Blog – Page 2 – Dr. Mazlan Abbas

What My PhD Actually Taught Me (And It Was Never Just About the Research)

People assume a PhD is about becoming an expert in a narrow field. You pick a topic, spend four years buried in it, defend your thesis, and walk away with a title in front of your name. That was how I thought about it when I started mine at Universiti Teknologi Malaysia back in 1989.

I was wrong. The research was almost the least important part.

What the PhD actually gave me, I only fully understood decades later, when I found myself building FAVORIOT from scratch. No corporate safety net. No budget approval processes. No team of fifty people to delegate to. Just a problem to solve, a market to convince, and a very steep learning curve. And somewhere in that pressure, I realised that every skill I was relying on, I had built it during those three years of doctoral work.

You Learn to Think, Not Just to Know

The biggest myth about a PhD is that it fills your head with knowledge. It does, but that is not the point. What it really trains is how you think. How to slow down when a problem looks unsolvable. How to question your own assumptions before anyone else does. How to look at a complex situation and break it into parts that can actually be examined.

Running a startup constantly throws you into situations where there is no clear answer. The market changes. A technology assumption turns out to be wrong. A partnership you counted on falls through. Most people freeze or react. The PhD habit of structured thinking means I almost always go back to basics: what do I actually know, what am I assuming, and what do I need to find out?

That thinking discipline has saved me more times than any specific technical knowledge I carry.

Working Alone Without Falling Apart

A PhD is a deeply solitary journey. Your supervisor guides you, but the work is yours. The thinking is yours. The doubt is yours. Nobody is going to sit with you at two in the morning when you cannot make sense of your own data.

Entrepreneurship feels exactly the same way in the early years. You make decisions that nobody else fully understands. You carry uncertainty that you cannot always share with your team because you are supposed to be the steady one. You have to be comfortable with ambiguity without becoming paralysed by it.

The PhD taught me how to keep working when I had no external validation. It taught me that the absence of certainty is not a reason to stop. You form a hypothesis, you test it, you adjust, and you keep going. That is the startup loop. That is also the research loop.

Finding Knowledge Instead of Waiting for It

Before the PhD, I consumed knowledge the way most students do: someone put it in front of me, and I absorbed it. The PhD changed that completely. You quickly discover that nobody is going to hand you what you need. You have to hunt for it. You learn to search literature strategically, to identify which sources actually matter, to read critically instead of passively.

At FAVORIOT, we operate in a field that moves fast. IoT, AIoT, edge computing, platform architecture, developer ecosystems. The landscape shifts every eighteen months. If I waited for someone to teach me what I needed to know, I would always be behind. The PhD habit of self-directed learning means I am always reading, always synthesising, always asking what the current evidence actually says rather than what the conventional wisdom assumes.

Making the Complicated Simple

The viva examination is the moment every doctoral candidate dreads. You stand in front of a panel of experts and defend work that you have spent years on. They will probe every weakness. They will ask you to justify every assumption. And they will not accept jargon as a substitute for understanding.

To survive a viva, you have to be able to explain your work clearly. Not just to people who already know the field, but to people who are testing whether you truly understand it, or whether you are hiding behind technical language.

That skill matters enormously in a startup context. I cannot afford to lose a potential customer, partner, or investor because I explained our platform in terms only an IoT engineer would recognise. I have to be able to take a genuinely complex system and present it in a way that is meaningful to a city mayor, a business owner, or a university faculty head. The PhD forced me to develop that translation ability.

Defending What You Believe Without Being Defensive

There is a particular kind of confidence that the PhD builds. Not arrogance, which is brittle. A well-grounded, evidence-based confidence that allows you to say: I believe this is right, and here is why. And then to genuinely listen to the counterargument rather than dismissing it.

In business, this matters in negotiation, in pitching, in product decisions, and in those moments when someone more powerful than you tells you that your idea will not work. The PhD trains you to distinguish between a challenge that reveals a real weakness and a challenge that is simply pressure. You learn to stand your ground when the evidence supports you, and to revise when it does not.

Evaluating Other People’s Work Without Ego

A less obvious skill the PhD builds is the ability to critically evaluate the work of others. Peer review, literature critique, comparative analysis of methodologies. You develop a framework for assessing quality, identifying gaps, and recognising when something looks impressive but is actually incomplete.

This translates directly into how I evaluate technology vendors, potential partners, and market claims. The IoT industry is full of noise. Announcements that overstate capability. Case studies that obscure the real cost. Research that is funded to reach a particular conclusion. The critical evaluation skills from doctoral training mean I read all of it with appropriate scepticism, and I draw my own conclusions.

The Scroll Was Never the Point

I do not display my PhD certificate in my office. Not because I am not proud of it, but because I stopped thinking of it as the output a long time ago. The output is how I think, how I work, how I handle uncertainty, and how I communicate. Those are the things the doctorate actually produced.

If you are a PhD holder wondering whether the years were worth it beyond the academic world, I would say this: you were trained for exactly the kind of work that startups and leadership roles demand. The certificate opened some doors, yes. But the habits it built have kept me going long after the doors were open.

So the real question is not whether your PhD is relevant to business. The question is whether you have recognised and applied everything it actually gave you.

The Moment I Realised Something Was Broken Why IoT Projects Fail to Scale, and What Nobody Wants to Admit

A few years ago, I walked into a facility that had spent close to a million ringgit on an IoT deployment. Sensors were installed. A dashboard was running. The operations manager proudly pulled up a screen showing hundreds of data points flowing in, live, in real time.

I asked him one question: “What decision did you make yesterday based on this data?”

He paused. He looked at the screen. He called a colleague over. A few minutes passed.

“Let me check the system,” he finally said.

That pause told me everything. The IoT project was not broken in the way most people imagine broken things to be. The sensors worked. The connectivity was stable. The platform was live. But something deeper had failed, and it had failed quietly, in a way that nobody in the room had named yet.

That was the moment I realised the industry had a serious problem. Not a technology problem. A thinking problem.

We Built the Infrastructure. We Forgot the Purpose.

When I look back at two decades of IoT work, across MIMOS, through my time at REDtone IoT, and now building FAVORIOT, the pattern I keep seeing is the same one. Organisations invest in sensors, platforms, and dashboards. They point to these things as proof of digital transformation. And then they wait for the results to come.

The results rarely come. Not because the technology failed, but because nobody asked the hard question before deployment: what decision are we trying to make faster?

The sensor was never the goal. The data was never the goal. The goal was always a better decision, made quicker, based on something real. But somehow, between the vendor pitch and the project sign-off and the go-live celebration, that goal got buried under the excitement of the technology itself.

I have seen this across manufacturing plants, utility companies, logistics operations, and smart city deployments. The pattern is consistent. The language around it is always optimistic. “We now have full visibility.” “We have a live dashboard.” “We are data-driven now.”

But ask the operations team what decision they made differently last Tuesday because of that dashboard, and the room goes quiet.

I Started Calling It Operational Blindness

Recently, I wrote a more technical definition of this condition for IoT World. I called it Operational Blindness. The full definition is this: it is the condition where an organisation invests in IoT infrastructure, collects operational data at scale, yet still cannot make confident, timely decisions because the data never closes the loop to action.

The organisation sees numbers. It does not see clearly.

This is not a criticism of the people involved. The engineers built what they were asked to build. The analysts produced their reports. The executives attended the strategy sessions. Everyone did their job. But the system, as a whole, was never designed to produce decisions. It was designed to produce data. And data without a decision is just noise dressed up in a dashboard.

When I started naming this condition clearly, something interesting happened. People began recognising it in their own organisations. System integrators told me they had seen this in nearly every project they had ever delivered. Operations managers said they had felt it for years but had no language for it. That recognition mattered to me, because you cannot fix what you refuse to name.

Why IoT Projects Stop Scaling

The question I get asked most often is: why do IoT pilots succeed but never scale?

The answer, in my experience, comes back to the same broken loop every time.

A pilot project works because it is small, focused, and closely watched. Someone with authority and curiosity is paying attention. Decisions get made. Results are visible. Everyone celebrates and writes a press release.

Then comes the scale-up. More sensors, more sites, more data, more dashboards. And suddenly the thing that made the pilot work, which was a human being with a clear question and the authority to act on the answer, gets diluted across a larger organisation with competing priorities, legacy processes, and departments that were never part of the original pilot conversation.

The data multiplies. The decision-making does not.

By the time the system is running at scale, there are alerts nobody reads, dashboards nobody opens, and reports that go straight from the inbox to the archive folder. The operations team, who were not involved in designing the IoT system, have learned to work around it rather than with it. They trust their own experience more than a dashboard they did not ask for.

This is not stubbornness. This is rational behaviour when you design a system without designing it around the people who are supposed to use it.

The Three Things That Actually Need to Change

After watching this pattern repeat itself across too many projects, I have settled on three things that genuinely move the needle.

The first is contextualisation. Raw data is not insight. A temperature reading means nothing unless it is translated into operational language. Not “82 degrees Celsius.” But “Motor 4B has been running above its safe operating threshold for 40 minutes and failure is likely within six hours.” Context converts measurement into meaning. Without it, the dashboard is just wallpaper.

The second is integration into workflow. The insight has to reach the right person through the channel they already use, at the moment they can act on it. Not a report that lands at 9am about something that happened at 2am. Not a dashboard on a screen in a control room that the relevant decision-maker never visits. The signal has to travel to where the decision actually gets made.

The third is closing the loop. The system needs to know whether action was taken, what action, and what the outcome was. Without that feedback loop, the organisation cannot learn from its data. It cannot improve. It cannot measure whether the IoT investment is working. The loop has to close: from sensor to decision to outcome and back.

These are not exotic ideas. But they are consistently absent in organisations that are spending heavily on IoT and wondering why the ROI never materialises.

What I Tell Every Organisation That Comes to Me

When organisations approach FAVORIOT, one of the first things I ask is: what decision do you want to make better, and who in your organisation makes that decision today?

If they cannot answer that question, the deployment is not ready. Not because the technology is not ready. The technology is almost always ready. But the organisation is not yet clear on what it is trying to do with what the technology reveals.

That clarity is everything. It is the difference between a sensor deployment that scales and one that stays a pilot forever. It is the difference between a dashboard that changes operations and one that gets opened once a month during the management review.

We built FAVORIOT precisely because we kept seeing this gap in the market. Not a platform gap. A gap between what the data says and what the organisation decides to do about it. That is the gap we are working to close.

