In Part 1, we introduced Maple Industrial Supply.

Maple's sales increased from $4.2 million to $4.8 million. Its EBITDA increased from $420,000 to $480,000.

Yet Maple has used $850,000 of its $900,000 operating line and is asking a supplier to double its credit limit.

If sales and EBITDA increased, why is Maple still looking for more credit?

Start with one sale

Suppose Maple sells $100,000 of inventory to a customer on 60-day terms. The inventory cost Maple $80,000.

Maple records the following:

Maple has recorded a $20,000 profit. But the customer has not paid yet.

The sale increased Maple's revenue and profit, but it did not immediately increase Maple's cash.

The income statement records the sale when it is earned. Cash flow reflects when the money is actually received or paid.

Now look at Maple's full year

During the year, Maple's accounts receivable increased from $700,000 to $1.1 million.

$1,100,000 - $700,000 = $400,000

This means Maple ended the year with $400,000 more tied up in customer accounts than it had at the beginning of the year.

Inventory also increased:

$900,000 - $650,000 = $250,000

Maple therefore had another $250,000 tied up in inventory. Together, receivables and inventory absorbed:

$400,000 + $250,000 = $650,000

Who helped finance the increase

Maple's accounts payable increased from $500,000 to $650,000.

$650,000 - $500,000 = $150,000

By carrying $150,000 more in accounts payable, Maple's suppliers financed part of the increase in receivables and inventory.

The remaining amount was:

$650,000 - $150,000 = $500,000

Maple therefore had to finance approximately $500,000 of the increase from other sources.

Compare that with EBITDA

Maple reported EBITDA of $480,000. A simplified comparison looks like this:

Using only these figures, Maple's receivables and inventory absorbed more cash than its EBITDA provided.

That helps explain why Maple could report higher sales and EBITDA while also using most of its operating line and requesting more supplier credit.

This is not Maple's complete operating cash flow. We have not included interest, taxes, capital expenditures, debt repayments or other balance-sheet changes.

It is a simplified calculation designed to show one thing: profitability does not tell us how quickly earnings are being converted into cash.

Working capital is a different calculation

Working capital is calculated at a particular date:

Current assets - current liabilities

For example:

Company A has positive working capital of $300,000 and negative EBITDA of $100,000. Both statements can be true.

Working capital describes the company's financial position at one date. EBITDA describes its operating performance over a period.

Now consider Company B:

Company B has negative working capital of $200,000 and positive EBITDA of $300,000. Again, both statements can be true.

The company may be profitable but have substantial short-term obligations. Alternatively, it may operate a business that collects from customers before paying suppliers.

The figures create questions. They do not provide the complete answer.

What should the credit professional learn

Three calculations are telling us different things:

·        EBITDA helps describe operating earnings.

·        Working capital compares current assets with current liabilities at one date.

·        Changes in receivables, inventory and payables help explain why cash increased or decreased during a period.

None should be interpreted alone.

For Maple, the issue is not simply whether EBITDA is positive. It is whether Maple can convert its sales, receivables and inventory into cash before its bank and supplier obligations become due.

How much cash is tied up, how long will it remain tied up, and who is financing the gap?

In Part 3, we will examine that timing through Maple's cash-conversion cycle.

A commercial banker and a B2B credit professional may assess the same business at the same time.

The banker may be reviewing its operating line. The credit professional may be deciding whether to increase its trade credit limit.

Both are assessing creditworthiness—but they are not necessarily making the same decision.

I asked AI to compare their perspectives using a simplified case.

Meet Maple Industrial Supply

Maple Industrial Supply has asked one of its suppliers to increase its credit limit from $75,000 to $150,000, with payment terms of 60 days.

At approximately the same time, Maple’s bank is reviewing its $900,000 operating line.

Maple provides the following year-end information:

Financial information	Previous year	Current year
Sales	$4,200,000	$4,800,000
EBITDA	$420,000	$480,000
Accounts receivable	$700,000	$1,100,000
Inventory	$650,000	$900,000
Accounts payable	$500,000	$650,000

We also know that:

• Maple’s customers pay in an average of 75 days;
• $850,000 of its $900,000 bank line is currently used; and
• Maple is requesting 60-day terms from its supplier.

