Category: AI

Don’t Wait Until AIoT Becomes Popular. Universities Must Prepare Their Lecturers and Students Now

What if the most valuable technology skill of the next decade is something our universities and polytechnics are only beginning to teach? And what if we miss the opportunity simply because we waited too long to prepare?

Throughout my career, from teaching at Universiti Teknologi Malaysia (UTM) to working at CELCOM, conducting research at MIMOS, and eventually building FAVORIOT, I have witnessed how technologies emerge and create opportunities for those who recognise their potential early.

When the Internet of Things (IoT) first attracted attention, very few people understood how to build complete connected systems. We had electronics engineers, telecommunications specialists and software developers, but finding someone who understood how everything worked together was difficult.

Those who started learning early gained valuable experience. They built prototypes, made mistakes, solved technical problems and gradually became the specialists that industries needed.

Today, I see a similar opportunity emerging with Artificial Intelligence of Things (AIoT). But this time, I believe our universities and polytechnics must act earlier.

AIoT Is More Than Learning Artificial Intelligence

Artificial Intelligence is becoming increasingly accessible. Students can use AI to generate code, analyse information, prepare assignments and develop applications. These are useful skills, but AIoT introduces another level of technical capability.

AIoT combines intelligence with the physical world. Imagine factories where machines detect unusual operating conditions, buildings that identify energy wastage, or environmental monitoring systems that provide earlier warnings of potential dangers.

Building these applications requires knowledge of sensors, communication networks, IoT platforms, machine learning, cybersecurity and operational processes.

Finding professionals who understand all these disciplines remains challenging. This creates an opportunity for students who begin developing AIoT expertise before it becomes widely available.

Before We Train Students, We Must Train Their Lecturers

Having spent years in academia, I strongly believe that educational change must begin with educators themselves.

We cannot expect lecturers to teach emerging technologies confidently if they have not been given opportunities to learn and experiment with them.

Universities and polytechnics should begin sending lecturers for specialised AIoT training, even before introducing dedicated AIoT programmes.

There are several reasons why this matters:

  1. Lecturers become the first generation of AIoT educators. They develop the knowledge required to introduce AIoT concepts across existing engineering and computing courses.
  2. Students benefit from practical teaching. Trained lecturers can guide students beyond theoretical lessons into real sensor connectivity, analytics and intelligent applications.
  3. Final-year projects become more relevant. Students can develop solutions involving predictive maintenance, energy monitoring, smart agriculture and environmental intelligence.
  4. Research opportunities expand. Lecturers can explore multidisciplinary research and collaborate with industry partners.
  5. Educational institutions gain an early advantage. Universities that prepare their lecturers now may become recognised centres for AIoT education and applied research.

Training lecturers should not be viewed simply as another professional development activity. It is an investment in the future capabilities of their students.

Why Early AIoT Skills Matter

Students who begin learning AIoT today may enjoy several advantages. They can develop specialist knowledge before competition increases, gain experience across multiple technical disciplines and explore opportunities in manufacturing, agriculture, energy, utilities and smart cities.

More importantly, they can begin building real solutions while others are still learning introductory concepts.

I have always believed that practical experience gives young engineers confidence. When students successfully connect sensors, collect real-time data, apply machine learning and demonstrate useful results, they begin seeing themselves as solution builders rather than simply graduates seeking employment.

That confidence can influence their entire careers.

Universities Should Not Wait for Industry Demand

One concern I often have is that educational programmes sometimes respond only after industries begin reporting skills shortages.

By then, preparing lecturers, developing laboratories, revising curricula and producing graduates may take several more years.

We should reverse that approach. Universities and polytechnics should identify emerging technical capabilities and prepare their educators before demand becomes widespread.

Start by sending a small group of lecturers for AIoT training. Allow them to develop practical projects, share their knowledge with colleagues and introduce AIoT components into existing courses.

Over time, these efforts can grow into specialised laboratories, research groups and industry collaborations.

My Message to Education Leaders

When we started building FAVORIOT in 2017, IoT was still developing. The experience taught me that entering an emerging field early can be difficult, but it also creates opportunities to build expertise that others may take years to acquire.

I do not want our next generation of engineers to discover AIoT only when companies begin urgently searching for experienced professionals.

If we want students to become future AIoT experts, we must first give their lecturers the opportunity to become AIoT educators.

The next generation of AIoT talent may already be sitting in our classrooms. What they need are educators who can recognise their potential, guide their experiments and prepare them for opportunities that are only beginning to emerge.

My hope is that Malaysian universities and polytechnics will start preparing today, not because AIoT has already become popular, but because they believe our students deserve to be among the pioneers of tomorrow.

