One of the biggest things I've learned from using AI at Global Fast Fit is that artificial intelligence can be extremely good at solving technical problems. But there's something it still doesn't automatically understand: the organization itself.

It can write code. It can suggest formulas. It can analyze data. It can explain Python. It can even help design a system. But it doesn't automatically know what that system means to the people using it.

The Difference Between a Technical Problem and a Real Problem

Suppose I tell AI: "I have several spreadsheets and I want to combine them into one master sheet." That sounds like a technical problem. AI can immediately suggest formulas, scripts, database structures, and different ways of importing information. But there are questions behind that request AI can't answer on its own.

Which spreadsheet is the authoritative source? Why are there several spreadsheets in the first place? Who enters the information? Why are the records structured differently? What happens when information is missing? Which fields actually matter? What should happen to an incorrect record? Who needs to use the final data?

Those aren't coding questions. They're organizational questions, and they require someone who understands what's actually happening on the ground.

AI Sees the Data. I See the Story Behind It.

When I work with GFF data, I know that a row is never just a row. There's usually a reason it exists. A participant record represents a person. A session represents an activity that actually happened. A video represents something that was recorded. An event represents people coming together. A piece of equipment represents something someone needs to use. A missing record may represent something that happened but was never properly documented.

AI can process the information I give it. But it doesn't automatically know the story behind that information — and that distinction became very important as I started building and connecting systems at GFF.

You Have to Teach AI Your Organization

The more complex the work became, the more context I had to provide. I had to explain what different programs were doing, what different columns meant, how sessions related to videos, how participants related to events, why certain IDs mattered, and what I considered a correct record. Only after providing that context could AI start giving me solutions that actually fit the organization.

That taught me something: AI doesn't arrive knowing your organization — you have to teach it. And the better you understand your own organization, the better you can use AI.

This Is Where Human Knowledge Becomes Valuable

There's sometimes a fear that AI will make human knowledge less important. My experience has actually made me think the opposite. The more capable AI becomes, the more valuable the person who understands the actual problem becomes.

If I know exactly what GFF needs, I can use AI very effectively. If I don't understand the organization, AI can produce a technically impressive solution that solves the wrong problem — and that can be worse than having no solution at all. A beautifully written script that does the wrong thing is still wrong.

I Learned This Through Trial and Error

My work with Apps Script taught me this repeatedly. I could ask ChatGPT to create a script, and it would look completely reasonable. Then I'd run it against the actual data and something wouldn't match — a row imported incorrectly, an ID generated in the wrong place, a record that didn't connect to the session I expected, an assumption that didn't match how our system actually worked.

At that point, I'd have to go back and explain the organization more clearly. The problem usually wasn't that the AI couldn't write the code. The problem was that I hadn't fully communicated the system. That was an important lesson.

Chess Gave Me Another Example

Chess provided another unexpected example. When we recently wanted to analyze tournament games stored in a PGN file, the technical task was relatively straightforward: read the PGN, extract the games, analyze the moves, produce useful information.

But even there, the interesting questions weren't purely technical. What do we actually want to learn from the games? Individual player performance? Blunders? Opening choices? Patterns across the tournament? How players made decisions? Those questions determine what the analysis should actually do. Python can process the PGN, and AI can help write the Python — but we have to decide what's worth analyzing.

AI Can Build the Tool. You Define the Purpose.

This has become one of my simplest ways of thinking about AI. It can help build the tool; humans have to define the purpose. It can help write the database query; humans have to decide what information matters. It can help write the Apps Script; humans have to decide what the workflow should be. It can help analyze the chess games; humans have to decide what they want to learn from them. That relationship works very well when both sides are doing what they're good at.

Context Is Everything

I've also learned that the quality of the answer I get from AI depends heavily on the quality of the context I provide. A vague description gets a generic solution. But when I explain the actual workflow, the data structure, the constraints, the mistakes I've already encountered, and what the final system needs to achieve, the quality of the response changes significantly.

That's forced me to become better at documenting my own thinking. I have to know what I'm asking. I have to understand what I'm trying to achieve. I have to separate the actual requirement from the solution I initially imagined — and sometimes admit my original approach was wrong.

AI Doesn't Walk Into the Building

There's another difference that's easy to overlook. AI doesn't experience the organization. It doesn't walk into GFF and see the people using the equipment. It doesn't watch a chess tournament unfold. It doesn't see someone struggling to record information, or notice that a process which looks perfect on paper is inconvenient for the person actually using it. It doesn't hear the conversations around a problem, or observe the unexpected things that happen during normal operations.

People do. And that real-world experience matters.

The Best Systems Come From Both

I've come to believe that the best results happen when human experience and AI capability are combined.

The human brings context, experience, judgment, purpose, organizational knowledge, an understanding of people, and awareness of what's practical. AI brings speed, technical knowledge, pattern recognition, coding assistance, ideas, alternative approaches, and the ability to work through large amounts of information.

Neither side is enough on its own. But put them together, and something interesting happens — a person who understands the problem can suddenly build things they might previously have considered beyond their ability.

The Lesson I Took From GFF

My experience at Global Fast Fit has taught me that technology should serve the organization, not the other way around. It's easy to get excited about building a sophisticated system. It's much harder to ask whether that system actually solves the right problem.

That's why I now try to start with the organization rather than the technology: first understand what's happening, then understand what's going wrong, then define what needs to change. Only after that should you decide whether the answer is a spreadsheet, a formula, Apps Script, Python, AI, or something completely different.

AI Can Be Powerful Without Being in Charge

I don't think the future of AI is necessarily about AI taking over every decision. From my experience, I see something more practical: AI becomes incredibly powerful when it works alongside people who understand the problems they're trying to solve.

At GFF, AI has helped me move into areas I wouldn't have expected to explore. But the organization still gives the work its meaning. The people still define the problems. The human still checks the result, and the human still decides whether the solution makes sense.

That's perhaps the most important lesson I've learned: AI can help you build the solution, but you still need to understand the problem. Because no matter how good the code is, a technically perfect solution to the wrong organizational problem is still the wrong solution.

AI didn’t teach me how to cook. Knowing how to cook taught me how to use AI to cook.

