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Amro Elhalwagy
@amro-elhalwagy
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
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Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
If you work in Business Intelligence but don’t understand the business… you’re just creating colorful reports.
Let me be direct.
A true BI professional doesn’t just know Power BI, Tableau, SQL, or Python.
He understands:
• How the company makes money
• Where it loses money
• What keeps the CEO awake at night
• Which KPIs actually drive decisions
Because dashboards don’t create impact.
Decisions do.
13
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2mo
Proud moment 👏🎉
He said he’ll pass CMA Part 1 from the first attempt… and he did 💪
As promised, reposting 😄
Hard work + consistency always win—keep going, this is just the beginning 🚀
8
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
just a reminder from my notebook
8
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
لقاء رائع لازم تسمعوه ....من زمان وانا بقول tokenization هيحل مشاكل في بزنس كتير و خصوصا العقارات بس لازم تشريعات و جهات تنظيمية ورقابية تكون جاهزة ليه و خصوصا الحزء المتعلق بالمزايا الاضافية اللي بياخدها مالك الtoken دي مهمه جدا مش بس المكلية لوحدها دي بتدي sustenability و. stability للtoken ....... شكرا جدا لمجهودكم و منتظر stake في قطر قريبا 😉
Rami Tabbara Youssef Salem Abdullah AlBaker Mansour Al Derei
العقار لا ينهار … المستثمر ممكن-3 من رواد الأعمال- في حلقة 4#من مدرسة الاستثمار مع الأصدقاء
7
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Dark humor… but it highlights a real problem: titles without real expertise
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Stop competing with a calculator.
Lately, I see more people comparing themselves to AI — almost treating it like a competitor instead of what it really is: a tool.
Yes, AI is fast.Yes, it can write, calculate, analyze, and automate tasks in seconds.But so can a calculator solve complex equations faster than any human.
And yet… no one compares their intelligence to a calculator.
The real issue isn’t AI It’s how some professionals define their own value.
If you believe your role is only to execute tasks — write code, build slides, generate reports — then of course AI will feel threatening. Because tools replace tasks But humans were never meant to be “tasks.”
We create context.We ask better questions.We connect strategy with reality.We understand business, emotions, and consequences — things no algorithm truly owns.
AI doesn’t replace thinking, it amplifies thinking.
So instead of asking, “How do I compete with AI?”Ask a better question:
👉 “How do I become the person who knows how and why to use it?”
You are not the tool.
4
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
6d
Most companies are not data-driven. They are data-justified.
The difference is the order of operations.
Data-driven: the analysis runs first, the decision follows, and leadership accepts answers it didn't expect.
Data-justified: the decision is already made — by instinct, politics, or precedent — and the data's job is to dress it for the board.
You can diagnose which one you are with a single question: when was the last time the data changed a decision someone senior had already announced?
If you can't remember one, the data function isn't driving anything. It's a tailor.
This isn't a technology gap. Companies with world-class analytics teams are data-justified every day. It's a governance question: does evidence have standing in the room, or only a seat outside it?
Instinct without data is gambling. Data without standing is decoration.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
Companies are now telling employees to use less AI. Reduce the tokens. Cut the cost. Use it wisely.
They are solving the wrong problem.
The reason most employees burn through AI budget without results is not because they use it too much. It is because they use it without two things that no tool can replace:
1- Deep understanding of the business problem they are trying to solve. If you cannot articulate the problem precisely in your own words, you cannot instruct AI to solve it. Vagueness in, vagueness out. The people spending the most tokens are usually the people who have not done the thinking before they opened the tool.
2- Understanding of how AI actually works. AI is not a search engine. It constructs answers based on what you give it. If you do not understand that, you will spend hours refining prompts and wondering why you are going in circles.
The organisations burning money on AI tokens do not have a cost problem.
They have a talent problem.
Rationing tokens without fixing that gap is like limiting fuel for a driver who does not know where they are going. The next competitive advantage will not go to the companies that use the most AI.
It will go to the ones who have people precise enough to use it with the least.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
Instead of stopping at the image, I pushed it one step further.
I asked why each element appeared in the illustration — and the explanation was surprisingly accurate (Damn… it is so deep 😶)
The exaggerated smile and sharp eyes reflected an analytical mindset that still values approachability.
The glasses symbolized clarity and structured thinking — data → insight → decision.
The beard and overall look leaned toward a modern strategist rather than a traditional analyst.
The headset wasn’t random either. It represented deep focus, long analysis sessions, and being constantly plugged into data, discussions, and problem-solving — closer to advisory work than solo number-crunching.
Coffee? Not just a cliché.
It stood for iteration, refinement, and the long hours behind strategic decisions.
The multiple screens told different stories:
One for hands-on work with data — Power BI, SQL, analytics logic
One for AI and automation — future-proofing decisions, not reacting to trends
One symbolic of complex challenges — taming ambiguity and high-stakes problems, not just tidy datasets
Even the playful elements had meaning:
A curious cat with a NASA cap → exploration, independence, long-term thinking
Dice and books → calculated risk backed by knowledge, not guesswork
Rockets and space themes → growth, ambition, and constant forward momentum
The overall message behind the image?
Engineer by mindset.
Strategist by execution.
Analyst by craft.
Leader in the making.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2d
Most companies bought AI to improve their reporting. That was the easy purchase. And the wrong one.
AI made their dashboards faster. It summarized the data, flagged the trend, generated the slide. Reporting got cheaper and prettier.
The decisions did not get better.
Because the bottleneck was never the reporting. It was what happens after the report: who decides, on what basis, against which alternatives, with what authority to act. Most organisations never built that. They built dashboards and hoped decisions would follow.
AI multiplies whatever system it lands in. Drop it into a company with real decision infrastructure and it compounds. Drop it into a company that confuses reporting with deciding and you get the same indecision, delivered faster and with better formatting.
Before asking what AI can do for your business, ask a harder question: when your best report lands on the table, what actually happens next?