From Teaching IoT to Solving Real Problems: Lessons I Learned the Hard Way

Looking back at my years of building IoT solutions, I can see how much the conversation around IoT has changed. In the beginning, our challenge was convincing people that IoT mattered. Later, people wanted to learn how to build it themselves. Today, building an IoT prototype has become much easier, especially with generative AI helping people write code and solve technical problems. Yet one uncomfortable reality remains: the IoT market has not grown as quickly as many of us expected.

That experience has forced me to rethink what customers actually need from IoT. After years of developing FAVORIOT, conducting training, working on projects and watching pilots succeed technically but struggle commercially, I have learned that the biggest challenge is rarely the technology itself. The harder question is whether we are solving an operational problem that matters enough for someone to take ownership, allocate a budget and keep the solution running.

The Early Days Were About Explaining IoT

In the early days, many of our conversations started with a basic question: “What is IoT?” People had heard the term, but few understood how sensors, connectivity, cloud platforms and applications could work together. We spent considerable time educating customers, students and organisations about what IoT could do and why connecting physical assets could change the way operations were monitored.

At that time, simply demonstrating the technology could create excitement. When someone saw a sensor transmitting data to a cloud platform and displaying the information on a dashboard, it felt like something new. We could demonstrate remote monitoring, automatic alerts and real-time data collection, and people immediately became curious about the possibilities. We believed that once awareness increased, adoption would naturally follow.

Then People Wanted to Build IoT Themselves

As awareness grew, the questions changed from “What is IoT?” to “How can I build an IoT solution?” This created another chapter for FAVORIOT, where training became an important part of what we did. We conducted many IoT programmes involving sensors, Arduino, ESP32, Raspberry Pi, MQTT, cloud platforms, dashboards and application development.

The appetite for learning was strong because people wanted practical experience. Students wanted to build projects, lecturers wanted to introduce IoT into teaching and research, while engineers and professionals wanted to understand how connected systems could be applied in their organisations. IoT was gradually moving from something people heard about at conferences into something they could actually build with their own hands.

Then generative AI arrived, and we noticed another shift. Demand for traditional IoT training appeared to decline. One possible reason is that people can now ask an AI assistant how to connect an ESP32, generate MQTT code, troubleshoot an error or explain how a sensor works. Knowledge that once required attending a two-day workshop can now be accessed within minutes.

If IoT Is Easier to Build, Why Is the Market Still Slow?

This became an uncomfortable question for many IoT solution providers, including us. Sensors became cheaper, connectivity improved, platforms matured and technical knowledge became easier to obtain. Generative AI lowered the technical barrier even further, yet the commercial market for IoT still seemed to move much more slowly than the technology.

For a long time, we believed demonstrating stronger technology would help overcome this problem. We showed customers devices, dashboards, alerts, analytics and different ways of connecting physical assets. A good demonstration could attract attention, lead to discussions and sometimes result in a pilot project. Technically, many of these pilots worked exactly as expected.

The problem came afterward. Some pilots simply stopped. The sensors were working, data was flowing and dashboards were displaying information, yet the project never expanded. We slowly realised that proving technology could work was very different from proving that an organisation needed it badly enough to continue investing in it.

A Successful Pilot Can Still Become a Failed Project

That experience taught us to ask a different question: who owns the project after the pilot? Someone might approve an experiment, another department might provide the equipment and an IT team might help with connectivity, but nobody may actually be responsible for turning the pilot into part of daily operations.

Without operational ownership, a pilot can easily become a technology showcase rather than a working system. If nobody is accountable for the problem being addressed, nobody feels enough urgency to expand the solution. There may also be no operational budget, measurable outcome or internal champion willing to push the project beyond its experimental stage.

This changed the questions I now believe we should ask at the beginning of an IoT project. What operational problem is the customer experiencing? Who suffers when that problem occurs? What does the organisation lose when the problem remains invisible? Who receives the information generated by the system, and what action will that person take? Most importantly, who owns the outcome after the pilot finishes?

Customers Rarely Wake Up Wanting IoT

A maintenance manager probably does not arrive at work thinking about buying an IoT platform. He may simply want to know whether a remote pump has stopped running before someone complains. A facilities manager may want to understand why electricity consumption suddenly increased, while an environmental officer may need an early warning when water quality begins moving outside acceptable limits.

The same applies to energy systems, buildings, factories and remote assets. Customers are usually concerned about downtime, wasted energy, unnecessary site visits, equipment failures, delayed responses and things happening in their operations that they cannot see quickly enough. IoT becomes valuable when it helps expose those problems early enough for someone to act.

This has changed the way I think about FAVORIOT as well. Connecting devices remains necessary, but connection alone is not the outcome. Data must help someone see something they could not previously see, understand what is happening and decide what to do next. That is why Connect → See → Act represents something much deeper to me today than simply describing how an IoT platform works.