I have been cooking for a long time. For years, my relationship with recipes was simple: I searched for them. If I wanted carrot cupcakes, pilau, bread, cake, or something I had never made before, I would go online, mostly to YouTube, search for what I wanted, compare a few recipes, choose one, check the ingredient list, and then work backwards from there. Do I have tomatoes? Yes. Onions? Yes. Ginger? No. Vanilla? No. Fine, add those to the shopping list. The recipe told me what I needed, and I went out to find it.

TikTok came later, and although my algorithm seems determined to feed me a recipe every third or fourth scroll, I have learned to be suspicious of TikTok food. Everything looks magnificent in thirty seconds. Butter sizzles. Cheese pulls. Cakes rise perfectly. Someone cuts into something impossibly moist. Then I try it, and more often than I would like, it flunks. So YouTube remained my safer place for recipes. At least there, someone usually had enough time to explain what they were doing. I could see textures. I could watch the batter. I could listen for the little warnings: don’t overmix this, brown that properly, wait until this changes colour.

But even YouTube has limitations. Sometimes you have to watch three or four videos before you find one that suits what you have. Sometimes the recipe assumes ingredients that are easy to find somewhere else but not in your kitchen. Sometimes it is nine at night, the shops are closed, you live far enough from everything that “just pop out and buy some baking soda” is not a realistic suggestion, and your children still need something to eat the next morning. That is where AI changed cooking for me.

But I think I have to go further back to explain why.

Growing up, my mother was not what people would call an impressive cook. She was a full-time nurse raising three children, mostly on her own. My father was a soldier, and when we were young he was away on missions for long periods of time, sometimes years. My mother would leave for work early, finish her shift in the late afternoon, collect us, shop for whatever was needed, get home and start again. We did not have the convenience of walking into the kitchen and turning on a gas burner. We cooked in an outside kitchen. If she got home and the fire was out, the kitchen was cold. Firewood had to be split. A fire had to be lit. Then dinner had to be cooked. There was homework. Laundry. Cleaning. Children to bathe. Clothes and school things to prepare. And somewhere in all of that, she also had to rest because the next morning was coming whether she was ready or not. By nine at night, our house was dark and silent.

So my mother cooked what worked. We had a small rotation of meals. They were healthy. They were fully cooked. They filled our stomachs. There was no garnish, no unnecessary flourish, no culinary performance. The objective was sustenance.

Other people noticed. My aunties, especially those who spent more time at home or had more support in their households, cooked differently. Their food looked different. There was more variety, more presentation, more of what people considered proper hosting. People sometimes joked about my mother’s cooking. I remember the jokes because they hurt me. They would say things like, “Your mother puts in the least effort,” or joke that they wished they could care as little as she did.

Once, my mother made rice, potatoes and beef. She had cooked the potatoes and beef together in one pot. Guests came over and refused to eat. They said they had already passed by Pizza Inn and had pizza. Maybe they had. But as a child, I saw the looks. I understood the joke beneath the politeness. My mother fed us. She gave the visitors tea. We ate the food later.

That stayed with me, because I could see what my mother was carrying even when other people could only see the plate. So when I started learning how to cook properly in my mid-teens, I was not simply learning a nice life skill. I was learning to cook to avenge my mother.

And because I am apparently incapable of doing anything halfway, I may have overcorrected.

When I later went to campus, started working, and eventually lived alone, cooking became something else entirely. I had time. I would come home and make elaborate, sensual meals for one person: me. People in the apartment block would smell the food and knock. “What are you cooking today?”

If I was making pilau, I did not simply pour ground pilau masala into a pot. I would open a packet of whole pilau spices and select what I wanted for that particular meal. I would look for the biggest, healthiest-looking pieces. I would boil water and soak the spices, then sieve them and rinse away the dust. I would dry-roast the whole spices in a pan until the kitchen became fragrant, then grind some of them and keep others whole. I would split open the cardamom pods to expose the seeds and break down pieces that were too large. I would prepare the peas separately. I would sometimes add a little sugar to the water so they stayed bright green, then remove them before they overcooked. The onions had to become perfectly golden brown. The meat had to brown properly.

There was music. There were podcasts. There was nowhere I needed to be afterwards. I could spend an absurd amount of time making dinner and then sit down and eat it by myself. Cooking was craft. It was entertainment. It was pleasure.

Then I got married. Then I had children, very quickly. Then the jobs became more demanding. Life filled up. And slowly, I started approaching the same line my mother had stood on years before. I no longer had endless time to roast spices while listening to music. Sometimes I was tired. Sometimes there were children demanding things from me. Sometimes work had followed me home. Sometimes dinner was not an artistic opportunity. It was 7 p.m. and people needed to eat.

I could feel myself getting dangerously close to cooking purely for sustenance, and I could not let that happen.

The funny thing is that adulthood also made me understand my mother differently. As a child, I thought the enemy of beautiful cooking was lack of skill. Growing up taught me otherwise. Sometimes the enemy is simply time. My mother had not failed to discover the magic of caramelised onions. She had three children, a nursing job, an absent husband, a wood fire and tomorrow morning coming at her very quickly.

Then, somewhere in the middle of my own busy life, I started a snack bar. And that is how AI cooking properly entered my life.

I had been using AI for other things, and while thinking through the snack bar, I started asking it for recipes. Not just searching for recipes. Building them. That was the lightbulb moment. For years, recipes had told me what I needed. AI was the first time I could tell the recipe what I had.

Before, the process might have been: I want carrot cupcakes. Search YouTube. Watch five videos. Compare them. Pick one. Hope. Now the conversation is more like: I have oat flour. I have carrots. I have yoghurt. I have no vanilla. I have no baking soda. I need enough for forty cupcakes. They are for children, so I do not want them very sweet. Make that work.

And it does. Or at least, it gives me somewhere very useful to begin.

That flexibility has completely changed the way I cook. I am very particular about how much sugar my children eat. Commercial baked goods are often ridiculously sweet. I do not need a cupcake designed to make a five-year-old’s eyeballs vibrate. So I can say that. Reduce the sugar. Make this mildly sweet. I only have honey. I have no butter. Can I use yoghurt? I have leftover rice. I have cumin seeds. What can I do with that?

And suddenly I have cumin rice that is so good I wonder why I was ever going to throw the rice away.

AI is particularly good at leftovers because leftovers rarely fit neatly into traditional recipes. You do not search YouTube for, “I have one and a half cups of rice from yesterday, half a capsicum, two eggs, cumin seeds, a tired spring onion and approximately one tablespoon of yoghurt.” But that is a perfectly normal thing to tell AI. It works with the kitchen that exists.