The Real Failure Mode

The IoT industry has spent years convincing organisations that more sensors, more data, and more dashboards are the path to operational excellence. They are not. They are the beginning of the path. What lies between that beginning and actual operational excellence is the hard work of turning data into decisions, and decisions into habits, and habits into results.

Most IoT projects fail to scale not because the technology lets them down, but because nobody built a bridge between the technology and the way the organisation actually makes decisions. The bridge is not a software feature. It is an organisational design question. And it is one that the IoT industry has, for too long, left for somebody else to answer.

If you are running an IoT deployment right now and it is not producing the results you expected, I would encourage you to ask one question before you spend another ringgit on more sensors or a better platform. Ask: what decision am I trying to make, and is my current system designed to help me make it?

The answer to that question will tell you more than any dashboard ever will.

I wrote a deeper technical take on this over at IoT World

Why Blogging When Nobody Was Reading Was the Best Business Decision I Ever Made

The long game of content, before it was called content marketing. How years of blogging to an empty room became the foundation of a business, a reputation, and a life’s work in IoT.

Let me ask you something. If you wrote something today and nobody read it, would you write again tomorrow?

Most people would say no. And I understand why. We live in a world that measures everything in likes, shares, reach and impressions. If the numbers don’t move, the assumption is that nothing is working. That you are wasting your time. That maybe you should stop.

I started blogging around 2009. At that time, IoT was barely a word in people’s vocabulary. Social media was young. And I was writing about telemetry, machine-to-machine communication, and connected devices… to what felt like an empty room. The analytics were humble, to put it kindly. Sometimes a few dozen readers a day. Sometimes less.

But here is what I know now that I didn’t fully appreciate then: that empty room was being recorded.

Planting Seeds Before Anyone Was Hungry

When I wrote in those early years, I wasn’t writing to go viral. I was writing because I had ideas that needed to get out of my head and onto paper. I was processing things I had seen in my career at MIMOS, CELCOM and UTM. I was trying to make sense of where technology was heading, and writing was how I thought through things properly.

There was no content calendar. No SEO strategy. No analytics dashboard I checked obsessively every morning. Just me, a laptop, and the belief that what I was writing about actually mattered, even if the world hadn’t caught up yet.

And then IoT exploded.

Between 2013 and 2015, the conversation around connected devices went from niche to mainstream almost overnight. And suddenly, people were looking for someone who understood the space, someone who had been thinking about it, writing about it, for years. That someone was already there. I had the archive. I had the track record. I had the voice.

That is when I understood what I had been doing all along. I had been building credibility on a platform I owned, quietly, consistently, before there was any social pressure to perform.

The Business That Content Built

When I co-founded FAVORIOT in 2017, I didn’t have to start from zero when it came to reputation. The blog had done years of work. Every article I had written about IoT platforms, smart city infrastructure, the challenges of data connectivity… those were bread crumbs that led people to me long before FAVORIOT existed as a company.

Investors ask you what makes you credible in your space. Customers ask why they should trust you over a larger competitor. Journalists ask why your opinion matters. What I could always point to was the body of work. Not a pitch deck, not a fancy website, but hundreds of posts that showed how I thought, what I believed, and how long I had been in this conversation.

You cannot fake a ten-year archive of consistent ideas.

That is what content does when you treat it seriously over a long period of time. It becomes proof. It becomes your reputation, made searchable and permanent.

When Nobody Is Watching, That Is the Real Test

I want to be honest about something. There were stretches of time when I wondered if I should just stop. The numbers were not rewarding. Nobody was sharing my posts to their thousands of followers. The inbox was quiet.

But I kept asking myself: do I believe what I am writing? Do I think this matters?

The answer was always yes. And that is what kept me going.

Looking back, I think the discipline of writing when nobody was watching was actually what built the quality of my thinking. When you write for an audience that doesn’t exist yet, you can’t rely on trending topics or viral hooks. You have to write something true. You have to be genuinely useful. You have to develop a real point of view, because there’s no performance to hide behind.

That pressure, the pressure of honesty over popularity, sharpened me in ways that no conference or workshop ever could.

The Compound Interest of Ideas

There is a concept in finance called compound interest. Money grows not just from what you put in, but from the returns building on themselves over time. Content works exactly the same way.

A post I wrote in 2011 about sensor networks brought me a speaking invitation in 2014. A talk in 2014 turned into a collaboration in 2016. A collaboration in 2016 led to a client for FAVORIOT in 2019. And so on. The chain of connections is rarely obvious when you are in the middle of it. You only see it clearly when you look backwards.

If I had stopped blogging in 2010 because the numbers were small, none of those downstream opportunities would have existed. The compound interest would never have started accumulating.

This is what people miss when they treat content as a short-term campaign. Content is infrastructure. You build it slowly, you maintain it, and it works for you even when you are sleeping, traveling, or running a startup that demands every hour of your attention.

What I Tell Founders and Professionals Who Ask Me About Content

People often ask me: “Mazlan, should I start a blog? Should I write on LinkedIn? Is it worth it?”

My answer is always the same. Ask yourself not whether anyone will read it today, but whether you believe what you are writing is true and useful. If yes, write it. Then write it again next week. And the week after that.

Stop thinking about content as a marketing activity. Start thinking about it as a thinking habit that happens to be public.

Because the day will come, and I genuinely believe this for anyone with real expertise and something honest to say, the day will come when someone will find that old post you wrote when nobody was looking, and it will open a door you never expected.

I have lived that story more times than I can count. And it all started with a decision to write anyway, even when the room was empty.

So let me leave you with this: what do you know right now, in your field, in your experience, that the world hasn’t fully caught up to yet? Are you writing it down? Are you sharing it?

Because someone out there is about to start searching for exactly what you already know.

Why I Want More Malaysian Students to Brag About Their Projects

Malaysian students build impressive projects but rarely share them publicly. Here is why that needs to change, and what students, lecturers, and institutions can do about it.

Have you ever built something and then kept it to yourself?

I have seen this happen too many times, and it bothers me more than I probably let on. A student spends weeks getting an IoT sensor to talk to a cloud platform, wires everything up, writes the code, stays up late debugging, and then… nothing. They submit the assignment, get their marks, and move on. Nobody outside that classroom ever knows the project existed.

That project deserved better. And honestly, so did the student.

The Culture of Hiding Our Work

I think we have a cultural habit in Malaysia of downplaying what we build. We are taught, from a young age, that showing off is rude. That humility means staying quiet. That if your work is good, people will somehow find out on their own.

But in the world we are living in today, that thinking is holding our students back.

I recently wrote about how some students in Malaysia are still learning IoT using Blynk, ThingSpeak or Favoriot, platforms that have genuinely served the community well over the years. My concern was not with the tools themselves, it was with the possibility that some students might complete their programmes without being exposed to more current, industry-relevant environments. The response to that piece was warm and it opened a much bigger conversation I want to continue here.

Because there is a second problem, and it sits right next to the tools issue. Even when students build something impressive, they rarely tell the world about it.

What “Bragging” Really Means

I want to be careful with my word choice here, because when I say I want Malaysian students to brag, I do not mean arrogance. I mean documentation. I mean visibility. I mean the courage to say, “I built this, and here is how I did it.”

In the global tech community, sharing your work is not showing off. It is how you contribute. It is how you invite feedback, attract collaborators, and build a reputation before you even graduate. Every time a developer writes a blog post about what they learned building a project, every time a student posts a short video demo on LinkedIn or YouTube, every time someone publishes their code on GitHub with a clear README, they are adding value to the community.

They are also building something that a CV alone cannot carry: a visible portfolio of thinking.

I Have Seen What Happens When Students Share

When students do step forward and share their work, remarkable things happen. I have seen students connect with mentors because someone stumbled across their blog. I have seen project ideas get picked up by companies that were looking for exactly that kind of solution. I have seen young engineers get job offers because a hiring manager found their GitHub repository.

And I have seen something perhaps more important: the student’s own confidence change. When you write about your project publicly, you are forced to explain it clearly. You start to understand what you actually built. You start to see what worked and, more importantly, what did not. That reflection is part of the learning that never shows up in an exam.

The Platforms Are Already There

There is no shortage of places where Malaysian students can share their work. LinkedIn is the obvious starting point, and yet I still see so many profiles with nothing more than a degree listing and a generic summary. Imagine if every final year IoT project at a Malaysian university had a proper LinkedIn post, with photos, a short explanation of the problem it solved, and the tech stack used.

GitHub is equally powerful. A well-documented repository says more about an engineer’s capability than almost anything else. Medium or WordPress and personal blogs remain excellent homes for longer technical write-ups. YouTube or TikTok are perfect for demo videos. Even short-form platforms have an audience hungry for genuine student work.

The tools to publish are free. The audiences are there. What is missing is the habit and the encouragement.

What Lecturers and Institutions Can Do

I want to be fair here: this is not entirely on the students. If we want a culture shift, we need to start from inside the institutions.

Imagine if project documentation and public sharing were built into assessment rubrics. Imagine if a student was graded not only on whether the sensor collected data correctly but also on whether they could explain the project clearly to a non-technical audience. Imagine if universities celebrated student projects the way they celebrate sports achievements.

Some institutions are already doing this, and I applaud them. But it needs to be more systematic. Students should graduate not just with technical skills but with the confidence and habit of communicating those skills to the world.

A Personal Challenge

If you are a student reading this, I want you to do one thing this week. Pick a project you are working on or have completed. Write about it somewhere public. It does not have to be perfect. It does not have to be long. Just write honestly about what you were trying to solve, what you used to solve it, and what you learned in the process.

That one post could be the start of something you cannot predict yet.

If you are a lecturer or a programme coordinator, think about how you can make sharing a formal part of the learning journey. Not as a burden, but as a skill that students will use for the rest of their careers.

Malaysia has genuinely talented engineering students. I have met many of them, worked alongside some of them, and been impressed by what they can build when given the space to do it. My concern is that too much of that talent is invisible to the industry because no one taught them that visibility is part of the job.

So tell me, if you could make one change to how Malaysian engineering education celebrates student work, what would it be?

Which IoT Platform Is Best for Students?

Every semester, I meet students who proudly show me their IoT projects. Some build smart dustbins. Some build flood monitoring prototypes. Some build temperature monitoring systems for cold-chain delivery. Some build smart parking models using cardboard, wires, sensors, and a lot of hope.

I always enjoy those moments because they remind me of why technology education is so powerful. There is something special about watching a student connect a sensor, upload data to the cloud, and suddenly realise, “Eh, I can actually build something useful.”

But there is one question I always ask.

“What IoT platform are you using?”