Would the banker and the B2B credit professional assess this information in the same way?

What they have in common

Both need to answer a fundamental question:

Where will the cash to repay the credit come from?

For a healthy operating business, repayment will normally come from cash generated through its operations.

The banker needs to understand how Maple will repay amounts borrowed under its operating line. The credit professional needs to determine how Maple will pay the supplier’s invoices when they become due.

Both may consider:

• sales and profitability;
• cash flow;
• receivables and inventory;
• existing obligations;
• payment history;
• customer concentration; and
• conditions affecting Maple’s industry.

Both will also want to know what could delay or interrupt the expected repayment.

Where their perspectives differ

The banker is evaluating Maple’s overall borrowing requirement and its ability to repay the operating line. Depending on the lending agreement, the bank may receive regular financial reporting, hold security, impose covenants and establish which assets support the facility.

The B2B credit professional is deciding whether to deliver additional goods or services and wait for payment. The supplier may receive less financial information and may not have the same security as the bank.

But the supplier may have information the banker does not see as quickly.

Changes in payment behaviour, requests for extended terms, unusual purchasing activity, deductions, disputes and broken payment commitments can reveal how Maple is managing its trade obligations.

The banker and supplier are therefore looking at the same business through different exposures and sources of information.

What do we see in Maple’s numbers?

There are favourable developments:

• sales increased;
• EBITDA increased; and
• the business appears to be growing.

There are also developments requiring explanation:

• receivables increased;
• inventory increased;
• accounts payable increased;
• most of the operating line is already in use; and
• Maple wants the supplier to double its credit exposure.

None of these facts, considered separately, proves that Maple is financially weak.

Receivables and inventory may increase because a healthy business is growing. Greater use of an operating line may be consistent with that growth. An increase in payables may reflect higher purchasing volume.

The same changes could also indicate slower collections, excess inventory or increasing dependence on borrowed money and supplier credit.

The figures tell us what changed. They do not yet tell us why it changed.

Could the banker and supplier reach different decisions?

Yes.

The bank and the supplier have different exposures, repayment arrangements, information and protections. One may approve credit while the other declines it or imposes additional conditions.

A bank might remain comfortable because its lending is supported by eligible assets, security and financial reporting.

A supplier might be less comfortable because it is being asked to double its exposure without receiving comparable protection.

The reverse is also possible. A supplier may approve a controlled amount based on strong payment experience and direct knowledge of the customer, even if the bank is unwilling to increase its facility.

Neither decision automatically proves that the other is wrong.

The question is not simply:

Would the bank lend to Maple?

For the B2B credit professional, the better question is:

Given our particular exposure, terms, information and protection, what evidence shows that Maple can and will pay us when our invoices become due?

In Part 2, we will examine Maple’s working-capital changes and ask a more difficult question: If sales and EBITDA increased, where did the cash go?

Moving from understanding bond mechanics to interpreting credit signals

I ended the first article in this series with a question:

What might the capital markets be telling me about a customer's ability to pay tomorrow that I can't yet see from how they're paying me today?

It came out of my attempt to understand bonds using ChatGPT as a tutor.

By the end of that exercise, I understood the basic relationship:

Bond prices go up → yields go down.

Bond prices go down → yields go up.

I also learned that a company's bond yield can provide another signal about how investors perceive its credit risk.

But that raised a more difficult question.

If a customer's bond yield suddenly rises, does that mean the company has become riskier?

Not necessarily.

So I went back to my AI tutor.

A rising yield doesn't tell me enough

Consider a customer whose bonds yielded 6% six months ago and now yield 9%.

My first reaction as a credit professional might be:

Something is wrong. Investors are demanding much more return to lend to this company.

Maybe.

But I am missing an important piece of information:

What happened to the rest of the bond market?

Suppose the comparable government yield also moved:

Government bond: 4% → 7%

Customer's bond: 6% → 9%

The customer's borrowing cost has certainly increased. That matters, especially if it needs to refinance.

But notice the difference between the two yields.

Six months ago: 6% - 4% = 2%

Today: 9% - 7% = 2%

The credit spread hasn't changed.

Investors are still demanding roughly the same additional return for taking this company's risk rather than government risk.