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Most KPI frameworks fail before they are built.
Not because the data is wrong. Not because the tools are inadequate.
Because nobody asked the three questions that determine whether a KPI will ever drive a decision.
Question 1: What decision does this metric need to support? A KPI without a decision attached to it is a number with nowhere to go. Before you track anything, name the decision it serves. If you cannot name it, the metric does not belong on the dashboard.
Question 2: Who acts when this number moves? Every KPI needs an owner — not a viewer. Someone whose job changes based on what the number does. Without an owner, a metric is decoration.
Question 3: What does good actually look like — and why? A target without a business rationale is a guess dressed as a goal. The benchmark needs to connect to how the business actually operates, not what looks ambitious in a spreadsheet.
Three questions. Most dashboards cannot answer any of them.
That is not a data problem.
That is a decision architecture problem.
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1mo
just watched a fantastic video from Founders' Academy on "Validating the Problem Before You Build Anything," and it completely reframes how we should approach product development.
https://lnkd.in/dMJx9mZ8
Founders' Academy | Lesson 2: Validating the Problem Before You Build
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
This week, one of the most powerful AI models on the market was released on a Tuesday and pulled by Friday.
Not because it failed. Because a government issued an export-control directive, and access was switched off — for entire categories of users, across borders, in a single evening.
The reaction online is predictable: don't depend too much on AI, keep your skills sharp, it's only a tool.
That misses the real lesson.
The risk was never that your people lean on a tool. The risk is that you built a decision around a capability you do not control — one that a regulator, an export rule, or a decision made in another country entirely can remove from under you between a Tuesday and a Friday.
That is not a productivity problem. It is a governance problem.
For years the question leadership asked about AI was "how do we adopt it faster?" The better question, the one this week exposed, is quieter: which of our decisions now cannot be made if this capability disappears tomorrow?
If the honest answer is "several, and we never mapped them" — that is the exposure. Not the cost. Not the skills. The dependency you never put on the risk register because it never occurred to you that the supplier could vanish by government order.
Resilient organisations don't avoid powerful tools. They stay aware of what they have quietly become unable to do without them.
The tool coming back online next week doesn't close the question.
It's the reminder that you were asking the wrong one.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
"The data shows progress." I have heard this in boardrooms for 13 years.
It is almost always wrong.
Not because the data is lying. Because the KPI was never connected to the business in the first place.
A number going up is not progress. It is a number going up.
Progress is when the business performs differently. When revenue moves. When costs drop. When a decision gets made faster or better than it would have been made before.
The gap between those two things — between what the dashboard shows and what the business actually does — is where most analytics investments quietly die.
Before you build a KPI, one question needs an answer:
If this number moves, what decision changes — and who makes it?
If you cannot answer that, you do not have a KPI.
You have a metric with nowhere to go.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Correlation is not causation.
A statistician once said:
Ice cream sales increase…And drowning incidents increase.
Does that mean ice cream causes drowning?
Of course not.
The real hidden variable?
☀️ Summer.
More people go to the beach.
More people buy ice cream.
More people swim.
More accidents happen.
Now let’s talk about today’s trend:
“Layoffs are happening because of AI.
Really?
Yes, AI adoption is increasing.
Yes, companies are becoming more efficient.
But are we sure AI is the real cause?
Or are we ignoring other possible variables:
• Financial restructuring
• Over-hiring during hyper-growth years
• Economic slowdown
• Geopolitical instability
• Poor strategic decisions
• Cash flow issues
• Market corrections
It’s easier to blame AI. It’s trendy. It gets attention. But companies didn’t fire accountants because calculators were invented. They didn’t fire designers because Photoshop appeared.
They didn’t fire analysts because Excel was created.
Technology changes how we work. It doesn’t automatically eliminate value. Before jumping to conclusions, we need to ask better questions.
As professionals — especially those working with data — we should know better:
👉 Just because two things move together…
👉 Doesn’t mean one caused the other.
Let’s think deeper.
Let’s analyze root causes.
Let’s not let headlines replace reasoning.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
تكملة على الكلام اللي قلته قبل كده عن الـ Tokenization وخصوصاً في العقارات…
الـ Token لو بيمثل "ملكية صامتة" فقط، هيفضل عرضة لتقلبات السوق. اللي بيحمي السعر وبيخلي التقييم منطقي ومستدام هو المزايا الإضافية (Utility).
في العقارات، ومع تطور الأطر التنظيمية الموضوع مابقاش مجرد مضاربة أو "Digital Ownership" وخلاص. القيمة الحقيقية بتظهر لما الـ Token يتحول لأداة حيوية بتدي حاملها:
1️⃣ حق التصويت والإدارة: المشاركة الفعالة في قرارات الأصل.
2️⃣ أولوية الوصول (Priority Access): ميزات حصرية في المرافق أو المشاريع القادمة.
3️⃣ دخل مرتبط بالأداء: عائد يعكس كفاءة التشغيل الحقيقية، مش مجرد رقم على الشاشة.
4️⃣ مزايا تشغيلية: تسهيلات داخل منظومة العقار نفسه.
هنا بيتحول الطلب من "مضاربي" لـ "استخدامي".
وقتها السعر بيرتبط بجودة الأصل وعوائده، مش بالمزاج العام للسوق أو "التريند".
خلّينا نكون صريحين:
لو الـ Token اختفى والأصل فضل شغال عادي بدون أي تأثر في قيمته أو إدارته… يبقى الاستقرار ده كان وهم.
العقارات محتاجة اقتصاد حقيقي، مش Hype عشان ما يحصلش تضخم بلا سبب و تبقي مجرد مضاربة.
وكما ذكرت سابقاً: الـ Tokenization حل ثوري.. لكن بدون تشريعات واضحة + Utility حقيقية، مفيش استدامة (Sustainability) ولا استقرار (Stability).
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
5h
Every now and then someone tells me, "I have an idea."
