As AI moves from experimentation to institution-wide adoption, the focus is shifting towards connected data systems, scalable content frameworks and responsible governance. Abhijeet Sethi, Strategic Business Head, LearningMate a straive company in an exclusive interaction with Kaanchi Chawla of Elets News Network, discusses the barriers to scaling AI, the future of multilingual learning and what fully AI-enabled institutions could look like over the next three to five years. Edited excerpts:
AI tools are already being used by students and educators. From Straive’s experience, what are the biggest barriers institutions face when trying to scale AI beyond pilot projects?
In education today, AI experimentation is not the problem. Scaling it is.
Most institutions successfully run pilots because they are limited in scope and operate in controlled environments. The difficulty arises when AI needs to interact with multiple systems across the institution. Student Information Systems, Learning Management Systems, and assessment platforms often operate independently, which means the data required for AI to function effectively remains fragmented.
Another common barrier is ownership. Many AI initiatives begin within innovation teams or specific departments, but scaling requires clear accountability for outcomes. Institutions need to decide who is responsible for model performance, data quality, and learner impact. Without that clarity, AI outputs remain difficult to operationalise.
From our experience at Straive, institutions that succeed treat AI as part of their academic infrastructure rather than a standalone tool. When data systems, learning content, and analytics frameworks are aligned, AI can move from isolated pilots to a reliable capability that supports better learning outcomes and institutional decision-making.
What does “future-ready enterprise” mean to you in the context of data, content, and AI services?
A future-ready enterprise is not defined by how much AI it adopts, but by how effectively it turns AI into consistent outcomes.
The fragmentation including Student Information Systems, Learning Management Systems, and assessment platforms, makes it difficult for institutions to act quickly. Identifying at-risk students, improving course outcomes, or optimising programs becomes harder when insights are scattered across systems.
The next phase, EdTech will focus less on scale and more on learner outcomes, retention, and program effectiveness. Future-ready organisations respond by building strong data foundations that unify systems and make content and curriculum AI-ready. When content, analytics, and learner data work together, institutions can enable personalised learning models, faster course development, and more responsive student support.
As AI becomes more embedded in academic workflows, governance also becomes essential. Institutions need clear validation mechanisms and human oversight to ensure reliability, fairness, and accountability.
Ultimately, a future-ready enterprise does not experiment endlessly with AI. It operationalises AI in a structured way so that it consistently improves outcomes across learning, content creation, and institutional operations.
Have you encountered a situation where AI implementation did not go as planned? What were the learnings from that experience?
AI implementations rarely fail because the models do not work. More often, the surrounding systems are not prepared for them.
In one engagement, an institution wanted to use AI to accelerate course content development. In testing environments the system performed well, but once it was deployed across real academic workflows, inconsistencies emerged. Different departments used different content structures, metadata standards were uneven, and editorial processes varied widely.
As a result, the AI system struggled to produce consistent outputs across courses.
The key lesson was that AI depends heavily on structured inputs and well-defined processes. If the underlying content and workflows are inconsistent, the technology will amplify those inconsistencies.
The solution involved standardising content structures, aligning metadata frameworks, and introducing governance across editorial workflows. Once those foundations were in place, AI could support course development far more effectively.
With the rise of global learners, how has Straive supported localisation or multilingual content strategies for EdTech platforms?
As EdTech platforms expand globally, localisation becomes essential to delivering meaningful learning experiences. Many organisations initially think of localisation as translation. In practice, effective global learning requires deeper adaptation. Content needs to align with regional contexts, curriculum standards, and cultural expectations while maintaining academic integrity.
At Straive, we approach localisation through a combination of AI-enabled language technologies and strong subject matter expertise. AI helps accelerate translation, transcription, and multilingual content creation, while human experts ensure the educational intent, terminology, and instructional design remain accurate.
Structured content frameworks also play an important role. When learning materials are designed in modular formats, they can be adapted more easily across languages and markets without rebuilding courses from scratch. This becomes especially important for global EdTech providers that need to scale learning experiences quickly while maintaining consistency and quality across regions.
For instance, we deployed AI-assisted course production workflows, centralised authoring systems, and localisation accelerators that enabled faster multilingual adaptation and more scalable global content delivery for a global EdTech provider. These initiatives helped reduce overall course production timelines by 40–45%, while ensuring instructional quality and contextual relevance remained intact across markets. AI-assisted workflows supported faster creation of narratives, assessments, and media assets, while human oversight ensured academic accuracy and learner relevance.
The approach also helped reduce duplication in content creation, enabled reusable and IP-controlled content frameworks, and minimised the manual rework traditionally associated with multilingual localisation efforts.
Also Read: Rajasthan Building an Industry-Connected Higher Education Ecosystem
Looking ahead, based on Straive’s ongoing projects, what does a fully AI-enabled institution realistically look like in the next 3–5 years?
Over the next three to five years, AI-enabled institutions will operate more like connected learning ecosystems rather than isolated technology deployments.
For students, learning journeys will become more personalised. AI will analyse engagement patterns, assessments, and participation to personalise learning paths and identify when students may need additional support.
For educators, AI copilots will assist with curriculum design, content creation, and feedback generation. This will significantly reduce administrative effort and allow educators to focus more on teaching and mentoring.
Institutional leaders will also gain access to unified data platforms that connect academic performance, operational metrics, and learner engagement. This will allow institutions to make faster and more informed decisions about programs, resources, and student success strategies.
At the content level, AI-enabled workflows will allow institutions to update and scale learning materials far more efficiently while maintaining academic standards.
Ultimately, the AI-enabled institution will not replace educators or academic leadership. Instead, it will augment them with intelligent systems that enable faster decisions, more personalised learning experiences, and stronger educational outcomes at scale.
_____________________________________________________________________
Stay informed with the latest developments shaping the future of education. Follow Elets Digital Learning for regular updates, insights, and policy news.
Also, join the conversation at the Elets World Education Summit 2026 this October in New Delhi, where education leaders, policymakers, and innovators will come together to discuss the future of learning.
