I attended both EdTech Week and Magic EdTech’s 35th Anniversary Summit 2025 this past October, and one theme struck me clearly: across higher education and workforce learning, priorities are shifting from feature-driven innovation to structural transformation. The change ahead isn’t defined by new tools but by a new architecture — AI-native systems designed to modernize infrastructure and strengthen educator capacity.
Over the past year, the narrative has largely centered on enhancing smart features within legacy systems and processes. But as we explored at the conferences, institutions are realizing that layering AI on top of outdated infrastructure doesn’t solve the real problems. The core issues are structural: interoperability, governance, compliance, and measurable impact. This finding is supported by research from Higher Ed Dive, which notes that many higher-ed institutions struggle with fragmented data systems, outdated records, and weak governance; barriers that make real progress difficult.
From AI‐Enabled to AI‐Native
As the sessions unfolded, it became clear that AI-native design is no longer a nice-to-have; it has become the baseline. The future belongs to systems that learn and evolve, not to those that add AI as an afterthought. Legacy systems don’t scale that way, and IT leaders are acutely aware of it. In fact, a recent EDUCAUSE poll showed that 42% of institutions expect an IT budget decrease next year, underscoring the need for future investments to yield enduring ROI. This means systems that increase operational efficiency now, while minimizing the need for reinvestment cycles later.
The shift is fundamental. If the infrastructure itself is built to adapt and learn, it mitigates long-term risk and cost. It’s no longer just about adopting AI; it’s about building systems that are inherently intelligent by design.
Integration, Not Hype, Is Driving Procurement
Higher education procurement priorities are clearly shifting. RFP data from the past two years indicate that institutions are emphasizing implementation, data governance, and integration over standalone AI capabilities. In an analysis conducted by Magic EdTech of 159 education RFPs from 2023–2025, implementation & support (34.1%) and data solutions (32.9%) far outpaced mentions of artificial intelligence (2.4%), while accessibility (7.2%) and integrations (4.6%) also ranked among the top priorities. This confirms what many in EdTech are already seeing — buyers want platforms that are interoperable, compliant, and measurable, not just AI-enabled.
This trend aligns with EDUCAUSE’s 2025 Horizon Report, which focuses on data management and trust infrastructure as near-term priorities for teaching and learning. Similarly, a recent industry research shows that institutions are channeling their budgets toward data infrastructure, integration, and long-term implementation support, investments that strengthen institutional agility rather than chase standalone AI tools. Institutions are investing in trust, connectivity, and efficiency. For product teams like those at WGU Labs, this is a reminder that innovation without interoperability and governance will not prevail.
Compliance as a Catalyst for Trust
At both events, I noted how compliance has evolved from a checklist item to a genuine value differentiator. Across the vendor booths and presentations, systems already certified for FERPA, COPPA, SCORM, and accessibility compliance were winning attention because they reduce adoption risk. In one case discussed, an institution cut onboarding time by half and support tickets by 50% thanks to compliant architecture.
This aligns with procurement best practices, where institutions that demonstrate audit readiness, accessibility, and regulatory compliance tend to gain buy-in more quickly. For Labs and other EdTech builders, compliance has become a value multiplier, reducing friction, accelerating deals, and delivering the kind of ROI that institutions now demand
AI’s Real ROI: Educator Capacity
A recent Gallup survey of 2,232 U.S. public school teachers found that those who use AI tools weekly save an average of 5.9 hours per week, equivalent to nearly six weeks of regained time over the course of a school year. That kind of efficiency has the potential to be transformative.
At the Summit, Dr. Karen Ferguson, Chief Transformational Officer at Education Affiliates, emphasized that the most persistent challenge in education isn’t content, it’s educator bandwidth. Classrooms are overflowing with digital materials, but teachers are running out of hours to individualize instruction, provide feedback, or innovate in meaningful ways. This is where AI’s true return on investment becomes clear. When technology lightens the administrative load and restores cognitive space, educators can devote their energy to what matters most: connecting with students, fostering growth, and sparking creativity. The real transformation happens in how that reclaimed time is used to reimagine lessons, build relationships, and reignite the joy of teaching.
What Comes Next
I left the conferences with a renewed conviction that our future success lies in infrastructure, not just features; in systems that work together, comply, adapt, and measure. Across both education and employment, the expectation is the same: trust and value must be demonstrated, not assumed. Institutions want technologies that are verifiable and interoperable; employers want talent whose skills are visible and validated. The shift isn’t complete, but it’s unmistakable. Trust, transparency, and measurable outcomes are becoming the shared currency of both EdTech innovation and workforce readiness.
For EdTech innovators and institutions alike, the challenge is clear: build ecosystems, not products. The future of AI-native modernization depends on integration, adaptability, and governance that scale trust and learning outcomes. It’s ambitious work, and exactly the kind worth doing.
