Welcome to the final installment of our cash flow series! Over the past five parts, we've covered why cash is king, what a cash flow statement is, how to read it, forecasting techniques, and effective cash utilization. Now it's time to bring everything together into a practical, actionable plan that you can implement in your business right away.

Using Your Cash Flow Forecast Proactively

Now that you understand the three areas of cash flow and how to optimize each, you can use your cash flow forecast as a proactive planning tool rather than just a reactive warning system.

Here's how to leverage your forecast for maximum impact:

• Scenario Planning: Run multiple cash flow scenarios (best case, worst case, and most likely) to prepare for different business conditions

• Investment Timing: Identify the optimal times to make investments based on projected cash surpluses

• Negotiation Leverage: Use your forecast to time supplier negotiations when your cash position is strongest

• Strategic Borrowing: If you need financing, your detailed forecast gives lenders confidence and may help secure better terms

Let me expand on each of these strategies with concrete examples:

Scenario Planning in Action

One of my landscaping clients built three different cash flow forecasts:

• Best Case: Assuming 90% of invoices paid on time, full crew utilization, and optimal weather conditions

• Most Likely: Assuming 80% of invoices paid on time, 85% crew utilization, and typical seasonal weather impacts

• Worst Case: Assuming 65% of invoices paid on time, 70% crew utilization, and severe weather disruptions

This simple exercise revealed that even in their worst-case scenario, they could maintain operations without layoffs if they delayed a planned equipment purchase by three months. This advance knowledge allowed them to communicate clearly with their supplier and arrange flexible timing without burning bridges.

Investment Timing That Works

A restaurant owner I worked with used her cash flow forecast to identify that September-November provided her strongest cash position each year. By timing her major equipment upgrades and interior refreshes during this window, she avoided the high-stress cash crunch that had previously occurred when making improvements during slower months.

The key insight here wasn't just to invest when cash was available, but to proactively schedule capital expenditures during specific periods each year - making these investments part of the annual business rhythm rather than reactive decisions.

The Art of Cash Flow-Based Negotiation

Using your forecast to time vendor negotiations is particularly powerful. One manufacturing client discovered through forecasting that they faced a predictable cash trough each February-March. Instead of borrowing to cover this period, they approached their top three suppliers in November (when cash was abundant) and negotiated 60-day payment terms specifically for January and February invoices.

In exchange, they offered to pay on 15-day terms during their high cash months. This created a win-win: the suppliers appreciated predictable early payments for most of the year, and my client smoothed out their cash flow without interest costs.

Borrowing from a Position of Strength

The worst time to seek financing is when you desperately need it. A detailed cash flow forecast allows you to approach lenders when your business looks healthiest, securing better terms and building banking relationships before you need them.

A retail client used their 12-month forecast to secure a line of credit during their strongest quarter, despite not needing the funds immediately. Six months later, when an unexpected opportunity to purchase inventory at a steep discount arose, they had immediate access to capital that ultimately yielded a 40% return.

Putting It All Together: The Cash Flywheel

Think of effective cash utilization as creating a flywheel effect. You start by building security (3-6 months of expenses), then use excess cash to fuel growth, which generates more cash, which can be reinvested again.

The most successful small businesses I've worked with follow this pattern religiously:

1. Secure your foundation with adequate cash reserves

2. Optimize operations to generate maximum cash from your core business

3. Make strategic investments with clear ROI expectations

4. Use financing sparingly and strategically

5. Monitor continuously using your cash flow forecast

6. Repeat, building momentum with each cycle

This approach transforms cash from simply a resource to be managed into a strategic weapon that drives sustainable growth.

Building Your 90-Day Cash Flow Action Plan

Let me give you a concrete template for putting these principles into immediate action. Here's a 90-day plan that has worked for numerous clients:

Days 1-30: Assessment & Foundation

• Pull your last 12 months of cash flow statements and identify patterns

• Calculate your current cash reserves in terms of months of operating expenses

• Set a specific target for your ideal cash reserve (pick your number between 3-6 months)

• Identify your top 3 cash drains in operations

• Build a simple 13-week cash flow forecast (even if it's just in Excel)

Days 31-60: Optimization & Strategy

• Implement one specific change to accelerate customer payments

• Implement one specific change to optimize vendor payments

• Identify one potential strategic investment that could improve cash generation

• Run best/worst/likely scenarios on your forecast

• Schedule regular (weekly/bi-weekly) reviews of your actual cash position against forecast

Days 61-90: Systems & Growth

• Refine your forecast based on actual results from the first 60 days

• Document your cash management system so others can follow it

• Establish cash flow-based triggers for major decisions (hiring, expansion, etc.)