That is the difference. I used to search for recipes. Now I build them.

One of my favourite moments came after I made a batch of oat cupcakes. Someone tried them and could not get enough. He came back for another, and then another, and eventually asked me to pack some takeaway for his wife. As I was packing them, I casually told him, “Did you know this is an AI recipe?”

He looked genuinely surprised. “Really?”

I told him it was an entirely AI-generated oat cupcake recipe.

He said he had never known anyone who used AI to cook.

I loved that reaction, because by that point the cupcakes had already passed the only test that really mattered. He had eaten them, enjoyed them, come back twice, and wanted to take some home. The fact that AI had helped build the recipe only became interesting afterwards.

And I probably cook about 80 percent of my meals with some form of AI involvement now. That sounds strange even to me, especially because I am not somebody who did not know how to cook and then discovered a robot chef. I already knew how to cook. That is precisely why it works.

I can look at a recipe and know when something seems wrong. Two cups of sugar? Absolutely not. That batter is going to be too wet. That quantity of flour will make it dense. That spice needs to bloom first. Those onions are nowhere near ready.

AI cannot smell my onions. It cannot taste my batter. It cannot know whether the oranges I bought this week have unusually bitter rinds. It cannot touch a dough and realise it needs another tablespoon of flour. It does not know that my particular oven runs slightly hotter on one side.

I am still the cook.

And I think that distinction matters. AI has not replaced my judgement. It has removed a lot of the friction around using it. It handles the mathematics of scaling twelve cupcakes to forty. It helps me think through substitutions. It can suggest what to do with ingredients that need using up. It can adjust sweetness. It can help me troubleshoot when the cocoa powder runs out halfway through a baking session. It can give me a starting point at 9 p.m. when the shops are closed, there is no bread for tomorrow, and I am standing in the kitchen looking at oats, yoghurt, carrots and determination.

It does not return me to the version of myself who could spend two hours hand-roasting pilau spices on a random Tuesday. I do not have that life anymore. What it does is help me preserve some of that woman inside the life I have now. The woman who still wants the food to be interesting. Who still wants her children to eat well. Who still likes discovering what happens if she changes something. Who still wants cooking to contain pleasure, not just responsibility.

The snack bar pushed the experiment further because cooking for your own family is one thing. Selling food is another. A home recipe can be slightly different every time. A product cannot. Now the questions become more serious. Can I make it again? Can I scale it? What does one batch cost? Can I substitute an ingredient without destroying the texture? Will children actually eat it? Will someone pay for it twice? And perhaps most importantly: when the AI-generated cookie comes out of the oven, does it actually taste good?

That is where the machine stops being impressive simply because it produced an answer. The kitchen gets the final vote.

Sometimes the recipe works beautifully. Sometimes it needs more flour, less sugar, or a longer bake. Sometimes I take one look at what it has suggested and change half of it before I even start. Which is why, despite how much AI has transformed the way I cook, I do not think the most interesting question is whether AI can write a recipe.

Of course it can.

The more interesting question is what happens when that recipe meets a cook. Because AI can generate the first draft. It cannot taste the cookie. The interesting part is what happens next when a cook can tell the machine, “That batch was too dry. Fix it.”

People usually think of table tennis as a relaxed basement hobby or a game played at a casual summer BBQ with a drink in one hand. But when you step onto the court competitively, the entire atmosphere transforms. It becomes a high-velocity chess match played at breakneck speed, where every single rally is a blur of intense physical focus, split-second tactical decisions, and explosive power. Living my life as a table tennis player means training my mind to process complex strategy in a matter of milliseconds while pushing my legs, core, and wrists to their absolute physical limit.

My daily training routine starts long before I ever pick up a paddle. Power in competitive table tennis doesn't actually originate from the arm, as many people assume. Instead, it starts in the legs, transfers through the hips, and snaps through the wrist. My mornings are filled with dynamic footwork drills and shadow swings to ensure I can cover all nine feet of the table in a fraction of a second. This is immediately followed by high-intensity drills where my robot feeds hundreds of balls at high speed. This repetition forces me to build pure muscle memory for every rotation, angle, and bounce, while conditioning my reflexes to instantly read subtle changes in incoming ball trajectories.

To keep pace with the game’s evolution, I’ve integrated smart AI technology into these high-volume sessions. Rather than firing balls in predictable, repetitive patterns, my app-controlled robot uses dynamic algorithms to instantly alter shot depth, interval timing, and spin variation in real time. Paired with smartphone computer vision tools tracking every rally, the AI maps my footwork speed and ball placement heatmaps across hundreds of shots. It turns raw multi-ball drills into an adaptive, match-like challenge—forcing my feet to move continuously and ensuring every micro-adjustment strengthens real, instinctive muscle memory under fatigue.

The intense physics of the sport make this level of preparation essential. In a high-level competitive match, a loop drive can travel upwards of 70 mph, with topspin reaching over 100 revolutions per second. With less than 0.25 seconds to react to an incoming ball, there is simply no time to stop, analyze, and deliberate. You have to trust your muscle memory, anticipate your opponent's next move, and execute your shot instantly without a moment of hesitation.

While the physical demands are rigorous, the mental battle is where matches are ultimately won or lost. Table tennis requires complete emotional control under immense pressure. When facing a rapid pace, I have to force myself to step forward toward the table rather than retreating backward. When dealing with heavy spin, I have to keep my body relaxed to generate a smooth counter-stroke. Tight scores demand that I reset my focus entirely between points, keeping my strategy simple and treating every ball as its own isolated contest.

Ultimately, being a table tennis player is about far more than just winning points or collecting trophies. It is about the pure, addictive feeling of hitting the perfect counter-loop, outsmarting a skilled opponent, and constantly striving to sharpen your focus. When I pack up my equipment at the end of a long day, my legs are tired and my energy is drained, but I wouldn't trade the thrill of life at the table for anything else.

Today, I am not blogging about fitness. Today, I am blogging about structure.

If you have followed most of my blogs, you probably already know what to expect from me. Fitness, exercise, nutrition, trainers, clients, lifestyle diseases and everything in between. But today, let's leave the gym for a moment.

Let's talk about organisations.