The market moved.

That is quite different from the market specifically becoming more worried about my customer.

Now change one number

Suppose instead:

Government bond: 4% → 3%

Customer's bond: 6% → 9%

Now something very different has happened.

The credit spread moved from:

2% → 6%

Investors are accepting less return to hold government debt while demanding much more to hold my customer's debt.

That doesn't prove the company is heading for financial trouble.

But if I'm extending trade credit to this business, I want to know why the market is suddenly demanding so much more compensation for its risk.

Perhaps leverage increased. Perhaps earnings expectations deteriorated. Perhaps free cash flow weakened. Perhaps there is a large debt maturity approaching. Perhaps an acquisition increased financial risk. Or perhaps the market has overreacted.

The bond market isn't making my credit decision for me.

It's giving me a reason to investigate.

Then came the recession question

ChatGPT tested me on another scenario.

A recession begins. Investors become nervous and rush into U.S. Treasuries as a safe haven.

What happens?

I correctly reasoned that investors would flock to Treasuries.

Then I got the next part wrong.

I said Treasury prices would fall and yields would rise.

But investors are buying Treasuries.

Demand ↑ → prices ↑ → yields ↓

At the same time, those investors could be selling riskier corporate bonds.

So we could have:

Treasury yields ↓

while:

Corporate bond yields ↑

That widening gap can tell us something about how investors' appetite for corporate credit risk is changing.

And it gives the credit professional some additional context.

The customer may still be paying me on time

This is perhaps the most important part.

Suppose my customer continues paying invoices within terms.

Its latest financial statements look reasonably good.

But its bonds are falling in price, its credit spread is widening and it has a substantial amount of debt coming due next year.

Which signal should I believe?

That's probably the wrong question.

I should consider all of them.

Payment history tells me how the customer has been paying.

Financial statements help me understand its financial position and past performance.

Cash-flow analysis helps me understand whether the business is generating the cash necessary to support its obligations.

And capital markets may tell me something about what investors expect next.

The value is in putting those signals together.

Refinancing can change the picture quickly

Consider a company with:

$500 million in cash

$2 billion in debt

negative free cash flow

and:

$800 million of debt maturing next year.

Looking only at the $500 million cash balance might give me some comfort.

But where will the company get the money to deal with that $800 million maturity?

Perhaps it intends to refinance it.

That may have been relatively easy when investors were willing to lend at 5%.

What if they now demand 10%?

Or what if financing becomes difficult to obtain at almost any reasonable price?

The company's factories didn't suddenly disappear.

Its customers may still be buying.

It may still be paying my invoices.

But its access to capital has changed.

For a business dependent on refinancing, that can eventually become a liquidity problem.

This changes the credit question

We often ask:

Is this company profitable?

We also ask:

Does it have enough liquidity?

But understanding the bond market added another question to my list:

How dependent is this business on continuing access to external financing—and what happens if that financing becomes significantly more expensive or harder to obtain?

That brings me back to where Part 1 ended.

The bond market isn't a crystal ball.

A widening credit spread doesn't tell me to reduce a credit limit.

A falling bond price doesn't tell me a customer will default.

And investors can certainly be wrong.

But these can be forward-looking signals that deserve attention alongside financial statements, payment behaviour, cash flow and everything else we use to assess creditworthiness.

Perhaps the better question isn't:

"What is the bond market telling me?"

It's:

"What is the bond market telling me to investigate?"

And that's where we move from understanding bonds to using the information as credit professionals.

Ready for Round 2?

The first quiz tested whether you understood how bonds work.

Round 2 is different.

This time, don't just think about the bond.

Think like the credit professional assessing the business behind it.

 

The quiz is at the bottom of this article.
Using ChatGPT as a tutor to move from bond basics to better credit questions

I understood the basic idea of a bond—or at least I thought I did.

A government or company needs money. It issues a bond. An investor lends the money and receives interest. At maturity, the investor gets the principal back.

Simple enough.

Then I started asking ChatGPT questions.

If I buy a Treasury bond and sell it three months later, who pays me the interest? Why would anyone sell a $1,000 bond for $950 when they could simply hold it to maturity? When people say investors are "selling bonds," where do those bonds go? And why do bond yields rise when prices fall?