• If at target reserve level, develop a plan for strategic use of excess cash

• If below target reserve, implement specific actions to accelerate reserve building

This 90-day plan isn't theoretical - it's the exact roadmap I've used with clients who went from constant cash stress to strategic cash management. The key is to take consistent action rather than trying to solve everything at once.

Conclusion: Becoming a Cash Flow Master

Over these six parts, we've covered the complete journey from understanding why cash is king to effectively deploying it for maximum impact. You now have the knowledge to not just survive but thrive by mastering your cash flow.

Remember that cash utilization isn't a one-size-fits-all formula. Your business's specific circumstances, growth stage, and risk tolerance will dictate the right approach for you. The key is making intentional decisions based on data rather than gut feelings or industry myths.

If you've made it this far, you're already ahead of most business owners. Put these principles into practice, and you'll be amazed at how much more resilient and successful your business becomes.

I hope you've found this series valuable. If you have questions about implementing these strategies in your specific business, let's talk.

Here's to your cash flow success!

In the comments below, I'd love to hear: What was your biggest cash flow insight from this series? And what specific action are you taking first to improve your cash position?

How Much Cash is Enough Cash?

The goal is to build a cash balance that keeps the business secure and then manage that cash balance to maximize cash generation long term.

Security is approximately defined as 3-6 months worth of operating expenses.

For example, if my business has $10K per month of expenses, I would want a $30K-$60K worth of cash on hand (3-6 months of expenses).

How far you are to three or six is largely a matter of preference. If you're conservative like me and your business is young and/or prone to seasonal swings in revenue, you'll want to be closer to six months ($60K).

If - on the other hand - you're more aggressive and/or the business is more mature (3-5 years of sustainable operations), you'll be just as comfortable with three months ($30K).

Any less than three months, and you're just begging for trouble to find you. COVID is a great example of this. Any more than six months, and you run the risk of deploying your cash inefficiently. In other words, just having your cash sit there and not be either a) producing more income via a security like stocks or b) being reinvested in the business to expand operations is by definition a waste of your money.

Suppose I only need $60K but I have $200K. This means, I have $140K of "excess cash" (a wonderful problem to have). If I want to maximize future cash flow, maybe I hire a crew and a new truck to expand my market and sell more services. That $140K today is an investment in future operation cash flow.

Think of this as a two step process in cash utilization:

• Step One: Ensure you have a 3-6 month emergency fund

• Step Two: Make strategic bets with anything above your emergency fund to maximize future cash flow

For small businesses it may take you a while to get to that part two or part two may never occur or you may not want it to occur.

How Do We Squeeze as Much Cash as Possible Out of the Business?

Ok, now that we know how much is "enough" cash. Let's discuss how to squeeze as much cash as possible out of the three areas of cash flow.

Operations

The strategies here are pretty simple:

1. Increase Profitability

2. Minimize the amount of time between invoice and collections

3. Maximize the amount of time between when you're billed and when you pay vendors

Sure, we would all love to just snap our fingers and increase profitability, but this is your job as a business owner. Think strategically about how you are going to increase revenue and minimize expenses.

The other two strategies I refer to as "Working your Working Capital." Working capital is defined as Accounts Receivable (how much customers owe you) and Accounts Payable (how much you owe your vendors).

Working your Working Capital

How do we work these two areas? Payment terms have entered the chat.

On the receivables side, we want to have as small of terms as possible. Look at your customers. How many pay upon receiving the invoice? How many have payment terms where they don't have to pay you for 15, 30, 60, or even 90 days? We want to get these terms as low as possible. In B2C businesses like retail this is likely already happening. In B2B businesses such as wholesale or consulting this is much more difficult.

If you're in the latter camp, I typically find success by offering a "pay early discount" where you offer a 10% discount if you pay before the invoice due date. You could also threaten late charges for customers that pay after the due date. I typically find that carrots work better than sticks in order to maintain customer retention and a healthy overall relationship.