One of the most interesting examples of planning and execution in modern history is the operation that led to the death of Osama bin Laden.

This is not an article about celebrating terrorism or revisiting the pain caused by 9/11. I am looking at the operation from a completely different perspective: planning, intelligence, reconnaissance, communication, clear instructions, resources and execution.

Think about it.

The operation did not simply begin with someone saying, "Let's go and get him."

There was intelligence gathering. There was surveillance and reconnaissance. There was analysis. There were people whose job was to understand the target and the environment. There was a chain of command. There were specialised teams. There were resources and equipment. There were instructions.

Then came the day of execution. The Navy SEALs were not walking into an unknown environment without preparation. They were executing a mission that had been planned, studied and rehearsed.

If that planning had not been done, the mission could have ended very differently — potentially as a catastrophe.

And this is where the lesson becomes relevant to every organisation.

Organisations Go to War Every Day 

Not the physical kind of war. Businesses, organisations, gyms, schools, hospitals, NGOs and government institutions are fighting their own battles every single day.

They are fighting for customers.

They are fighting for efficiency.

They are fighting against competition.

They are fighting to meet targets.

They are fighting to retain employees.

They are fighting to deliver better services.

In a way, organisations are trying to capture their own Osama every month, every quarter, every year. The target could be poor sales, low productivity, high staff turnover, poor customer retention, financial problems, competition or simply a target that the organisation has failed to achieve for years.

And if the structure is poor, Osama keeps escaping. 

You keep setting the same target.

You keep identifying the same problem.

You keep holding the same meetings.

You keep discussing the same weaknesses.

Yet months later, you are still chasing the same thing. Why?

Because wanting to capture the target is not enough.

You need a mission.

Every Organisation Has Soldiers and Artillery. 

Every Organisation has soldiers. The employees are the soldiers.

Every organisation has artillery .The tools, equipment, technology, systems and resources available to employees are the artillery.

And most importantly, every organisation needs a general. Not necessarily one person sitting in an office giving orders, but a leadership structure that understands the mission, develops the strategy and communicates it clearly through the chain of command. Take away any one of these and the mission becomes vulnerable.

You can have the best soldiers in the world, but if they have no equipment, they are limited. You can have the best equipment, but without people who know how to use it, it becomes useless.

You can have excellent employees and excellent resources, but without a plan, everyone can be moving in different directions.

Soldiers + artillery + strategy.

That combination gives an organisation a fighting chance.

Planning Should Come Before the Battle. 

One of the biggest mistakes organisations make is expecting employees to figure everything out as they go.

A new month begins.

Everyone reports to work.

Targets are announced halfway through the month.

Instructions change.

Priorities change.

People are moved from one responsibility to another. Then, when the results are poor, someone asks:

"Why didn't we achieve our target?"

But perhaps the better question is:

"Did everyone know the mission?"

Every organisation should have a clear structure before entering its next "battle."

It could be a monthly plan.

A quarterly plan.

A yearly plan.

Whatever the period, the principle remains the same:

People need to know where they are going before you ask them to run.

At the beginning of every month, every employee should understand:

- What are we trying to achieve?

- What is my role?

- What are my targets?

- What worked last month?

- What did not work?

- What are we maintaining?

- What are we changing?

- What resources do I have?

- Who do I report to?

- Who do I work with?

- How will success be measured?

This may sound simple.

Because it is.

And perhaps that is the frustrating part.

Some of the things that make organisations fail are not complicated problems. They are simple things that were never properly structured.

Give Every Soldier a Mission 

Imagine sending ten soldiers into a battlefield and telling them:

"Go and do your best."

But nobody tells them the objective.

One goes left, Another goes right, Another waits, Another starts fighting a completely different battle, Another does not even know why they are there.

Would we blame the soldiers?

Maybe.

But the bigger problem would be the command structure. The same thing happens in organisations.

An employee cannot be expected to perform at their best when their responsibilities are unclear.

Give every employee a mission. Not just a job title. A job title tells you what position someone occupies.

A mission tells them what they are expected to accomplish.

And once the mission is clear, allow the employee to perfect their role.

But Don't Kill Individual Brilliance. 

Structure does not mean turning employees into robots.

In fact, one of the biggest mistakes leaders can make is creating such a rigid system that people are afraid to think.

Every organisation has individuals who can see things differently. Someone may notice a problem nobody else has noticed. Someone may have a better way of serving customers. Someone may discover a more efficient process.

Someone may have an idea that changes the entire organisation.

Individual brilliance is an asset. Leadership should not suppress it.

Leadership should give it direction. A good structure should tell people where the organisation is going, while still giving them enough room to figure out how to get there better.

The Danger of Changing The Mission Mid-Battle. 

Here is where many organisations lose the battle. They start the month with Plan A. Halfway through the month, leadership introduces Plan B.

Then Plan C appears.

An employee who was given one responsibility suddenly receives another.

A system that was working is abandoned.

Targets change without explanation.

Priorities shift without a replacement strategy. And sometimes, there is not even a backup plan. Change is necessary. Organisations must adapt.

But changing direction without communicating the new mission creates confusion.

If Plan A is no longer working, explain why. Develop Plan B.

Give people new instructions.Provide the resources required.

Explain what has changed.Then let everyone execute.

Don't change the mission and expect the soldiers to somehow know the new battlefield.

Q&A is Part Of Perfecting the Mission. 

Questions are not a sign of weakness. They are part of preparation.

If an employee asks:

"What exactly do you want me to achieve?"

That question should not irritate a leader.

It should make the leader happy.

Because it gives the organisation an opportunity to remove confusion before confusion becomes failure.

Regular Q&As, reviews and check-ins are therefore not simply meetings. They are opportunities to refine the mission.

What worked?

What failed?

Why did it fail?

What did we learn?

What should we maintain?

What should we stop?

What should we change?

That is how an organisation becomes better. The Mission Is Bigger Than One Person

There is another important lesson here.

The success of an organisation should not depend entirely on one individual.

A great employee without support will eventually become frustrated.

A great manager without a capable team will eventually become overwhelmed.

Great equipment without skilled people will sit unused.

And great strategy without execution remains just a document.

Everything is interconnected.

The soldier needs the artillery. The artillery needs the soldier.

Both need a plan.

And the plan needs leadership, communication and execution.