Lessons I Learned the Hard Way

After years of explaining IoT, teaching people how to build it, developing our own platform and watching projects move through different stages, several lessons have become difficult to ignore.

  1. Awareness does not automatically create demand. Someone can understand IoT very well without having a strong enough operational reason to buy a solution.
  2. Technical success is different from commercial success. Getting a sensor to transmit data and displaying it beautifully proves that the technology works. It does not prove that the organisation will continue paying for it.
  3. Every pilot needs an operational owner. Someone inside the customer’s organisation must care about the outcome, be responsible for the problem and have enough influence to move the project forward.
  4. Start with the pain, not the platform. Conversations about downtime, energy losses, unnecessary site visits, equipment failures and delayed responses are usually more meaningful than conversations about protocols, dashboards and technical specifications.
  5. IoT creates value when visibility leads to action. Collecting more data is not enough. The real value comes when information helps someone recognise a problem earlier and respond before the consequences become more expensive.

Perhaps this has been one of the hardest lessons from building IoT solutions over the years. We spent the early years proving that IoT technology worked. We then spent years teaching people how to build it. Today, I believe the conversation has to move beyond both.

The future of IoT will not be decided by how many sensors we can connect or how impressive our dashboards look. It will be decided by whether we can help organisations see operational problems they could not see before and give the right people enough information to act.

That lesson took years of projects, pilots, training programmes, successes and disappointments for me to fully appreciate. Stop trying to sell customers IoT. Find the operational problem they desperately need to see, and make that problem visible.

Six Stories from Founder’s August 2026

The August Field Notes
Field notes · Issue No. 08

Six stories from a founder’s August.

A month’s worth of writing from Dr. Mazlan Abbas — on pitches that failed, pilots that stalled, and the uncomfortable questions a founder keeps circling back to. Grouped by what they’re really about, not just when they were posted.

6 articles August 2026 mazlanabbas.com
01

Startup Journey & Lessons

The unglamorous middle of building FAVORIOT — the pitch that didn’t land, the partnership that stalled after the photo op, the pilot that worked and still went nowhere.

Aug 24, 2026

I Pitched My Elderly Monitoring Startup to Nearly 100 Investors. Here’s Why They All Said No.

Before FAVORIOT was an IoT platform, it was a smartwatch for elderly care — and almost a hundred investors turned it down flat. The question none of them actually asked out loud is the one that still shapes how he pitches today.

Read the full story → Aug 22, 2026

The MoU Photograph Is the Easy Part

The handshake, the signing, the photo — that’s the simple bit. What actually happens to a partnership once the cameras stop and everyone drives back to their real jobs is a different story entirely.

Read the full story → Aug 15, 2026

I Thought a Successful Pilot Would Naturally Become a Real Project. I Was Wrong.

Two pilots, both technically flawless, neither one a paying customer. What he learned about the wide, quiet gap between “it works” and “someone will actually buy it.”

Read the full story →
02

Leadership & Reflection

Slower, more personal essays — the kind written after hours, when the day’s meetings are over and the bigger questions surface.

Aug 14, 2026

Would We Listen to Advice From Our Future Selves?

A time machine, one message, your past self on the other end of it. The question is easy to ask — the honest answer he arrives at is a lot less comfortable.

Read the full story → Aug 7, 2026

The Moment I Realised Our Dashboard Was Not the Answer

For years, more devices and better dashboards meant better decisions — that was the whole industry’s belief, his included. Then he watched organisations with beautiful dashboards keep making the exact same mistakes.

Read the full story →
03

National Tech Vision

Written for Merdeka week — a wider lens on Malaysia’s technology industry and where confidence in it still runs short.

“
Aug 31, 2026

Malaysia Is Independent. Our Technology Mindset Should Be Too.

Sixty-nine years of independence, and one question he couldn’t shake over the long weekend: have we ever actually trusted what we build ourselves? A hard look at why homegrown IoT still gets treated as the backup option — using his own platform as the case in point.

Read the full story →

Compiled from mazlanabbas.com — the personal writing of Dr. Mazlan Abbas, co-founder & CEO of FAVORIOT.

For the more technical, IoT-industry take on these ideas: iotworld.co

August 2026 – Mazlan Abbas Monthly Compilation – Ideas and Lessons

August 2026 Collection | Dr. Mazlan Abbas
Dr. Mazlan Abbas · Monthly Reading Collection

AUGUST Ideas, lessons and hard-earned realities from 2026

Six essays from August 2026, curated into one reading collection spanning operational visibility, startup commercialisation, partnerships, personal reflection and confidence in Malaysian technology.