On the payables side, you likely have a lot less leverage. You are incredibly reliant on your suppliers and they have their own businesses to run. Therefore, they're going to be highly unlikely to give you terms longer than what is already in writing.

So, here you take what you can get and you do your best to extend terms as much as possible at the beginning of the relationship. If they offer 30 days at first, counter with 60. You could also offer a deposit in exchange for lengthier terms (pay 10% today and pay the rest in 60 days).

Investing Cash Flow

This bucket is primarily for reinvestment in your business through two primary areas:

1. Investment in expanded operations

2. Park unutilized cash in investments that outperform the market

In the first scenario, you want to spend cash with the intent of making more money in your operations. There are a number of ways to project these kinds of investments using Payback Period and Return on Investment (ROI) analysis.

For example, I just did this recently with a business development investment. I hired a firm that came in and built out an end-to-end customer acquisition program. They helped me identify ideal clients, write outreach messaging to cold contact potential clients, and then coached me on how to land them. They charged me $9,000 for this service over three months ($3,000 per month).

They helped me land two additional clients each of whom I charged $250/hr for roughly 15 hrs. per week worth of work.

So, I expanded my operation by $15,000 (15 hrs x 4 weeks x $250/hr) per month or $180K per year. My cash outlay for that expansion was $9,000.

My payback period = $9,000 (initial cash outlay) / $180,000 (annual cash inflow) = 0.05 years or 0.6 months (roughly 18 days).

I'll take that kind of bet all day long.

Another way to look at it is that my marketing investment generated a net gain of $171K ($180K - $9K). My Return on my $9,000 investment = 171,000 / 9,000 = 1,900%.

I'm not saying that these results are typical, but this is an example of how I utilize cash within my own business to maximize future operations.

If you're not in a position to make investments like this, it's also effective to simply work with an investment advisor to park unutilized cash in securities like stocks and bonds to generate returns higher than a bank account. Just make sure you don't put any of your 3-6 month emergency fund in these securities because it'll take time to liquidate those assets and convert them to cash so that you can actually access it.

Financing Cash Flow

Now I'm relatively averse to debt, but equity is also extremely difficult to raise, especially if you are fledgling business. I typically advise clients against getting into debt beyond simple lines of credit because the kinds of bets you'd be making with those funds are pretty risky and if they don't pan out you're going to be out more than just the cash behind the investment, you could even be personally liable.

However, there are times when financing makes sense. Here are some smart financing strategies that can help your business grow without putting it at risk:

Debt Financing: Use It Wisely

If you do pursue debt, consider these options carefully:

• Short-term lines of credit to manage seasonal cash flow fluctuations

• Equipment financing where the asset itself serves as collateral

• SBA loans which typically offer more favorable terms than traditional bank loans

Remember: Only take on debt that directly contributes to cash generation. The debt service payments should be comfortably covered by the additional cash the investment will generate.

Equity Financing: When It Makes Sense

Bringing in equity partners means giving up some ownership, but it can be the right move when:

• You need significant capital for rapid expansion

• Your business model requires substantial upfront investment before generating returns

• You would benefit from the expertise and connections investors bring

The key is finding partners who share your vision and timeline. I've seen too many entrepreneurs take money from investors with misaligned expectations, leading to painful conflicts down the road.

In the final part of this series we will bring everything together into a cohesive cash flow management strategy.

It’s a terrifying place to be, and if you’re ready to not be quite so scared, you better have a 13-week cash flow forecast in place.

Keep it Simple Stupid

Forecasting—especially in the age of AI—often seems mystical. It’s as if you need to be an oracle on a mountaintop reading chicken entrails to predict next Tuesday's sales.

In reality, all you need is a spreadsheet and to dedicate some time to plan. You can always make it fancier later. The important thing is to draft a plan and execute against it.

The Secret CFO (a newsletter superior to my own) offers an excellent, straightforward crash course on forecasting that's worth your time.

I'll share my perspective later. For now, here's a simple template to get you started.

13-Week Cash Flow Template

45.6KB ∙ XLSX file

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A blank template that you can deploy today.

