Part 1: Boring Economics

I dropped economics in secondary school and chose further mathematics instead. At the time, economics felt abstract. It bored me to death. I have since had to take courses on economics at post-graduate level. And I think it is one of the most practical subjects to understand.

A few days ago I overheard a conversation in a supermarket. Someone was complaining about the price of petrol. The price had gone up again, and the explanation came quickly.

“Dangote is at it again.”

Their reasoning was simple. What does a war in Iran have to do with petrol refined in Lekki? The conclusion was that the usual suspects were simply taking advantage of the masses.

You hear this kind of argument everywhere.

It is not confined to the beer parlour or the street corner. You hear it in supermarkets, in taxis, in WhatsApp groups, and sometimes even in places that are supposed to be centres of learning and policy.

We argue constantly about the economy, yet we rarely understand the mechanisms that shape it. And that misunderstanding is not harmless. When people cannot see the system that produces economic outcomes, they begin to look for villains instead. Price increases become conspiracies. Currency movements become manipulation.

The difficulty with these explanations is that prices rarely exist in isolation. Even when fuel is refined locally, the forces that shape its price remain global. Crude oil prices are set in international markets. If they had to import crude oil, shipping costs move with global logistics conditions.

This is not unique to Nigeria. The economist Bryan Caplan argues in The Myth of the Rational Voter that economic misunderstanding is widespread even in wealthy democracies; voters routinely misunderstand trade, markets, and price mechanisms.

Advanced economies possess a stabilising layer. Independent central banks, credible statistical agencies, professional civil services, and policy institutions anchor economic decisions in reality. Even when public opinion drifts, technocratic institutions can pull policy back toward economic constraints.

In many developing countries that corrective layer is weaker than it should be. Politicians are as economic-illiterate as the people they lead, and they keep interfering with technocrats who might otherwise stabilize policy. Daron Acemoglu and James Robinson make a related point in Why Nations Fail. When elites misunderstand how economic institutions function, development suffers.

Economic development depends not only on resources or geography. It depends on whether a society develops a shared understanding of how its economy actually works.

This is where Nigeria struggles most visibly. Public debates frequently swing between contradictory instincts, higher government spending with lower taxes, more jobs alongside policies that make business increasingly difficult. Individually these preferences are understandable. Together they violate the constraints of the economic system.

Nigeria’s economy is complex. But complexity is not the same as mystery. The economy is not chaos. It can be understood as a set of interacting systems that govern the flow of production, money, labour, and capital.

Once you see the structure, economic events stop feeling random. They begin to look like symptoms, traceable back to causes.

Part 2: Two Engines

Nigeria’s economy runs on two engines at the same time. The domestic engine is what happens inside the country: production, employment, credit conditions, government spending, and the decisions of the Central Bank of Nigeria.

The global engine is what happens in the wider world: oil markets, international capital flows, and the interest rate decisions of the Federal Reserve.

Most Nigerians focus on the domestic engine when thinking about the economy. That instinct is understandable. But the global engine is equally powerful, and it operates whether or not anyone is paying attention.

A decision made in Washington can influence the cost of borrowing in Lagos. A shock in global markets can reduce the capital available to Nigerian businesses.

Both engines feed into the six systems that actually drive the economy.

Part 3: Six Systems

System 1: The Production System

The production system is the real economy, where goods are grown, made, and delivered, and where services are provided.

The broadest measure of this system is GDP, the total value of goods and services produced in the economy over a given period. When GDP grows, the economy expands. When it contracts for two consecutive quarters, economists call it a recession.

But GDP is an average, and averages are deceptive. The economy can be growing on paper while most people feel poorer. If growth is concentrated in one sector while agriculture and manufacturing stagnate, the headline number flatters the reality on the ground.

This is why economists also track sectoral growth rates; how fast each part of the economy is expanding or contracting relative to the whole.

Agriculture employs the largest share of Nigerians and determines food supply. When farming is disrupted by insecurity, infrastructure failures, famine or shortages of inputs, food inflation often begins there, in the farms.

Manufacturing converts raw materials into finished goods. But Nigeria’s manufacturing base has long been constrained by unreliable electricity, high logistics costs, and dependence on imported inputs that become more expensive whenever the naira weakens.

Services: telecommunications, banking & finance, retail, transportation, healthcare, and education, account for the largest share of GDP. But service activity depends heavily on household purchasing power.

Beneath all of this sits the informal economy, which employs the majority of Nigerians. Street traders, artisans, transport operators, and small-scale service providers form the real labour market. Most are there because formal opportunity is scarce.

System 2: The Employment System

Employment is the bridge between economic activity and everyday life. Production creates output, but employment converts that output into income and distributes it across households.

Nigeria’s labour challenge is not only unemployment but underemployment. Many people work, but not in jobs that fully use their skills or generate stable income. A university graduate selling phone accessories is technically employed. The economy has not absorbed their full potential.

Real wages, adjusted for inflation, determine whether workers are actually gaining or losing ground. If wages rise ten percent but inflation rises twenty percent, purchasing power falls even when the payslip shows more money.

Employment is the human summary of everything happening elsewhere in the economy.

System 3: The Money and Credit System

Money and credit form the circulatory system of the economy. Without them, production cannot be financed, businesses cannot expand, and economic activity stalls.

When credit flows freely, the economy moves. When credit tightens, everything slows.

The Central Bank of Nigeria sets the Monetary Policy Rate, the benchmark from which commercial lending rates are derived. When inflation rises, the central bank typically raises interest rates to slow demand and stabilise prices.

But higher rates also make borrowing more expensive for businesses and households. It means that investments are deferred and hiring slows. The tool that fights inflation also restrains growth.

In Nigeria inflation is often driven by supply-side forces such as currency depreciation, fuel costs, or agricultural disruption rather than excessive borrowing. When rates rise in response to supply-driven inflation, businesses feel the pressure even though credit was never the original problem.

System 4: The Fiscal System

The fiscal system is the government’s financial architecture, how public money is raised, allocated, and managed.

In Nigeria it is dominated by oil.

When oil prices are high and production is stable, government revenue rises and public spending expands. When prices fall or production is disrupted, revenue contracts sharply across all levels of government.

Federal allocations get shrunk. State governments struggle to pay salaries. Nigeria’s spending has historically been dominated by recurrent costs such as salaries and debt service rather than capital investment. So when revenues decline, infrastructure spending is cut first.

Public debt adds another pressure. When Nigeria borrows in foreign currency and the naira weakens, the local currency cost of repayment rises automatically. Debt service already consumes a large share of government revenue. Currency depreciation makes that share even larger.

System 5: The External Account

The external account describes Nigeria’s economic relationship with the rest of the world, particularly the flow of foreign currency into and out of the country.

Nigeria requires foreign currency to import many of the goods its economy depends on: fuel, machinery, pharmaceutical inputs, food commodities, and industrial materials. Most of those dollars come from crude oil exports.

When oil revenues are strong, dollar supply is adequate, foreign reserves accumulate, and the central bank has the capacity to stabilise the naira.

When dollar inflows weaken, reserves fall and the naira comes under pressure. The exchange declines. A weaker naira raises the cost of imported inputs across the economy. Those costs move through supply chains and eventually appear in the prices people pay.