6 Articles5 ThemesAugust 2026
The August Edition

What experience teaches after the presentation ends

August’s writing repeatedly returns to one idea: appearances can mislead. A dashboard may look impressive without helping people act. A pilot may work without becoming a business. An MoU may generate photographs without generating results. An admired idea may still lack market evidence. The collection brings those lessons together with reflections on experience, judgement and Malaysia’s confidence in its own technology.

06original essays, arranged by the question each one helps the reader answer.
01 · Operational Visibility

Beyond the dashboard

Why connected devices and attractive charts still leave organisations struggling to understand what needs attention and what action should follow.

7 August 2026

The Moment I Realised Our Dashboard Was Not the Answer

For years, better dashboards seemed like the natural destination of IoT. Real deployments told a different story. Operators still made site visits, problems were still discovered through calls and WhatsApp, and colourful screens did not always create trusted operational awareness. This article explains the shift from collecting and displaying data toward solving Operational Blindness, and why the real objective is helping people know what is happening early enough to act.

Read the full article →
01
02 · Startup & Commercialisation

Proof is not the same as a business

Two founder stories examine what happens when technical success, a compelling pitch and genuine social value meet the harder test of commercial reality.

15 August 2026

I Thought a Successful Pilot Would Naturally Become a Real Project. I Was Wrong

A pilot can prove that technology works while proving very little about budgets, buying commitment, timing or the durability of the market. Drawing from real projects, this essay explores why founders and technology teams must qualify the commercial path before treating a successful pilot as the beginning of scale.

Read the full article →
02
24 August 2026

I Pitched My Elderly Monitoring Startup to Nearly 100 Investors. Here’s Why They All Said No

Favorwatch had a meaningful purpose and a convincing story, yet almost 100 investor approaches ended without funding. The retrospective is less about rejection and more about the missing evidence behind the pitch: paying customers, repeatable demand and traction. It is a useful read for founders tempted to mistake interest in an idea for proof of a market.

Read the full article →
03
03 · Partnerships

The photograph is not the partnership

A practical founder’s view of why formal collaboration matters far less than ownership, a first project, resources and people who keep the relationship moving.

22 August 2026

The MoU Photograph Is the Easy Part. The Real Work Begins After Everyone Goes Home

MoU ceremonies create recognition, publicity and the feeling that a partnership has begun. Yet signatures cannot appoint a champion, fund an activity or create a customer project. This essay looks behind the ceremonial photograph and identifies the practical conditions that turn good intentions into actual work, including named owners, a first activity, resources, timelines and measurable outcomes.

Read the full article →
04
04 · Leadership & Reflection

Would the younger self listen?

A short reflection on the strange relationship between experience and risk, and whether wisdom can exist without the mistakes that produced it.

14 August 2026

Would We Listen to Advice from Our Future Selves?

If an older and wiser version of a person could send one message backwards in time, would the younger version even accept it? This brief piece asks whether courage, curiosity, wrong turns and risks are simply problems to avoid, or whether they are part of the process that eventually creates judgement and perspective.

Read the full article →
05
05 · Malaysia & Technology

Merdeka of the technology mindset

A Merdeka reflection on what it means for a country to aspire to create technology while its own buyers may still instinctively place greater trust in foreign brands.

31 August 2026

Malaysia Is Independent. Our Technology Mindset Should Be Too.

Malaysia has spent decades building engineers, universities, industries and local technology companies, yet technological self-confidence cannot be created by policy or certification alone. Using Favoriot’s experience as a Malaysian-built platform, this Merdeka essay asks buyers to judge local technology by capability, support and results rather than by the country printed on the brand.

Read the full article →
06

August 2026 was less about technology itself and more about learning to distinguish what looks like progress from what actually creates progress.

Curated from the August 2026 articles of Dr. Mazlan Abbas

All article titles and outbound links lead to the original posts on mazlanabbas.com. Abstracts on this collection page are editorial summaries written for discovery and do not replace the original articles.

Continue reading Dr. Mazlan Abbas

AIoT, startups, operational visibility, technology leadership and lessons drawn from building real products and companies.

Visit mazlanabbas.com →

Why I Started Calling It Operational Blindness

“There has to be a better way to describe what I’ve been seeing.”

For years, that thought kept returning whenever I met customers. It followed me into boardrooms, factories, utility plants, government agencies, and Smart City command centres. Every meeting seemed different on the surface, yet they all ended with a similar feeling that something important was missing.

At first, I believed the problem was technology. I thought organisations simply needed more sensors, better connectivity, or a more capable IoT platform. Later, as artificial intelligence became the latest trend, I wondered if AI would finally solve the problem that had frustrated me for years.

The more I observed, the more I realised I had been asking the wrong question. The problem was never about having more technology. It was about why organisations continued making poor operational decisions despite having so much technology around them.