Most of the time, the answers are almost the same.

Blynk.

ThingSpeak.

Sometimes Firebase.

Sometimes a custom dashboard built in a hurry the night before presentation day.

I smile. Not because those platforms are wrong. They are not. In fact, they have helped many students start their IoT projects. But I also feel a small discomfort because the question is bigger than the platform name.

Is the student learning IoT as a complete system, or only learning how to make a project look alive for demo day?

That is where the real conversation begins.

For universities, choosing the right IoT platform is not only about which platform is free, popular, or easy to connect with Arduino. It is about what kind of graduates we want to produce. Do we want students who can display sensor readings on a graph, or do we want students who understand how real IoT systems collect data, manage devices, trigger alerts, support decisions, and grow beyond the classroom?

That is why the question “Which IoT platform is best for students?” deserves a practical answer.

Easy Is Good, But Too Easy Can Become a Trap

Let me say this carefully. Easy platforms are helpful, especially for beginners. When students are just starting, they need confidence. They need to see results quickly. If the first IoT experience becomes too painful, they may give up before they even understand the power of connected devices.

No lecturer wants to spend three weeks helping students debug Wi-Fi passwords, missing libraries, wrong ports, incorrect tokens, and mysterious error messages that seem to appear only at 2 a.m.

So yes, an easy learning curve matters.

But I also believe that if the platform only teaches the easiest path, students may never understand the deeper structure of IoT. They may know how to connect a sensor, but they may not know what happens after the data arrives in the cloud.

A proper IoT learning experience should expose students to the full flow of an IoT system:

Device connectivity
Data upload
Data storage
Dashboard design
Alerts and rules
APIs
User access
Security awareness
Analytics
Real-world use cases

When I look at student projects, I do not only look at whether the sensor works. I look at whether the student understands the purpose of the data.

What will you do with this data?

Who needs to see it?

When should the system alert someone?

Can this project survive outside the lab?

That is where many student projects become weak. They are good prototypes, but they are not always ready to become useful solutions.

“Students should not learn IoT only to pass a subject. They should learn IoT to solve problems they can see, touch, and understand.”
Dr. Mazlan Abbas

The Five Things Universities Should Look For

If a university wants to choose an IoT platform for students, I suggest looking at five practical areas. These are not complicated criteria, but they can make a big difference in how students learn and how lecturers teach.

1. Learning Curve

Students need a platform that does not frighten them on the first day. A student-friendly IoT platform must allow them to connect common devices such as ESP32, ESP8266, Arduino, Raspberry Pi, or industrial gateways without needing months of technical preparation.

The learning curve should be gentle at the beginning, but it should not remain too shallow forever. Students should be able to start with basic data upload, then gradually move into dashboards, APIs, alerts, analytics, and deployment thinking.

This is one reason platforms like Blynk and ThingSpeak became popular. They are easy to start. Tutorials are everywhere. Many students can follow YouTube videos and get something working within a few hours.

That is useful, especially for first exposure.

But universities should ask a second question.

After students learn the basics, where do they go next?

If the platform becomes too limited after the first prototype, students may hit a ceiling. They may know how to make a nice demo, but they may not learn how to design a proper IoT system.

For universities, the best platform should support both beginner learning and advanced learning. It should help students start simple, then grow deeper.

2. Project Readiness

A final year project should not feel like a toy. I know that sounds a bit harsh, but I have seen many projects where the idea is strong, yet the platform choice makes the project look small.

Imagine a student builds a flood monitoring system. The sensors work. The dashboard shows water levels. The graph looks nice. Then I ask a few practical questions.

Can the system alert the local council?

Can it support multiple locations?

Can it store historical data?

Can another user log in and monitor only one area?

Can the system be expanded into a city-level monitoring system?

Usually, the student pauses.

That pause tells me something. The student was taught how to connect devices, but not always how to think about real deployment.

A good IoT platform for students must help them move from prototype to project readiness. It should support features that resemble real-world IoT systems, not just simple charts.

These include:

Multiple devices
Multiple data streams
User access control
Event-based alerts
API connectivity
Data history
Dashboard sharing
Application-level use cases

This is where I see strong value in FAVORIOT for universities. FAVORIOT is not only a place for students to test sensors. It can help them build use cases that look closer to industry needs.

A student building a smart agriculture project should not stop at soil moisture graphs. The project should show irrigation alerts, crop condition trends, farm dashboards, and possible decisions.

A student building a smart building project should not stop at temperature and humidity readings. The project should show comfort levels, abnormal readings, equipment behaviour, and suggested actions.

That is real learning.

3. Local Support

This is the part many universities forget.

When a platform is based overseas, the tutorials may be good, but the support is usually generic. If a student faces a problem, they search forums, Reddit, YouTube, GitHub, or old blog posts. Sometimes they find the answer. Sometimes they get trapped in a maze of outdated libraries, broken links, and comments from 2017.

I have seen students spend days trying to fix small issues that could have been solved quickly with proper local guidance.

This is where local support becomes a serious advantage.

For Malaysian universities, having access to a local IoT platform means lecturers and students can ask questions, request training, discuss use cases, and get guidance in a context they understand.

Malaysia has its own education environment. We have our own final year project culture, our own industry needs, our own smart city challenges, and our own local problems that students can solve.

An IoT platform for Malaysian students should not only teach global examples. It should also expose them to local problems such as:

Flood monitoring
Cold-chain logistics
Campus energy monitoring
Smart agriculture
Vehicle tracking
Water quality monitoring
Smart city dashboards
Industrial machine monitoring

These are not imaginary textbook problems. These are problems students can see around them.

When support is local, the conversation becomes easier. A lecturer can say, “I want my students to build a smart campus project.” A student can ask, “How do I send ESP32 data to the platform?” A university can plan, “Can we train 50 students in one workshop?”

That kind of closeness matters.

“A local platform is not just about geography. It is about understanding the problems our students are trying to solve.”
Dr. Mazlan Abbas

4. Tutorials and Teaching Materials

Students love tutorials. Lecturers love tutorials even more because nobody wants to rebuild the same lesson from scratch every semester.

A good IoT platform for universities must have step-by-step materials that students can follow. The documentation should not only be written for professional developers. It should also guide beginners from zero to a working project.

Useful teaching materials should include:

Device setup
Coding examples
Platform account setup
Data upload steps
Dashboard creation
Alert configuration
Common errors
Sample use cases
Project ideas
Extension tasks for advanced students

The best tutorials do not only show students what to click. They explain why each step matters.

This is important because IoT is not only a technical subject. It is a systems-thinking subject. When students learn IoT properly, they begin to understand the relationship between physical devices, networks, cloud platforms, data, people, and decisions.

A tutorial should not only say, “Copy this code.”

It should make students think.

Why do we collect this data?

How often should the device send data?

What happens if the network fails?

Who owns the data?

How secure is the device?

These questions are becoming more important, especially when IoT devices are connected to buildings, cities, factories, farms, and public infrastructure. Students should learn this early, not after they graduate.

5. Showcase Value

Universities need platforms that help students showcase their work clearly and professionally. This may sound like a small thing, but it is not.

A student project with a messy dashboard can make a good idea look weak. A student project with a clean dashboard, proper data flow, alerts, use-case explanation, and a working demo can impress panels, industry partners, and potential employers.

The platform should help students explain their project in a way that makes sense.

Not only:

“This is my sensor.”

But also:

This is the problem.
This is the data I collect.
This is how the platform receives it.
This is the dashboard.
This is the alert.
This is the decision that can be made.
This is how it can be expanded.

That is the difference between a lab experiment and an industry-ready concept.

For universities, showcase value is important because good student projects can become competition entries, research prototypes, industry collaboration demos, grant proposal examples, teaching lab assets, open-day exhibits, or even startup ideas.

A good IoT platform should help students tell the story behind the data.

Comparing Common IoT Platforms for Students

Let us look at the platforms students often use and where each one fits.

Blynk

Blynk is popular because it is beginner-friendly. Students can create mobile dashboards quickly and connect devices without too much difficulty. For early learning, it works well.

Its strength is simplicity.

Its weakness is that students may become too focused on app display rather than full IoT architecture. It is good for quick prototypes, but universities may need something broader when teaching project readiness, multi-user systems, local industry use cases, and longer-term data thinking.

ThingSpeak

ThingSpeak is also popular in universities. It is useful for basic sensor data logging and graphing. Many students use it because tutorials are easy to find.

Its strength is simple data visualisation.

Its weakness is that students may treat IoT as “sensor plus graph.” That is only one piece of the puzzle. For deeper learning, students need more exposure to alerts, roles, APIs, dashboards, application context, and deployment design.

Firebase

Firebase is powerful for app developers. Students who build mobile apps may find it useful for storing and syncing data.

Its strength is app development support.

Its weakness is that it is not an IoT-specific platform by default. Students may need to build many IoT-related features themselves. This can be useful for advanced software students, but challenging for beginners who are still learning sensors, networks, and data formats.

ThingsBoard

ThingsBoard is a strong open-source IoT platform with many features. It can be useful for advanced students and lecturers who want control and flexibility.

Its strength is depth.

Its weakness is setup and maintenance. Universities need technical confidence to install, configure, teach, and support it. For some classes, that may be too heavy. For postgraduate or advanced labs, it can be a good option.

FAVORIOT

FAVORIOT sits in a practical position for Malaysian universities. It gives students a platform that can be used for learning, prototyping, dashboards, alerts, APIs, and real use-case development. It is also local, which means the support, training, examples, and industry context can be closer to what Malaysian students and lecturers need.

Its strength is the balance between learning and project readiness.

Students can start with basic data uploads, then grow into stronger projects that reflect real use cases. Lecturers can use it for teaching. Universities can use it for labs, workshops, competitions, and industry collaboration.

For students, the platform becomes more than a place to display data. It becomes a place to understand the flow from device to decision.

That is the real lesson.

Why FAVORIOT Makes Sense for Universities

I do not believe universities should choose an IoT platform only because it is local. That is not a strong enough reason. A platform must earn its place in the classroom by being useful, practical, teachable, and relevant.

This is where I believe FAVORIOT can contribute.

1. It Helps Students Learn the Full IoT Flow

Students can understand how devices send data to the cloud, how data is stored, how dashboards are created, and how alerts can be triggered.

This teaches IoT as a complete system, not as a loose collection of sensors.

2. It Supports Real Project Ideas

FAVORIOT can support many student project themes, including:

Smart agriculture
Smart campus
Smart building
Smart city
Flood monitoring
Cold-chain monitoring
Vehicle monitoring
Environmental monitoring
Energy monitoring
Industrial machine monitoring

This gives lecturers flexibility across engineering, computing, data science, agriculture, and business faculties.