Remittances from Nigerians abroad provide a second important source of dollars. Unlike oil revenues, remittances tend to remain relatively stable and often increase during difficult periods.

The opposite movement also occurs through capital flight, when businesses and households move savings into dollars or foreign accounts during periods of uncertainty. Individually this behaviour is rational. Collectively, it reduces the dollars available in the economy and intensifies pressure on the naira.

System 6: The Global Capital System

This system operates largely outside Nigeria but shapes its economic conditions continuously.

Global investors move capital across countries in search of returns. Two signals strongly influence those movements.

The first is interest rates set by the United States Federal Reserve. When American interest rates rise, investors can earn higher returns on safe US assets. Capital that had previously flowed into emerging markets begins to move back toward the United States.

Countries like Nigeria experience this as reduced investment inflows, higher borrowing costs, and additional pressure on their currencies.

The second signal is global financial uncertainty, often measured by indicators such as the CBOE Volatility Index. When uncertainty rises, investors tend to move their capital into safer assets and withdraw funds from riskier markets.

Economists increasingly describe this dynamic as the global financial cycle. Capital expands and contracts in waves, often driven by financial conditions in major economies.

Part 4: How the Systems Connect

These systems do not operate independently. They form a chain.

When global financial conditions tighten, foreign investment slows. Dollar inflows weaken. Reserves come under pressure and the currency depreciates.

A weaker currency raises import costs and inflation rises. The central bank raises interest rates to stabilise prices. Borrowing becomes more expensive. Businesses slow investment and hiring.

Household incomes stagnate. Consumption weakens. Demand in the production system falls. If oil revenues are also declining at the same time, government finances weaken just as the shock arrives.

What appears to the public as disconnected economic events is often a transmission mechanism moving through the system.

Nigeria has experienced this sequence repeatedly across economic cycles. The actors change. The mechanism does not.

Seeing the structure changes how you read economic news. Most economic headlines are signals about one of these systems. Once you know which system is moving, and why, the noise begins to resolve into something legible.

The economy is not a conspiracy. It is a system. And systems, once understood, become readable.

Production creates value. Employment distributes income. Capital finances activity. Fiscal policy allocates public resources. The external account stabilises trade. Global capital determines investment flows.

1. Silicon Valley: An Ecosystem View

Most startup analyses begin and end at the firm.

Did they raise capital?

Did they survive?

Did they become profitable?

How much was the return to investors?

What about founder outcomes?

These are the primary lenses. And they are very important. But when we move a step further they are incomplete.

When we talk about Silicon Valley, we point to trillion-dollar companies. But few people have heard of how the Valley was actually built. An older man born in 1957 in that area told me the story a fews ago. And I have was fascinated.

In 1957, eight engineers left Shockley Semiconductor Laboratory and formed Fairchild Semiconductor. Fairchild did not just manufacture chips. It concentrated talent. Inside that firm, people learned how to scale semiconductor production, raise venture capital, and run high-technology organizations. Then they left and did it again.

From Fairchild came Intel and Advanced Micro Devices.

But more importantly, from Fairchild came the capital foundation of the Valley. Eugene Kleiner, one of the original Fairchild founders, started Kleiner Perkins. Don Valentine, Fairchild’s head of sales, started Sequoia Capital.

Before them, technology startups relied on East Coast bankers. Bankers understood balance sheets. They did not understand silicon risk. Kleiner and Valentine were the first VCs local to the valley. They were operators funding operators.

Kleiner Perkins seeded Sun Microsystems, Netscape, Genentech, Amazon, and Google.

Sequoia backed Apple, Cisco Systems, Oracle, Yahoo, Paypal, Airbnb, Stripe and WhatsApp.

They took the operational density of a single company and turned it into the circulatory system of a geography.

Fairchild eventually declined, but Silicon Valley thrived. The ecosystem is a different unit of progress.

Forty years later, PayPal received early backing from Sequoia and carried that operator-led capital model into the internet era. It survived fraud wars and operational chaos before being acquired by eBay in 2002. But the acquisition was not the story. Its alumni went on to found Tesla, LinkedIn, YouTube, Palantir Technologies, Yelp, SpaceX, Affirm, Yammer and others. PayPal is valued today at $60-70 billion. The companies founded by the early alumni are valued at roughly north of $1.5 trillion dollars.

The ecosystem view is that the firm matters. But in the long term, its impact on the ecosystem matters even more.

2. E-commerce in Nigeria: An Ecosystem View

When we evaluate Jumia or Konga, we default to the same firm-level questions.

But in the early 2010s, large-scale e-commerce was not operationally real in Nigeria.

Customers were skeptical of remote payments. Last-mile delivery systems did not exist. Digital demand generation at scale was new. Marketplace coordination across multiple cities was largely untested. Early platforms had to solve problems that did not previously have local playbooks.

They had to train customers through repeated transactions. They had to experiment with logistics models under poor infrastructure. They had to design trust mechanisms like payment on delivery. They had to build internal operator density from scratch.

That learning did not disappear.

Employees left and joined logistics firms, fintech companies, retail chains and new startups. Customers who placed their first successful online order did not revert to pre-digital behavior. Operational templates were discovered. Failure patterns were mapped. Trust baselines shifted. The environment changed.

So much has been written on the Jumia Mafia, but here I will focus on customer education and the building of trust.

3. Schwartz’s Pyramid of Awareness & The Education Tax

Markets do not begin fully formed. They have to be constructed in the mind before they function in reality.

Eugene Schwartz described this progression through what is often called the Pyramid of Awareness. Customers move through stages:

Unaware.

Problem-aware.

Solution-aware.

Product-aware.

Most aware.

In mature markets, most consumers are already solution-aware. They know e-commerce works. They are simply choosing between platforms.

In early markets, the constraint is different.

When large-scale e-commerce launched in Nigeria, the dominant constraint was was awareness and trust.

Customers had to move from:

“This sounds risky.”

to

“This might work.”

to

“This works for me.”

That transition is expensive. It requires marketing intensity, operational reliability, repeated successful transactions, and public absorption of failure.

Millions of Nigerians placed their first online order through early platforms. That first successful delivery permanently expands behavioral possibility.

E-commerce moves from abstract theory to lived experience. Future companies inherit that shift. They do not need to prove that online ordering works. They only need to prove that they execute it better.

4. Payment on Delivery: Engineering Trust in a Low-Trust Market

In low-trust environments, innovation is behavioral before it is technological. Payment on delivery was not a convenience feature.

It acknowledged reality. Customers feared fraud. Digital payments were uncharted territory.

Consumer protection systems were weak. And there was always a risk of bad actors.

Rather than demand full upfront trust, early platforms absorbed operational complexity.

Deliver first. Collect later.

This simple design choice did three things at once.

It reduced perceived risk for first-time users. It accelerated category education. It created repeated successful experiences that gradually normalized remote commerce.

Trust was engineered through the process. And once customers experienced reliable delivery enough times, the baseline shifted. Subsequent companies entered a market that was already more trusting than the one that existed before.

5. An Ecosystem View: Capability Compounds

Capability compounds.

Jumia built scale on Payment on Delivery. That decision unlocked trust. It moved customers. It expanded the market. But scale built around a solution creates dependence on that solution.