Fine. How long has it been sitting with you — two years? Three? Still a note in a drawer? Then it isn't an idea. It's a wish.
An idea on its own is worth nothing. Nobody pays for an idea. What gets paid for is the distance between the idea and the decision — turning it into a proposal, studying it, working out how it actually gets executed, and sitting with a hard decision while knowing you might be wrong.
That's the real work. And people run from it, straight into the easy part — falling in love with the idea itself. Because an idea is comfortable, and nobody is held accountable for one. A decision is ugly. It carries responsibility. It can turn out wrong.
And honestly? Even now, after all these years, I still catch myself loving an idea before I've tested it properly. The only difference is I learned to see it early — I start turning the idea into a study before I make any decision or begin to execute.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
I spent ten years preparing reports that I knew would be challenged.
Board-level reporting, under sustained external scrutiny, where every number could be questioned by people whose job was to question it.
That environment teaches you something most analysts never learn: the difference between what is interesting and what is defensible.
Interesting is easy. Patterns, correlations, impressive charts. Interesting fills slides.
Defensible is different. Defensible means you know where every number came from, what it assumes, where it breaks, and what you would say when someone smarter than you asks the question you hoped they wouldn't.
Here is what surprised me: working defensibly made me faster, not slower. When you build analysis to survive challenge, you stop producing volume and start producing judgment. Half the slides. Twice the weight.
Most organisations reward interesting because nobody challenges it. The discipline only develops when someone is paid to push back.
If nobody ever challenges your analysis, that is not a sign it's good.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Every KPI must answer three questions. Most answer only one.
1- What happened? This is where almost every KPI system stops. Sales versus target. Cost versus budget. Coverage versus plan. Useful, but it describes the past. The decision was already made — by someone, somewhere, often without you.
2- What is happening? Fewer systems answer this. Not last month's number — the live trajectory. Is the gap closing or compounding? A KPI that updates monthly on a business that moves daily is a history lesson.
3- What should we do next? Almost no system answers this. Yet it is the only question leadership is actually paid to answer. If your KPIs cannot point to a decision — reallocate, stop, double down, renegotiate — they are not performance indicators. They are performance descriptions.
The test for any metric in your business: can you name the decision it exists to drive? If you can't, you're not measuring performance. You're documenting it.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Everyone in analytics talks about understanding the business.
Very few actually do.
And the gap shows up fast.
It shows up when the analyst recommends a solution that is technically correct and operationally impossible. When the dashboard answers a question nobody was asking. When the insight is real but lands in the wrong room with the wrong person at the wrong moment.
Understanding the business does not mean knowing the industry.
It means knowing how decisions actually get made inside the organisation — who holds the real constraints, where the pressure lives, what finance is worried about that commercial has not been told yet, what the operations team knows that never makes it into a report.
It means sitting with the architect and understanding why a design decision affects project profitability. Sitting with the sales team and understanding why a route plan looks efficient on paper but fails in the field. Sitting with the hotel manager and understanding why occupancy numbers move differently than the model predicted.
You cannot build decision intelligence for a business you only understand from the data layer.
The data shows you what is happening.
The business tells you why it matters — and who needs to act.
Most analytics professionals master one side.
The ones who master both are the ones who end up in the room when the real decisions get made.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
A 10% growth rate sounds impressive.
Until you find out it went from 10 units to 11.
Ratios are the most comfortable place for an analyst to hide.
They smooth the ugly. They make flat businesses look dynamic. They turn a marginal improvement into a headline number. And they almost never tell you whether the business is actually healthy or quietly declining.
I have sat in rooms where leadership celebrated percentage growth while absolute performance was eroding beneath the surface. The ratio was climbing. The business was shrinking.
The question that exposes this every time:
Growth relative to what — and does the base actually matter?
A KPI without that answer is not intelligence. It is arithmetic dressed up as insight.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
This is bad
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
Did anyone use codex? I’d love to learn what worked well and what didn’t. 🙌
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
If you work in Business Intelligence but don’t understand the business… you’re just creating colorful reports.
Let me be direct.
A true BI professional doesn’t just know Power BI, Tableau, SQL, or Python.
He understands:
• How the company makes money
• Where it loses money
• What keeps the CEO awake at night
• Which KPIs actually drive decisions
Because dashboards don’t create impact.
Decisions do.
13
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
just a reminder from my notebook
8
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Dark humor… but it highlights a real problem: titles without real expertise
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
6d
Most companies are not data-driven. They are data-justified.
The difference is the order of operations.
Data-driven: the analysis runs first, the decision follows, and leadership accepts answers it didn't expect.
Data-justified: the decision is already made — by instinct, politics, or precedent — and the data's job is to dress it for the board.
You can diagnose which one you are with a single question: when was the last time the data changed a decision someone senior had already announced?
If you can't remember one, the data function isn't driving anything. It's a tailor.
This isn't a technology gap. Companies with world-class analytics teams are data-justified every day. It's a governance question: does evidence have standing in the room, or only a seat outside it?
Instinct without data is gambling. Data without standing is decoration.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
Instead of stopping at the image, I pushed it one step further.
I asked why each element appeared in the illustration — and the explanation was surprisingly accurate (Damn… it is so deep 😶)
The exaggerated smile and sharp eyes reflected an analytical mindset that still values approachability.
The glasses symbolized clarity and structured thinking — data → insight → decision.
The beard and overall look leaned toward a modern strategist rather than a traditional analyst.
The headset wasn’t random either. It represented deep focus, long analysis sessions, and being constantly plugged into data, discussions, and problem-solving — closer to advisory work than solo number-crunching.
Coffee? Not just a cliché.
It stood for iteration, refinement, and the long hours behind strategic decisions.
