Appliances Electrical and Electronics Manufacturing

Why the next decade belongs to intelligent classroom ecosystems

Over the last two decades in the AV & EdTech industry, I’ve seen hardware cycles rise and fall, panels get thinner, pixels get sharper, and OPS specs become a competitive battleground. But nothing- not 4K/8K, not IR bonding, not Android upgrades - has changed our industry the way AI has in the past 24 months. The shift is quiet. It’s not a flashy “robots replacing teachers” storyline. It’s a deeper transformation of daily workflow, classroom expectations, school procurement behavior, and the value chain that sits behind educational technology.

What has changed recently and never existed before

AI is now the default expectation. A few years ago, AI was positioned as a premium add-on. Today, teachers start with a simpler question: How much does your AI reduce my effort in the classroom? It is no longer a checkbox; it’s a primary differentiator.

Teachers want automation, not more features. “200 apps” is not a selling point. What matters is less work: fewer clicks, faster explanations, instant content, and smoother classroom flow. Utility beats variety.

Security has moved into the boardroom. As blended learning expanded, so did exposure. Institutions are now asking for encryption, access controls, auditability, and admin dashboards, not as nice-to-haves, but as table stakes.

Buying decisions have shifted from specs to ecosystem readiness. The winning panel is rarely the brightest or the largest. It’s the one that fits naturally into the institution’s teaching workflow, integrates cleanly with current systems, and can be supported reliably over time.

Students have changed, too. Gen Alpha expects technology to work effortlessly. Slow, complex, or unintuitive interfaces are abandoned immediately. Experience is no longer a layer on top; it is the product.

How technology assists teachers today

AI reduces classroom friction. Summaries, assessments, auto-layouts, and transcription quietly save minutes in every class, freeing teachers to focus on explanation and interaction rather than setup.

Remote device management has matured. Administrators can now lock, update, and monitor devices across buildings with far less manual effort, making extensive rollouts more predictable.

Content ecosystems are cleaner and more usable. Educational bodies increasingly expect mapped curricula, ready-to-use templates, and structured lesson flows rather than raw content repositories.

Service readiness has become a differentiator. Schools and institutes look for on-site response, predictable turnaround times, as well as proactive maintenance, not just warranties on paper.

Core reliability has improved across leading brands. Bonding quality, touch precision, and thermal stability are significantly better, reducing classroom interruptions and improving day-to-day trust in the technology.

Where technology still falls short

Despite rapid adoption, today’s classroom technology still breaks in predictable places. The gaps are less about features and more about context, constraints, and field execution.

AI still struggles with local context. India’s multilingual spectrum and code switching patterns challenge global models. Accuracy drops when language, accent, and classroom slang mix in real time.

Offline intelligence remains limited. Many schools operate with inconsistent connectivity. When systems degrade without the internet, day-to-day usefulness drops, and trust erodes quickly.

Teacher adoption is still the main bottleneck. Most teachers use only a fraction of what panels can do, often 15–20%. Training, time pressure, and habit loops matter more than new capabilities.

Digital systems don’t talk to each other. Schools running mixed-brand infrastructure face fragmentation across content, devices, and analytics. The result is duplicated effort and uneven experiences across classrooms.

Security and compliance are uneven. Practices vary widely across vendors and institutions. Consistent safeguards, audits, and enforceable standards are improving, but uniform compliance is still years away.

What AI-first IFPD solutions are getting right

The panels gaining traction in Indian classrooms share a pattern. They offer instant lesson summaries that save teachers 10-15 minutes per class. They auto generate quizzes from handwritten notes and diagrams. They respond to voice commands - "summarize this," "explain that" - so teachers stay in flow instead of navigating menus. The best ones run AI locally on the device, solving the connectivity problem that plagues rural and semi-urban schools. They're built for India's multilingual reality, not retrofitted from global platforms. And critically, they keep classroom data on-premise unless explicitly exported. The adoption numbers tell the story: minimal, easy-to-use interfaces get teachers to 70-80% feature usage within weeks. Complex ones gather dust. The technology that wins isn't the most powerful - it's the one teachers actually use.

What’s coming next

Based on current adoption patterns, several shifts now feel less like predictions and more like inevitabilities. They are already visible at the edges of the market and will move to the center faster than many expect.

Edge AI becomes the default, not the exception. Constraints on speed, privacy, and reliability will push intelligence closer to the classroom. Cloud-only AI will increasingly feel slow and exposed, while edge-based models become the standard for instantaneous engagement.

Interfaces move from touch-first to voice-first. Touch will not disappear, but voice will handle most day-to-day workflows. In instructional environments, voice is faster, more natural, and better aligned with how teachers already operate.

Lesson creation becomes automated and mainstream. Uploading a chapter will be enough. Slides, diagrams, questions, and summaries will be generated in minutes. The bottleneck will no longer be content creation, but judgment and curation.

Institution-wide analytics begin to shape decisions. Participation, engagement, and learning outcomes will increasingly be reflected in dashboards. Over time, these signals will influence curriculum design, asset allocation, and performance measurement.