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13-Week Cash Flow Demo

48.9KB ∙ XLSX file

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A fully filled-out version of the template.

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Why do I Love this Tool?

Whenever I engage a new Fractional CFO client, I always start with this 13-Week Cash Flow forecast. If necessary, I spend my first two weeks just standing this up. Why?

It's Simple to Implement

Filling the template out is no more complicated than a three-month version of your own household budget. How much are you going to get paid? How much will you need to pay to someone else?

That's it. With a little time and elbow grease, you'll have something workable—if you follow the detailed steps that I’ll lay out later, you can create a draft in about forty-five minutes.

Now, as your business grows, it is worthwhile investing in more sophisticated forecasting methods. These can range from out-of-the-box solutions to simple Excel-based tools that pack a surprisingly powerful punch and are much more accurate than the back of your napkin.

For now, let’s not let perfect be the enemy of good enough.

It’s a Powerful, Tactical Decision Making Tool

This forecast serves as a control mechanism to ensure wise cash management while helping you identify problems and take decisive actions to address them. Say goodbye to those sleepless nights wondering about making payroll. Instead, you'll anticipate cash flow challenges and eliminate them through confident decision-making.

Why 13 weeks specifically? It's short enough that you can predict with reasonable confidence and accuracy—and the more you use it, the more precise your forecasts become.

It's also long enough that significant tactical decisions made today will materialize within the forecast's timeframe. If the tool shows you're running out of cash in 10 weeks, you'd better start working on solutions immediately (whether that's securing a line of credit or making tough decisions about staffing).

It’s a platform for further transformation

After building a customized forecast with clients, we layer in automations and tools for greater accuracy. When needed, I implement predictive algorithms for cash receipts and set up the ERP to automatically refresh actuals weekly.

Once I'm confident in the short-term picture and how to influence it, I shift focus to longer-term strategy—like building a cash reserve to ensure we can meet payroll 9 months from now, or identifying which vendors we should negotiate with to get better terms and stretch out our accounts payable.

Steps to Setting this up

Step One: Enter your Settings

On the Settings tab simply fill in the necessary information:

• Company Name

• File Name

• Current Year

• Current Month (dropdown)

• Week Start: 1 - Sunday, 2 - Monday, etc

  

  .

Next, on the Forecast tab, enter the forecast start date (defaults to today), your business's cash balance at the start of the forecast week, and your Cash Reserve.

The Cash Reserve is your minimum cash balance that provides peace of mind—a target you never want to hit.

Why have it? It creates a buffer that's better than zero. The amount varies person to person, but I generally tie it to one of these:

• enough to pay off all debt in a doomsday scenario (e.g., $10,000 to clear your line of credit)

• 3-6 months of expenses (e.g., $10,000 monthly expenses × 3 months = $30,000)

The first option lets you close shop cleanly if absolutely necessary. The second buys you 3-6 months during a downturn.

Either way, it establishes your "safe" minimum before hitting absolute zero. It’s the amount that allows you to exhale and say “I at least have enough cash to weather the next three months.”

When customers pay you late or the economy takes a dump on you, believe me, you’ll be glad that you have that buffer in place.

Step Two: Fill out Actuals

Now fill out your Actuals. Ideally, use the statement of cash flows from your bookkeeping service. If your bookkeeper can't help with this, it's time to get serious and sign up for QuickBooks or similar software.

As a last resort, use your business bank account(s). Take the most recent month and categorize each cash transaction individually.

This exercise alone will reveal more about your business than you might be comfortable knowing.

Step Three: Fill out Forecast

Plan Receipts

Next, plan out the payments you expect from customers over the next 13 weeks. For B2B businesses, this is typically straightforward. Look at what you've already billed that will be collected in the coming month, then estimate your billings for the following two months and project collections accordingly.

If that's not feasible, or if you're in a B2C industry like Retail, leverage a trailing 13-week cash flow statement instead.

Running QuickBooks Online? That report is already right at your fingertips—assuming your bookkeeping is clean.

You can even just look at your last few weeks of direct sales, calculate an average, and apply it going forward. These forecasts then become weekly targets for you to hit.

Plan Payments

Next, plan out your expenses for the next 13 weeks. The first month is straightforward—just use the invoices already in hand. Beyond that, identify your recurring expenses first, then add in your non-recurring costs.