One question led to another.

What started as an attempt to understand bonds became something else: an experiment in using AI as a personal tutor.

A Few Bond Terms Before We Start

The distinction to remember: The bond's face value and coupon are contractual. Its market price and yield can change every day.

Starting with the basics

I started with a simple assumption:

Me: Bond yields go up, bondholders make more return, but bond issuers have to pay more?

ChatGPT immediately introduced an important distinction.

If a company issued a $1,000 bond paying 4%, the bondholder continues receiving the contractual coupon. If market interest rates subsequently rise to 6%, the company doesn't suddenly start paying the existing bondholder 6%.

Instead, the market price of the existing bond changes.

Why?

Because investors can now obtain approximately 6% elsewhere. The old 4% bond becomes less attractive at its original price.

Its price therefore falls until the return available to a new buyer becomes competitive with prevailing market rates.

Bond prices and bond yields move in opposite directions.

Price goes up → yield goes down.

Price goes down → yield goes up.

I understood the words.

As I would later discover, I hadn't completely internalized them.

What happens after Treasury sells a bond?

My next questions concerned where bonds actually come from.

Suppose the U.S. Treasury needs to borrow money.

Treasury auctions securities to investors in what is called the primary market.

Simplifying the transaction:

Treasury issues bond → Investor provides cash → Treasury owes interest and principal

But what happens if the investor wants to sell the bond before it reaches maturity?

They don't normally sell it back to Treasury. Instead, they sell it to another investor in the secondary market.

Investor A → Bond → Investor B

Investor B → Cash → Investor A

Treasury receives nothing from this transaction. The identity of its creditor has simply changed.

Treasury still owes the promised interest and principal, except Investor B now owns that claim.

If the investor instead holds the bond to maturity, Treasury simply repays the face value as promised.

That led to another question.

Why would someone sell a $1,000 bond for $950?

Suppose I paid $1,000 for a Treasury bond.

Interest rates subsequently rise and the bond is now worth only $950 in the secondary market.

Why would I sell?

Why not simply hold it until maturity and collect the $1,000 Treasury promised me?

There can be many reasons.

An investor might need cash. A fund might face withdrawals. A bank might need liquidity. An investment manager might want to shorten the portfolio's duration. Or another investment might now provide a better expected return.

There was also an important lesson here.

Not selling doesn't mean the loss doesn't exist.

If an asset I purchased for $1,000 can now be sold for only $950, its market value has declined by $50. I haven't realized the loss, but economically the asset is nevertheless worth less today.

If I hold the Treasury to maturity, however, and Treasury makes all the promised payments, I will still receive the contractual principal.

The $950 is therefore not Treasury changing its promise.

It is the market changing the price it is willing to pay for that promise today.

That distinction turned out to be fundamental.

Who gets the interest if I sell halfway through?

Then I pushed the example further.

Suppose I own a Treasury paying 5% and sell it three months into a six-month coupon period.

Do I receive anything for having held the bond for those three months?

Economically, yes.

Interest accumulates while the bond is held. This is accrued interest.

If the six-month coupon is $25 and I sell halfway through the period, approximately $12.50 has accrued during my ownership.

Treasury doesn't need to divide the next coupon between the two investors.

Instead, the bond-market settlement process accounts for the accrued interest. The new owner eventually receives the full coupon from Treasury, while the previous owner is compensated for the portion attributable to their period of ownership.

That prompted another question: What if I desperately need money and am prepared to take a haircut?

Again, the market provides the answer.

I can accept a lower price for my bond, but I still need someone willing to buy it.

Which brought me back to supply and demand.

What does "nobody is buying U.S. bonds" actually mean?

I had heard variations of this expression many times.

But taken literally, it doesn't make much sense.

If a bond is sold in the secondary market, someone bought it.

There aren't enough investors willing to buy at the current price and yield.

Suppose sellers want $1,000. Buyers aren't interested. The price falls to $980. Still insufficient demand. $960. More buyers appear. $940. Now perhaps enough investors consider the return attractive. The market clears.

Selling pressure → price falls → yield rises → additional buyers become interested

This also explains why a Treasury-market selloff can eventually affect the U.S. government's borrowing costs.