Customer expectations harden. Operational systems optimize. Fraud controls, routing logic, working capital cycles, all adjust to that architecture. This is path dependence. The bridge that delivered trust is no longer temporary. If Jumia removes PoD, revenue will likely fall.

Meanwhile, new entrants enter a partially educated market without carrying that legacy structure. They push upfront payments and they inherit trust without inheriting constraint.

Today, even a random foreign seller can enter the Nigerian market, demand upfront payment, and operate at scale, because the customer has already been educated.

Thousands of SMEs sell through WhatsApp and Instagram. Delivery across cities is normal. Remote payments are routine. None of this was structurally normal fifteen years ago.

Someone moved first. The pioneers absorbed the skepticism and they absorbed the cost of education. Now the firm’s financial return is one metric. The market that becomes possible afterward is another.

Part I: The System

W. Edwards Deming argued that 94–96 percent of performance comes from the system, not the individual. It is easy to gloss over his argument.

A friend of mine was a rainmaker in a second-generation bank. Starboy. Targets bent around him. Corporate deposits. Structured deals. Big clients. When numbers were tight, management called him. He understood credit. He understood politics. He knew how to move files through committees. He could close.

Then a fintech recruited him. Higher upside. Faster growth. Less bureaucracy. He was bored, so he moved.

Six months in, the magic slowed. Same man. Different results.

What changed?

At the bank, he was backed by a strong balance sheet, flexible pricing, deep product suites, brand trust, and institutional gravity. At the fintech, margins were thinner. The cost of capital was higher. Credit models were rigid. Risk tolerance was tighter. The brand carried less weight. Product options were narrower.

His skill did not disappear. The system changed. In one case, he had system leverage. Talent matters, but it converts into outcomes only within an enabling system.

The system always wins.

Part II: Five Dimensions, Not Two

Most people divide work into two categories: strategy and execution. That division is too crude.

Work is layered.

Some work sets direction. Some work is design. Some work aligns movement. Some work builds new capability. Some work runs existing capability. These are not job titles. They are dimensions.

Hierarchy distributes authority. It does not determine the layer of work being performed.

There are five dimensions of work. Strategy. Design. Coordination. Projects. Operations. When we collapse all of that into “strategy versus execution,” we lose precision. And when leaders lose precision, they mistake activity for progress.

A CEO can spend a day doing operations. A product manager can spend a day shaping strategy. Labels do not determine the dimension.

Performance is the movement of intention across five dimensions. Failure is distortion across them.

Part III: The Upstream Layers

1. Strategy: Direction Under Uncertainty

Strategy is constrained intent. It answers: What are we trying to achieve, and what are we willing to sacrifice to achieve it?

If nothing is eliminated, there is no strategy.

Strategy defines the playing field. It sets the boundaries within which every other dimension operates. When strategy is vague, downstream functions invent their own meaning. Fragmentation begins at the top.

Strategy shapes the work before the work begins.

2. Design: Form and Architecture

Design converts intent into structure.

Externally, it defines what we build: products, services, value propositions.

Internally, it defines how we deliver: workflows, incentives, structure, policies, decision rights, information flow.

Design encodes behavior. Incentives shape choices. Structure shapes outcomes. Culture is repeated architecture.

The governing design principle is simply to make the right action easy and the wrong action hard. Most downstream pain is design failure, wearing an execution mask.

3. Coordination: Alignment of Purposeful Agents

Organizations are not machines. They are collections of intelligent, self-directed humans. Alignment is not natural. It must be constructed.

Coordination synchronizes timing, sequencing, dependencies, and decision rights. It subordinates local optimization to system optimization.

Strategy sets direction. Coordination sets rhythm. And without rhythm, direction collapses into noise.

Part IV: Two Kinds of Execution

Every organization runs two paths at once: running the business and changing the business.

4. Projects: Building New Capability

Projects are temporary risk containers designed to produce permanent capability.

A new product. A new system. A new market entry. A new structure.

Projects absorb volatility so operations can remain stable. And when projects never land, you get innovation theater. When operations absorb unfinished change, you get burnout.

Projects build the future.

5. Operations: Running Existing Capability

Operations delivers consistency, cadence, quality, and feedback.

Strategy operates at the level of intent. Operations operates at the level of constraint. If operations struggles, something upstream failed. And when leaders ignore operational feedback, they drift into narrative delusion.

Operations is where system-truth becomes visible.

There are two coherent flows of work.

Strategy → Design → Coordination → Operations.

Strategy → Design → Coordination → Projects.

One flow runs current capacity. The other creates new capability.

Part V: System Coherence and Upstream Correction

Last week I argued that functions are a myth. A strong sales team cannot rescue a broken portfolio. A disciplined finance team cannot compensate for an incoherent strategy.

When a result is missed, the instinct is to exert pressure on the operations layer — the Starboy. Work harder. Stay later.

But if performance is a translation problem, failure rarely originates where it surfaces.

The leader’s job is not just to intensify pressure downstream. It is to diagnose the distortion upstream. Most organizations lack the discipline to move upstream. Causality flows downstream, and correction must move upstream.

When operations fail, examine the operations layer, but also check the design. Are incentives misaligned? Are tools inadequate? Is the workflow fighting the worker?

When projects stall, examine coordination. Were dependencies synchronized? Were decision rights clear? Was the sequencing disciplined?

When coordination breaks down, examine design and strategy. Is the structure ambiguous? Are priorities conflicting? Did we attempt to align around a direction that was never truly chosen?

When Design produces a misfit, examine Strategy. Did we define trade-offs? Did we choose what to sacrifice? Or did we attempt to be everything at once?

So, the real question is not always who missed the number.

It is which dimension broke first.

Part I: Local Optima

I recently came across an email from Microsoft CTO Kevin Scott explaining internal tensions at OpenAI. And what led to the ousting of Sam Altman. He wrote:

“I could tell you stories like this from every place I’ve ever worked, and it boils down to, even if you have two important, super successful things you’re trying to work on simultaneously, folks rarely think about the global optima. They believe that their thing is more important, and that to the extent that things are zero-sum, that the other thing is a cause of their woes.”

That dynamic is universal.

Large systems are built from smaller systems that work. But strong functions do not automatically translate into organization-wide performance.

I used to wonder why.

The culprit is the Myth of Functions.

It is counterintuitive. But when competent people optimize for their local goals, they constrain the business. Sales optimizes for top-line revenue. Finance optimizes cost control. Marketing optimizes lead volume. Operations optimizes for unit efficiency.

Each leader defends a pristine dashboard. Each function improves its own metrics.

Locally, performance rises. Globally, coherence falls.

What is optimal for the part is often parasitic for the whole.

Part II: Local Optima and Politics

Local optimization persists because it is politically rational.

Power is vertical. Career progression is vertical. Functions are not only skill containers, but are also power structures. Within that context, it is rational to win locally even if the system suffers.

I recently reviewed a business where the friction was visible from the parking lot.

Consider what was happening inside their order-to-cash cycle, the subsystem that carries a customer from signed contract to collected revenue.