3. It Gives Local Relevance

Students can build projects that solve problems they see in Malaysia. That makes learning more meaningful.

A student in Kelantan can build a flood alert project. A student in Johor can build a smart factory monitoring project. A student in Selangor can build a campus energy dashboard. A student in Sabah can build a remote environmental monitoring system.

The platform becomes a bridge between classroom learning and local problem-solving.

4. It Can Support Training and Workshops

Universities often need structured learning sessions. FAVORIOT can support this through training, tutorials, and guided project development. This is helpful for lecturers who want their students to move faster without spending too much time solving repeated technical issues.

When students receive proper guidance, they can focus on the project idea, data logic, and outcome.

Of course, wiring errors will still happen. Somewhere in every IoT lab, one jumper wire is always guilty. That is part of the learning experience.

5. It Helps Students Think Like Problem-Solvers

This is the most important part.

IoT is not about gadgets. IoT is about solving problems using connected data.

When students use a platform that supports dashboards, alerts, analytics, and real use cases, they begin to ask better questions.

Not only, “Can my sensor work?”

But, “What decision can this data support?”

That is the shift universities should encourage.

“The best student project is not the one with the most sensors. It is the one that helps someone make a better decision.”
Dr. Mazlan Abbas

So, Which IoT Platform Is Best for Students?

The honest answer is that it depends on the learning goal.

If the goal is to introduce very basic IoT in one lab session, Blynk or ThingSpeak may be enough. If the goal is app development with some connected data, Firebase may be suitable. If the goal is advanced open-source platform control, ThingsBoard can be useful.

But if the goal is to help students build IoT projects that are easier to teach, locally supported, project-ready, and closer to real-world use cases, then FAVORIOT deserves serious attention from Malaysian universities.

Not because it is local only, but because it helps students move from “my sensor sends data” to “my project can support decisions.”

That is the difference.

Universities Should Stop Teaching IoT as a Demo-Only Subject

This is my concern. Too many students treat IoT as a final year project checkbox.

Buy sensor.

Connect microcontroller.

Send data.

Show graph.

Present.

Graduate.

Then what?

The world does not need more abandoned prototypes sitting inside lab cupboards. The world needs graduates who can design connected systems that work outside the classroom.

Universities have a huge role to play here. They can choose platforms that stretch students a little further. Not too much until they drown, but enough to make them stronger.

A good IoT platform should teach students how to think about reliability, security, data quality, user needs, deployment, maintenance, alerts, decisions, business value, and social impact.

That is how we prepare students for the real world.

My Final Thought

When a student builds an IoT project, I do not want them to say only, “My dashboard can show temperature.”

I want them to say, “My system can help a cold-chain operator detect temperature problems before goods are damaged.”

I want them to say, “My system can help a campus reduce energy waste.”

I want them to say, “My system can help farmers monitor soil conditions.”

I want them to say, “My system can help local councils respond faster to floods.”

That is when IoT becomes meaningful. That is when a student project becomes a story of impact.

So, which IoT platform is best for students?

The best platform is the one that helps them learn fast, build properly, get support, showcase confidently, and think beyond the demo.

For many universities in Malaysia, I believe FAVORIOT can be that platform. It gives students a practical path from classroom learning to real-world IoT thinking.

And maybe the next great smart city, smart agriculture, or smart campus idea will not come from a giant corporation. Maybe it will come from a student who started with one sensor, one platform, one dashboard, and one simple question.

What if this small project can solve a real problem?

That is the kind of question worth building for.

What do you think? Should universities continue using the usual IoT platforms, or is it time to introduce students to more locally relevant and project-ready platforms like FAVORIOT? I would love to hear your thoughts in the comments.

Why Malaysia Must Stop Treating IoT as a Final Year Project Toy

Every year, I meet students who proudly show me their IoT projects, and I can almost predict what will appear on the table before the demonstration even begins. There will be a sensor, a microcontroller, a few wires, a mobile app, and a dashboard showing temperature, humidity, distance, motion, water level, or some other familiar reading. The student will explain how the system works, the lecturer will nod, the evaluator will ask a few questions, and everyone will feel relieved when the demo runs without embarrassment. For a few minutes, the project looks alive, impressive, and full of promise.

Then, after the presentation, something strange happens. The device is switched off. The components are kept in a box. The dashboard is no longer opened. The video may be uploaded somewhere, but after that, the whole thing quietly disappears into the graveyard of final year projects. Nobody continues collecting data. Nobody studies the pattern. Nobody asks whether the system can be deployed in a real site. Nobody asks whether the idea can be improved, commercialised, or connected to an actual industry problem. It simply ends where it started, on the classroom table.

Each time I see this, I ask myself, “Is this what IoT has become in Malaysia? A temporary demo to pass an assessment?”I am not saying this to mock students. Far from it. I respect students who spend sleepless nights trying to make their projects work. I know the pain of troubleshooting something that refuses to behave when people are watching. Anyone who has handled hardware knows that sensors have a strange sense of drama. They work perfectly at midnight, then suddenly become shy during the presentation.

But my concern is bigger than the student project itself. My concern is that Malaysia has treated IoT for too long as something small, experimental, academic, and temporary, when in reality, IoT should already be part of how we manage our cities, farms, buildings, factories, transport systems, utilities, campuses, and public services. IoT is not a toy. It is infrastructure intelligence. Malaysia must start treating it that way.

The Problem Is Not the Students

Let me say this clearly. The problem is not the students. Many of them are creative, hardworking, and eager to learn. They work with limited budgets, limited time, borrowed components, weak WiFi, and sometimes unclear project guidance. They do what they can with what they have, and for that, they deserve encouragement. The issue is not their effort. The issue is the way we have framed IoT in education, industry, and sometimes even government projects.

For many years, IoT has been taught and demonstrated as a sensor to dashboard exercise. Connect the device. Send the data. Show the graph. Done. That is useful as a starting point, but it is a dangerous place to stop. When IoT is presented only as a dashboard project, students naturally assume that the job is complete once data appears on the screen. They do not always ask what happens after the data appears, who will use it, what decision it supports, what action must follow, what happens when the device fails, or whether the system can survive outside the classroom.

Maybe we taught them to build the wrong finish line, I thought to myself. The finish line should not be “the data appears.” The finish line should be “someone makes a better decision because the data appeared.” That is the difference between a demo and a real solution.

A Sensor Is Not a Solution

One of the biggest misunderstandings about IoT is the belief that once a sensor is connected, the problem is solved. This is like saying a thermometer cures fever. A thermometer does not cure anything. It only tells you something is wrong. What matters next is the interpretation, the decision, the treatment, the follow up, and the responsibility of the person who acts on that reading.

IoT works the same way. A temperature sensor in a cold room does not protect vaccines or food by itself. It only provides signals. The real value begins when the system detects abnormal readings, alerts the right person, records the evidence, helps the operator respond quickly, and supports compliance reporting. A water level sensor near a river does not prevent flooding by itself. It becomes useful only when the data is trusted, the alert reaches the correct agency, the response process is clear, and residents receive warnings early enough to act. A vibration sensor on a machine does not prevent downtime by simply producing numbers. It becomes useful when those numbers are analysed, compared against patterns, and linked to maintenance decisions before the machine fails.

That is why I always say the dashboard is not the destination. The dashboard is only the window. The real question is what we do after looking through that window. If we see a problem and nobody acts, then the system has failed quietly. It may still look beautiful on a screen, but it has not changed anything in the real world.

Malaysia Has Too Many Real Problems for IoT to Remain in the Lab

Malaysia does not lack problems that need better visibility. We have floods that still catch people by surprise, buildings wasting energy after office hours, farms that need better monitoring of soil, water, and climate, logistics operators that must prove temperature compliance, factories that want to reduce downtime, public facilities that need better maintenance, and local councils trying to manage waste, traffic, drainage, parking, lighting, and public complaints with limited manpower.

These are not imaginary classroom problems created for a semester project. These are real operational issues that cost money, time, trust, and sometimes safety. Yet many organisations still operate using manual checks, WhatsApp updates, delayed reports, and reactive decisions. Someone must visit the site. Someone must take a photo. Someone must key in data. Someone must send a message. Someone must wait for approval. By the time the information reaches the decision maker, the problem may already have grown bigger.

This is where IoT should become serious. IoT should help Malaysia see earlier, respond faster, and plan better. It should reduce blind spots in operations. It should help people move from guessing to knowing, and from reacting late to acting early. But that will not happen if IoT continues to be treated as a decorative technology for exhibitions, competitions, and one off prototypes.

A smart city is not built from disconnected demos. A smart factory is not built from one sensor reading on a laptop. A smart farm is not built from a project that stops after the student graduates. Real IoT needs continuity, ownership, maintenance, platforms, training, security, and people who care about the outcome, not just the device.

The Final Year Project Mindset Is Too Small

Final year projects are important. They help students learn, test ideas, make mistakes, and gain confidence. I fully support that. But when the final year project mindset becomes the national mindset, we have a problem. The final year project mindset usually asks whether the system can work for the demo, whether the data can appear on a screen, whether the poster looks good, whether the report can be submitted, and whether the panel can be impressed.

A national IoT mindset asks much harder questions. It asks whether the system can solve a real problem, work outside the lab, scale to multiple sites, be maintained for years, protect the data, support daily decisions, reduce cost, reduce risk, improve service delivery, and continue after the project team leaves. These are the questions Malaysia must start asking more seriously.

We cannot keep celebrating prototypes while ignoring adoption. We cannot keep launching pilots that never move into operations. We cannot keep confusing activity with progress. There is a big difference between building something and making it useful. Malaysia has built many IoT things. Now we must make them useful.

Stop Asking Only “Can It Connect?”

One of the most common questions in IoT is, “Can it connect?” Of course, connectivity matters. Without connectivity, the device becomes an island. But connectivity alone is not enough. A connected device that sends meaningless, unreliable, unsecured, or unused data is not a success. It is only a noisy device.

The better questions are more practical. Can it connect reliably? Can it recover when the network fails? Can it send clean and meaningful data? Can it protect that data? Can it trigger the right response? Can it help the user decide faster? Can it reduce manual work? Can it be supported for years, not weeks?

This is where many IoT projects become weak. They focus too much on the first connection and not enough on long term usefulness. I have seen systems where the data appears nicely on the dashboard, but nobody knows what threshold should trigger an alert. I have seen dashboards that look impressive, but no department has agreed who is responsible when something abnormal happens. I have seen projects where the devices were installed, but after a few months, nobody checked whether the data was still accurate.