If existing 10-year Treasuries are trading at yields around 5%, Treasury cannot realistically expect investors to enthusiastically buy a comparable new issue yielding only 3%.

New Treasury debt has to compete with securities already available in the secondary market.

Then I asked ChatGPT to stop teaching me

At this point, I thought I understood it.

So I changed the exercise.

Instead of asking ChatGPT another question, I said:

"Test my understanding starting with the basics. Five questions to get started."

This changed the role of AI.

ChatGPT stopped being an answer engine and became an examiner.

It asked what would happen to the price of my existing 4% bond if newly issued comparable Treasuries yielded 6%.

I answered correctly:

"The price will go down because there won't be as much demand for a 4% yield when new bonds are paying 6%."

Good.

Then the questions became slightly harder.

ChatGPT asked:

You own a $1,000 Treasury paying a 5% coupon. Market yields suddenly fall to 3%. What should happen to the market price of your bond?

My answer?

"Market price would fall."

Wrong.

I had just demonstrated that I understood the explanation but hadn't fully internalized the relationship.

If my bond pays 5% while comparable new bonds offer only 3%, my bond has become more attractive.

Investors should therefore be willing to pay more for it.

Demand rises → price rises → yield falls toward the prevailing market return.

Then ChatGPT asked what would normally happen if a recession caused investors to rush into Treasuries as a safe haven.

I made essentially the same mistake again.

I said prices would fall and yields would rise.

But if investors are rushing into Treasuries:

Demand rises → prices rise → yields fall.

ChatGPT had identified the weakness in my mental model.

I understood why investors behaved as they did. What I hadn't yet mastered was translating that behaviour into the correct price-and-yield movement.

The tutoring session had found something that simply continuing to read explanations might not have exposed.

The rule I needed to remember

PRICE ↑ = YIELD ↓

PRICE ↓ = YIELD ↑

Or perhaps an even more intuitive version:

When everyone wants to lend you money, you don't have to pay them as much.

When people become reluctant to lend to you, you have to pay them more.

That applies not only to governments.

It brings us directly back to business credit.

What does any of this have to do with assessing a customer?

Quite a lot—particularly when assessing publicly traded companies with bonds trading in capital markets.

A corporate bond isn't simply an investment security.

Its market price and yield can also provide information about how investors perceive the company's future credit risk.

Consider a hypothetical customer.

Six months ago:

10-year government yield: 4.0%

Customer's bond yield: 5.5%

The difference—or credit spread—is 1.5 percentage points, or 150 basis points.

Now suppose six months later:

10-year government yield: 4.1%

Customer's bond yield: 8.5%

The government yield barely changed.

But investors now require 8.5% to hold the customer's debt.

The credit spread has widened from approximately 150 to 440 basis points.

Something has changed.

That doesn't automatically mean the customer is going bankrupt.

It does mean I should probably start asking questions.

The market may be telling us something

A conventional commercial credit review might tell me:

·        The customer is paying within terms.

·        Its latest financial statements appear acceptable.

·        There have been no returned payments.

·        The credit rating hasn't changed.

·        Management isn't reporting a liquidity problem.

Yet thousands of investors collectively buying and selling the company's debt are suddenly demanding substantially greater compensation for taking its credit risk.

That deserves attention.

Perhaps investors are concerned about upcoming debt maturities. Perhaps free cash flow is deteriorating. Perhaps leverage is increasing. Perhaps earnings guidance has weakened. Perhaps an acquisition changed the company's capital structure. Perhaps refinancing that previously looked straightforward is becoming much more expensive.

Or perhaps the market is simply wrong.

The bond yield doesn't give me the answer.

It gives me a signal worth investigating.

That is an important distinction.

From backward-looking credit to forward-looking signals

Commercial credit analysis naturally relies heavily on historical information.

Financial statements tell us what happened during a previous reporting period. Payment history tells us how the customer behaved yesterday. A credit report largely aggregates information that already exists.

But our real question is forward-looking:

Will this business be able and willing to pay us six months from now?

For companies with publicly traded debt, bond markets provide another piece of evidence.

Not an oracle. Not a replacement for financial analysis. And certainly not an automatic credit decision.