The Promise That Didn’t Match Reality

When I co founded Favoriot, I genuinely believed that if organisations could connect their devices and continuously collect operational data, better decisions would naturally follow. It sounded logical because connected systems should produce better visibility, and better visibility should improve operations.

That belief shaped much of our early thinking. We focused on connecting sensors, collecting data, building dashboards, and making information available in real time. Technically, everything worked exactly as we intended.

Then I started spending more time with customers.

Everything Looked Digital

I visited factories with sophisticated production monitoring systems. I walked into utility control rooms filled with SCADA screens and large video walls. I met building operators who proudly demonstrated their Building Management Systems, while Smart City command centres showcased dashboards that displayed hundreds of live data feeds.

Everything looked impressive. From the outside, these organisations appeared highly digital, highly connected, and fully in control. If someone judged only by the technology they saw, they would probably conclude that these organisations had already solved their operational challenges.

Yet the conversations told a completely different story.

The Same Answers Everywhere

Whenever I asked simple operational questions, I kept hearing remarkably similar responses.

“We’re still investigating the root cause.”

“Nobody realised the equipment had been deteriorating.”

“The maintenance team wasn’t informed in time.”

“Operations thought Engineering was handling it.”

“We have the data, but we’ll need time to retrieve it.”

These weren’t isolated incidents. They appeared in manufacturing, water utilities, agriculture, energy, healthcare, environmental monitoring, and Smart City projects. Different industries were using different technologies, but they were all struggling with surprisingly similar operational problems.

Technology Wasn’t the Missing Piece

As an engineer, I naturally tried to solve the problem by looking at the technology. Perhaps organisations needed more sensors. Perhaps they needed more dashboards or more sophisticated analytics. When AI became widely available, I even wondered whether intelligent algorithms would finally close the gap.

But the gap remained.

The more technology organisations deployed, the more puzzled I became. Multi million ringgit projects still depended on phone calls to verify incidents. Teams still relied on WhatsApp groups during operational emergencies. Reports still arrived after problems had already caused financial losses, and managers continued making decisions using fragmented information collected from disconnected systems.

The Frustration Started Growing

There were many evenings when I drove home replaying customer conversations in my head. Why does this keep happening? Why are organisations still surprised by problems they should have detected much earlier? Why do sophisticated dashboards still fail to prevent operational failures?

Those questions stayed with me because I couldn’t find a satisfying answer. Every new customer seemed to reinforce the same pattern instead of challenging it. It became increasingly difficult to believe that another dashboard or another AI model would somehow solve the underlying issue.

Slowly, I realised that I wasn’t looking at a technology problem. I was looking at something much deeper.

It Was Never About Data

One of the biggest turning points came when I realised organisations were not suffering because they lacked data. In fact, many had more operational data than they knew how to use. Sensors, PLCs, SCADA systems, ERP platforms, and IoT solutions were already generating enormous amounts of information every second.

The problem was that data does not automatically create understanding. Thousands of sensor readings cannot explain which issue deserves immediate attention. Beautiful dashboards cannot tell people which decision will prevent tomorrow’s failure. Artificial intelligence cannot produce reliable recommendations when operational information is incomplete, delayed, or disconnected from business context.

What organisations lacked was not data.

They lacked operational visibility.

Finding the Right Words

The phrase didn’t appear during a conference or while preparing a presentation. It arrived during one of those quiet moments when years of observations finally began connecting together.

This isn’t a data problem.

This isn’t an IoT problem.

This isn’t even an AI problem.

People are making decisions without seeing the complete operational reality.

Almost immediately another thought came to mind.

They’re operationally blind.

Operational Blindness.

The moment I spoke those words, everything suddenly made sense.

Suddenly the Pattern Was Obvious

Operational Blindness explained why organisations reacted instead of anticipated. It explained why departments worked in isolation despite sharing the same objectives. It explained why dashboards could display everything in green while operational performance quietly deteriorated underneath.

I began seeing Operational Blindness everywhere. A leaking pipeline that remained unnoticed until millions of litres of treated water had been lost. A machine whose vibration gradually increased until production stopped unexpectedly. A hospital where maintenance records were scattered across disconnected systems. A commercial building where energy costs quietly increased month after month because nobody recognised abnormal patterns early enough.

Different industries. Different technologies.

Exactly the same operational condition.

It Changed How I Saw Favoriot

That realisation also changed how I described our own company.

For years, I introduced Favoriot as an IoT platform because that was technically correct. Over time, I realised customers were rarely looking for another platform. They were looking for a way to stop recurring operational surprises, reduce uncertainty, and gain confidence in their daily decisions.

Customers don’t buy dashboards simply because they look attractive. They invest because they want fewer failures, lower operating costs, better compliance, improved reliability, and stronger business outcomes. The platform is only one part of that journey.