Sales closed deals with non-standard payment terms to hit quarterly targets. Finance, protecting margins, flagged those contracts for review, adding three to five days before invoicing even began. Operations, measured on unit efficiency, batched fulfillment weekly instead of processing orders as they arrived. Logistics, optimizing route density, held shipments until trucks were full.

Each decision made sense locally. Together, they broke the promise. The customer who signed expecting delivery in two days received their order in ten. No one violated policy. No one failed their KPI. Every function did its job. Yet the system failed.

Then we looked at how their OKRs were structured. Sales targeted quarterly revenue with no constraint on deal structure. Finance targeted DSO reduction to 14 days, penalizing slow-collecting contracts. Operations targeted a 15% improvement in fulfillment cost per unit, rewarding batching. Each OKR was reasonable in isolation. Together, they created a system where the customer’s needs became an afterthought.

The incentives required internal competition. For one team to hit its targets cleanly, another team had to absorb friction. Star performers were emerging by exploiting bottlenecks, not by resolving them. A star performer in a broken system is often just a symptom of a deeper design failure.

Leadership had engineered it. Because what you measure is what you manufacture. What you reward is what you reproduce. If you want folks to optimize for the system, you must bake in incentives that reward that behavior.

Part III: Containers vs. Subsystems

The deeper issue is not that functions behave tribally. It is that we have misidentified what functions actually are.

We have confused functions with subsystems.

Functions are containers. Sales, marketing, supply chain, finance, HR, product, engineering, design. These are not systems. They are containers of specialized skill. Capability clusters. Professional tribes.

A subsystem is something different.

A subsystem is a sequence of interdependent processes that produces a defined outcome. It serves either a customer or another internal unit.

Procure-to-pay is a subsystem, from identifying a need to paying the supplier. Order-to-cash is a subsystem, from customer order to revenue collection. Lead-to-revenue. Issue-to-resolution. Concept-to-Launch. Customer Retention.

These subsystems cut horizontally across the organization’s functional silos. And they depend on coordinated handoffs. When you treat a function as a subsystem, you strengthen the department and weaken the delivery. You cannot optimize a container and expect a subsystem to improve.

Part IV: Designing for Flow

Businesses are organized around departments, but they win or lose on how value moves. Customers do not experience your structure; they experience a journey. Throughput is governed by the narrowest constraint in the value stream, not the weakest department.

Leaders often obsess over functional excellence and ignore subsystem integration. The result is a disconnected enterprise. The Myth of Functions is the illusion that increasing capacity automatically increases output.

It does not.

You can strengthen every function and still fail to improve global performance. Most inefficiencies live in the white space between functions, the handoffs that the org chart does not show.

Big systems are built from smaller systems that work. But those smaller systems are not departments. They are value streams. If you want systems thinking to work, you must design around those streams.

1. Map the Journey. Trace the full path from trigger to outcome, ignoring departmental lines. A lead-to-revenue stream, for example, begins not when Marketing qualifies a prospect but when the first signal of intent appears, and it ends not when Sales books the deal but when Finance collects the cash. Until you draw the full path, you cannot see where value stalls.

2. Assign Stream Accountability. Give someone the authority to fix the handoffs. Someone whose incentives are tied to end-to-end cycle time rather than any single function’s dashboard. Without this, handoff failures become orphan problems that every function acknowledges, and no function owns.

3. Measure the Flow. Prioritize cycle time, quality, and throughput over how busy each function appears. Utilization tells you how much of a team’s capacity is occupied. It says nothing about whether that activity advances the journey. A team running at 95 percent capacity can still be the bottleneck.

4. Align Incentives. Reward shared outcomes so that “winning locally” feels like losing. For example, the VP of Sales is no longer measured on booked revenue but on collected revenue. Now she shares accountability with Finance and Operations. The Head of Operations is not rewarded for unit efficiency alone but for delivery time and customer satisfaction. Now efficiency cannot come at the expense of experience.

When incentives shift from departmental metrics to system outcomes, behavior follows. If compensation and prestige are tied to flow health, collaboration becomes rational. If they remain tied to silo performance, tribalism will persist. You cannot preach alignment and reward fragmentation.

Design influences behavior.

When you do this, functions assume their proper role. They become service providers to subsystems. Functions contribute expertise. Subsystems create outcomes. In the end, the market does not reward siloed excellence. It rewards synchronized execution.

The tragedy of the “Myth of Functions” is that rational behavior at the desk level often creates irrational outcomes at the enterprise level.

How did Elon build the most technical startups in the world and fail at a government job?

Most of what we call systems thinking in business is quietly built on a mechanistic view of systems.

It assumes the system is a machine with compliant parts. Find the bottleneck, elevate it, subordinate everything else, repeat. That logic works beautifully when the system is mostly physical flow, stable constraints, and low politics. So it gets you results in a factory line or a call center. This is why the Theory of Constraints is such an effective model in those regimes.

Now go use TOC in a government agency. Goodluck.

You see across scientific fields, we recognize that systems differ fundamentally. And each kind of system requires different kinds of laws.

Physics, non-purposeful systems. Physics studies objects with no will and no preference. Planets do not negotiate gravity. Causality is stable. If you repeat an experiment a thousand times, you expect the same outcome.

Chemistry, constrained interaction systems. Chemistry studies substances that interact in predictable patterns but with more complexity than physics. But still, no intention. A reaction either has the required activation energy or it does not. Chemistry is a world of reliable rules, only more conditional.

Biology, adaptive purposeful systems. Biology breaks the pattern. Biological systems respond rather than behave. They adapt. They compensate. They pursue survival. And they respond to their environment. And this means the laws become probabilistic. You predict tendencies, not certainties.

Social sciences, fully purposeful systems. And then you reach social systems. Clubs, markets, governments, and businesses made up entirely of purposeful agents. Individuals with goals. Teams with incentives. Managers with fears. Leaders with biases.

Here, nothing behaves. Everything responds. Causality becomes conditional. Effects depend on context, interpretation, history, power, trust, and incentives. Physics gives you universal laws. Social science gives you moving targets. The laws of one domain do not translate cleanly to another. What holds true in a mechanistic system often fails to apply in a social system.

I have a friend who’s an engineer, and he tried to explain everything through that lens. When conflict showed up, he’d reach for Newton. Every action triggers an equal and opposite reaction. Not a bad instinct, honestly, just incomplete.

When people weren’t performing, he called it inertia. “They won’t move unless you push them.” And when a process was slow, he blamed friction and went looking for ways to lubricate it.

But the more he pushed and lubricated, the more the system changed shape. People didn’t just “move.” They interpreted. They resisted. They complied on the surface and defected underneath. They protected status. They conserved energy. They negotiated meaning.

He wasn’t dealing with particles. He was dealing with purposeful agents. He was importing laws from one domain and demanding they behave like universals in another.

That’s why someone can send rockets into orbit with breathtaking reliability, yet struggle to reform a ministry. A rocket is obedient. A bureaucracy is not.

And even within social systems, the rules differ. Markets, bureaucracies, political arenas, and firms do not run on the same mechanics. There are at least 8 kinds of social systems.

First-principles thinking wins where constraints are physical. It struggles where constraints are political.