Remove one piece and the entire system becomes weaker. Every Month Is Another Mission

Perhaps this is how organisations should approach their work.

At the beginning of the month:

Here is our mission.

Here is what we achieved last month.

Here is what did not work.

Here is what we are maintaining.

Here is what we are changing.

Here is your role.

Here are your targets.

Here are your resources.

Here is who you report to.

Then let everyone execute. At the end of the month:

Did we win?

If yes, why?

If no, why?

Then learn and prepare for the next mission. Because the objective is not to have a perfect month every month.

The objective is to build an organisation that learns from every mission.

The Final Lesson 

The operation that led to Osama bin Laden was successful because it was more than simply sending people to a location.

It involved intelligence, planning, preparation, resources, leadership, communication, specialised personnel and execution.

Had those elements not come together, the consequences could have been catastrophic.

Organisations should understand the same principle.

Do not send people into battle without telling them what they are fighting for.

Give them the tools.

Give them the information.

Give them the structure.

Give them clear instructions.

Give them room to use their individual brilliance.

And when the plan changes, communicate the change.

Because at the end of the day:

A mission without soldiers cannot be executed.

Soldiers without artillery are limited.

Artillery without soldiers is useless.

And soldiers and artillery without a plan can still lose the war.

Today, I am not talking about fitness. I am talking about structure.

And perhaps the greatest competitive advantage an organisation can have is not having the most talented people or the most expensive equipment.

It is having the right people, the right tools, the right structure and a clear mission — all moving in the same direction.

Disclaimer: I am not a doctor, and I stand to be corrected by medical professionals. This is simply an observation based on my experiences, conversations with health professionals, and the things I have been learning about health and lifestyle.

In the Global Fast Fit fraternity, most of you know Dr. James. But today—just today—you can call me Dr Kelvin. 

I have listened to Dr. James speak about lifestyle diseases, read articles, listened to health professionals, and observed people around me. One thing has become increasingly clear to me:

There is a thin line between preventive health care and curative health care—but both come at a cost.

And perhaps the bigger question is:

Which cost are you willing to pay?

Prevention has a price.

Preventive health care sounds simple when we talk about it.

Wake up early.

Exercise.

Go to the gym—or engage in whatever form of physical activity you enjoy.

Watch what you eat.

Reduce excessive sugar and unhealthy foods.

Avoid smoking and excessive alcohol.

Get enough sleep.

Go for medical check-ups.

Maintain a healthy weight.

Sounds easy, right?Not necessarily.

Prevention requires discipline. It requires saying no when everyone else is saying yes. It may mean waking up when your bed is still warm and going for a walk. It may mean choosing water instead of another sugary drink. It may mean reducing foods you love.There are sacrifices involved. And sometimes you may even ask yourself, “Why am I doing all this when I feel perfectly healthy?”

That's the tricky part about prevention. You are making sacrifices today for a problem you may never experience tomorrow.

But curative health care also has a price 

Now consider the other side.

Someone develops a lifestyle-related condition and needs treatment. Suddenly, there may be medication to take every day. There may be foods to reduce or completely avoid—not because you chose to, but because your doctor has advised you to. There may be regular hospital visits, tests and monitoring. There may be complications. There may be financial costs. And perhaps most importantly, there may be things you once did freely that you can no longer do in the same way. The irony is that some of the sacrifices we resist making for prevention can eventually become sacrifices we are forced to make because of illness. The difference is that prevention gives you more room to choose.

The choice is not always ours 

Of course, we should be careful not to oversimplify this. Not every disease can be prevented by going to the gym or eating well.

Genetics matter.

Age matters.

Environment matters.

Accidents happen.

Some illnesses occur despite people doing everything “right.” And sometimes people simply do not have the resources, information or circumstances necessary to make healthy choices. So this isn't about blaming sick people for their illnesses. It is about recognizing the things that are within our control.

We already know many of the choices 

This is perhaps the most interesting part. Most of us already know what we are supposed to do. We know smoking is harmful. We know excessive alcohol consumption can be harmful. We know physical inactivity isn't good for us. We know that constantly eating poorly isn't ideal. We know that sleep matters. We know that carrying excessive weight can increase the risk of certain health problems. We know we should get checked when something doesn't feel right.

The problem is rarely a complete lack of information. The problem is often action.

We want the benefits of good health without paying the price that comes with maintaining it. But health doesn't work that way.

Every choice has a price 

This is where my “Dr Kelvin” theory comes in. 😂

Maybe the question isn't: “How do I avoid paying a price?” Maybe the question should be:

“Which price am I willing to pay?”

Preventive health may cost you early mornings, discipline, exercise, dietary changes and saying no to certain habits.

Curative health may involve medication, hospital visits, dietary restrictions, monitoring, treatment costs and sometimes complications. Neither path is completely free. Both require sacrifices.

The difference is that with prevention, you are often paying a smaller price today in the hope of avoiding a much bigger price tomorrow. And that, to me, is the real value of preventive health.

Don't wait for your body to negotiate on your behalf

One of the biggest mistakes we make is waiting until the body forces us to pay attention. We ignore the small signs.

We postpone check-ups. We say, “I'll start exercising next month.”

We say, “I'll change my diet when I start feeling sick.”

We say, “I'm still young.”

But health doesn't always give us a warning before the bill arrives.

Sometimes prevention is simply choosing to take responsibility for the things we can control while we still have the opportunity to do so.

Exercise.

Eat reasonably well.

Sleep.

Avoid harmful substances.

Get appropriate medical check-ups.

Listen to your body.

And when something is wrong, seek professional medical advice rather than trying to diagnose yourself from Google—or from Dr Kelvin. 

So, which poison suits you best?

Maybe that sounds like a strange question. But in reality, both choices come with inconveniences.

One asks you to sacrifice some comfort now for the possibility of a healthier future.

The other may require you to sacrifice some comfort later because your health has already demanded attention. You cannot always choose whether illness will come. But you can influence how you treat your body while you have the chance. Preventive health isn't a guarantee that you will never get sick.

Curative health isn't a failure.

And choosing treatment when you are sick is not something to be ashamed of. But if there are things we can reasonably do today to reduce our risk tomorrow, perhaps they are worth doing.

Because at the end of the day, both prevention and treatment have a cost.