A Bigger Mission

Today, I no longer see Favoriot simply as a company that connects devices. I see it as helping organisations reduce Operational Blindness by connecting fragmented operational data, providing meaningful context, supporting faster decisions, and enabling coordinated actions across different teams.

That mission extends beyond IoT. It applies equally to manufacturing, utilities, healthcare, agriculture, logistics, smart buildings, critical infrastructure, ESG reporting, and even cybersecurity. Wherever operational decisions depend on trusted information, Operational Blindness can exist.

As artificial intelligence becomes increasingly capable, this challenge becomes even more significant. AI cannot compensate for missing operational context, disconnected information, or delayed data. Before organisations can become AI driven, they must first become operationally aware.

More Than Just a New Phrase

Looking back, I sometimes smile at how long it took me to recognise what had been sitting in front of me all along. I thought I was building an IoT platform, but what I was really trying to solve was a much larger operational problem that existed across almost every industry.

The phrase Operational Blindness represents years of customer conversations, personal frustrations, lessons learned, and moments of clarity. It gave me a way to describe a problem that many organisations experience every day but struggle to explain.

Perhaps the most meaningful breakthroughs don’t always come from inventing new technologies. Sometimes they come from finding the right words to describe a challenge that everyone has experienced but nobody has been able to name.

Have you ever experienced Operational Blindness in your own organisation? I would love to hear your story because every experience helps us understand this challenge better, and every lesson brings us one step closer to building organisations that can truly connect, see, and act.

Malaysia’s IoT Market Has The Infrastructure. So Why Is Adoption Still Crawling?

Every few months a new market research report lands in my inbox with a headline number for Malaysia’s IoT market. Billions of dollars. Double-digit CAGR. Smart city spend projected to grow at 27% a year through 2034. On paper, we look like a country sprinting toward a connected future.

I have been in this industry long enough, from MIMOS to REDtone IoT to building FAVORIOT from scratch, to know the difference between a market projection and a market. The projection is what analysts model when they extrapolate 5G rollout numbers and government budget announcements. The market is what actually gets deployed, paid for, and kept running past the pilot phase. In Malaysia right now, there is a real gap between the two, and I think it is worth naming exactly where that gap comes from.

The Infrastructure Story Is Genuinely Good

Let’s give credit where it belongs first, because the connectivity layer has moved fast. 4G coverage sits above 96% of populated areas. 5G adoption crossed 52% by the end of 2024, and coverage reached over 82% of populated areas by 2025. Malaysia has more than 7,000 5G sites deployed and pulled in over RM86 billion in data center investment in 2024 alone, making the country Southeast Asia’s leading data center hub.

That is not a small achievement. A country cannot build an IoT economy without pipes to carry the data, and Malaysia’s pipes are, by regional standards, excellent. If you had told me in my MIMOS days that we would have this level of physical infrastructure by 2026, I would have been thrilled.

But infrastructure was never the constraint. Demand was.

The Roadmap We Wrote, And The Reality We Got

Go back to the National IoT Strategic Roadmap, the document MIMOS helped shape more than a decade ago. It forecast IoT applications and services reaching RM34 billion by 2025, up from RM7.5 billion in 2020. Device production was projected to hit RM4.3 billion in the same window.

Nobody in this industry seriously believes we hit those numbers. I do not say that to be cynical about the roadmap. I say it because the gap between what we projected and what materialized is the single most instructive data point in this entire market. It tells us the bottleneck was never technological capability. It was adoption discipline, and adoption discipline is a much harder problem to solve with a budget allocation.

Seven Reasons Growth Keeps Stalling

I want to walk through the actual mechanics of why, because “slow adoption” is too vague to act on. Here is what I see happening on the ground, backed by what the data confirms.

SMEs are still building the foundation IoT depends on. Malaysia’s economy runs on small and medium enterprises, and a recent survey found only about 35% of surveyed SMEs had implemented basic digital systems beyond simple accounting software. You cannot sell predictive maintenance sensors to a factory floor that has not digitized its production logs yet. IoT vendors keep pitching step five to companies that are still working through step one, and the pitch lands as noise rather than opportunity.

The payback math does not match buyer expectations. Deploying an IoT platform requires real upfront investment in hardware, software, and integration with legacy systems, and for SMEs this cost is a genuine barrier to entry even when the long-term ROI is positive. Malaysian buyers, especially in manufacturing and plantation sectors, want a return inside 12 to 18 months. Most honest business cases for connected infrastructure deliver in 24 to 36. That gap kills deals at the finance desk long before the technical evaluation even starts.