Most business leaders make the same mistake as Elon. They apply the logic of mechanistic systems to social systems. They assume that if they pull a clean lever, business outputs will change proportionally. If they add pressure, performance will rise automatically. If they tighten the rules, behavior will stabilize.

But organizations do not behave like machines. They are made up of people. And every person is a purposeful part, responding, interpreting, protecting, and optimizing for their survival. A rocket responds to equations. A company responds to incentives, fear, power, identity, meaning, history, and interpretation. That is the nature of the systems we lead. Even under extreme coercion, human beings retain agency.

Once you understand that organizations are social systems, your approach to systems thinking will change.

Work is not the task on paper. Work is the translation from strategic intent to action through purposeful agents. It passes through interpretation, incentives, capacity, coordination, local tradeoffs, competing priorities, fear and ambition, negotiation and conflict.

This phenomenon is why outcomes often surprise leaders. They erroneously assume that they are managing a well-oiled machine. But in reality, they are leading a network of purposeful translators who would optimize for themselves when incentives are not aligned.

Once, a leader called me crying, “Our best salesman just quit.” So I empathized with him first. And then I picked up my notes.

Ontology. The system changed. Team composition and structure shifted overnight.

Dynamics. Execution capacity took a hit.

Ecology. The talent market and competing offers enabled the exit.

Teleology. The exit revealed meaning. When your best people leave, it is rarely only money. It is often misalignment on value, growth, fairness, or what the work is for.

He did not find it funny, but we must learn systems thinking.

In social systems, events are not incidents. They are signals. And if we refuse to read them as system feedback, we will keep treating symptoms and calling it management.

In my previous essay, I argued for simple models. But simple does not mean easy and cheap. There is a minimum viability to what passes as a useful model.

Aristotle taught something that can sound like a burden. If you wanted to explain a thing fully, you had to explain it in 4 distinct ways. Not one. Four. It reads like a checklist, and checklists sometimes feel like punishment.

1. The material cause. What it is made of?

2. The formal cause. What pattern makes it what it is? How it is structured.

3. Efficient cause. What makes it happen?

4. Final cause. What it is for?

But he was not being complicated. He was forcing intellectual rigor. He was trying to prevent a common human error. Mistaking a fragment for the whole. Seeing one slice of truth and acting as if the slice is the whole object.

And when you extend his 4 causes, you could have a universal model for explaining almost anything. And for understanding any system.

I. Ontology, what is it?

Ontology is identity, not just naming. Ontology answers two Aristotelian questions: what it is made of, and what form makes it what it is.

It is the Ship of Theseus question applied to real systems: when parts change, what still makes it the same thing.

In practice, ontology includes three elements.

Material, the raw stuff of it. What is it made of?

Form, the organizing pattern that makes the substance into a coherent thing.

Boundary, the edge that separates this from that. What counts as inside, what counts as outside.

II. Dynamics, how does it work?

This is Aristotle’s efficient cause, the source of motion, what initiates change.

Dynamics is the physics of change. It is not only how things move forward. It is how they keep their shape against decay.

Dynamics includes three elements.

Drivers, the engine, the catalyst, the trigger.

Constraints, the limits that bound capacity.

Maintenance and replenishment. What must be continuously supplied to keep the system from degrading?

III. Ecology, How is it shaped by its environment?

Ecology is the surrounding field of causes that shapes a system from outside. The actors and forces that load, control, feed and push back on it.

In practice, ecology reduces to four checks.

Agents, what the system is coupled to, the other bodies or forces it interacts with.

Dependencies, what it relies on from its surroundings but does not fully control.

Flows, what crosses the boundary.

Feedback loops, what amplifies or dampens behaviour over time.

IV. Teleology, What is it’s purpose?

Teleology is the logic of direction and significance. Aristotle’s final cause.

Teleology has two regimes.

Regime A, natural systems, direction without intention. Here, teleology is not moral purpose. It is convergence. Systems move toward attractors and drift towards stable states. The system does not want anything.

Regime B, purposive systems, direction with intention. Here, teleology is purpose. The system is for something. It can succeed or fail relative to that end.

In this regime, teleology has an internal structure.

Telos, the why.

Goals, the what we are aiming for.

Evaluation, the judgment loop that detects drift and corrects courses.

So people end up with an original sin. Reductionism. Treating purposive systems as purely mechanical, ignoring self-interest, status, fear, and ambition.

Let’s get practical.

In order to fully explain the circulatory system in biology, we would need to understand all four. And it takes years of med school.

Ontology (anatomy, what it is). Heart, arteries, veins, capillaries, blood, valves, chambers, vessel walls, and blood composition.

Dynamics (physiology, how it works). Pressure gradients, heart contraction cycle, flow rates, resistance, oxygen exchange at capillaries, regulation, adaptation under stress, failure modes (shock, clotting, arrhythmia).

Ecology (the external causal field). The surrounding systems and conditions that load, constrain, and regulate circulation. Lungs (oxygenation), kidneys (fluid and electrolytes), nervous and endocrine control (signals and hormones), temperature, hydration, altitude, and activity level, etc

Teleology (purpose/function, what it is for). Deliver oxygen and nutrients, remove carbon dioxide and waste, distribute hormones, regulate temperature and pH, and maintain internal stability so the body can function.

When a doctor says a heart is failing. What does he mean?

So, when a leader tells me “our systems are failing”.

I ask “What do you mean?”.

And now I wait until they and the team can answer it in four ways. Because if you cannot articulate the system or its issues in depth. You do not understand it. And you are not ready; you have no business improving what you do not understand.

Welcome to systems thinking.

My father was a professor of physics, a fine mind who lived to teach. Teaching was his calling.

Last year, I spent five unbroken weeks with him. It was by far the longest time we spent together since I left home. We had a million stories and memories to trade, and in the simplest ways, he made my year.

One afternoon, he noticed I was deep in thought and asked what I was wrestling with.

I told him I was trying to build a conceptual model that fused strategic management and development economics to explain business outcomes in emerging markets.

He listened quietly, the way he always did when a student was overcomplicating things. Then set down his cup of water, smiled, and said something simple but profound.

“All models are wrong, but some are useful,” he said. He was quoting George E.P. Box.

And then he continued, “A useful model is more powerful than an elaborate one. All scientific models aim to simplify reality. Complexity is only justified if it improves utility.”

Newton’s laws of motion dominated physics for centuries. Even after Einstein revealed its limits, it remained the default tool because it was useful in the right regime. Physicists do not abandon simple models when complexity exists; they use the simplest model that serves the question at hand. Relativity did not kill Newton’s law; it drew the boundary.

F = ma is not powerful because it captures every nuance of nature. It is powerful because, within its regime, it lets us build cars, design aircraft, and, paired with his law of gravitation, put satellites in orbit.

Months later, my professors would echo the same truth in different words. And I have come to realize that this principle is not confined to physics or academia; it defines the work I am called to do.

I see it everywhere now.

A mid-sized manufacturer I recently worked with maintained a 30-metric scorecard that required two full-time analysts to update. Yet the leadership team did not know the handful of drivers behind performance. The dashboard was elaborate. It was not useful.

The opposite is also true. A startup founder I know runs his entire sales operation on a single-page visual. Twelve boxes. One page. His team knows exactly where they are winning and where they are stuck.