The multiple screens told different stories:
One for hands-on work with data — Power BI, SQL, analytics logic
One for AI and automation — future-proofing decisions, not reacting to trends
One symbolic of complex challenges — taming ambiguity and high-stakes problems, not just tidy datasets
Even the playful elements had meaning:
A curious cat with a NASA cap → exploration, independence, long-term thinking
Dice and books → calculated risk backed by knowledge, not guesswork
Rockets and space themes → growth, ambition, and constant forward momentum
The overall message behind the image?
Engineer by mindset.
Strategist by execution.
Analyst by craft.
Leader in the making.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Most KPI frameworks fail before they are built.
Not because the data is wrong. Not because the tools are inadequate.
Because nobody asked the three questions that determine whether a KPI will ever drive a decision.
Question 1: What decision does this metric need to support? A KPI without a decision attached to it is a number with nowhere to go. Before you track anything, name the decision it serves. If you cannot name it, the metric does not belong on the dashboard.
Question 2: Who acts when this number moves? Every KPI needs an owner — not a viewer. Someone whose job changes based on what the number does. Without an owner, a metric is decoration.
Question 3: What does good actually look like — and why? A target without a business rationale is a guess dressed as a goal. The benchmark needs to connect to how the business actually operates, not what looks ambitious in a spreadsheet.
Three questions. Most dashboards cannot answer any of them.
That is not a data problem.
That is a decision architecture problem.
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
This week, one of the most powerful AI models on the market was released on a Tuesday and pulled by Friday.
Not because it failed. Because a government issued an export-control directive, and access was switched off — for entire categories of users, across borders, in a single evening.
The reaction online is predictable: don't depend too much on AI, keep your skills sharp, it's only a tool.
That misses the real lesson.
The risk was never that your people lean on a tool. The risk is that you built a decision around a capability you do not control — one that a regulator, an export rule, or a decision made in another country entirely can remove from under you between a Tuesday and a Friday.
That is not a productivity problem. It is a governance problem.
For years the question leadership asked about AI was "how do we adopt it faster?" The better question, the one this week exposed, is quieter: which of our decisions now cannot be made if this capability disappears tomorrow?
If the honest answer is "several, and we never mapped them" — that is the exposure. Not the cost. Not the skills. The dependency you never put on the risk register because it never occurred to you that the supplier could vanish by government order.
Resilient organisations don't avoid powerful tools. They stay aware of what they have quietly become unable to do without them.
The tool coming back online next week doesn't close the question.
It's the reminder that you were asking the wrong one.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Correlation is not causation.
A statistician once said:
Ice cream sales increase…And drowning incidents increase.
Does that mean ice cream causes drowning?
Of course not.
The real hidden variable?
☀️ Summer.
More people go to the beach.
More people buy ice cream.
More people swim.
More accidents happen.
Now let’s talk about today’s trend:
“Layoffs are happening because of AI.
Really?
Yes, AI adoption is increasing.
Yes, companies are becoming more efficient.
But are we sure AI is the real cause?
Or are we ignoring other possible variables:
• Financial restructuring
• Over-hiring during hyper-growth years
• Economic slowdown
• Geopolitical instability
• Poor strategic decisions
• Cash flow issues
• Market corrections
It’s easier to blame AI. It’s trendy. It gets attention. But companies didn’t fire accountants because calculators were invented. They didn’t fire designers because Photoshop appeared.
They didn’t fire analysts because Excel was created.
Technology changes how we work. It doesn’t automatically eliminate value. Before jumping to conclusions, we need to ask better questions.
As professionals — especially those working with data — we should know better:
👉 Just because two things move together…
👉 Doesn’t mean one caused the other.
Let’s think deeper.
Let’s analyze root causes.
Let’s not let headlines replace reasoning.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
5h
Every now and then someone tells me, "I have an idea."
Fine. How long has it been sitting with you — two years? Three? Still a note in a drawer? Then it isn't an idea. It's a wish.
An idea on its own is worth nothing. Nobody pays for an idea. What gets paid for is the distance between the idea and the decision — turning it into a proposal, studying it, working out how it actually gets executed, and sitting with a hard decision while knowing you might be wrong.
That's the real work. And people run from it, straight into the easy part — falling in love with the idea itself. Because an idea is comfortable, and nobody is held accountable for one. A decision is ugly. It carries responsibility. It can turn out wrong.
And honestly? Even now, after all these years, I still catch myself loving an idea before I've tested it properly. The only difference is I learned to see it early — I start turning the idea into a study before I make any decision or begin to execute.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Every KPI must answer three questions. Most answer only one.
1- What happened? This is where almost every KPI system stops. Sales versus target. Cost versus budget. Coverage versus plan. Useful, but it describes the past. The decision was already made — by someone, somewhere, often without you.
2- What is happening? Fewer systems answer this. Not last month's number — the live trajectory. Is the gap closing or compounding? A KPI that updates monthly on a business that moves daily is a history lesson.
3- What should we do next? Almost no system answers this. Yet it is the only question leadership is actually paid to answer. If your KPIs cannot point to a decision — reallocate, stop, double down, renegotiate — they are not performance indicators. They are performance descriptions.
The test for any metric in your business: can you name the decision it exists to drive? If you can't, you're not measuring performance. You're documenting it.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
A 10% growth rate sounds impressive.
Until you find out it went from 10 units to 11.
Ratios are the most comfortable place for an analyst to hide.
They smooth the ugly. They make flat businesses look dynamic. They turn a marginal improvement into a headline number. And they almost never tell you whether the business is actually healthy or quietly declining.
I have sat in rooms where leadership celebrated percentage growth while absolute performance was eroding beneath the surface. The ratio was climbing. The business was shrinking.
The question that exposes this every time:
Growth relative to what — and does the base actually matter?