That is not IoT maturity. That is technology theatre. It looks good from far, but when you stand closer, you realise the system has no operational backbone.

IoT Must Belong to the People Who Own the Problem

Another mistake I often see is when IoT is handed entirely to the technical team. The technical team can connect devices, configure networks, set up platforms, and build dashboards. That part is important. But they cannot define business value alone. Technology people can build the pipe, but the people who face the pain must define what should flow through it and what should happen after that.

If the project is about energy, the energy manager must be involved. If the project is about farming, the farm operator must be involved. If the project is about city services, the relevant local council department must be involved. If the project is about machine maintenance, the maintenance team must be involved. If the project is about cold chain, the operations and compliance teams must be involved.

IoT fails when it becomes a technology project without an operational owner. The people who feel the pain must be part of the design from day one. They know the messy details. They know where problems happen. They know which alerts matter and which alerts will be ignored. They know what kind of information is useful at 8 a.m. on a busy Monday, and what kind of dashboard nobody will ever open after the vendor leaves.

Who owns the pain? That is the question I often ask myself. Because the person who owns the pain should also own the outcome.

Cybersecurity Must Be Built In From the Beginning

When we connect more devices, we also create more openings. This is the side of IoT that many people do not like to talk about during cheerful demos. A connected device can provide visibility, but a poorly secured device can also become a weak door into a larger system. That is why IoT cannot be treated casually, especially when it starts moving into buildings, factories, campuses, farms, utilities, transport systems, and critical services.

Every IoT project should ask security questions from the beginning. Who can access the device? How is the device authenticated? Can the firmware be updated safely? Can the data be altered? Can someone fake a reading? Can unusual device behaviour be detected? What happens when the device is stolen, damaged, or hijacked? How do we separate normal failure from suspicious activity?

AI can help detect abnormal patterns and support faster response, but AI is not a magic shield. A weak IoT architecture with AI added later is still weak. It is like putting a fancy lock on a wooden door that is already cracked. Security must be designed into the system, not sprinkled on top after the project becomes popular.

This is why I often remind people that IoT is no longer just an engineering conversation. It is also a cybersecurity conversation, a governance conversation, and a trust conversation. Once a device is connected to a larger environment, it becomes part of a bigger responsibility.

The AI Conversation Makes IoT More Important

Today, everyone wants to talk about AI. AI is in every conference, proposal, workshop, policy conversation, and almost every company profile. Sometimes it feels as if even the office pantry will soon claim to have an AI powered coffee strategy. I understand the excitement because AI is powerful, but we must remember something very basic.

AI needs data. Not just old data sitting in spreadsheets, but live, real world, operational data. Data from machines, buildings, vehicles, rivers, farms, cold rooms, energy meters, production lines, and public facilities. Where does that data come from? It comes from IoT.

IoT is the bridge between the physical world and digital intelligence. Without IoT, many AI systems are left guessing from outdated, incomplete, or manually entered information. This is why I find it strange when people say IoT is old news and AI is the future. To me, that is like saying the brain is important, but the nerves are no longer needed.

AI may be the brain, but IoT is part of the nervous system that senses what is happening in the real world. If the nerves are weak, the brain receives poor signals. When the signals are poor, the decisions will also be poor. Malaysia cannot build serious AI for real world operations while treating IoT as an afterthought. The two must grow together.

We Need Local Capability, Not Permanent Dependency

There is another issue that we must discuss honestly. Many students and developers in Malaysia still use overseas IoT platforms by default. They use them because tutorials are easy to find, examples are everywhere, and seniors have used them before. I understand this completely. When a student is rushing to complete a project, the easiest path becomes the most attractive path.

But at a national level, we must ask a bigger question. Do we want to remain only users of other people’s platforms, or do we want to build our own capability as well? This is not about rejecting global platforms. There is nothing wrong with learning from global tools. They have their strengths, and they serve many use cases well. But if every university, student, agency, and company only learns using external platforms, then our local ecosystem will remain thin.

We will produce users, not builders. We will produce dependency, not confidence. We will produce projects, not capability. Malaysia needs local platforms, local examples, local documentation, local support, local case studies, and local success stories. We need students who can say they built something using a Malaysian IoT platform like Favoriot, and that project can be extended into a real solution. We need lecturers who can expose students to platforms that understand local industry needs. We need system integrators who can build faster because they are not starting from zero every time. We need government and industry buyers who care about long term ownership, data governance, and local support.

This is how an ecosystem grows. Not through slogans, but through usage, trust, and repeated real deployments.

Pilots Must Stop Dying After the Launch Photo

Malaysia loves pilot projects. We launch pilots with banners, speeches, handshakes, group photos, and sometimes a nice gimmick where someone presses a button on stage. I have nothing against pilots. A pilot is useful when it helps us learn, reduce risk, and prepare for wider adoption. But a pilot becomes wasteful when it ends at the launch photo.

Too many IoT pilots do not answer the most important questions. What did we learn? Did the system solve the original problem? Who used the data? What decision changed? What cost was reduced? What risk was avoided? What process improved? What failed? What should be changed before scaling? Can this model be repeated in another location?

If we cannot answer these questions, then the pilot was not a learning exercise. It was a performance. A good pilot must have a path to adoption. It must be designed with scale in mind, even if the first version is small. It must include training, maintenance, user feedback, support, and measurable outcomes.

Small pilots are fine. Small thinking is not.

Universities Must Raise the Standard

Universities have a major role to play because they are not just producing graduates. They are shaping how the next generation understands technology. If IoT is taught only as a technical connection exercise, students will graduate thinking that connectivity is the main achievement. But if IoT is taught as a complete system, students will begin to think like solution builders.

They will understand sensors, communication, platforms, data quality, cybersecurity, analytics, user needs, operations, and business value. They will learn that the real world is not as friendly as the lab. In the lab, the WiFi usually works. In the field, the signal disappears when you need it most. In the lab, the power supply is stable. In the field, someone may unplug the adapter because they need the socket. In the lab, the sensor is clean. In the field, it faces heat, rain, dust, insects, vibration, curious hands, and sometimes people who have no idea why the device is there.

This is why students need exposure to real problems. Let them work with local councils, farms, factories, hospitals, logistics companies, buildings, and campuses. Let them talk to actual users. Let them understand frustration, constraints, budgets, maintenance, and accountability. A student who has seen real operational pain will build differently. That student will not simply ask whether a value can appear on a dashboard. That student will ask whether the data can help someone act before the problem becomes worse.

That is the kind of graduate Malaysia needs.

Government and Industry Must Demand Outcomes

The responsibility is not only on universities. Government agencies, local councils, GLCs, enterprises, and private companies must also change how they buy and evaluate IoT. Do not buy IoT because it sounds modern. Do not install sensors because other cities have sensors. Do not ask for dashboards because dashboards look impressive in meeting rooms. Ask for outcomes.

Before starting an IoT project, the buyer should ask what decision the system supports, who will respond to alerts, how success will be measured, how the system will be maintained, how the data will be protected, and how the project will continue after year one. They should also ask what happens when the vendor is no longer standing beside the dashboard during a demo. That is where the truth normally appears.

This is where procurement must become smarter. If the tender only asks for devices and dashboards, the result will be devices and dashboards. If the tender asks for operational outcomes, response workflows, security design, data ownership, maintenance, training, and measurable impact, then vendors will have to design more serious solutions.

Malaysia must stop buying gadgets and start buying operational intelligence.

IoT Is a Long Term Commitment

Many people underestimate what happens after installation. The project is not finished when the devices are installed. In many ways, that is when the real work begins. Devices need monitoring. Sensors need calibration. Users need training. Alerts need tuning. Dashboards need improvement. Data needs review. Network issues need troubleshooting. Reports need refinement. Processes need updating. Budgets need planning.

IoT is not a one day event. It is a living system. This is why the final year project mindset is dangerous when applied to real deployments. In a student project, the goal is often to complete and submit. In a real deployment, the goal is to operate, improve, and sustain.

The system must still work after the excitement fades. It must still provide value when nobody is clapping. It must still help the user on an ordinary Tuesday morning when something goes wrong and people need answers fast. That is the true test of IoT. Not the demo. The ordinary day.

From Prototype Pride to Operational Discipline

We should still be proud of prototypes, but we should not stop there. A prototype is a question. A real deployment is an answer. A prototype asks whether an idea can work. A real deployment answers by showing how it improves operations.

Malaysia has enough prototypes. What we need now is discipline. We need discipline in defining problems, designing systems, securing devices, managing data, training users, measuring outcomes, scaling what works, and stopping what does not. This is not glamorous work. It may not look as nice as a launch ceremony, but this is where real progress happens.

Behind every useful IoT system, there are people doing boring but necessary things. They check data quality, fix device issues, improve alerts, train staff, review reports, update workflows, and make sure the system continues to serve the people who depend on it. That is how IoT becomes part of daily operations. Not through magic. Through discipline.

The Malaysia I Want to See

I want to see Malaysian students building IoT projects that do not disappear after final presentations. I want to see lecturers guiding students toward real world problems, not repeated versions of the same safe ideas. I want to see universities using local platforms and building stronger links with industry. I want to see local councils using IoT to manage floods, waste, parking, lighting, and public facilities more intelligently.

I want to see factories using IoT to reduce downtime and improve maintenance. I want to see farms using IoT to improve productivity and reduce waste. I want to see buildings using IoT to cut energy costs and support sustainability goals. I want to see local system integrators building reusable solutions instead of starting from scratch for every customer. I want to see Malaysia take IoT seriously as a foundation for AI, smart cities, and national competitiveness.

Most of all, I want us to stop underestimating our own ability. We have the talent. We have the problems. We have the technology. We have local platforms. We have universities. We have industries that need better data. What we need now is the courage to connect all of these pieces into something bigger.

IoT Was Never Meant to Stay on the Classroom Table

The blinking LED was never the final achievement. It was only the first sign that something could be connected. The real achievement comes when that connection creates awareness, that awareness leads to action, and that action improves lives, services, businesses, and national capability.

Malaysia must stop treating IoT as a final year project toy because the world has already moved on. IoT is now part of infrastructure, cybersecurity, AI, sustainability, smart cities, industrial growth, and operational decision making. If we continue treating it as a small student experiment, we will produce many demos but too few outcomes. That would be a waste, not just of components or project budgets, but of potential.