But potentially an early-warning signal.

If the market suddenly demands substantially more compensation for lending to one of my customers while government yields and comparable-company yields remain relatively stable, I want to understand why.

What does the bond market think is changing that isn't yet visible in my traditional credit information?

That is a very different way of looking at a bond yield.

What I learned about using AI

I started this exercise trying to understand bonds.

But I also learned something about using AI.

There is a significant difference between asking AI:

"Explain bond yields to me."

and saying:

"Test whether I actually understand bond yields."

The first gives me information.

The second forces me to retrieve the information, apply it and expose weaknesses in my reasoning.

And when I answered incorrectly, ChatGPT didn't simply give me another generic explanation. It could see the particular relationship I was getting wrong and concentrate the lesson there.

That suggests a simple way of using AI for professional development:

Ask → challenge → apply → get tested → make mistakes → correct the mental model → apply it to your work.

The objective isn't to have AI do the thinking for us.

It's to use AI to make us do more of the thinking ourselves.

And in this case, I started by asking ChatGPT how bonds work.

I ended up asking a much more useful question about credit:

What might the capital markets be telling me about a customer's ability to pay tomorrow that I can't yet see from how they're paying me today?

First ask: Which cash flow is negative?

Large-company examples establish the principle

Recent public-company results show that negative cash flow can arise for very different reasons:

Amazon generated approximately US$161.4 billion in operating cash flow for the 12 months ended June 2026, yet reported negative free cash flow of US$7.6 billion after exceptionally heavy property and equipment spending, largely for artificial-intelligence infrastructure.

Moderna used US$630 million in operating cash during the first quarter of 2026 while continuing to invest in research, development and its product pipeline. Here, the cash consumption occurs within operations and carries the risk that future products may not succeed.

Uber reported US$2.862 billion of operating cash flow and US$2.792 billion of free cash flow in the second quarter of 2026. After years of spending to build its platform and customer base, it illustrates the outcome investors hope a growth strategy will eventually produce.

These examples are instructive, but most businesses do not operate with billion-dollar cash reserves. What does the issue look like for a company with roughly $10 million in annual revenue?

Three $10-million-company examples

The following cases are hypothetical, simplified examples designed to show how the same cash-flow principles appear in a privately held business.

1. A distributor building inventory

 

The distributor purchased inventory for a new product line or seasonal sales period. Negative operating cash flow may be reasonable if the inventory is supported by demand, sells quickly and produces an adequate margin.

Credit concern: Inventory can become obsolete, sell more slowly than forecast or require price reductions. The lender or supplier should examine inventory turnover, purchase commitments, customer orders and available borrowing capacity.

2. A manufacturer adding capacity

 

The existing business generates cash, but the equipment purchase creates temporary negative free cash flow. The investment may be sound if it increases output, reduces unit costs or enables new contracts.

Credit concern: The company must fund both the equipment and the working capital required by higher production. Capacity does not automatically create demand, and growth can increase receivables and inventory before producing cash.

3. A profitable service company running short of cash

 

The income statement reports a profit, but customers are not paying quickly enough. Unlike the first two examples, the cash shortfall may not represent a deliberate investment. It may reflect weak credit decisions, billing disputes, poor receivables management or deteriorating customer quality.

Credit concern: Sales growth can conceal the problem. Review aging trends, customer concentration, disputes, bad-debt experience and the difference between reported revenue and cash received.

The questions management—and creditors—should ask

·        What exactly is consuming the cash?

·        Is the outflow planned, measurable and time-limited?

·        What return is expected, and when should it appear?

·        How much liquidity remains if the plan takes longer than expected?

·        Is the company funding productive growth—or covering weaknesses in its existing operations?

What this exchange demonstrates about learning with AI

An AI tutor is most useful when the user remains active. Instead of treating the first response as final, ask for examples, challenge assumptions, request another level of explanation and connect the answer to a situation you actually understand.

·        Ask the model to explain the concept in plain language—and then in professional terms.

·        Request examples, counterexamples and a comparison between healthy and unhealthy cases.

·        Ask to see the concept inside a calculation, document or financial statement.

·        Scale the example to your industry, organization or level of responsibility.