A KPI without that answer is not intelligence. It is arithmetic dressed up as insight.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
Did anyone use codex? I’d love to learn what worked well and what didn’t. 🙌
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2mo
Proud moment 👏🎉
He said he’ll pass CMA Part 1 from the first attempt… and he did 💪
As promised, reposting 😄
Hard work + consistency always win—keep going, this is just the beginning 🚀
8
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
لقاء رائع لازم تسمعوه ....من زمان وانا بقول tokenization هيحل مشاكل في بزنس كتير و خصوصا العقارات بس لازم تشريعات و جهات تنظيمية ورقابية تكون جاهزة ليه و خصوصا الحزء المتعلق بالمزايا الاضافية اللي بياخدها مالك الtoken دي مهمه جدا مش بس المكلية لوحدها دي بتدي sustenability و. stability للtoken ....... شكرا جدا لمجهودكم و منتظر stake في قطر قريبا 😉
Rami Tabbara Youssef Salem Abdullah AlBaker Mansour Al Derei
العقار لا ينهار … المستثمر ممكن-3 من رواد الأعمال- في حلقة 4#من مدرسة الاستثمار مع الأصدقاء
7
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Stop competing with a calculator.
Lately, I see more people comparing themselves to AI — almost treating it like a competitor instead of what it really is: a tool.
Yes, AI is fast.Yes, it can write, calculate, analyze, and automate tasks in seconds.But so can a calculator solve complex equations faster than any human.
And yet… no one compares their intelligence to a calculator.
The real issue isn’t AI It’s how some professionals define their own value.
If you believe your role is only to execute tasks — write code, build slides, generate reports — then of course AI will feel threatening. Because tools replace tasks But humans were never meant to be “tasks.”
We create context.We ask better questions.We connect strategy with reality.We understand business, emotions, and consequences — things no algorithm truly owns.
AI doesn’t replace thinking, it amplifies thinking.
So instead of asking, “How do I compete with AI?”Ask a better question:
👉 “How do I become the person who knows how and why to use it?”
You are not the tool.
4
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
Companies are now telling employees to use less AI. Reduce the tokens. Cut the cost. Use it wisely.
They are solving the wrong problem.
The reason most employees burn through AI budget without results is not because they use it too much. It is because they use it without two things that no tool can replace:
1- Deep understanding of the business problem they are trying to solve. If you cannot articulate the problem precisely in your own words, you cannot instruct AI to solve it. Vagueness in, vagueness out. The people spending the most tokens are usually the people who have not done the thinking before they opened the tool.
2- Understanding of how AI actually works. AI is not a search engine. It constructs answers based on what you give it. If you do not understand that, you will spend hours refining prompts and wondering why you are going in circles.
The organisations burning money on AI tokens do not have a cost problem.
They have a talent problem.
Rationing tokens without fixing that gap is like limiting fuel for a driver who does not know where they are going. The next competitive advantage will not go to the companies that use the most AI.
It will go to the ones who have people precise enough to use it with the least.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2d
Most companies bought AI to improve their reporting. That was the easy purchase. And the wrong one.
AI made their dashboards faster. It summarized the data, flagged the trend, generated the slide. Reporting got cheaper and prettier.
The decisions did not get better.
Because the bottleneck was never the reporting. It was what happens after the report: who decides, on what basis, against which alternatives, with what authority to act. Most organisations never built that. They built dashboards and hoped decisions would follow.
AI multiplies whatever system it lands in. Drop it into a company with real decision infrastructure and it compounds. Drop it into a company that confuses reporting with deciding and you get the same indecision, delivered faster and with better formatting.
Before asking what AI can do for your business, ask a harder question: when your best report lands on the table, what actually happens next?
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1mo
just watched a fantastic video from Founders' Academy on "Validating the Problem Before You Build Anything," and it completely reframes how we should approach product development.
https://lnkd.in/dMJx9mZ8
Founders' Academy | Lesson 2: Validating the Problem Before You Build
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
"The data shows progress." I have heard this in boardrooms for 13 years.
It is almost always wrong.
Not because the data is lying. Because the KPI was never connected to the business in the first place.
A number going up is not progress. It is a number going up.
Progress is when the business performs differently. When revenue moves. When costs drop. When a decision gets made faster or better than it would have been made before.
The gap between those two things — between what the dashboard shows and what the business actually does — is where most analytics investments quietly die.
Before you build a KPI, one question needs an answer:
If this number moves, what decision changes — and who makes it?
If you cannot answer that, you do not have a KPI.
You have a metric with nowhere to go.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
تكملة على الكلام اللي قلته قبل كده عن الـ Tokenization وخصوصاً في العقارات…
الـ Token لو بيمثل "ملكية صامتة" فقط، هيفضل عرضة لتقلبات السوق. اللي بيحمي السعر وبيخلي التقييم منطقي ومستدام هو المزايا الإضافية (Utility).
في العقارات، ومع تطور الأطر التنظيمية الموضوع مابقاش مجرد مضاربة أو "Digital Ownership" وخلاص. القيمة الحقيقية بتظهر لما الـ Token يتحول لأداة حيوية بتدي حاملها:
1️⃣ حق التصويت والإدارة: المشاركة الفعالة في قرارات الأصل.
2️⃣ أولوية الوصول (Priority Access): ميزات حصرية في المرافق أو المشاريع القادمة.
3️⃣ دخل مرتبط بالأداء: عائد يعكس كفاءة التشغيل الحقيقية، مش مجرد رقم على الشاشة.
4️⃣ مزايا تشغيلية: تسهيلات داخل منظومة العقار نفسه.
هنا بيتحول الطلب من "مضاربي" لـ "استخدامي".
وقتها السعر بيرتبط بجودة الأصل وعوائده، مش بالمزاج العام للسوق أو "التريند".
خلّينا نكون صريحين:
لو الـ Token اختفى والأصل فضل شغال عادي بدون أي تأثر في قيمته أو إدارته… يبقى الاستقرار ده كان وهم.
العقارات محتاجة اقتصاد حقيقي، مش Hype عشان ما يحصلش تضخم بلا سبب و تبقي مجرد مضاربة.
