sectors facing similar pressures.

Hesitation is rational – designing governance before action

Hesitation around AI that acts is understandable. Leaders worry about data quality, over-automation, and loss of control, especially in regulated or trust-based environments. These concerns are not reasons to pause indefinitely. What’s often missing is the role of governance as an enabler, not a constraint.

Nearly half of organizations report that inadequate governance and trust frameworks are limiting their ability to realize value from AI. The same research shows that companies investing in responsible AI practices are better positioned to scale impact.

AI that acts cannot succeed without clear guardrails. Moving from recommendations to execution requires explicit decisions about who AI can act for, what actions it is authorized to take, when human review is required, and how exceptions are escalated.

Organizations that move forward successfully treat governance as part of the product and process design, not an afterthought. In practice, that means establishing:

• Defined approval paths for when AI can act independently versus when human sign-off is required.
• Auditability and traceability so actions can be reviewed, explained, and reversed.
• Clear escalation rules that route uncertainty to human owners.
• Privacy and data controls aligned to regulatory expectations.

This kind of governance doesn’t slow AI down, it enables action with confidence. Leaders shouldn’t ask whether they can afford governance, but whether they can afford AI that can’t act because governance was never designed into the system from the beginning.

AI readiness in 2026

In 2026, AI maturity will be defined less by whether organizations use AI and more by how effectively they let it act.

AI-ready institutions share several characteristics:

• Clear outcome targets tied to enrollment, retention, engagement, or fundraising lift.
• Governance frameworks that include privacy controls, approvals, audit trails, and escalation.
• Unified data and integrations that allow AI to execute, not just recommend.

The next phase of AI adoption will be led by organizations that design for responsible action,  enabling AI to increase capacity, support better outcomes, and help teams do more with less – without losing the human touch that matters most.