The question is:

Which price are you willing to pay? — Dr  Kelvin 😂

Disclaimer: This is a personal reflection and is not medical advice. I am not a doctor. For medical concerns, diagnosis, treatment and individual health decisions, consult a qualified healthcare professional.

Every movement discipline has its gold standard—that single, uncompromising metric that separates high-level athletic effort from absolute peak performance. In the world of high-intensity functional speed, few benchmarks test the spectrum of aerobic capacity, explosive power, and raw mental grit quite like the Global Fast Fit standard routine: 15 push-ups, 15 leg lifts, 15 bodyweight squats, and a 250-meter full-throttle sprint.

To most disciplined athletes, breaking the two-minute barrier is a milestone. Crossing into sub-70-second territory requires elite conditioning. But stopping the clock at 59 seconds? That places an athlete in a tier all their own.

That exact mark is held by Luke Ngasha, a Nakuru-based athlete, fitness trainer, and community leader whose path to elite fitness was forged through relentless everyday consistency.

The Architecture of the 59-Second Burn

To understand what sub-60 seconds on the GFF standard routine actually means, you have to break down the biomechanics and time domain:

15 Push-Ups (~8–10 seconds): Explosive chest, shoulder, and triceps engagement with zero room for partial depth or wasted momentum.

15 Leg Lifts (~10–12 seconds): Instant transition to core bracing, controlling flexion without allowing the hip flexors to fatigue before standing.

15 Squats (~10–12 seconds): Dropping to parallel and driving upward rapidly, forcing immediate lactate clearance in the quads and glutes.

250m Sprint (~25–27 seconds): Transitioning directly from bodyweight resistance into a dead sprint while operating on near-zero oxygen.

Executing these movements back-to-back without a single breath of recovery demands more than physical strength—it requires absolute anaerobic efficiency and immediate transition speed between the floor and the track.

Community First: Building at Lanet

For Luke, holding a benchmark record is only half the picture. As manager and lead trainer at the Lanet Community Center in Nakuru County, the focus extends well beyond personal metrics.

Whether directing morning strength sessions, structuring progressive squat challenges, or organizing multi-sport community events—from chess and table tennis to high-energy fitness trials—the core philosophy remains the same: accessible, data-driven, and high-standard functional training for everyone.

By combining structured metabolic tracking with practical, real-world athletic movement, the center operates as a hub for local fitness culture—proving that elite performance isn't built in luxury facilities, but through structured discipline, clear systems, and community support.

The Takeaway

True fitness performance isn't about arbitrary numbers or fleeting trends. It’s about setting a standard, tracking progress with precision, and executing consistently day after day.

You know how when you were young and you wanted to go somewhere — maybe to eat lunch at your friend’s house, maybe to play, maybe even to go for a sleepover — and then your mother said no? So you had to go back, very helplessly, and say, “Sorry, my mum said No.” That was me in this situation. Except I am now a grown woman helping run a community-based organization, the thing in question was not a sleepover, and the mother who said no was ChatGPT.

This whole thing started very innocently. At Global Fast Fit C.B.O, we had started running regular chess events, and they were slowly getting attention in Nakuru. The events were not huge, but they had life. They had a community feeling around them — children, adults, beginners, stronger players, friendly competition, and the kind of buzz that makes you feel something is growing. Somewhere along the way, a local event-management and ticketing platform noticed what we were doing and approached us with what sounded like a very simple offer: send us your poster, send us your event details, we can advertise and ticket for you.

At first, that sounded useful. As a small organization, you are always looking for more visibility. More people posting your event means more eyes on the poster. More eyes can mean more attendance. The offer sounded like extra support, not a takeover. In my mind, it was simple: we would continue promoting our own events through our own networks, and this platform would also help put the events in front of more people. Let us advertise. Let them advertise. Let everyone bring people. Very harmless, very practical, very “why not?”

But then the simple thing started developing a personality. It was no longer just “send us the poster.” It became “all tickets should go through our platform.” Then it became “all the money should go through us.” Then it became “we deduct our percentage and later remit the balance to you.” That is where the first discomfort started, because anyone who has ever planned even a small event knows that event money is not decorative. It is not just a figure waiting quietly for the event day. It moves before the event. Snacks have to be bought. Some snacks need preparation before the day itself. Branding has to be done early. T-shirts, medals, water bottles, printing, logistics, transport, venue preparation — these things do not wait for the morning of the event.

The other issue was the payment journey. We already had our own paybill. We already had our own community. Many people who attend our events hear about them directly from us — through WhatsApp, friends, family, neighbours, parents, workmates, chess contacts, and people who know the centre. When someone is already in your inbox asking, “I saw your poster, how do I pay?” the easiest and most natural thing is to give them the paybill immediately. You do not want to send them on a long pilgrimage through a third-party link, extra steps, and a different payment process. When someone gets the urge to give you money, you should not create an obstacle course. Some people will tolerate a complicated process because they like you. Others will quietly disappear.

So from where I stood, there was already a mismatch. What we needed was additional visibility and support. What was slowly being requested was control of the ticketing pathway. That difference matters. We were not looking for someone to take over the event. We were looking for more people to help us amplify it.

Eventually, a meeting happened. I attended mainly as the note-taker, which is a role I actually enjoy. I like sitting in the background during these kinds of conversations because I get to observe how people present themselves, how proposals are framed, how negotiations move, and how much is said without being said directly. I am still learning that world, so I like listening. The meeting was meant to clarify what this proposed working relationship would look like, especially because the ticketing arrangement was no longer sounding as simple as it had first appeared.

During the meeting, one thing became clear to me: we needed the offer put in writing. Not because anyone wanted to create unnecessary bureaucracy, but because verbal conversations are too slippery once money starts moving. People forget what they said. People remember things differently. People become very creative with memory when an event starts making money. The right time to disagree is before the event, before the ticket sales, before the sponsors, before the crowd, before the pressure. So the request was simple: send us a written MOU or agreement showing what you want, what you are offering, what you expect from us, and how the money and responsibilities will work.

The meeting ended without a concluded agreement. That part is important. We did not leave with signed terms. We did not leave having accepted the proposed structure. We left with the understanding that a written document would be sent for review. So when the document eventually arrived, I treated it as a draft. A proposal. Something to be read carefully before any decision could be made.

Then the document came.

And my goodness.

The audacity arrived in PDF form.