Maybe the problem is not that Malaysia lacks IoT projects, I thought to myself. Maybe the problem is that too many of them are never allowed to grow up.

So let us allow them to grow. Let us move IoT from the classroom table to the operations room, from assignment marks to measurable outcomes, from dashboards to decisions, from prototypes to platforms, and from toys to infrastructure. Malaysia does not need more blinking LEDs to prove that we can connect things. Malaysia needs connected systems that help us think, act, and build better.

What do you think? Are we ready to treat IoT as serious national capability, or are we still too comfortable celebrating prototypes that never leave the lab? I would love to hear your thoughts in the comments.

Life on Stage: The Journey of a Speaker Who Never Asked to Become One

Mazlan Abbas · Founder Reflections · 2025

Life on Stage: The Journey of a Speaker Who Never Asked to Become One

From a single university talk to hundreds of stages across four continents

If someone asks me, “Mazlan, how many times have you given a presentation?” I’ll just smile. Not out of arrogance. But because I honestly can’t remember. What I do remember is that it didn’t start because I wanted to be a speaker. It started because I had something worth sharing, and people began to listen.

This isn’t a post about achievements. It isn’t a resume list. This is a story about what happened between all those numbers. What I felt standing in front of a room. What I carried back to the hotel. What changed in me, one presentation at a time.

Every time I stepped onto a stage, I asked myself the same question: am I here to teach, or am I here to learn?

The answer was always both.

The expert in anything was once a beginner who refused to stop showing up.

Helen Hayes

2013 – 2015 · Seeds Planted

In the early days, I was talking about something most people hadn’t fully grasped yet. IoT. Internet of Things. Back then, when people heard “IoT” their faces went blank. I remember giving a talk at UTHM with barely any questions at the end… but I knew the seeds had to be planted early. Someone needed to walk away thinking, “Huh, that’s actually interesting.”

When I delivered the keynote at the International Conference on Soft Computing in Data Science 2015 at Pullman Putrajaya, I could feel the shift. People were beginning to take it seriously. “Internet of Things and Big Data: The Perfect Marriage” was the title I chose myself. Not for the glamour, but because I genuinely believed these two things were made for each other.

2016 – 2017 · The Stage Gets Bigger

These years were relentless. Singapore. Sydney. Jakarta. Kuwait. I was standing in front of people from different countries, talking about smart cities, IoT ecosystems, how technology could reshape the way we live. At the Smart Cities Expo World Forum in Sydney 2016, my message was about citizen engagement. Not just infrastructure. Not just data. People.

That’s what everyone always misses. Technology can be brilliant. But if citizens don’t engage, a smart city is just a smart facade.

Kuwait, Singapore, Sydney, Jakarta… not to collect passport stamps. But to bring back perspectives that no office or lab could ever give you.

2017 was also the year I realized something important: I was no longer just an academic or an industry guy. I had become a bridge. Universities invited me. Industries listened. Government agencies started calling. And with that position came a responsibility I didn’t take lightly.

2018 – 2019 · Local to Global

There is one moment I will never forget. Keynote at the Smart Cities Global Technologies and Investment Summit in Algiers, Algeria, June 2018. A single Malaysian standing before an audience from North Africa, talking about the IoT innovation ecosystem. There was a flutter of nerves. But far more than that, there was exhilaration.

Because what I brought to that room wasn’t just theory. It came from the real, lived experience of building Favoriot. From every failure and every struggle that no one in the audience could see behind the clean, polished slides.

2019 was even more intense. IIUM. UTeM. UCSI. Polytechnics. Borneo. Istanbul. Sarawak. Topics widened too, from IoT for the Construction Industry, to IR 4.0 for MINDEF. I had stopped talking about IoT in a narrow box. I was talking about transformation. About the future. About how human beings need to adapt or be left behind.

We cannot solve our problems with the same thinking we used when we created them.

Albert Einstein

2020 – 2021 · The World Changes, So Does the Stage

COVID arrived. Everything moved online. Webinars became everyone’s new language. And I, someone who had grown used to feeling the live energy of a room, had to adapt. Talking to a camera. Q&A via chat box. A different kind of connection, but the same message.

What I valued most during that period was that people still wanted to listen. MaGIC, UTHM, UiTM, USIM, ADTEC, Politeknik Mersing, UKM… the 2021 webinar list was endless. That told me something: curiosity about technology doesn’t stop, even when the world is falling apart.

I gave a talk on “Jobs That Don’t Exist Yet.” Halfway through, it hit me: several of the things I do today didn’t exist ten years ago either.

The talk at MMU on “The Entrepreneurship Journey of Pre and Post Covid-19” was when I felt closest to my audience. Not because the slides were great. But because the story was real. Favoriot faced the same tsunami. We survived, but we carry the scars.

2022 – 2023 · Deeper and More Specific

By 2022, the nature of invitations had shifted. People no longer called to hear a general talk about IoT. They wanted specifics. ESG. Manufacturing. Financial services. TVET. Smart cities. The energy sector.

That is a sign of maturity, both in the industry and in myself. Penang, Smart Factory Conference, October 2023: ESG in Manufacturing. That same month, ICSIMA at Tamu Hotel: Smart Measurement with IoT. Then WCIT 2023: 10 Ways IoT Can Drive ESG Compliance.

Some weeks had two or three events. The body was exhausted. But the mind couldn’t stop.

And then there was the keynote I gave with the most heart: ICoICT 2023, August that year. “Humanizing IoT: Placing People at the Centre of Technology.” That wasn’t just a title. That was my philosophy. After years of talking about devices, sensors, and connectivity, I needed to remind the room, and myself, that at the end of every data pipeline there is a human being. A mother who wants to know her child is safe. An elderly man who wants to live with dignity at home. A farmer who needs to know tomorrow’s weather.

2024 – 2025 · Rethinking Everything

The DSA 2024 talk on secured IoT communications marked my entry into the cybersecurity conversation in a more serious way. Smart applications demand secure communications. That is not optional. It is a requirement.

UTAR Talk, November 2024, “The Ultimate Things about IoT.” The audience was energetic students, full of questions. The best one: “Sir, in ten years, will IoT still be relevant?” I smiled. In ten years, IoT will be like electricity. You won’t see it, but you’ll need it everywhere.

And most recently, WCSC 2025. “From Smart to Regenerative: Rethinking Urban Transformation through IoT.” This represents a major shift in my thinking. Smart cities are no longer enough. We need cities that can heal themselves, adapt on their own, regenerate. Not just connected, but alive in a deeper sense.

Through all of it, I saw one common thread: I was never really talking about technology. I was talking about the people who use it, and the people who get left behind when they don’t.

At the National Address Conference, July 2025 at WTCKL, I carried a message about Technology and Data Sovereignty. This wasn’t purely a technical talk. It was about the future of a nation. Whoever holds the data holds the power. Malaysia must understand this.

200+Presentations

12+Countries

12Years on Stage

The purpose of life is not to be happy. It is to be useful, to be honorable, to be compassionate, to have it make some difference that you have lived and lived well.

Ralph Waldo Emerson

From 2013 to 2025. Hundreds of times standing on a stage. From UTHM to Algeria. From a small webinar to an international keynote. From speaking in front of 20 students to thousands of delegates.

What do I bring back every single time? Not the applause. Not my name in a programme book. Not a selfie with the organizer.

What I bring back are the questions people ask after the session. Simple questions, but deep ones. Questions that remind me why I started doing all of this in the first place.

This journey isn’t over. The next stage is already waiting. And I still have a great deal left to say.

Because technology keeps moving. And people need to move with it.

The Day a Small Malaysian Company Stood Shoulder to Shoulder with Microsoft, PwC and Mastercard

FAVORIOT has been named one of the Top 50 Thought Leading Companies on Innovation 2026 by Thinkers360, alongside Microsoft, PwC, Mastercard, and Tata Consultancy Services. As the only Malaysian company on this global list, here is what that recognition truly means.

When was the last time you sat quietly and let something truly sink in?

I had one of those moments recently. I was scrolling through the Thinkers360 announcement for their 2026 Top 50 Thought Leading Companies on Innovation, half-expecting to scan through the usual suspects and close the tab. Then I saw it. FAVORIOT. Right there in the list. Alphabetically sandwiched between the giants of the global technology and consulting world.

I had to read it twice.

Thinkers360 is not a popularity contest. It is not the kind of list you get onto by having a big marketing budget or by gaming social media algorithms. That is precisely what makes it different from most rankings out there. Their leaderboard is powered by a patented algorithm that evaluates companies based on authentic, personally authored thought leadership content, which includes articles, books, keynotes, media interviews, podcasts, whitepapers, and speaking engagements. You cannot buy your way in. You cannot inflate your score with fake followers or reshared third-party content. What counts is what your people genuinely know and are willing to share with the world.

So when FAVORIOT appeared alongside Microsoft, PwC, Mastercard, Tata Consultancy Services, HCLTech, and ServiceNow, I did not feel triumphant in the way you might after winning a business pitch. I felt something quieter and deeper than that. I felt grateful, and I felt a kind of responsibility.

Let me put this into context, because context matters enormously here.

FAVORIOT is a Malaysian company. We are not a billion-dollar multinational. We did not emerge from Silicon Valley or London or Singapore’s Orchard Road. We were built in Kuala Lumpur, by a team that believes deeply in the power of IoT to transform how cities are managed, how industries are monitored, and how businesses make decisions with real-time data. We have been doing this since 2017, quietly and consistently, publishing our thoughts, sharing our frameworks, speaking at conferences, writing when others are sleeping, and building a body of knowledge that goes far beyond what our company size might suggest.

And I think that is exactly the point.

Thought leadership has never been about size. It has always been about substance. It has always been about the courage to have a perspective, to write it down, to share it publicly, and to do so consistently even when the audience is small and the applause is quiet.

I remember the early days of FAVORIOT, when I would publish an article about IoT adoption in developing markets and wonder if anyone was reading. I remember speaking at regional conferences where I was one of the few voices pushing the narrative that Southeast Asia did not have to be a technology consumer, we could be a technology contributor. I believed that deeply, and I kept writing, kept speaking, kept building.

What Thinkers360 has done with this ranking is validate something I have always believed: that genuine expertise, consistently shared, eventually finds its audience. Their methodology deliberately looks beyond social media reach and evaluates the full body of work a company produces. The articles your team writes. The keynotes your leaders deliver. The books, the whitepapers, the podcasts, the depth of contribution to the global conversation. That is a hard score to fake, and I am proud that FAVORIOT earned it the hard way.