In business, we have developed an addiction to elaboration. We chase novelty, layering new frameworks upon old ones. We build dashboards that track hundreds of metrics and craft strategic plans so detailed they collapse under their own weight. Leaders end up with tools they cannot wield, while the real drivers get lost in the noise.

This is not a call for simplistic thinking. Some problems genuinely require complexity. But in business, we have inverted the default. Physics does the opposite: start simple, add complexity only when utility demands it.

The real task of modern leadership and scholarship is to distill theories into useful models. Because often, the models that work in the global north do not translate properly in our own context. So our problem is not a lack of knowledge. It is the scarcity of useful models.

A useful model is not a universal truth. It is a thinking tool, one that collapses complexity into clarity and turns scattered knowledge into a single, answerable question.

What is the most important thing we can do right now?

My writings are dedicated to Prof. A. A. Ubachukwu, father, mentor, teacher, and friend.

My father is a professor of physics and a thoroughbred physicist in every sense of the word. Growing up, it amazed me how he could apply universal laws of physics to almost everything. Concepts like the conservation of energy, Newton’s laws of motion, or Einstein’s theory of relativity weren’t just scientific principles to him - they became lenses through which he understood the world.

And to be fair, physics is a powerful discipline. Some of its laws are so fundamental that they echo in fields far beyond science. But even the most universal theories begin to break down when stretched beyond their natural domain. Eventually, mental models that once offered razor-sharp clarity start offering no more judgment than common sense, or worse, they mislead.

That’s when I began to understand something deeper: good judgment isn’t just about being smart or mastering a specific field. It’s about knowing how to understand problems. It’s the ability to discern what kind of problem you’re facing, and then knowing whether your toolbox fits the domain or whether it’s time to admit you need different tools entirely.

We often fall into the trap of treating our expertise like a Swiss army knife: good for everything. But in truth, most solutions collapse when applied outside their native context. The real danger is not in having a narrow expertise; it is in refusing to acknowledge its limits.

Good judgment lies in that space between confidence and humility. In having a mind flexible enough to learn across domains, and wise enough to know when to call for reinforcements.

In 2016, I was traveling by road from Uyo to Calabar. "That's a power plant," said my friend. Here, in the middle of nowhere, was a massive complex of a power station, and it stood proud like a Nigerian. And then the gist started, apparently, the Federal Government had built an IPP project in the middle of nowhere and did not think of the gas pipeline that would supply the power station. The irony was that I saw some security lights on, and was told that "NEPA" supplied power to the complex.

The power problem in Nigeria is a wicked problem. A wicked problem is a social or cultural problem that is difficult or impossible to solve for as many as four reasons: incomplete or contradictory knowledge, the number of people and opinions involved, the large economic burden, and the interconnected nature of these problems with other problems.

Rittel and Webber described 10 characteristics of a wicked problem in their paper "Dilemmas in a General Theory of Planning" written in 1973.

1. There is no definitive formulation of a wicked problem. This implies that in order to describe a wicked-problem in sufficient detail, one has to develop an exhaustive inventory of all conceivable solutions ahead of time.

2. Wicked problems have no stopping rule: there are no criteria that tell when the solution has been found.

3. Solutions to wicked problems are not true-or-false, but good-or-bad

4. There is no immediate and no ultimate test of a solution to a wicked problem: solutions tend to generate waves of consequences over an extended period of time.

5. Every solution to a wicked problem is a "one-shot operation"; because there is no opportunity to learn by trial-and-error, every attempt counts significantly. Actions are irreversible and every trial counts.

6. Wicked problems do not have an enumerable (or an exhaustively describable) set of potential solutions, nor is there a well-described set of permissible operations that may be incorporated into the plan.

7. Every wicked problem is essentially unique

8. Every wicked problem can be considered to be a symptom of another problem

9. The existence of a discrepancy representing a wicked problem can be explained in numerous ways. The choice of explanation determines the nature of the problem's resolution.

10. The planner has no right to be wrong.

Let's take a look at our power sector. Nigeria's energy needs are estimated at 170,000 MW. Installed capacity is 13,000 MW. Available generation is 7500 MW. The transmission capacity is 5000 MW. Distribution to homes is less than 4000 MW. 47% of Nigerians do not have access to grid electricity, and of these, only 44.6% are metered. Absence of cost-reflective tariffs, inadequate metering, intermittent failure of transmission infrastructure, gas shortages, inability of discos to pay NBET, etc. are valid reasons why power is not solved.

Ghana as of 2016, had power cuts so bad that they called their ex-president, Mr. Mahama, the power-cut president. Between 2015 and 2020, total installed capacity grew from 3,174 MW to 5,171.6 MW, and average available capacity of 2,058.6 MW to 3,363 MW. The problem with wicked problems is that solutions tend to generate waves of consequences over an extended period. By the end of 2019, Ghana had fixed the incessant power cuts. There was a new problem: independent power producers were owing gas suppliers $400 million, and the state-owned distribution company was owing power producers $1.5 billion. By January 2020, net arrears in the power sector totaled $2.5 billion, and excess supplies of power and gas were expected to cost Ghana $1 billion. In 2019, the Nigerian government spent some 540 billion naira subsidizing electricity.

The Obasanjo administration emphasized power generation and built IPP stations everywhere. The focus on generation did not have a commensurate focus on increasing transmission wheeling capacity. $16 billion was spent on the sectors between 1999 and 2007, while power generation fell from 3500 MW in 1999 to 1200 MW in 2007. The Yaradua government initially halted the NIPP projects on suspicion of corruption. By the time funding was released in 2009, completion dates had to be pushed to 2011. The Jonathan administration unbundled the sector and privatized Gencos and Discos. The Buhari administration has a deal with Siemens to expand Distribution to 7,000 MW and 11,000 MW by 2021 and 2023, respectively, and then drive generation and overall grid capacity to 25,000 MW.

A report claims that when Bola Ige handled the Ministry of Power, he promised to solve it in six months. When Rilwan Lanre Babalola entered in 2008, he promised to double power generation by 2009. By September 2010, capacity had dropped by 26.7%. Babatunde Fashola said a serious government would solve the power problem in 6 months. After 4 years in office as Minister of Power, power issues continued. In Nigeria, wicked problems are always reduced to a single cause - CORRUPTION. The fact that they outlive all manner of administrations should teach us that wicked problems always require new ways of thinking - design thinking.

In 2016 and 2017, I was part of a series of classes based on business cases from the Harvard Business School. The CEO of the company I worked with, was a Harvard student and had insisted that we go through her cases once a week. One of the most striking business cases was on a company in Nigeria, and the challenges they had faced in the poultry value chain.

Some facts from the case study. As at 2015, Chicken prices in Nigeria were 30 to 100% higher than in the US, EU or Brazil. A major driver of this cost was chicken feed. Nigerian demand for chicken was some 1.3 million metric tones while production was between 300,000 to 400,000 metric tones so there had to be a black market.

Feed makes up 65 - 80% of broiler production cost. Feed in Nigeria is double global prices. Typical broiler feed mix was made of 58% Maize. Nigerian maize sold at double the average world price. Maize yields per hectare was 2 MT vs 3.8MT for South Africa and 11 MT for the USA. Amongst other things, Maize yields were low because the typical Nigerian farmer applied 5.5kg of fertilizer per hectare versus an optimal 50-100kg. Only about 19% of maize farms were fertilized at all in 2010.