What I expected was maybe a simple MOU: here is the event, here is what we will do, here is what you will do, here is our fee, here is when money will be remitted. What arrived was a long, formal agreement with clauses on ticketing, gate verification, refunds, insurance, liability, permits, data protection, event safety, crowd control, cancellations, branding, complimentary guests, deductions, and responsibilities. It was no longer giving “send me the poster.” It was giving “welcome to the legal department.”

I read it. Then I read it again. I wrote notes in my book. I tried to break it down clause by clause, but the document had so much legal language that parts of it kept slipping past me. I understood maybe 65% of it, but the remaining 35% was making me deeply uncomfortable. It felt like being insulted in a language you do not fully understand. You may not know the exact words, but your spirit knows something has happened. That was the feeling. I could not explain every clause perfectly, but I knew the agreement was not as innocent as the original offer had sounded.

So I did what any modern manager operating slightly outside her expertise might do. I took it to ChatGPT. Not to make the decision for me, not to replace a lawyer, not to become the board of directors, and not to give the final word on behalf of the organization. I needed a first layer of translation. I needed the kind of help you ask from that lawyer friend you wish you had in the room — the one you call and say, “Please read this thing and tell me what I am actually agreeing to.”

That is where the story became funny to me, because ChatGPT did not scream. It did not dramatize. It did not say, “Run.” It calmly translated the agreement into plain English, and the more it explained, the more the answer became obvious. Clause by clause, the document stopped looking like a simple ticketing support proposal and started looking like a risk-transfer ceremony. The ticketing company would handle ticketing, ticket verification, ticket reports, and ticket proceeds, but GFF would remain responsible for most of the things that could go wrong.

In plain language, the agreement was saying that they would collect ticket money, deduct fees, control the ticketing process, and later remit the balance. Meanwhile, we would carry the burden of the actual event: permits, venue safety, insurance, refund exposure, crowd control, complimentary guest lists, gate arrangements, data duties, legal compliance, and operational risks. The 6% fee itself was not even the main issue. The bigger issue was the position the agreement placed us in. They held the platform, the money pathway, and the protective clauses. We held the event risk.

That is when my AI mother said no.

Politely. With reasons. But no.

The funny part is that the original offer had been so casual. It started as “send us the poster.” By the time the agreement was translated into ordinary language, it had become “all money passes through us, we deduct our percentage, we pay later, you carry the event risk, and here are several clauses about permits, insurance, refunds, data, liability, and safety.”

And this is where the management lesson became bigger than the document itself. Many small organizations are vulnerable in exactly this way. Someone approaches you with what sounds like an opportunity. The language is friendly. The offer sounds useful. The other party appears experienced. Then a formal document arrives, and because it looks professional, you feel pressure to treat it as reasonable. Sometimes you even feel embarrassed to admit that you do not fully understand what you are reading. That embarrassment is dangerous, because people do not only sign bad documents because they are careless. Sometimes they sign because the other person sounds confident, the document looks official, and they do not want to look difficult.

This is why I appreciated having AI as a first layer of protection. It did not replace judgment. It improved judgment. It slowed me down. It turned legal language into operational language. It helped me see where the money was flowing, where the risk was sitting, where the power was being placed, and where the agreement did not match the original conversation. It helped me move from vague discomfort to specific questions. That is a very useful management tool.

The relationship eventually wrapped itself up quietly. We did not proceed with that agreement. The person later attended one of our events as a participant, not as our ticketing provider. He paid, played, and left. And that was fine. Not every conversation has to become a partnership. Not every opportunity has to be accepted. Sometimes the most important outcome of a meeting is learning what not to enter.

The lesson was not that ticketing platforms are bad. They are not. For the right event, with the right terms, they can be very useful. The lesson was also not that AI is a lawyer. It is not. The lesson was that as a growing organization, we need document literacy just as much as we need programs, events, sponsors, and ideas. A partnership is not a partnership just because the language is polite. A document can smile at you while quietly moving all the chairs in the room.

So now I have a new management habit. When something looks official but feels strange, I do not panic, and I do not pretend to understand. I read it slowly. I translate it. I ask what each clause means in real life. I check where the money goes. I check who carries the risk. I check who has control. I check whether the document matches the conversation that came before it. And when necessary, I go back with my newest excuse in the book:

Sorry, my AI said no.

https://docs.google.com/document/d/138EH7eB-leoGZF-tlnHK0zD-_OSdbGjiFK7UZbt5k40/edit?usp=drivesdk

Sylvia was supposed to start Secondary School at the beginning of 2026, but that didn’t happen. Right now she should be sitting her end-of-second-term exams, but instead she’s on the pitch, training in soccer.  

She knelt to greet me, as is the custom in Uganda. It’s rare in Kampala, but her respect and her smile were genuine.  

At 15, a girl out of school is vulnerable. But on the turf, Sylvia is fearless. Sprinting, turning, passing — you’d think it was a boy. She’s the only girl out there, and she plays like she belongs.  

That’s exactly why I was at Soccer Stars Soccer Academy with Isaac, the Founder and Head Coach. We were there to introduce the Global Fast Fit Fitness Benchmark — GFF’s simple test to measure strength, speed, endurance, and agility but it takes discipline determination in young athletes.  

The moment I explained the drill, Sylvia stepped forward. “Now,” she said. No fear. No excuses.  

Coach Isaac gathered the teenagers to share their stories. Unlike big academies that charge hefty fees, Soccer Stars exists to lift children from the slums of Katwe, Kisenyi, and Kampala suburbs. Coach Isaac trains them and leverages their talent to secure school scholarships. Six girls have already gotten that chance and joined elite teams. Sylvia is still waiting for hers.  

Then came the GFF test.  

Sylvia ran the benchmark and scored 2:10 — two minutes and ten seconds.  

In GFF, we don’t just record times. We look for heart.  

And Sylvia showed it.  

GFF is about determination. It’s about showing up when no one expects you to. It’s about a 15-year-old girl choosing the ball over idleness, training over risk, and hope over circumstances.  

At Global Fast Fit, we believe every child deserves a chance to be tested, to be seen, and to be supported. Sylvia reminded us why we do this work.  

Congratulations, Sylvia. The benchmark is just the beginning.

When I started working as a trainer, I thought knowing exercises and how to perform them correctly was enough. With time, I realised that training is much bigger than lifting weights, counting repetitions, or telling someone to “push harder.”