Being the only Malaysian company on this list carries a weight I do not take lightly. This is not just a FAVORIOT achievement. It is a moment for the Malaysian technology ecosystem to recognise that we belong in the global conversation, not as observers, but as contributors. Our ideas about IoT, smart cities, and enterprise connectivity are not local curiosities. They are relevant globally, and this recognition confirms it.

Standing in the same list as PwC, a firm with over 360,000 employees across 151 countries, or Microsoft, which shapes the technology infrastructure of virtually every organisation on earth, is not something I will pretend feels ordinary. It is extraordinary. But it also tells me something important: the criteria that matter, depth of thought, consistency of publishing, willingness to educate and elevate an industry, those criteria do not require a headcount of thousands. They require commitment.

I often tell my team that we are not just building an IoT platform. We are building a body of thought. We are documenting what works, sharing what does not, and contributing to a global understanding of how connected technologies can solve real problems for real people in real cities. Every article published, every keynote delivered, every insight shared on LinkedIn or our blog is a brick in that structure.

Today, Thinkers360 has told us that structure is visible. That it stands tall enough to be counted among the world’s most influential companies in innovation.

What comes next matters more than the recognition itself. We will continue writing. We will continue speaking. We will continue challenging assumptions about what IoT can do and who gets to lead that conversation. And we will continue carrying the Malaysian flag into rooms where, honestly, not many have taken it before.

If you are building something in this part of the world, something real, something you believe in, and you are doing the quiet, consistent work of sharing your knowledge with the world, I want you to know this: it accumulates. It compounds. And one day, it gets seen.

Are you investing in thought leadership the same way you invest in your product? Because in the end, the companies that shape industries are the ones that shape the conversation first.

Explore the full Thinkers360 50 Thought Leading Companies on Innovation 2026 list. If your organization is building something worth sharing, the global conversation is still wide open, and the world is still listening.

How AI Has Lightened My Load as Chief Everything Officer

Being a founder means being the Chief Everything Officer. Here is how AI has become the leverage that makes the load just a little lighter.

If there is one title that most accurately describes my life right now, it is not CEO. Not Founder. Not even the glamorous-sounding “Technopreneur.”

It is Chief Everything Officer.

And that is not something to be proud of. It is an exhausting reality.

The Monday Morning That Never Ends

Picture a Monday morning. Before 9am, my head is already full. A client proposal still half-done. A LinkedIn post I have not updated in three days. An email from an overseas partner waiting for a reply. A pitching deck for next week that is still blank. A financial report the accounts team has been asking for since yesterday.

All of it, simultaneously, inside the same head.

Back when I worked in large organisations, there were teams for all of this. Someone for marketing. Someone to handle social media. Someone to prepare slides. A PA to filter emails. My role was specific, focused, and contained. The moment I left to build my own company, I realised… all of that now falls on me.

That is the part nobody tells you about the startup world. They talk about freedom. About being your own boss. But nobody talks about those Sunday nights sitting alone in front of a laptop, trying to finish a company blog post, with a pounding headache from exhaustion.

Burnout Is Real

I have experienced burnout. Not once. More than once.

There were moments I would sit in front of the screen, hands on the keyboard, with nothing coming out. Not because I had no ideas, but because my mind was so overloaded it could no longer process anything. Like a browser with too many tabs open until the laptop freezes.

That is the most frightening feeling as a founder, because if you stop, everything stops.

I still remember one night, past midnight, trying to write a proposal for a major client. Hands tired. Eyes tired. But my brain could not stop because the deadline was the next morning. I asked myself at that moment, “How long can I keep doing this?”

The answer did not come in the form of rest. It came in the form of technology.

“Successful entrepreneurs are not those who work the hardest. They are those who are smartest about using every resource available to them.”

When AI Entered the Picture

The first thing AI helped me with was writing. Before this, a single article for the company blog could take half a day. Research, outline, draft, edit, proofread… hours of work. Now I sit down, have a conversation with AI, explain what I want to convey, and within a short time I have a working draft.

Not copy-paste verbatim. But a starting point. And that starting point is incredibly valuable when time is your most limited resource.

Then I started using AI for social media. Facebook, LinkedIn, TikTok, Instagram… each platform has its own tone. Before, even thinking of captions felt like a burden. Now I brief the AI on the message I want to deliver, the platform, the audience, and it helps me draft. I edit to match my voice. Fast. Efficient.

Presentation slides too. I outline what I want, AI helps me structure the flow, drafts content for each slide, and suggests what to keep and what to cut. I walk into meetings more confident, more prepared, calmer.

My 11pm Brainstorming Partner

This is what I love most. Before, when I wanted to think something through, I had to wait for a meeting, for other people in the room, for a discussion to happen. Now I can brainstorm with AI at 11pm after everyone is asleep.

I ask questions, push back, ask it to argue against me. It gives me perspectives I had not considered. There are times I come in with an idea I think is brilliant. The AI gives me a counter-argument that makes me think twice. That is not a bad thing. It is far better than proceeding with an idea that has holes in it.

AI also helps me prepare for meetings and pitching sessions. I describe the client I am about to meet, their industry, their likely problems, my product. I ask the AI to simulate the questions they might ask, the objections that might come up, the best way for me to respond. It is like a rehearsal. And it makes me walk into the room far more prepared.

Since using AI, I feel like I have an assistant who never sleeps, never takes leave, never asks for a raise, and is always ready when I need them.

“The best technology is not the most sophisticated. It is the one that saves the most of our time and energy for what truly matters.”

Honest Truth: AI Is Not the Answer to Everything

I want to be clear about this. AI is not a cure for every problem.

AI cannot replace human relationships. When I meet a client, what makes them trust me is not a beautiful slide deck or a well-written proposal. It is the way I speak, the way I listen, the way I show that I truly understand their problems. That comes from experience and empathy, not from a prompt.

AI also cannot make strategic decisions for me. It can provide data, perspectives, and options. But ultimately, I am the one who must decide. Those decisions come from gut instinct built over years of experience, mistakes, and lessons learned.

And AI cannot replace real networking. Coffee with an old friend, connecting with another founder who understands the same struggles, attending an event and having organic conversations… all of that still matters, and still has to be done personally.

So the way I see AI now is this: it is leverage. Not a replacement. When you have good leverage, you can lift heavier things with the same amount of energy. AI is leverage for my time and energy as a founder doing many things alone.

To Founders Who Are Still Skeptical

I see fellow founders who have not yet fully embraced AI. Some are skeptical, some afraid, some feel it is “cheating” or inauthentic. I understand that feeling. I felt the same way.

But when I watch them struggling with things I can now resolve much faster, I feel for them, because I know how exhausting life is without that leverage. The world has changed. The tools have changed. The way we work must change too.

I am not telling you to hand over all control to AI. I am simply saying: try it first. Use it for one small thing. See what happens. Give it a chance to prove its value.

“Do not fear new tools. Fear the unwillingness to learn, because that is what will leave us behind.”

Still Chief Everything Officer, But With a Reliable Partner

One day, perhaps AI will be able to do even more. Handle customer service, manage projects, negotiate with vendors. Maybe the role of Chief Everything Officer will truly be shared between me and AI in a more balanced way.

But for now, I am still the Chief Everything Officer. Only now, I have a reliable partner. One I can “wake up” at 2am when an idea suddenly strikes. One that never complains, never has bad days, and always tries to give me the best answer.

And that is enough to make a founder’s life just a little bit lighter. Just a little. But in the startup world, a little means everything.

What about you? Are you still doing everything alone, or have you found your own leverage? Share your thoughts in the comments below.

Social Media, Business, and the Invisible War Every Founder Must Learn to Fight

Long before I became a founder, I was already experimenting with social media, not because it was fashionable, not because someone told me personal branding would become important, and not because I had a grand strategy written neatly inside a notebook, but because I could sense that these platforms were quietly changing the way people discovered ideas, trusted experts, followed companies, and made decisions.

It started with Twitter, then Facebook came along, followed by YouTube, LinkedIn, Instagram, and later TikTok and Threads, and with every new platform I joined, I found myself learning the same painful but useful lesson: every platform has its own behaviour, its own audience, its own rhythm, and its own strange way of rewarding or punishing your content.

There were no formal classes for me, no coach sitting beside me, no step-by-step manual that said, “Mazlan, this is exactly how you should build your voice online.” I learned the old-fashioned way by reading, testing, failing, adjusting, and trying again, which sounds noble now, but at that time, it was mostly trial and error with a lot of silent head-scratching in between.

When FAVORIOT started to grow, social media was no longer just a personal space where I shared thoughts, opinions, and reflections. It became part of the business itself, and suddenly I was not only speaking as Mazlan Abbas, the person, but also carrying the voice of FAVORIOT, the company, the brand, the team, and the mission we were trying to build.

That was when things became complicated.

I thought to myself, “So now I have to be myself, represent the company, educate the market, promote the product, build trust, and still sound human at the same time?”

Yes, apparently.

And I had to do all of that while running a startup.

When Social Media Starts Feeling Like a Battlefield

At one point, managing social media felt like being a soldier defending too many frontlines at the same time, because LinkedIn wanted professional insights, Facebook preferred a more personal tone, X rewarded sharp and quick comments, TikTok demanded visual storytelling, Threads wanted casual conversations, Instagram needed strong visuals, and YouTube required patience, planning, and a completely different level of commitment.

Every platform seemed to ask for something different, yet all of them demanded the same thing from me: time, attention, consistency, and energy.

The difficult part was not just posting. Anyone can post. The real challenge was knowing what to say, how to say it, where to say it, and which version of myself should be speaking.

For my personal account, people followed me because they wanted my thoughts, my stories, my experiences, and my reflections. They wanted to see the human side of the founder, not a walking advertisement. For the FAVORIOT account, the expectations were different because the company needed to sound clear, professional, relevant, and trustworthy to customers, partners, developers, investors, universities, and the wider IoT community.

That was where the first real problem appeared: mixed messaging.

The Fine Line Between Personal Voice and Company Voice

The line between a founder and the company can become blurry, especially in a startup where the founder’s face, voice, reputation, and personality are often tied closely to the brand, and while this can be powerful, it can also create confusion if we are not careful.

Whenever I posted too much business content on my personal account, engagement usually dropped because people did not follow me just to receive product updates. They followed me because they wanted perspective, stories, lessons, observations, and sometimes a little honesty about what it really feels like to build something from the ground up.

At the same time, if the company account became too personal, it risked weakening the professional image of the brand, because a company page must serve a different purpose. It must help customers understand what the company does, why it matters, how it solves problems, and why people should trust it.