Ecosystem lock-in matters more than hardware. The panel itself will matter less than the ecosystem around it. Winning platforms will behave like hubs, integrating content, analytics, and services rather than competing solely on device specifications.

Service culture becomes a differentiator. Adoption at scale will favor brands with strong on-ground support. Training, reliability, and responsiveness will matter more than feature lists.

AI shifts from assistant to co-creator. Teachers will depend more on AI not just to assist, but to design, refine, and personalize their teaching flow. The relationship will feel collaborative rather than transactional.

What we’ve learned after thousands of classrooms

Teachers forgive missing features, but not complexity. If something takes more than a few seconds to understand or execute, it simply won’t be used, no matter how powerful it is.

Institutions value reliability instead of new developments. Steady functionality and dependable support matter more than frequent or flashy upgrades.

Students pull the adoption curve upward. They acclimate to new technology faster than teachers and often set expectations that institutions are forced to meet.

Hardware differentiation is narrowing. As physical capabilities converge, software, integration, and service will increasingly determine which platforms win.

Trust matters most. Schools are looking for long-term partners who understand their context and stand by their deployments, not vendors chasing the next sale.

The bottom line

We're standing at a quiet but profound turning point. The shift from hardware centric classrooms to intelligence-driven ecosystems has already begun. AI isn't replacing teachers. AI is removing the friction that keeps them from teaching at their best. The brands getting this right aren't just adding features - they're reimagining the teaching workflow. And for those of us building the future of Indian EdTech, the opportunity is clear: create technology that saves time, reduces mental workload, and respects the realities of Indian classrooms. If we keep doing that, we won't just respond to the shift; we'll be part of it. We'll lead it.

Clean energy, EV, sustainability, leadership insights

India’s clean-energy shift is part of a larger global movement, driven by rapid advancements in electric vehicles (EVs), sustainable energy solutions, and government policy. As India aims to become a global leader in clean energy, its progress is influenced by international market growth, emerging technologies, and evolving policy trends. For those of us building inside this transformation, it is also a personal journey, shaped by lessons, missteps, and the constant pressure of scaling something new.

My own path into this industry was never planned. In fact, my career started by selling petrol and diesel for a living, and the energy transition in the early phases of my career looked more theatrical in the short term. I worked across roles that had nothing to do with EVs or power systems at the time. But each role taught me something essential: how to build teams, think long-term, and stay calm when things go wrong. These were the foundations I carried with me when I joined the clean energy sector in a leadership role. What I didn’t expect was how quickly the industry and my job would change around me.

"The market has shifted faster than anyone predicted"

India’s EV sector was still young in 2019-20 when I joined. Many customers questioned the viability of EV charging infrastructure and the future of clean mobility in India: Every deployment seemed like a test case. Every charger installed was monitored like a newborn.

Today, OEMs are launching advanced platforms and investors are backing long term plays in sustainable energy. Consumers expect fast, reliable EV charging infrastructure with zero excuses. Safety, cyber standards, and uptime are under the spotlight in a way we never imagined years ago. This shift pushed companies in the sector into a new phase: from pilot deployments to large-scale national infrastructure. From basic chargers to high-power, software-led platforms. From an India-focused team to a global engineering and manufacturing organization, partnering with global partners and sharing best practices.

For me as a leader, the question changed from “How do we prove this works?” to “How do we make it work every day at scale?”. That mindset change has defined the last few years.

Technology helps, but it doesn’t solve everything
Technology sits at the center of our industry’s progress. Smart diagnostics, advanced electronics, remote monitoring, and AI-led predictive tools have changed the way we operate. These systems enable operators to detect problems early, optimize performance, and better support customers.

But technology also forces you to face the reality: reliability is not created by algorithms alone. It comes from disciplined engineering, a culture that values quality, and teams that understand the stakes on the ground. Connectors don’t always match perfectly. Field conditions stress hardware in ways engineers never predict. This is where my earlier career lessons became valuable. You cannot outsource judgment. You cannot automate ownership. You cannot scale without systems. We learned this as we shifted from reactive maintenance to data-led field operations. The technology enabled it, but the real change came from people and teams who adopted new processes and raised the bar on reliability.

The road ahead will demand depth, not just speed
Looking at the next five years, India’s energy and mobility ecosystem - including electric vehicles, renewable energy, and advanced battery technologies - is entering a decisive phase. What we build now will define the market for a decade, and help shape India’s leadership in the global clean energy transition. The choices made here will set examples for other emerging markets worldwide.

High-power charging will move from a niche to the norm. Software will become the brain of every hardware product. Safety and cybersecurity will become non negotiable. Customers will measure value not just by innovation, but also by reliability and service. Global standards will shape how Indian companies design, manufacture, and operate, ensuring India’s solutions stay competitive and applicable worldwide.

Use technology to amplify discipline, not replace it
The turning point in my own journey was recognizing that leadership isn't just about solving the biggest problem of the day. It is about designing organizations that solve problems better than any one person can. This is the mindset India’s energy transition now requires from all of us. We are still in the early chapters of a massive transformation.

