By
Shirin Khosravian
Artificial intelligence is quickly becoming a baseline operational requirement. Universities, colleges, and education systems are finishing with the experimentation phase and transitioning more into scaled execution. AI directly influences how institutions operate, how learning is delivered, and how trust is maintained in academic outcomes. The institutions that lead will be those that treat AI as a strategic asset embedded across the organization.
Legacy systems that can’t support intelligent automation and fragmented data architectures often undermine strategic insight. Platforms like Salesforce (when implemented with intent) are becoming foundational to any future AI deployments. With a unified data model, embedded AI capabilities, and scalable security, Salesforce enables education institutions to optimize AI responsibly while improving student, faculty, and staff experiences at scale.
We see 2026 as the inflection point where AI maturity separates institutions that modernize from those that fall behind. The trends outlined below reflect where education is actually heading. From AI as a core institutional capability to academic integrity frameworks and human-centered transformation, these trends define what it will take to remain credible, competitive, and resilient in the next era of education.
1. AI will become a necessary organizational capability
By 2026, AI in education is moving beyond experimentation into an enterprise capability that directly impacts institutional efficiency and scalability. Universities that treat AI as embedded infrastructure (across operations, data, and security) are outperforming those still deploying isolated tools.
AI is reshaping institutional operations
AI-driven automation is materially reducing administrative overhead across enrollment, advising, student services, and finance. Salesforce research shows 78% of higher education staff report AI improves service delivery, while 77% say it enhances institutional planning and decision-making. This is not theoretical value. AI is already absorbing high-volume, low-value work such as routine inquiries, scheduling, and operational reporting, allowing staff to focus on higher-impact outcomes.
Data architecture is the bottleneck
AI performance is constrained by data fragmentation. Poor data integration and legacy architecture as the primary barriers to scaling AI across the enterprise. Without a unified data model, institutions cannot operationalize AI at scale or trust the outputs it produces. By centralizing student, faculty, and operational data into a single system of record, institutions enable real-time insights, AI-powered analytics, and cross-functional decision-making, without compounding technical debt.
Security and privacy are core requirements
As AI becomes embedded in institutional workflows, risk exposure increases. Salesforce reports that 75% of IT security leaders expect AI to play a central role in threat detection and compliance, yet fewer than half feel prepared to secure AI systems today. This gap is untenable for education institutions managing sensitive student, financial, and research data. Security and privacy are now enablers of scale, not inhibitors of innovation.
In 2026, AI maturity in education is defined by institutional integration. Universities that leverage AI through automated processes, unified data architecture, and enterprise security frameworks will scale faster and make better decisions. Those that don’t will stall in constant experimentation and pilot modes.
Related Article: Leveraging Salesforce for Higher Education Centralization
2. Expect more AI ethics and governance frameworks
AI ethics and governance will become operational imperatives. As generative AI proliferates across industries, higher education institutions must move from fragmented guidelines to structured governance frameworks that assign accountability, mitigate risk, and preserve trust in institutional outcomes.
Governance will improve operations
AI governance in education means concrete structures and processes embedded into institutional decision cycles, which will require more than the typical annual policy refreshes. Accrediting bodies and federal guidance increasingly expect institutions to demonstrate evidence of governance practices that protect privacy, reduce bias, and uphold accessibility and civil rights. Institutions without formal frameworks expose themselves to measurable compliance and reputational risk.
Ethical principles must map to actions
Research shows that responsible AI governance in education must explicitly address privacy and data protection, algorithmic fairness, transparency, explainability, student well-being, and human oversight. This is built through governance mechanisms that assign responsibility and authority. These mechanisms include bias audits, documented decision protocols, and transparent reporting processes.
Shared governance across stakeholders
AI governance in education markets is shifting from siloed IT checklists to cross-institution committees that combine legal, IT, academic affairs, instructional design, and student representation. Multi-unit structures ensure that decisions about ethical use of AI (whether for advising bots, automated grading, or adaptive learning systems) are informed by diverse perspectives and lived experience, reducing blind spots around risk and equity.
Institutional trust requires transparency
Students and faculty demand clarity on how AI affects their learning, privacy, and academic outcomes. Governance frameworks that include explainability protocols, clear use disclosures, and channels for recourse build institutional legitimacy and protect academic integrity from erosion.
AI governance is a competitive differentiator for education institutions. Those that streamline ethical oversight will be positioned to scale AI without exposing students or institutions to harm. Those that defer will face audit findings, compliance penalties, and weakened trust in credential value.
3. AI will be leveraged as a learner and faculty enablement tool
Students increasingly view AI as a resource for understanding concepts, getting timely feedback, and supplementing instruction, while educators are beginning to integrate AI into instruction and support functions, even as adoption levels across faculty vary. These capabilities are redefining how education is delivered and experienced.
AI is designed to amplify educators’ impact. Adaptive learning systems and intelligent tutoring technologies can provide real-time, personalized support that complements classroom instruction, helping students reinforce learning outside class time. Meanwhile, faculty can leverage AI for content development, formative feedback, research assistance, and targeted interventions, enabling more meaningful engagement with learners. Real data shows students are using AI extensively, and institutions are responding with tools designed for both learners and instructors.
What AI enablement looks like in practice
- 24/7 personalized tutoring and study support: AI tools act like on-demand tutors, answering questions and guiding review outside traditional hours.
- Adaptive learning pathways: Systems analyze performance and adjust content pacing and difficulty to match individual needs.