In the few years I have worked with Global Fast Fit, training has been a continuous learning process for me. It has changed the way I look at exercise. I now see it as a very wide field that requires understanding, observation and, most importantly, the willingness to keep learning.

A good trainer needs to understand exercises—not just how to perform them, but why they are being prescribed, who they are appropriate for, how they can be modified, and what adaptations they are supposed to produce.

But exercise knowledge is only one part of the job.

A trainer should have some understanding of first aid and basic safety, a reasonable understanding of nutrition and diet, good communication skills, and the ability to work with different personalities and abilities.

We also need to understand our limits. You don't have to know everything. In fact, knowing when something is beyond your expertise is part of being a good trainer. Sometimes the most professional answer is, “I don't know, let me find out.”

Your client may know more than you think.

We are training in an age where information is everywhere. A client can watch a hundred videos about squats before they ever meet you. They may know about calories, protein, mobility, progressive overload or different training methods.

That shouldn't threaten a trainer.

Instead, it should challenge us to become better.

Clients don't necessarily expect us to know absolutely everything, but they expect us to be competent enough to understand what they already know, correct misconceptions where necessary, and add something valuable to the conversation.

You should be able to sit across from an informed client and have a meaningful conversation—not simply rely on your position as “the trainer.”

Don't become rigid.

Fitness is full of recycled thoughts.

“This exercise is always good.”

“That exercise is bad.”

“Everyone needs this workout.”

“This is the only way to lose weight.”

The more I learn, the more I realise that exercise is rarely that simple.

Every client is different. Their goals, abilities, limitations, lifestyle, experience and response to training can all be different. A trainer therefore needs to observe, question and adapt.

And sometimes, we need to accept that the programme isn't always the problem.

Take responsibility but do not take all the blame 

As trainers, we have a responsibility to provide good programmes, educate our clients, monitor progress and give appropriate guidance.But we don't live with our clients.

We cannot monitor what they eat 24 hours a day. We cannot watch how long they sleep. We cannot control whether they follow the programme when they leave the gym. We cannot force someone to recover properly, manage their stress or make better choices every day.

Sometimes a client isn't getting the expected results because of habits outside the training session.

That doesn't mean we should immediately blame the client. We should first ask ourselves whether we have done our part properly.

But once we have genuinely done our part, we also have to accept that the client has responsibility for their own results.

Don't beat yourself up over every outcome you cannot control.

Your job is to provide the knowledge, structure, guidance and support. The client still has to live the lifestyle.

Keep learning 

For me, this has been one of the biggest lessons from my time at Global Fast Fit. I have stopped seeing training as simply a collection of exercises. It is a field that demands curiosity.

Read. Ask questions. Observe other trainers. Study the human body. Learn from your clients. Challenge your own beliefs. Keep up with new information. And don't be afraid to change your mind when better knowledge comes along.

Most importantly, remember that being a trainer doesn't mean you have finished learning. It means you have taken responsibility for someone else's learning and physical development.

To my fellow trainers 

Don't let the fact that you are called a trainer convince you that you have finished learning.

Your clients are not your experiments, and your confidence should never be greater than your competence.

Keep learning. Keep questioning. Listen to your clients. Accept when you are wrong. Know your limits. Take responsibility for what you can control—and don't destroy yourself over what you cannot.

The best trainer isn't the one who knows everything. It's the one who is committed to knowing more tomorrow than they knew today.

There is a common saying in data science:

"A model is only as good as the data it learns from."

As artificial intelligence becomes increasingly integrated into our daily lives, one question becomes more important than ever:

Where does high-quality data come from?

Recently, I completed the DataUniversa Data Ingestion Training System and was honored to receive certification after achieving a 10/10 score in the Functional Fitness dataset assessment.

While receiving the certificate was rewarding, the real value was understanding the discipline required to transform human experience into structured knowledge that AI systems can actually learn from.

Data Is More Than Information

Many people think data collection simply means filling in forms or recording numbers.

In reality, meaningful data ingestion is about preserving context.

It means capturing not only what happened, but also:

• the original problem,
• the starting conditions,
• observations,
• reasoning,
• interventions,
• failures,
• outcomes,
• supporting evidence,
• and the lessons that others can apply.

Without that context, data becomes isolated facts. With context, it becomes knowledge.

Applying These Principles in Practice

The timing of this certification could not have been better.

Over the past several months, I have been redesigning the Global Fast Fit (GFF) organizational data system, transforming multiple independent Google Sheets into a centralized, governed information system.

That work involved:

• designing permanent unique identifiers,
• improving data integrity,
• implementing centralized governance,
• creating provenance tracking,
• automating synchronization using Google Apps Script,
• and documenting the complete engineering journey as a HOSI (Human-Originated Solution Intelligence) case study.

The DataUniversa training reinforced something I had already begun to appreciate: Good AI starts with good human documentation.

If the reasoning behind a solution is never recorded, AI can only learn the outcome—not the process that produced it.

The Importance of Human-Originated Solution Intelligence

One of the most exciting ideas introduced through DataUniversa is that people should not only contribute data—they should contribute solutions.

Every solved problem represents valuable knowledge.

Whether the challenge involves healthcare, education, engineering, business, agriculture, or organizational systems, documenting how a solution was discovered allows both humans and AI systems to learn from real-world experience.

This philosophy aligns closely with the HOSI framework, where complete solution journeys are preserved rather than simply recording successful outcomes.

Receiving this certification is not an endpoint.

It marks the beginning of a deeper commitment to building systems that are:

• reliable,
• auditable,
• evidence-based,
• reproducible,
• and designed for long-term learning.

As organizations increasingly adopt AI, the quality of their future systems will depend heavily on the quality of the knowledge we preserve today.

I'm grateful to the DataUniversa team for developing a training program that emphasizes structured thinking, evidence, and responsible data ingestion.

I look forward to applying these principles in future projects and contributing additional HOSI case studies that help build better knowledge for both people and intelligent systems.

Certificate Achievement

• Certification: DataUniversa Data Ingestion Training System
• Dataset: Functional Fitness
• Score: 10/10
• Certificate Awarded: July 18, 2026

This achievement reminds me that the future of AI isn't built solely by better algorithms—it's built by better knowledge, carefully documented by people who solve real problems.