وكما ذكرت سابقاً: الـ Tokenization حل ثوري.. لكن بدون تشريعات واضحة + Utility حقيقية، مفيش استدامة (Sustainability) ولا استقرار (Stability).
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
I spent ten years preparing reports that I knew would be challenged.
Board-level reporting, under sustained external scrutiny, where every number could be questioned by people whose job was to question it.
That environment teaches you something most analysts never learn: the difference between what is interesting and what is defensible.
Interesting is easy. Patterns, correlations, impressive charts. Interesting fills slides.
Defensible is different. Defensible means you know where every number came from, what it assumes, where it breaks, and what you would say when someone smarter than you asks the question you hoped they wouldn't.
Here is what surprised me: working defensibly made me faster, not slower. When you build analysis to survive challenge, you stop producing volume and start producing judgment. Half the slides. Twice the weight.
Most organisations reward interesting because nobody challenges it. The discipline only develops when someone is paid to push back.
If nobody ever challenges your analysis, that is not a sign it's good.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Everyone in analytics talks about understanding the business.
Very few actually do.
And the gap shows up fast.
It shows up when the analyst recommends a solution that is technically correct and operationally impossible. When the dashboard answers a question nobody was asking. When the insight is real but lands in the wrong room with the wrong person at the wrong moment.
Understanding the business does not mean knowing the industry.
It means knowing how decisions actually get made inside the organisation — who holds the real constraints, where the pressure lives, what finance is worried about that commercial has not been told yet, what the operations team knows that never makes it into a report.
It means sitting with the architect and understanding why a design decision affects project profitability. Sitting with the sales team and understanding why a route plan looks efficient on paper but fails in the field. Sitting with the hotel manager and understanding why occupancy numbers move differently than the model predicted.
You cannot build decision intelligence for a business you only understand from the data layer.
The data shows you what is happening.
The business tells you why it matters — and who needs to act.
Most analytics professionals master one side.
The ones who master both are the ones who end up in the room when the real decisions get made.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
This is bad
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
If you work in Business Intelligence but don’t understand the business… you’re just creating colorful reports.
Let me be direct.
A true BI professional doesn’t just know Power BI, Tableau, SQL, or Python.
He understands:
• How the company makes money
• Where it loses money
• What keeps the CEO awake at night
• Which KPIs actually drive decisions
Because dashboards don’t create impact.
Decisions do.
13
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
لقاء رائع لازم تسمعوه ....من زمان وانا بقول tokenization هيحل مشاكل في بزنس كتير و خصوصا العقارات بس لازم تشريعات و جهات تنظيمية ورقابية تكون جاهزة ليه و خصوصا الحزء المتعلق بالمزايا الاضافية اللي بياخدها مالك الtoken دي مهمه جدا مش بس المكلية لوحدها دي بتدي sustenability و. stability للtoken ....... شكرا جدا لمجهودكم و منتظر stake في قطر قريبا 😉
Rami Tabbara Youssef Salem Abdullah AlBaker Mansour Al Derei
العقار لا ينهار … المستثمر ممكن-3 من رواد الأعمال- في حلقة 4#من مدرسة الاستثمار مع الأصدقاء
7
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
6d
Most companies are not data-driven. They are data-justified.
The difference is the order of operations.
Data-driven: the analysis runs first, the decision follows, and leadership accepts answers it didn't expect.
Data-justified: the decision is already made — by instinct, politics, or precedent — and the data's job is to dress it for the board.
You can diagnose which one you are with a single question: when was the last time the data changed a decision someone senior had already announced?
If you can't remember one, the data function isn't driving anything. It's a tailor.
This isn't a technology gap. Companies with world-class analytics teams are data-justified every day. It's a governance question: does evidence have standing in the room, or only a seat outside it?
Instinct without data is gambling. Data without standing is decoration.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2d
Most companies bought AI to improve their reporting. That was the easy purchase. And the wrong one.
AI made their dashboards faster. It summarized the data, flagged the trend, generated the slide. Reporting got cheaper and prettier.
The decisions did not get better.
Because the bottleneck was never the reporting. It was what happens after the report: who decides, on what basis, against which alternatives, with what authority to act. Most organisations never built that. They built dashboards and hoped decisions would follow.
AI multiplies whatever system it lands in. Drop it into a company with real decision infrastructure and it compounds. Drop it into a company that confuses reporting with deciding and you get the same indecision, delivered faster and with better formatting.
Before asking what AI can do for your business, ask a harder question: when your best report lands on the table, what actually happens next?
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
This week, one of the most powerful AI models on the market was released on a Tuesday and pulled by Friday.
Not because it failed. Because a government issued an export-control directive, and access was switched off — for entire categories of users, across borders, in a single evening.
The reaction online is predictable: don't depend too much on AI, keep your skills sharp, it's only a tool.
That misses the real lesson.
The risk was never that your people lean on a tool. The risk is that you built a decision around a capability you do not control — one that a regulator, an export rule, or a decision made in another country entirely can remove from under you between a Tuesday and a Friday.
That is not a productivity problem. It is a governance problem.
For years the question leadership asked about AI was "how do we adopt it faster?" The better question, the one this week exposed, is quieter: which of our decisions now cannot be made if this capability disappears tomorrow?
If the honest answer is "several, and we never mapped them" — that is the exposure. Not the cost. Not the skills. The dependency you never put on the risk register because it never occurred to you that the supplier could vanish by government order.
Resilient organisations don't avoid powerful tools. They stay aware of what they have quietly become unable to do without them.
The tool coming back online next week doesn't close the question.
It's the reminder that you were asking the wrong one.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
تكملة على الكلام اللي قلته قبل كده عن الـ Tokenization وخصوصاً في العقارات…
الـ Token لو بيمثل "ملكية صامتة" فقط، هيفضل عرضة لتقلبات السوق. اللي بيحمي السعر وبيخلي التقييم منطقي ومستدام هو المزايا الإضافية (Utility).