- Reduced faculty workload for routine tasks: AI can automate feedback loops, assist with content generation, and provide initial drafts for syllabi or assessments.
- Data-informed instructional adjustments: AI analytics surface patterns in engagement and performance that help educators refine teaching tactics.
- Support for equity and access: AI tools make personalized learning scalable, especially in large or under-resourced classes where individual attention is constrained.
AI as an enablement tool in education in 2026 is about augmenting human capacity, not replacing it. Students increasingly rely on AI for on-demand help and reinforcement, often seeing it as a study partner that boosts confidence and preparation. Faculty are beginning to integrate AI into their own workflows, though broader institutional support and training are still needed to close adoption gaps and unlock full instructional value. When aligned with strong academic policy and thoughtful implementation, AI can enhance learning outcomes and make faculty more efficient contributors to student success.
4. A push for academic integrity and transparency frameworks
Academic integrity is all about preserving trust in what a credential represents in an AI-enabled world. As generative AI becomes ubiquitous, institutions are being forced to rethink assessment models, validation mechanisms, and transparency standards. The focus is shifting from “Did a student use AI?” to “Can the institution stand behind the learning outcome?”
Traditional integrity models are breaking down because they were designed for a pre-AI environment. AI detection tools are unreliable at scale and disproportionately penalize certain student populations. As a result, leading institutions are moving away from policing behavior and toward redesigning assessments to emphasize applied learning, critical thinking, and process transparency. This means fewer high-stakes, static submissions and more iterative, experiential, and contextual evaluation. These frameworks might include:
- Assessment redesign: Project-based, oral, and applied assessments that require demonstration of understanding, not content generation
- Transparency over prohibition: Clear disclosure policies that define acceptable AI use and require students to document how AI supported their work
- Process visibility: Emphasis on learning artifacts, drafts, reflections, and checkpoints rather than final outputs alone
- Faculty enablement: Training instructors to design AI-resilient assessments and evaluate learning in AI-augmented contexts
- Student trust models: Framing integrity as a shared responsibility that protects the long-term value of degrees, not a disciplinary mechanism
Salesforce can play a critical role here by supporting process tracking, portfolio-based assessment, and auditable learning journeys across the student lifecycle. When assessment data, engagement signals, and learning artifacts are unified, institutions gain defensible transparency into how outcomes were achieved, not just what was submitted.
Academic integrity leaders will be those who stop fighting AI and start designing around it. Institutions that modernize assessment and embed transparency into learning workflows will protect credential credibility and student trust. Those that cling to detection-first strategies will face growing skepticism—from students, employers, and accreditors alike.
Related Article: Preparing Higher Education for Salesforce Agentforce
5. Striking a balance between humans and AI
Digital transformations in education must be human-centered. They need to be designed around the real needs, constraints, and behaviors of students, faculty, and staff rather than around technology capabilities alone. Institutions that prioritize human outcomes over technical novelty are seeing better adoption, stronger impact, and lower risk of unintended consequences such as equity gaps and disengagement. Human-centered digital transformation integrates human needs into strategy, design, implementation, and governance at every stage.
Generic digital transformation initiatives that focus solely on platform rollouts or automation will fail to deliver value if they neglect the lived experience of users. In human-centered transformation, technology (especially AI) serves human goals such as meaningful learning, accessible support, and equitable opportunity. This requires listening to stakeholders, testing with real users, and adapting solutions based on feedback loops rather than assuming that functionality equals adoption. Here’s a few examples of how that might look:
- User voice integrated into design: Research and co-design with students, faculty, and staff to ensure tools solve real problems instead of creating new ones.
- Empathy-driven interfaces: AI and systems that respect cognitive, cultural, and accessibility differences in how users make decisions and interact with technology.
- Instruction alignment: Digital workflows that enhance instruction and learning rather than disrupt or distract from core educational goals.
- Continuous evaluation and iteration: Feedback loops that refine digital solutions based on performance data, human outcomes, and changing needs.
- Leadership and culture change: Training and support for leaders and faculty so transformation is socially embedded, not just technically implemented.
Human-centered approaches avoid common pitfalls of digital transformation by anchoring innovation in observable human behavior. UNESCO’s global initiatives on AI in education emphasize that technology is a tool, not the heart of learning, and that teacher autonomy and learner experience must drive integration strategies.
Institutions that align AI strategies with human experience will not only achieve higher adoption and satisfaction, but also protect the legitimacy of learning outcomes, strengthen faculty trust, and close the gap between institutional goals and lived reality. Those that prioritize technology over users risk deploying solutions that look modern but fail operationally and reputationally.
6. AI fluency will become part of the core curriculum
Like spreadsheets or data literacy before it, AI must be embedded directly into existing coursework across disciplines. Isolating AI into standalone electives limits impact and fails to prepare students for environments where AI is simply part of how work gets done.
True AI fluency is about application and judgment, not tool training. Students need to understand when and how to use AI in their field, how to evaluate outputs for accuracy and bias, and how to apply human oversight responsibly. Deloitte notes that workforce readiness now depends on contextual AI skills integrated into day-to-day workflows, not surface-level familiarity.
Salesforce research reinforces that students increasingly expect institutions to prepare them for AI-enabled careers through practical, curriculum-level integration. Institutions that embed AI fluency into the core curriculum will graduate adaptable, employable professionals. Those that don’t risk leaving students academically credentialed but operationally unprepared.