We have a talent bottleneck on both sides of the table. Adoption requires skills in IoT, data analytics, cybersecurity, and cloud computing, and Malaysia faces a real shortage of professionals who can manage these deployments end to end. This hits twice. Customers cannot maintain what they buy, and system integrators cannot scale delivery teams fast enough to take on more projects. I see this constraint directly in how far my own SI partners can stretch their capacity.

We have become excellent at pilots and mediocre at scale. I have written about this before, going back years: many organizations in Malaysia are still exploring what IoT can do rather than actually implementing it at scale. Government and GLC procurement cycles routinely stretch 12 to 24 months, and by the time a tender closes, the internal champion who pushed for the pilot has often moved to a different role. The institutional memory that would carry a pilot into a fleet-wide rollout simply evaporates.

5G hype outran 5G reality, and it dented confidence broadly. This one deserves more attention than it gets. 5G speeds actually fell 46% as user numbers scaled during 2024 and 2025, with real-world performance landing at roughly half of what marketing promised. Enterprise deployments involving sensors and remote machinery hit ceiling effects faster than expected. The irony is that most industrial IoT use cases never needed 5G in the first place. NB-IoT, LoRaWAN, and plain 4G cover the majority of real deployments. But 5G dominated the boardroom conversation for two years, and when it underdelivered, some of that disappointment bled into skepticism about connected technology in general.

Security fear has outpaced security literacy. IoT devices expand the attack surface for cybercriminals, and data privacy regulations around data ownership add another layer of complexity. Malaysia’s breach history has not helped confidence here. 2023 recorded the highest number of data breaches on record, with fifteen ransomware cases reported per week. For risk-averse government agencies and GLCs, “where does my data actually sit” has become a conversation-ending question, and few local vendors carry the certifications to answer it with authority.

Policy attention has moved on to AI, and IoT lost its dedicated spotlight. Look at where Budget 2026 puts its weight. RM53 million for the Malaysia Digital Acceleration Grant targeting blockchain, AI, and quantum computing. RM18.1 million for the National AI Office. RM2 billion earmarked for a Sovereign AI Cloud. IoT is barely named as a standalone priority anymore. Compare that to Budget 2025’s allocation of just RM15.1 million for smart city development under the Housing and Local Government Ministry, a rounding error next to the AI figures. Without a dedicated policy push, and without a compliance mandate the way India’s FASTag requirement forced RFID adoption almost overnight, Malaysian IoT adoption stays voluntary. Voluntary adoption is slow adoption by definition.

The Pattern Underneath The Pattern

Put these seven factors together and a single story emerges. Malaysia does not have a technology problem. It has a demand-side maturity problem sitting on top of a fragmented supply side. The billions poured into 5G, fiber, and data centers were supply-push investments, and they worked. The AI Nation agenda is also supply-push. What never got properly addressed is demand-pull: regulatory mandates that force adoption where voluntary uptake has failed, financing instruments that convert IoT capex into manageable opex, and system integrator capacity that can deliver at the scale procurement teams keep promising.

Where This Actually Gets Interesting

Here is the part I think most people in the industry are missing, and it is the reason I am not pessimistic about where this goes next.

AI cannot function in the physical world without sensor data. Every company now scrambling to bolt AI onto their operations is going to discover, within the next year or two, that they have nothing meaningful to feed their models. You cannot build a predictive maintenance AI without machine telemetry. You cannot build a smart agriculture AI without soil and climate sensors. You cannot build a smart building AI without occupancy and energy data. The instrumentation layer that IoT was supposed to build over the last decade is exactly the layer AI needs and mostly does not have.

Malaysia spent ten years underinvesting in that instrumentation layer relative to its own roadmap. That is the bad news. The good news is that the correction is coming, driven not by an IoT mandate but by AI ambition that will hit a wall without sensor data behind it. Companies that quietly kept building connected infrastructure through this slow period, the ones who treated IoT as the unglamorous plumbing rather than the headline act, are positioned to become the data supply chain for Malaysia’s AI Nation agenda.

That is not a consolation prize. That is the actual opportunity. The market did not fail. It just took longer to arrive at the reason it needed to exist.

Malaysia’s IoT-specific market figures vary considerably across research houses, and several published reports use templated methodology that should be treated with caution. The directional pattern across every credible source, however, is consistent: infrastructure has outpaced adoption, SMEs remain the binding constraint, and enterprise pilots are not converting to scale at the rate the roadmap assumed.

Why Seeing Your Brand Once Is Never Enough: The Science Behind the B2B Buying Decision

In B2B technology marketing, posting once and hoping for conversion is one of the most common and costly mistakes. The Rule of 7 is just the starting point. For IoT platforms, AIoT training, and smart city solutions, the real buying journey demands 12 to 20 meaningful touchpoints before serious action begins.

How many times does a potential customer need to encounter your brand before they are ready to act?