This non-recurring (One Offs) bucket is crucial—it's a key lever for controlling cash burn. For instance, if a large inventory purchase next week would drop you below your minimum cash threshold, consider buying when you have a surplus or spreading purchases over time.

Still struggling to map out these outflows? An aging Accounts Payable Report can help (assuming your books are in order).

Plan One-Time Cash Draws or Cash Infusions

Finally, capture any one-time cash flows that fall outside normal operations. These could be either inflows or outflows.

For example, mark your tax estimates for upcoming installment weeks, or include any debt service payments you need to make.

If you anticipate needing to inject cash into the business, include these under the Equity Deposits and New Debt lines.

Step Four: Copy to the Prior Forecast tab and Review

Once you've completed your forecast, copy and paste (values only) to the prior forecast tab, then wait for the week to end.

First thing Monday morning, update the actuals tab and head to the review tab.

You'll immediately see variances across two key comparisons:

1. The Prior Week's actuals vs the Forecast

2. The Current Forecast vs the Prior Forecast for go-forward weeks

What do we see?

• Red Rock Diner didn’t pay me last week as expected even though their invoice was due. I better get on their add the week of 3/2 because I’m owed that money

• I saw a cash crunch coming at the end of march, I should draw on the line of credit for $2,000 the last full week of the month

• With that new debt, I’ll have a new payment starting 5/1

• Things are looking better in May, let’s pay down the debt with some of our excess cash

When examining the prior week's variance, you need to understand exactly where and why you were off. After identifying these discrepancies, adjust your forecast accordingly, allowing you to compare your new forecast against the prior one.

This exercise will hit you square in the face with necessary actions to influence your business's cash flow.

By Tuesday, you should have an updated forecast (repeat step three) that you'll manage toward until refreshing again the following Monday.

First Steps into a Larger World

That’s it, now rinse and repeat. The first 2-3 weeks this will likely feel like a chore, but if you commit yourself to it, you’ll reap a ton of rewards least of which is a few more hours of restful sleep.

In our next installment, we’ll discuss timing strategies for payments and collections, and how to make informed decisions that prioritize long-term cash generation.

Work with me: build your 13‑week cash flow forecast

If you are still worried about white‑knuckling your cash, I’ve helped entrepreneurs and small business owners stand up a simple, tactical 13‑week cash forecast that reduces surprises and unlocks confident decisions.

Here’s what we’ll do together:

• Quick assessment of your current cash picture and tools

• Stand up a tailored 13‑week forecast you can run every Monday

• Identify the 2–3 moves that improve cash the fastest

Ready to get relief and clarity?

• Book a free 30‑minute consultation

• Or email me directly at greg@ggernetzke.com

If you’re a fractional CFO, I’m also happy to collaborate on a streamlined setup you can roll out across clients with automated tools that allow you to focus on action rather than plugging and chugging through data.

Below is an example cash flow statement that I pulled from the QuickBooks sandbox. It's a made up landscaping company that I lovingly refer to as "Mow Money Mow Problems."

Start at the Bottom

First let's look at the bottom line. What does it tell us? It tells us how much cash flowed in (or out) in that month. Simple enough.

In customer cash flow reporting I also like to layer in the beginning and ending balance of cash so we can see how the net cash increase for a period actually materializes in the cash balance (i.e. the bank account).

This bottom-line view is your financial vital sign. If your ending cash balance is consistently decreasing month over month, you've got a problem that needs addressing – regardless of what your P&L might say.

Where Did It Come From and Where Did It Go?

Next, we can see how much cash flowed in (or out) by three broad categories:

1. Operations

This is by far the most important area. Think of this as the cash equivalent of your profitability. It reflects the actual amount of cash flowing into your business from revenue generating activities (sales, collections, etc.), and how much cash is flowing out of your business from your expenses (vendor payments, payroll, etc.).

There is nowhere to hide in this section. If customers are behind on paying you, this is where you'll feel it. I cannot tell you how many times I've worked with clients who say something like "I'm profitable, but there is never enough money in the bank." If you have ever had this thought, it's a telltale sign that you are not paying enough attention to your operating cash flow.

Some key items to look for in the operations section:

• How much of your net income is actually converting to cash?