·        Ask what assumptions, risks or exceptions may have been overlooked.

The caveat is important: An LLM can produce a confident answer that is incomplete, outdated or wrong. It does not know your full circumstances, and it cannot replace accountable professional judgment. Verify material facts against primary sources, protect confidential information and consult qualified professionals when the consequences matter.

Sources and credits

1. Amazon.com, Inc., Second Quarter 2026 Results, July 30, 2026. View source

2. Moderna, Inc., Quarterly Report for the period ended March 31, 2026 (Form 10-Q). View source

3. Uber Technologies, Inc., Second Quarter 2026 Results, August 5, 2026. View source

Disclaimer

This article was developed through an iterative conversation with generative artificial intelligence and reviewed for publication. AI-generated explanations may contain errors, omissions, outdated information or invented details. This material is provided for general educational and discussion purposes only and does not constitute accounting, financial, investment, legal, tax or credit advice. It should not replace independent verification, professional judgment or advice tailored to a specific organization. Public-company figures are drawn from the cited disclosures and may include non-GAAP measures such as free cash flow. The $10-million-company examples are hypothetical and do not represent actual companies. Do not submit confidential, personal or proprietary information to a public AI service without appropriate authorization and safeguards.
PS: Want to refresh or advance your financial analysis skill, check out our CCP and ECCP courses

Collections Trend Snapshot:
Our latest survey reveals a mixed but cautiously strained picture in Canadian accounts receivable. While 43% of respondents reported collection periods remaining the same, a notable 33% saw them increase (slower collections), outpacing the 22% who experienced faster recoveries. This net tilt toward extended payment times aligns with Canada’s subdued economic growth, persistent high household and business debt levels, and sector-specific pressures amid trade uncertainties and elevated interest rates.

What are you seeing in the trenches? We’d love to hear your firsthand insights on customer payment behavior, credit risk signals, or strategies that are working (or not) right now. Drop your observations in the comments or reply directly; your perspective helps paint a clearer picture for the community.

In this week's I ASKED AI blog, I asked AI to create an AI Readiness Assessment. 

Give it a try here! Just remember, AI can be a little biased!

What shaped the business landscape as we enter 2026

As we kick off a new year, it’s worth reflecting on the business developments that shaped decision-making, risk, investment, and strategy throughout 2025. Here are the 10 business stories that dominated headlines last year:

1. AI Became Core to Business Strategy
   Artificial intelligence moved beyond experimentation. In 2025, AI became embedded in operations, finance, marketing, customer service, and decision-making — reshaping productivity expectations and competitive advantage.
2. China’s BYD Overtook Tesla as the World’s Largest EV Maker
   The shift marked a major realignment in global manufacturing power, highlighting China’s growing dominance in electric vehicles and supply chains.
3. A Record Year for Mega-Mergers and Acquisitions
   2025 saw one of the strongest M&A environments in decades, with a surge in $10B+ deals driven by consolidation, scale, and AI-related synergies.
4. AI Drove Massive Wealth Creation — and Valuation Debates
   AI-linked stocks added hundreds of billions in market value, reigniting conversations around asset bubbles, sustainability of growth, and long-term fundamentals.
5. Regulatory Enforcement Softened in Key Markets
   Fines related to financial crimes and compliance violations dropped sharply in the U.S., signaling a shift in enforcement priorities and regulatory posture.
6. Corporate Leadership Structures Were Rebuilt Around AI
   Major firms reorganized leadership teams, roles, and reporting lines to prioritize AI integration and commercialization.
7. Markets Grappled With Volatility and Valuation Reality Checks
   Strong earnings expectations collided with economic uncertainty, creating sharp market swings — particularly in tech and growth sectors.
8. Trade Policy and Tariff Uncertainty Returned to the Spotlight
   Shifting geopolitical alliances and trade policies once again influenced supply chains, pricing strategies, and global risk planning.
9. Global Growth Slowed — But Avoided a Hard Landing
   Economic growth moderated worldwide, forcing businesses to focus on efficiency, cash flow management, and credit discipline rather than expansion alone.
10. Consolidation Accelerated Across Multiple Industries
    Beyond tech, industries such as logistics, media, energy, and financial services saw increased consolidation to manage costs, scale operations, and reduce risk.