في العقارات، ومع تطور الأطر التنظيمية الموضوع مابقاش مجرد مضاربة أو "Digital Ownership" وخلاص. القيمة الحقيقية بتظهر لما الـ Token يتحول لأداة حيوية بتدي حاملها:
1️⃣ حق التصويت والإدارة: المشاركة الفعالة في قرارات الأصل.
2️⃣ أولوية الوصول (Priority Access): ميزات حصرية في المرافق أو المشاريع القادمة.
3️⃣ دخل مرتبط بالأداء: عائد يعكس كفاءة التشغيل الحقيقية، مش مجرد رقم على الشاشة.
4️⃣ مزايا تشغيلية: تسهيلات داخل منظومة العقار نفسه.
هنا بيتحول الطلب من "مضاربي" لـ "استخدامي".
وقتها السعر بيرتبط بجودة الأصل وعوائده، مش بالمزاج العام للسوق أو "التريند".
خلّينا نكون صريحين:
لو الـ Token اختفى والأصل فضل شغال عادي بدون أي تأثر في قيمته أو إدارته… يبقى الاستقرار ده كان وهم.
العقارات محتاجة اقتصاد حقيقي، مش Hype عشان ما يحصلش تضخم بلا سبب و تبقي مجرد مضاربة.
وكما ذكرت سابقاً: الـ Tokenization حل ثوري.. لكن بدون تشريعات واضحة + Utility حقيقية، مفيش استدامة (Sustainability) ولا استقرار (Stability).
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Every KPI must answer three questions. Most answer only one.
1- What happened? This is where almost every KPI system stops. Sales versus target. Cost versus budget. Coverage versus plan. Useful, but it describes the past. The decision was already made — by someone, somewhere, often without you.
2- What is happening? Fewer systems answer this. Not last month's number — the live trajectory. Is the gap closing or compounding? A KPI that updates monthly on a business that moves daily is a history lesson.
3- What should we do next? Almost no system answers this. Yet it is the only question leadership is actually paid to answer. If your KPIs cannot point to a decision — reallocate, stop, double down, renegotiate — they are not performance indicators. They are performance descriptions.
The test for any metric in your business: can you name the decision it exists to drive? If you can't, you're not measuring performance. You're documenting it.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
This is bad
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2mo
Proud moment 👏🎉
He said he’ll pass CMA Part 1 from the first attempt… and he did 💪
As promised, reposting 😄
Hard work + consistency always win—keep going, this is just the beginning 🚀
8
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Dark humor… but it highlights a real problem: titles without real expertise
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
Companies are now telling employees to use less AI. Reduce the tokens. Cut the cost. Use it wisely.
They are solving the wrong problem.
The reason most employees burn through AI budget without results is not because they use it too much. It is because they use it without two things that no tool can replace:
1- Deep understanding of the business problem they are trying to solve. If you cannot articulate the problem precisely in your own words, you cannot instruct AI to solve it. Vagueness in, vagueness out. The people spending the most tokens are usually the people who have not done the thinking before they opened the tool.
2- Understanding of how AI actually works. AI is not a search engine. It constructs answers based on what you give it. If you do not understand that, you will spend hours refining prompts and wondering why you are going in circles.
The organisations burning money on AI tokens do not have a cost problem.
They have a talent problem.
Rationing tokens without fixing that gap is like limiting fuel for a driver who does not know where they are going. The next competitive advantage will not go to the companies that use the most AI.
It will go to the ones who have people precise enough to use it with the least.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Most KPI frameworks fail before they are built.
Not because the data is wrong. Not because the tools are inadequate.
Because nobody asked the three questions that determine whether a KPI will ever drive a decision.
Question 1: What decision does this metric need to support? A KPI without a decision attached to it is a number with nowhere to go. Before you track anything, name the decision it serves. If you cannot name it, the metric does not belong on the dashboard.
Question 2: Who acts when this number moves? Every KPI needs an owner — not a viewer. Someone whose job changes based on what the number does. Without an owner, a metric is decoration.
Question 3: What does good actually look like — and why? A target without a business rationale is a guess dressed as a goal. The benchmark needs to connect to how the business actually operates, not what looks ambitious in a spreadsheet.
Three questions. Most dashboards cannot answer any of them.
That is not a data problem.
That is a decision architecture problem.
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
"The data shows progress." I have heard this in boardrooms for 13 years.
It is almost always wrong.
Not because the data is lying. Because the KPI was never connected to the business in the first place.
A number going up is not progress. It is a number going up.
Progress is when the business performs differently. When revenue moves. When costs drop. When a decision gets made faster or better than it would have been made before.
The gap between those two things — between what the dashboard shows and what the business actually does — is where most analytics investments quietly die.
Before you build a KPI, one question needs an answer:
If this number moves, what decision changes — and who makes it?
If you cannot answer that, you do not have a KPI.
You have a metric with nowhere to go.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
5h
Every now and then someone tells me, "I have an idea."
Fine. How long has it been sitting with you — two years? Three? Still a note in a drawer? Then it isn't an idea. It's a wish.
An idea on its own is worth nothing. Nobody pays for an idea. What gets paid for is the distance between the idea and the decision — turning it into a proposal, studying it, working out how it actually gets executed, and sitting with a hard decision while knowing you might be wrong.
That's the real work. And people run from it, straight into the easy part — falling in love with the idea itself. Because an idea is comfortable, and nobody is held accountable for one. A decision is ugly. It carries responsibility. It can turn out wrong.
And honestly? Even now, after all these years, I still catch myself loving an idea before I've tested it properly. The only difference is I learned to see it early — I start turning the idea into a study before I make any decision or begin to execute.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
Everyone in analytics talks about understanding the business.
Very few actually do.
And the gap shows up fast.
It shows up when the analyst recommends a solution that is technically correct and operationally impossible. When the dashboard answers a question nobody was asking. When the insight is real but lands in the wrong room with the wrong person at the wrong moment.
Understanding the business does not mean knowing the industry.