This was not just a technical content issue. It was an identity issue.

Before pressing publish, I often had to ask myself, “Am I speaking as Mazlan, or am I speaking as FAVORIOT?”

That small question became very important, because personal branding and company branding may support each other, but they should not become the same thing. A personal account earns attention through authenticity, while a company account earns trust through clarity.

“Your personal account earns attention through authenticity. Your company account earns trust through clarity.”

It took me years to truly understand that, and even now, I still remind myself of it whenever I prepare content for different platforms.

The Hardest Battle Is Still Time

If anyone asks me what the biggest challenge is in managing social media as a founder, my answer is very simple: time.

Many people assume content creation means typing a few lines, adding a nice image, and clicking publish, but anyone who has done it seriously knows that one good post can involve research, choosing the right angle, writing the hook, shaping the message, checking the tone, preparing visuals, proofreading, deciding where to post, adjusting the format for each platform, and then monitoring the response after it goes live.

Now multiply that by several platforms.

Then multiply it again by two accounts, one personal and one company.

Then add meetings, proposals, customer follow-ups, speaking engagements, product discussions, investor conversations, staff matters, and the never-ending demands of running a startup.

That is when social media stops looking like a simple marketing activity and starts feeling like another full-time job quietly hiding inside your actual full-time job.

There were moments when I honestly felt like shutting everything down and focusing only on the “real work,” but each time that thought appeared, another thought answered it almost immediately.

“But this is part of the real work now.”

That is the reality for founders today, because social media is no longer optional if you want people to discover you, understand you, evaluate your credibility, and eventually trust you enough to have a serious conversation.

A startup can have a good product, a committed team, and a powerful vision, but if nobody sees it, hears about it, or understands it, the market may assume that nothing much is happening.

And that is dangerous.

Doing Everything Alone Can Drain You Quietly

For a long time, I handled most of the social media work by myself, which meant writing, editing, posting, choosing angles, creating variations, checking responses, replying to comments, and thinking about how to balance the personal voice with the company voice.

From the outside, people only see the final post, but they do not see the hesitation behind it, the drafts that never get published, the captions that are rewritten five times, the posts that are deleted because they do not feel right, or the late-night moment when you stare at the screen and wonder whether the message is useful or just noise.

Founders know this feeling very well.

We are not only building products. We are also building trust, visibility, confidence, and a story that the market can understand.

Many times, we do this with limited people, limited budget, limited time, and limited emotional energy, yet social media continues to demand freshness, consistency, and relevance as if we have a large content department hiding somewhere inside the company.

I thought to myself, “If people knew how much effort goes into one simple post, maybe they would forgive me for occasionally disappearing.”

But the market rarely forgives silence for too long.

If you disappear, people forget.

If you only appear when you want to sell, people resist.

If you post without direction, people get confused.

That is why founders need not only content, but also a system.

Then AI Changed the Way I Work

When AI tools like ChatGPT arrived, my content process changed in a major way, not because AI replaced my thinking or my voice, but because it helped me organise what I already had inside years of writing, speaking, presenting, and explaining.

Before AI, I wrote almost everything from scratch, and that meant every post felt like a new battle with a blank page. Now, I can start from existing material: old blog posts, keynote notes, training slides, customer questions, proposal ideas, or reflections from past experiences.

Over the years, I had written many articles on IoT World and my personal blog, and those articles were filled with experience, analysis, stories, opinions, and lessons that were still relevant. What I did not fully realise before was that all of that content was not old material. It was a content bank waiting to be reused.

A single blog article can now become several Threads posts, a polished LinkedIn article, a casual Facebook update, a short video script, a newsletter idea, or even a talking point for a presentation. The main idea remains the same, but the structure, tone, and length can be adjusted for each platform.

That saved me a lot of time.

More importantly, it reduced the pressure of always creating from zero.

I thought to myself, “Why should I keep starting from an empty page when I already have years of thoughts sitting quietly in my blog?”

That was a turning point.

AI gave me a better rhythm, but it did not remove my responsibility. I still had to decide what mattered, what should be published, what should be edited, what should be removed, and whether the final content sounded like me.

AI can help shape the clay, but the clay must still come from real experience.

“AI should not replace your voice. It should help your voice travel further.”

That is how I see it, because the danger is not in using AI, but in allowing AI to make us sound like everyone else.

Your Old Content May Be More Valuable Than You Think

Many founders underestimate the value of what they already have, because they assume content must always be new, fresh, and created from scratch, when in reality, some of the best content comes from old ideas explained in a clearer and more current way.

Old blog articles can become new social media posts.

Past presentation slides can become short educational content.

Customer questions can become articles.

Training notes can become carousels.

Proposal explanations can become LinkedIn posts.

Mistakes can become lessons.

Founder reflections can become trust-building stories.

Even a simple WhatsApp explanation to a customer can become the seed of a good post, because if one person asked that question, many others may be thinking about the same thing silently.

The key is to stop looking at content as a one-time activity.

Content should be treated like an asset.

You create it once, then reshape it, reuse it, update it, and distribute it in different ways depending on the platform and audience.

This is where AI becomes useful, because it can help turn raw material into different formats quickly, but the source must still be yours. Your voice, your judgement, your stories, and your understanding of the audience cannot be outsourced completely.

AI can help you move faster, but it should not make you disappear from your own content.

Lessons I Learned From Managing Personal and Business Accounts

After many years of managing both personal and company social media accounts, I have learned that clarity is more important than volume.

The first lesson is to separate the purpose of each account. Your personal account should carry your stories, reflections, opinions, values, and human experiences, while your company account should focus on customer problems, product value, use cases, industry education, credibility, and business outcomes.

The second lesson is to build a content bank before you need it. Do not wait until you are tired, busy, or under pressure before thinking about what to post, because that is when content becomes stressful. Save ideas continuously, collect useful questions, keep links to your old articles, and record your best explanations when they appear naturally.

The third lesson is to use AI as an assistant, not as a replacement. Let it help with structure, tone, rewriting, repurposing, and idea expansion, but never surrender your judgement, because AI may produce words, but only you know whether those words carry the right meaning for your audience.

The fourth lesson is to master one or two platforms first instead of trying to be everywhere at the same time. Many founders try to appear on every platform and end up weak on all of them, when they should first understand where their audience spends time and what kind of content works best there.

The fifth lesson is to respect the character of each platform. LinkedIn is suitable for professional insights and deeper reflections, Facebook allows a more personal touch, Threads feels more conversational, TikTok needs visual storytelling, and YouTube rewards those who can explain ideas with patience and consistency.

The sixth lesson is to measure what matters. Likes and views are pleasant, but meaningful comments, direct messages, serious enquiries, partnership discussions, speaking invitations, and sales leads are much better signals that your content is creating real value.

The seventh lesson is to accept that consistency matters more than perfection. A steady flow of useful and honest content is more powerful than one perfect post every few months, because people trust those who show up repeatedly with something worth saying.

Social Media Is a Marathon, Not a Firework Show

Social media is not a one-week campaign, a single viral post, or a magic trick that suddenly turns a quiet startup into a famous brand overnight. It is a long game of showing up, learning from your audience, improving your message, and staying visible without becoming a slave to the algorithm.

Some posts will perform well.

Some will disappear quietly.

Some will attract customers.

Some will attract critics.

Some will open doors you never expected.

Some will teach you what your audience truly cares about.

That is part of the process.

The founders who survive social media are not always the loudest or the most polished. Many times, they are the ones who know how to pace themselves, reuse their content wisely, protect their energy, stay clear about their message, and keep showing up even when the response is not immediate.

After all these years, I am still learning.

I am still experimenting.

I am still adjusting.

But now, with AI beside me, I no longer see social media as a monster waiting to eat my time. I see it as a set of channels that can carry my thoughts, my work, and FAVORIOT’s story to the people who need to hear it.

Not perfectly.

But consistently.

And for a founder, consistency is already a very big win.

So let me ask you this.

Are you managing your personal and business social media accounts on your own, or do you already have a team helping you? What has been your biggest challenge so far?

Share your experience in the comments.

Source based on the uploaded text.

You Learn to Think, Not Just to Know

Working Alone Without Falling Apart

Finding Knowledge Instead of Waiting for It

Making the Complicated Simple

Defending What You Believe Without Being Defensive

Evaluating Other People’s Work Without Ego

The Scroll Was Never the Point

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We Built the Infrastructure. We Forgot the Purpose.

I Started Calling It Operational Blindness

Why IoT Projects Stop Scaling

The Three Things That Actually Need to Change

What I Tell Every Organisation That Comes to Me

The Real Failure Mode

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Planting Seeds Before Anyone Was Hungry

The Business That Content Built

When Nobody Is Watching, That Is the Real Test

The Compound Interest of Ideas

What I Tell Founders and Professionals Who Ask Me About Content

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The Culture of Hiding Our Work

What “Bragging” Really Means

I Have Seen What Happens When Students Share

The Platforms Are Already There

What Lecturers and Institutions Can Do

A Personal Challenge

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Easy Is Good, But Too Easy Can Become a Trap

The Five Things Universities Should Look For

1. Learning Curve

2. Project Readiness

3. Local Support

4. Tutorials and Teaching Materials

5. Showcase Value

Comparing Common IoT Platforms for Students

Blynk

ThingSpeak

Firebase

ThingsBoard

FAVORIOT

Why FAVORIOT Makes Sense for Universities

1. It Helps Students Learn the Full IoT Flow

2. It Supports Real Project Ideas

3. It Gives Local Relevance

4. It Can Support Training and Workshops

5. It Helps Students Think Like Problem-Solvers

So, Which IoT Platform Is Best for Students?

Universities Should Stop Teaching IoT as a Demo-Only Subject

My Final Thought

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The Problem Is Not the Students

A Sensor Is Not a Solution

Malaysia Has Too Many Real Problems for IoT to Remain in the Lab

The Final Year Project Mindset Is Too Small

Stop Asking Only “Can It Connect?”

IoT Must Belong to the People Who Own the Problem

Cybersecurity Must Be Built In From the Beginning

The AI Conversation Makes IoT More Important

We Need Local Capability, Not Permanent Dependency

Pilots Must Stop Dying After the Launch Photo

Universities Must Raise the Standard

Government and Industry Must Demand Outcomes

IoT Is a Long Term Commitment

From Prototype Pride to Operational Discipline

The Malaysia I Want to See

IoT Was Never Meant to Stay on the Classroom Table

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Life on Stage: The Journey of a Speaker Who Never Asked to Become One

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The Monday Morning That Never Ends