As of November 2025, the global economic outlook remains one of moderate growth with considerable headwinds. In many developed economies (e.g., the U.S., Japan, the eurozone) growth is positive but modest; business confidence remains cautiously optimistic amid elevated uncertainty. In the UK growth is weak and risks remain elevated, although I did not find clear data of declining employment across the board.

Global trade tensions — particularly between the U.S. and China — and geopolitical risks continue to weigh on investment and trade flows. In China, growth prospects have improved somewhat, with official forecasts nudged upward, though structural and policy risks persist.

Inflation remains a concern in various regions, and central banks continue to navigate the trade-off between inflation control and growth support.

In Canada, recent data show the economy grew about +0.5 % annualized in Q3, after a trade-driven contraction in Q2. While output in manufacturing and trade-exposed industries remains under pressure, household consumption has held up reasonably well and inflation has eased to about 2.2% in October.

The Bank of Canada’s latest projections point to annual growth near 1.4% in 2026-27, and while rates remain elevated. Business investment remains cautious, and down-side risks (labour market softness, housing pressures, trade uncertainty) remain relevant.

Overall, the global and Canadian economies are navigating a fragile mix of modest growth, persistent uncertainty (trade, geopolitical, policy) and inflationary pressures — as we approach the end of 2025.

I Asked AI…Which industries around the world are showing signs of economic downturn?

Many industries worldwide are displaying clear signs of economic downturn as of 2025, with several sectors particularly vulnerable due to factors like global trade tensions, high tariffs, and weakening demand.

 

Most Affected Industries

 

Manufacturing and Industrial Sectors

The global manufacturing sector is widely expected to enter a recession in 2025. This is driven by tariff wars, rising input costs, and disruptions to cross-border supply chains, especially impacting sectors relying heavily on imported inputs (e.g., automotive, machinery, electronics outside of semiconductors). Major economies such as the U.S., Europe, and Canada are seeing contractions in manufacturing output, particularly in durable goods like vehicles and machinery.

 

Wholesale and Supply Chain Businesses

Supply chain challenges have intensified since early 2025, most notably affecting wholesale and cross-border trade sectors. In Canada, both wholesale and manufacturing are facing contractions tied to U.S.-Canada border delays, increased tariffs, and diminished private investment.

 

Retail and Big-Ticket Consumer Goods

Sales in retail, especially for big-ticket items such as new vehicles, appliances, and furniture, are down as both business and consumer sentiment decline. Higher prices due to tariffs and uncertainty over incomes have led to postponed or canceled purchases.

 

Agriculture and Food Processing

Agriculture, forestry, and food manufacturing are hit hard by tariff changes and reduced cross-border demand, as retaliatory tariffs and supply disruptions persist globally. This contraction is mirrored in upstream (input providers) and downstream (exporters) segments.

 

Aerospace, Chemicals, and Primary Metals

Sectors like aerospace, chemicals, and primary metals (including aluminum) are especially vulnerable due to direct exposure to new tariffs, increased production costs, and changing supply relationships. This is cited as a significant risk in both North American and European markets.

 

Public Sector and Contract Services

The public sector in the U.S. is facing historically large workforce reductions and contractor cuts, removing a typical stabilizer for economic downturns and impacting associated industries such as consulting, infrastructure, and support services.

 

Key Drivers of the Downturn

·       Global tariff escalation: Widespread, frequently changing tariffs have disrupted trade, increased costs for manufacturers, and led to retaliatory moves by other countries.

·       Supply chain instability: Border delays and input shortages amplify risk for production and trade-dependent companies.

·       Reduced business investment and consumer spending: Economic uncertainty deters investments in new capacity and capital outlays, leading to further weakness across affected sectors.

 

Resilient Sectors

Some industries—such as healthcare, consumer staples, utilities, and essential services (e.g., auto repair, home maintenance)—are showing more resilience and are less likely to experience a major downturn in 2025.

In summary, manufacturing, supply chain, retail (durable goods), agriculture, aerospace, chemicals, and public sector contractors are currently among the industries facing the most pronounced economic headwinds globally.

What are you seeing through your crystal ball?