It means knowing how decisions actually get made inside the organisation — who holds the real constraints, where the pressure lives, what finance is worried about that commercial has not been told yet, what the operations team knows that never makes it into a report.
It means sitting with the architect and understanding why a design decision affects project profitability. Sitting with the sales team and understanding why a route plan looks efficient on paper but fails in the field. Sitting with the hotel manager and understanding why occupancy numbers move differently than the model predicted.
You cannot build decision intelligence for a business you only understand from the data layer.
The data shows you what is happening.
The business tells you why it matters — and who needs to act.
Most analytics professionals master one side.
The ones who master both are the ones who end up in the room when the real decisions get made.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
Did anyone use codex? I’d love to learn what worked well and what didn’t. 🙌
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
just a reminder from my notebook
8
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Stop competing with a calculator.
Lately, I see more people comparing themselves to AI — almost treating it like a competitor instead of what it really is: a tool.
Yes, AI is fast.Yes, it can write, calculate, analyze, and automate tasks in seconds.But so can a calculator solve complex equations faster than any human.
And yet… no one compares their intelligence to a calculator.
The real issue isn’t AI It’s how some professionals define their own value.
If you believe your role is only to execute tasks — write code, build slides, generate reports — then of course AI will feel threatening. Because tools replace tasks But humans were never meant to be “tasks.”
We create context.We ask better questions.We connect strategy with reality.We understand business, emotions, and consequences — things no algorithm truly owns.
AI doesn’t replace thinking, it amplifies thinking.
So instead of asking, “How do I compete with AI?”Ask a better question:
👉 “How do I become the person who knows how and why to use it?”
You are not the tool.
4
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
4mo
Instead of stopping at the image, I pushed it one step further.
I asked why each element appeared in the illustration — and the explanation was surprisingly accurate (Damn… it is so deep 😶)
The exaggerated smile and sharp eyes reflected an analytical mindset that still values approachability.
The glasses symbolized clarity and structured thinking — data → insight → decision.
The beard and overall look leaned toward a modern strategist rather than a traditional analyst.
The headset wasn’t random either. It represented deep focus, long analysis sessions, and being constantly plugged into data, discussions, and problem-solving — closer to advisory work than solo number-crunching.
Coffee? Not just a cliché.
It stood for iteration, refinement, and the long hours behind strategic decisions.
The multiple screens told different stories:
One for hands-on work with data — Power BI, SQL, analytics logic
One for AI and automation — future-proofing decisions, not reacting to trends
One symbolic of complex challenges — taming ambiguity and high-stakes problems, not just tidy datasets
Even the playful elements had meaning:
A curious cat with a NASA cap → exploration, independence, long-term thinking
Dice and books → calculated risk backed by knowledge, not guesswork
Rockets and space themes → growth, ambition, and constant forward momentum
The overall message behind the image?
Engineer by mindset.
Strategist by execution.
Analyst by craft.
Leader in the making.
3
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1mo
just watched a fantastic video from Founders' Academy on "Validating the Problem Before You Build Anything," and it completely reframes how we should approach product development.
https://lnkd.in/dMJx9mZ8
Founders' Academy | Lesson 2: Validating the Problem Before You Build
2
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
3mo
Correlation is not causation.
A statistician once said:
Ice cream sales increase…And drowning incidents increase.
Does that mean ice cream causes drowning?
Of course not.
The real hidden variable?
☀️ Summer.
More people go to the beach.
More people buy ice cream.
More people swim.
More accidents happen.
Now let’s talk about today’s trend:
“Layoffs are happening because of AI.
Really?
Yes, AI adoption is increasing.
Yes, companies are becoming more efficient.
But are we sure AI is the real cause?
Or are we ignoring other possible variables:
• Financial restructuring
• Over-hiring during hyper-growth years
• Economic slowdown
• Geopolitical instability
• Poor strategic decisions
• Cash flow issues
• Market corrections
It’s easier to blame AI. It’s trendy. It gets attention. But companies didn’t fire accountants because calculators were invented. They didn’t fire designers because Photoshop appeared.
They didn’t fire analysts because Excel was created.
Technology changes how we work. It doesn’t automatically eliminate value. Before jumping to conclusions, we need to ask better questions.
As professionals — especially those working with data — we should know better:
👉 Just because two things move together…
👉 Doesn’t mean one caused the other.
Let’s think deeper.
Let’s analyze root causes.
Let’s not let headlines replace reasoning.
1
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
1w
I spent ten years preparing reports that I knew would be challenged.
Board-level reporting, under sustained external scrutiny, where every number could be questioned by people whose job was to question it.
That environment teaches you something most analysts never learn: the difference between what is interesting and what is defensible.
Interesting is easy. Patterns, correlations, impressive charts. Interesting fills slides.
Defensible is different. Defensible means you know where every number came from, what it assumes, where it breaks, and what you would say when someone smarter than you asks the question you hoped they wouldn't.
Here is what surprised me: working defensibly made me faster, not slower. When you build analysis to survive challenge, you stop producing volume and start producing judgment. Half the slides. Twice the weight.
Most organisations reward interesting because nobody challenges it. The discipline only develops when someone is paid to push back.
If nobody ever challenges your analysis, that is not a sign it's good.
Decision Intelligence & Strategic Analytics Executive | Board & C-Suite Advisory | GCC
2w
A 10% growth rate sounds impressive.
Until you find out it went from 10 units to 11.
Ratios are the most comfortable place for an analyst to hide.
They smooth the ugly. They make flat businesses look dynamic. They turn a marginal improvement into a headline number. And they almost never tell you whether the business is actually healthy or quietly declining.
I have sat in rooms where leadership celebrated percentage growth while absolute performance was eroding beneath the surface. The ratio was climbing. The business was shrinking.
The question that exposes this every time:
Growth relative to what — and does the base actually matter?
A KPI without that answer is not intelligence. It is arithmetic dressed up as insight.