Since we are talking about chicken, feed, maize and fertilizer. I did a short study on Brazil.

• Chicken: Second highest producer in the world. 6.3 billion dollars in chicken exports in 2019. 13.6 million tons produced in 2019.

• Feed: Chicken feed is made up of 70% corn and soybean. third largest feed producer in the world.

• Maize: Brazil produced 106 million tons of corn in 2019. It's the world's third largest producer.

• Fertilizer: Brazil has a major fertilizer short fall. Worse than Nigeria.

How does Brazil solve her fertilizer problem?

In 2018, Brazil imported 76% of her fertilizer needs, some 8.7 million tons. This cost her 8.62 billion dollars. In contrast, Nigeria's fertilizer consumption in 2017 is 1.56 million tons.

The world's biggest chicken exporter, the world's third biggest feed and maize producer, imports 76% of her fertilizer needs. Nigeria that cannot produce enough of chicken or feed or maize to feed herself may have to look again at her approach.

So how does the Nigerian government solve her poultry value chain problems.

• Ban Chicken imports

• Ban Maize imports

• Ban fertilizer imports

• State-control of fertilizer supply chain

I don't have all the answers but this is not how to solve a chicken problem.

Alvarez, Jose, and Natalie Kindred. "Chicken Republic." Harvard Business School Case 516-052, December 2015. (Revised April 2019.). https://www.hbs.edu/faculty/Pages/item.aspx?num=50241

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A number of tools and techniques are available for problem-solving. Most can be used across multiple methodologies and approaches. Sometimes, tables speak as loudly as pictures, so I will summarize most of the learnings here in tables.

Next, we look at how to choose the right problem-solving tool. Not all problems are created equal, and not all tools work the same across different contexts. The key to effective problem-solving is diagnosis: first, understand the type of problem you're facing, then choose a tool that fits.

1. Use the Cynefin Framework to classify the nature of the problem: The Cynefin Framework classifies problems into 5 types: Simple, Complicated, Complex, Chaotic, and Disorder.

1. Match the Tool to the Task: The table maps tools to the generic problem-solving process. The best tools don’t replace thinking—they structure it. Mastering the toolbox helps you: control ambiguity, solve more effectively, and teach others how to think better

Ask yourself, Is the problem urgent or emergent? Are the causes known or unknown? - Can I test multiple solutions safely? Is expertise available, or is experimentation the only way? You then need to use good judgment. Frameworks help, but choosing the right tool is as much an art as it is a science. The most effective leaders and problem-solvers build range - they know when to switch tools and when to combine them.

Problem-solving is rarely linear. It is iterative, meaning each step loops into the next, allowing for feedback, adjustment, and continuous improvement. This cyclical nature is crucial because every solution typically generates new problems, requiring further refinement or intervention.

Clarifying Terminology: Approaches, Frameworks, and Methodologies

• Approach: A broad philosophy or style of tackling problems. It may be high-level and conceptual or structured enough to guide detailed processes. Example: Human-centered design is an approach.

• Framework: A structured outline or skeleton that provides a general guide to solving a problem. Frameworks are flexible and can be tailored to different contexts. Example: The GROW model or OODA Loop.

• Methodology: A systematic set of rules and principles used to derive specific methods or techniques for solving problems. It is usually more detailed and prescriptive. Example: DMAIC, PDCA.

The progression also reveals why organizations sometimes struggle with implementation; they might adopt a methodology when they need an approach, or try to apply a rigid framework when they need flexible principles. Understanding these distinctions helps leaders choose the right level of structure for their specific context and organizational maturity.

The Core Problem-Solving Process. Although specific methodologies may differ in terminology or emphasis, most build on the same fundamental steps:

Each step involves tools, techniques, and data-driven analysis to ensure decisions are sound and sustainable.

Many modern methodologies trace their roots to Total Quality Management (TQM), Six Sigma, and Lean Manufacturing, each developed in different contexts but widely adopted across industries.

1. Total Quality Management (TQM): Founded by W. Edwards Deming. TQM is a management philosophy that emphasizes continuous improvement, customer satisfaction, and process excellence. It holds that quality must be built into every part of the organization, and problems should be addressed at their root to meet or exceed stakeholder expectations.

2. Six Sigma: A statistically driven methodology that focuses on eliminating variation and improving process performance. “Six Sigma quality” indicates that a process is well controlled (fewer than 3.4 defects per million opportunities). And emphasizes the use of data, measurement, and statistical tools to improve outcomes.
3. Lean Manufacturing: Originating from Toyota, Lean emphasizes the elimination of waste (“muda”) to improve efficiency and value delivery.

The 8 types of waste (acronym: DOWNTIME) are: Defects, Overproduction, Waiting, Non-utilized talent, Transportation, Inventory, Motion, and Extra-processing.

4. Lean Six Sigma: A hybrid philosophy combining Lean’s focus on waste elimination with Six Sigma’s focus on variation reduction. It is fact-based, data-driven, and emphasizes standardization, flow, and customer satisfaction.

Popular Problem-Solving Methodologies

1. PDCA / PDSA Cycle: Plan – Do – Check (Study) – Act. An iterative loop for continuous improvement of processes, products, or services. Widely used in TQM and process management.

2. DMAIC (Six Sigma). Define – Measure – Analyze – Improve – Control. Used to improve existing processes. Heavily data-driven and structured, ideal for identifying root causes and implementing measurable changes.

3. DMADV (Six Sigma). Define – Measure – Analyze – Design – Verify. Used when designing new processes or products. Focuses on ensuring new systems meet customer expectations from the outset.

4. A3 Problem Solving (Lean): A structured template-based approach used by Lean practitioners (especially in automotive). Named after the A3-size paper used to present the problem, analysis, and solution concisely.

5. 8D Methodology (Ford Motor Company) - Eight Disciplines of Problem Solving: A structured, team-oriented approach designed to identify, correct, and eliminate recurring issues. Especially useful for root cause analysis and corrective action in manufacturing settings.

6. Design Thinking: A human-centered, iterative methodology that seeks to understand the user deeply, challenge assumptions, and prototype solutions rapidly.

Phases include:

• Empathize

• Define

• Ideate

• Prototype

• Test

It is widely used in innovation, UX/UI, education, and organizational design.

Niche Approaches

For highly specialized contexts, the following models may be valuable:

• GROW: Used in coaching and personal development.

• OODA Loop: Effective in chaotic or ambiguous settings (e.g., startups, crisis strategy).

• TRIZ: Powerful in R&D, engineering, and technical innovation.
  
  

There is no one-size-fits-all methodology. The key is to:

• Understand your problem type and context

• Select an appropriate framework or methodology

• Apply it consistently while allowing for flexibility

• Learn from each iteration and adapt as needed

The next table is a guide on how to choose what works for you.

Great problem solvers don’t just act, they adapt. They don’t just fix, they transform.

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1. Problems Are Inevitable: “Whatever can go wrong will go wrong.” - Murphy’s Law. Problems are not unusual; they do not care if you are in a growth stage, decline, or stagnation. Something always goes wrong, despite planning and good intentions. This isn’t pessimism. It’s realism. Expect it. Prepare for it.