My belief is simple: the clean energy industry, in India and globally, rewards those who build for the long game by committing to sustainability, innovation, and leadership. If we stay committed to quality, invest in people, and continue to see substantial results, we won’t just observe the future of energy; we will help shape it.

Why adaptability, not optimization, is emerging as the real business model - seen from inside industrial and environmental intelligence.

I often remind my teams and myself that the most dangerous assumption a leader can make is believing tomorrow will simply be a faster version of yesterday. While this belief once served us well, it is now increasingly clear that holding onto it is not only risky but has become a liability.

I work in measurement, spanning industrial systems, weather, and environmental intelligence. We build instruments, software, and data services for customers operating in safety-sensitive, regulated, and mission-essential environments. For decades, the fundamentals of this business barely moved. Accuracy mattered. Reliability mattered. Processes hardened. Business models stabilized. The guidelines were straightforward, and optimization was rewarded.

Those certainties are now dissolving, and this is happening faster than many realize. The core problem I face and that I see across our industry - is that organizations that fail to adapt continuously risk becoming outdated in an environment defined by constant disruption.

What has changed that did not exist before?

The first major shift is the diminishing need for highly structured data to generate insight. Previously, insight followed a predictable sequence: systems were designed, schemas defined, data cleaned, and only then were questions asked. Entire IT ecosystems and operating models were built on this approach.

That sequence is breaking down. Advances in machine learning, computing, and data fusion now enable value extraction from messy, incomplete, and uncorrelated data. Signals surface before structure is imposed, and patterns emerge without established models. Insight is no longer limited by data perfection, fundamentally changing who can generate value and how quickly.

The second shift is the active challenge to long-standing business models and processes. In industrial and weather measurement, we traditionally sold products, contracts, and services in stable, predictable cycles. Revenue followed delivery, forecasts followed orders, and operations were optimized for efficiency and scale.

That logic is eroding. Customers now expect outcomes rather than instruments. They seek uptime, forecasts, and decisions, not just measurements, and value tied to impact rather than ownership. This shift is evident in procurement language, partner expectations, and competitive dynamics. Processes built for stability now struggle with speed, and those built for efficiency struggle with adaptability.

Unlocking cross-domain insights with technology

Today, technology excels at accelerating cross-domain insights. We can now correlate weather data with industrial performance, logistics, energy systems, and environmental risk. Signals that were never intended to interact can now be connected meaningfully and at scale.

At Vaisala, I’ve seen firsthand how insight now emerges at the intersection of domains, not from a single dataset or product line. Weather influences energy markets, and environmental conditions affect industrial yield. Measurement data becomes far more valuable when it is combined across contexts. This level of correlation simply wasn’t feasible just a few years ago.

At the same time, these  capabilities challenge the dominance of highly structured enterprise systems. When insights can be generated directly from raw signals, logs, images, and text, rigid schemas become less essential. Systems like ERP, CRM, and PLM were built on the assumption that the world could be modeled in advance, but this assumption is increasingly fragile.

Digital technology is also enabling new business models. Value-based offerings and outcome-based pricing are now viable. Demand forecasting combines real time behavior and external signals, not just historical sales curves. This changes how companies plan, price, and prioritize.

Yet technology has limitations. Many digital transformations focus on digitizing existing processes instead of rethinking decisions. They automate outdated logic, reinforce silos, and generate dashboards without clarity. Too often, enterprises confuse data volume with insight and tools with progress.

The greater challenge is operational and cultural, not technological. Technology can present options and accelerate decisions, but it cannot define intent or make choices. That gap is still a human responsibility.

Adaptability as the new business model

I believe the future belongs to autonomous enterprises. By autonomous, I do not mean without people, but organizations where systems constantly sense, decide, and act within clear boundaries. In these enterprises, insight is real-time, contextual, and immediately actionable.

In these organizations, many structured systems become optional rather than central. ERP, CRM, PLM, and financial systems primarily address latency and uncertainty by recording the past for future decision-making. As latency decreases, their role shifts. Decisions no longer wait for monthly or quarterly cycles; the enterprise becomes event-driven instead of calendar-driven.

This shift does not eliminate governance; it transforms it. Rules move from static workflows to dynamic policies, and controls shift from checkpoints to real-time tracking. Humans move up the value chain, with leaders focusing on shaping intent, constraints, and learning loops rather than approving transactions.

For data-centric businesses like ours, this shift is especially powerful. When data is the product, autonomy amplifies our advantage. Systems learn from every signal, models improve continuously, and offerings evolve during use. The boundaries between product, service, and decision support are beginning to blur.

This future will not arrive overnight. Legacy systems will coexist with autonomous layers. Regulation will slow some developments and accelerate others. Trust will become more important than speed, but the direction is clear. Successful enterprises will not be those with the most data or tools, but those that convert uncertainty into decisions faster than their peers.

I often tell fellow operators that the biggest risk is not adopting the wrong technology, but holding onto outdated models that assume the world remains predictable. In a world of continuous disruption, adaptability is no longer a capability; it is the business model. Enterprises that thrive will make adaptability their core strength.