Most marketers have heard of the Rule of 7, the long-standing principle that a buyer needs at least seven brand exposures before taking action. The University of Maryland traces this back to early advertising research, and while the number has been cited across decades of marketing literature, the principle itself has never been more relevant than it is today. In fact, for B2B technology companies selling complex solutions such as IoT platforms, AIoT training, smart city systems, or system integrator services, seven may not even be close to enough.

The Rule of 7 Is a Starting Point, Not a Ceiling

The original Rule of 7 was conceived in a world where buyers had far fewer choices and far fewer distractions. Today, a prospective customer is bombarded with hundreds of brand messages daily across social media, email, search, video, and messaging apps. Cutting through that noise requires more than a single touchpoint or even a handful.

A more practical and realistic framework looks like this. For low-cost, low-risk consumer products where the customer already understands the need, three to five exposures may be sufficient. For B2B software, IoT platforms, training programs, and consulting services where trust must be established before any commitment is made, seven to twelve exposures is a more reasonable expectation. For high-value, technical, government, enterprise, or long sales-cycle decisions, the number often climbs beyond fifteen.

The buyer in a B2B context is rarely a single person making a spontaneous decision. There are budget holders, technical evaluators, procurement officers, and end users, each requiring their own reassurance at their own pace.

The Buying Journey Is No Longer a Straight Line

Google’s research into the modern customer journey confirms what many sales teams already suspect: the path from first awareness to final purchase is not linear. Buyers today move fluidly between searching, scrolling, streaming, and comparing. They might encounter a LinkedIn post on a Monday, watch a product demonstration video on a Wednesday, read a case study on a Friday, and then not think about it again for three weeks, until a colleague mentions the same solution.

This fragmented, non-linear journey means that brand presence must be consistent, varied, and sustained. A company that posts once and then goes quiet is invisible in the decision-making process.

The real question to ask is not simply how many times a buyer must see the brand. The more important question is whether they are seeing the right message at the right stage of their journey.

Mapping Exposures to the Buyer’s State of Mind

For a company like Favoriot, which operates in the IoT and AIoT space and targets enterprise clients, government agencies, and system integrators, the exposure journey can be mapped across three distinct stages.

At the awareness stage, five to seven exposures serve to establish name recognition. The buyer begins to register that Favoriot exists and that it is active in the space they care about. They are not yet evaluating, but they are noticing.

At the trust-building stage, eight to twelve exposures shift the conversation from recognition to understanding. The buyer starts to grasp what Favoriot actually does, why it matters, and how it differs from alternatives. This is where thought leadership content, technical deep-dives, and customer stories begin to do their heaviest lifting.

At the purchase confidence stage, twelve to twenty meaningful exposures bring the buyer to a point where they are ready to initiate contact, request pricing, attend a training session, or discuss a potential project. This is not a passive audience anymore. They have been quietly building a case internally, and now they are ready to move.

Repetition with Variation Is the Real Strategy

The mistake many technology companies make is treating content as a one-time announcement rather than an ongoing conversation. They publish a product update, send one email campaign, and then wait for the pipeline to fill. When nothing happens, the conclusion is often that the market is not ready, when the real issue is that the message has not been delivered enough times or in enough forms.

The approach that works is repetition with variation. The core message stays consistent, but the angle changes with each touchpoint. One week it is a problem statement framed around a real challenge facing a city or an enterprise. The next week it is a customer story showing how a specific outcome was achieved. Then comes a webinar invitation, followed by a demonstration video, a data sheet, a LinkedIn article, a referral from a trusted partner, and eventually a direct proposal.

Each of these formats reaches a different segment of the buyer’s brain at a different moment in the journey. The cumulative effect is what transforms a vague sense of familiarity into a genuine willingness to engage.

Building a Brand That Earns the Right to Be Remembered

For any B2B technology company competing in a space where contracts take months and stakeholders number in the double digits, the discipline of sustained, multi-channel presence is not optional. It is the entire game.

The practical target for companies in this space is a minimum of seven meaningful touchpoints, with a realistic planning horizon of twelve to twenty before serious buying conversations begin. That means building a content calendar that spans LinkedIn posts, email sequences, webinar series, case study libraries, video demonstrations, partner referrals, and direct outreach, all carrying the same core message from a different direction.

The goal is to move the buyer from “I think I have seen that name before” all the way to “I think we should set up a meeting with them.”

How many touchpoints has your company planned for this quarter, and are they designed to move buyers forward or simply to fill a content calendar?

This article draws on principles from Favoriot’s market strategy for IoT and AIoT adoption in B2B and enterprise environments. Favoriot provides IoT platforms, AIoT training, and smart city solutions for system integrators, enterprises, and government agencies across Southeast Asia.

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?

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.