2. Solutions Are Intentional: Newton’s First Law of Motion: “An object remains in its state unless acted upon.” Systems, like people and organizations, tend toward inertia. Nothing changes until energy, thought, and action are deliberately applied. Solutions require intentional intervention.

3. Problem Solving Comes at a Cost and Requires Change:

Real problem solving will cost you: money, time, cognitive effort (grey matter), physical and emotional exertion. Motivational slogans won’t substitute for the work. “Inspire to perspire to refire” may sound good on a poster, but what you need is wisdom, discipline, and sometimes a bit of luck.

Also, you cannot solve problems with the same mindset, processes, or structures that created them. Solutions require: a shift in perspective, a change in systems or processes, and a willingness to adapt. If nothing changes, nothing improves.

4. Start with Root Cause Analysis

Defining the problem properly is half the battle. Go beneath the surface. Ask why, five times if you must. Even spiritual problems tend to have natural roots. Identify them.

Without root cause analysis, you’re treating symptoms, not problems.

5. Problems are Opportunities in Disguise: This one sounds like motivational talk, but problems are your chance to learn, innovate, or improve.

6. Every Problem Has Multiple Solutions: Even the hardest math problem has multiple ways of solving it. Business problems, by comparison, often have dozens, some better than others. Your job is to find the effective one.

7. Most Problems Are Multi-Dimensional and Dynamic: Problems can evolve, change, or even disappear over time. Complex problems rarely have simple answers. They’re entangled in history, relationships, systems, and culture. The best approach? Break it down. Like eating an elephant, one bite at a time.

8. Solve for Context: Culture will eat strategy for breakfast. Always solve problems in the context in which they exist, industry, people, politics, history, and values matter. One-size-fits-all models usually don’t solve anything worthwhile.

9. You Can’t Solve Every Problem Today: You’re not here to save the world. Focus. Solve one thing well. Leave room for others to solve the rest. Great leaders don’t do everything; they do what matters.

10. Firefighting ≠ Problem Solving: Firefighting is a contingency plan. It may save you today, but over time it will burn you out. If you’re always reacting, never redesigning, you’re not solving. You’re surviving.

11. Every Solution Brings New Problems: Solutions are not ends. They are transitions. Every fix creates ripple effects. Every decision introduces new variables. There is no holy grail. Good problem solvers anticipate risks, test assumptions, and stress-test solutions before they scale.

12. Use Proven Tools and Frameworks: There are tested, reliable problem-solving methods: Root Cause Analysis, 5 Whys, Fishbone Diagrams, PDCA (Plan-Do-Check-Act) Cycle, DMAIC, Design Thinking, Systems Mapping. Master the tools. Choose the ones that suit your context. Don’t reinvent the wheel.

13. Problem Solving Requires Collaboration: Sometimes, the symptom of the problem is in operations, but the root cause is in sales or finance, and vice versa. You need to work across functional boundaries in a lot of cases to solve a problem.

14. Measure What Matters: “When anecdotes and data disagree, the anecdotes are usually right.” - Jeff Bezos. If your KPIs say “green” but customers are complaining, something is off. Customer experience trumps vanity metrics. KPIs exist to serve the customer, not the other way around. This one was so hard to learn early in my career. But do not just benchmark. Listen. Observe. Respond. Solving problems is not just a skill. It’s a discipline. A mindset. A responsibility. It requires curiosity, courage, and clarity.

I think I gave the 14 Commandments. These things happen.

"We cannot solve our problems with the same thinking we used when we created them” - Albert Einstein

Problem solving is the act of defining a problem; determining the cause of the problem; identifying, prioritizing, and selecting alternatives for a solution; and implementing a solution - ASQ.

Fire fighting is not problem solving.

Fire fighting, according to the Business Dictionary, is Short-term 'fixing' of problems, or suppression of their symptoms, rather than understanding and addressing the factors that cause the problem.

In healthcare, diagnosis requires that you need to look at the clinical history, interview the patient, perform a physical exam, consider differential diagnoses, perform diagnostic tests, reach a final diagnosis, and sometimes consult with a specialist.

This can be applied to the business context; in this way, you are working with the history/context, subjective complaints, objective data, physical observations, and layers of expert opinion before reaching conclusions. This simple process ensures that your doctor isn't treating the wrong ailment.

Dr Russell Ackoff, a pioneer management thinker, discussed four approaches to solving problems:

1. Absolution - This is where we ignore the problem and hope that it goes away.

2. Resolution - This is where we dig into the past, figure out how we handled it in the past, and do the same again. This is a common-sense approach using trial and error and qualitative judgment.

3. Solution - This is where we use research, experimentation, and quantitative analysis to get optimized outcomes based on current constraints.

4. Dissolution - This is where we redesign the system, business, or its environment in a way that eliminates the problem and positions the business to function above current constraints.

“Successful problem solving requires finding the right solution to the right problem. We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.” - Russell Ackoff

These solutions might require that you:

• Implement or change business strategies

• Implement or change technologies

• Create, improve, or fix products and/or services

• Create, improve, or modify processes.

• Change the organizational structure.

• Change the organizational culture.

• Get new capabilities: Capabilities are the ways people and resources are combined for a specific outcome.

• Change materials or components.

One or more projects would usually have to be initiated when implementing solutions to most complex problems.

According to the Business Dictionary, a problem is “a perceived gap between the existing state and a desired state, or a deviation from a norm, standard, or status quo.” While many problems can have multiple solutions (i.e., ways to close the gap or correct the deviation), difficulties arise when those solutions are not immediately obvious or readily accessible.

In business, problems tend to recur when we fail to address their root causes. The Chartered Institute of Internal Auditors identifies three basic categories of problem causes:

1. Physical Causes – e.g., a material component fails.

2. Human Causes – e.g., someone makes an error.

3. Organizational Causes – e.g., a faulty process, policy, or system.

In complex systems, most problems do not stem from a single root cause. Instead, they are multifactorial, with causes spanning across people, structures, and systems.

If you’re Nigerian, you might be tempted to add a fourth category: “Spiritual forces” or simply “bad luck.”

And honestly, you’re not alone. I, too, sometimes wonder about the invisible hand of misfortune. But here’s the reality: wherever ‘spirits’ exist, they often act through the physical, human, or organizational channels we ignore.

So, pray, yes, but also probe. Ask: “What did misfortune take advantage of? What weak link allowed it to pass through the cracks?

According to Geitner & Bloch (2012), physical failures typically result from the following:

1. Faulty design

2. Material defects

3. Fabrication or processing errors

4. Assembly or installation defects

5. Off-design or unintended service conditions

6. Maintenance deficiencies

7. Improper operation or usage

Each of these technical causes, if not anticipated or mitigated, can lead to repeated failure, downtime, or worse.

Problem-solving is not just a technical or managerial function; it’s a life skill. It becomes even more essential when you are leading others, as people look to you not only to identify issues, but also to resolve them sustainably.

Whether you’re managing a business, a team, a household, or just your own life, your capacity to identify root causes and act on them often determines whether you grow or repeat the same cycles.