• Are your accounts receivable growing faster than your revenue? (That's bad – it means customers are taking longer to pay)

• Are you building up too much inventory that isn't moving? (Also bad – that's cash sitting on shelves)

• Is there a seasonal pattern to your operational cash flow that you need to plan for?

2. Investing

This is where we measure how much cash is being generated (or paid out) to build future operating opportunities. Mow Money Mow Problems bought a truck so it could serve more clients. This is an investment for their operations.

If there is a crew running that truck, that is additional revenue that can be earned that isn't being earned today.

Similarly, the cash paid to build a new store location, acquisition of another company, or even computer equipment is considered an investment activity. All of these things go toward enabling future operations.

Any activity that requires you to pony up cash (i.e. the car purchase) is reflected as a negative number. Any activity that pays you in this category (i.e. dividend payments from stocks in the company's name) is reflected as a positive number.

Smart questions to ask about your investing cash flow:

• Are these investments actually going to generate a return that exceeds their cost?

• Is the timing right, or should we delay some capital expenditures until our operating cash flow improves?

• Are we neglecting critical investments that will hurt us in the long run?

3. Financing

For our purposes consider financing roughly equivalent to "fundraising." This typically comes in two forms:

1. Debt: cash you get today and you pay back over time

2. Equity: cash you get today in exchange for a share of the profits later (ownership)

Any activity that brings in cash is reflected as a positive number (i.e. debt principal amounts). Any activity that pays out (i.e. debt repayment) cash is reflected as a negative number.

Key financing considerations:

• Is your business relying too heavily on debt to fund operations? (This is usually unsustainable)

• Are you in a position to pay down debt, or should you preserve cash for operations?

• If you're taking on investors, what expectations do they have for returns, and how will this affect your cash strategy?

What Constitutes "Good"?

Most of this is dependent on exactly what you're trying to do with your business. If you're trying to grow operations, you may be more aggressive with your Investing Cash Flow.

If you're staring down the barrel of a cash shortfall, you may need to focus your efforts on financing.

In addition, there are a few key ratios that I use to help me judge the buckets:

• Operating Cash Flow Ratio: OCF ÷ Current Liabilities. This tells you how well your operations can cover your short-term obligations. A ratio above 1.0 means you're generating enough operational cash to cover immediate debts.

• Cash Flow to Debt Ratio: OCF ÷ Total Debt. This indicates your ability to repay both short and long-term debt from operations. Higher is better, with anything above 0.20 considered decent for most businesses.

• Free Cash Flow: OCF - Capital Expenditures. This tells you how much discretionary cash you have after funding necessary investments to maintain your current operations.

Spotting Red Flags

Your cash flow statement can reveal several warning signs that demand immediate attention:

• Consistent Negative Operating Cash Flow: If your core business operations aren't generating positive cash flow over time, your business model might be fundamentally flawed.

• Growing Gap Between Net Income and Operating Cash Flow: If profits look good but cash from operations is poor, investigate why. Often this means customers aren't paying, or you're building up too much inventory.

• Heavy Reliance on Financing: If you constantly need debt or equity to fund operations, you're not sustainable.

• Insufficient Investment: Sometimes the problem isn't spending too much, but spending too little on critical infrastructure, technology, or equipment.

Putting It All Together

A healthy business typically shows a pattern of:

• Consistently positive operating cash flow

• Strategic investing cash outflows that are proportional to the business's growth plans

• Prudent use of financing that doesn't create an unsustainable debt burden

• A growing (or at least stable) ending cash balance that provides sufficient cushion for contingencies

Now that we know how to read a cash flow statement, next we'll explore cash flow forecasting techniques that will keep you on top of your bank balance without sucking up all of your time.

Don't forget – the cash flow statement is telling you a story about your business. Take the time to understand what it's saying, and you'll make better decisions that keep your business not just surviving, but thriving.

Introducing the Statement of Cash Flow

The cash flow statement measures the movement of cash through your business. It tells you how much cash you started and ended with, how much cash came in and went out (the "flow"), and through which channels this movement occurred.

These channels typically fall into three categories:

• Operating Activities: Cash generated from your core business operations

• Investing Activities: Cash used to create future cash flow through asset purchases or investments

• Financing Activities: Cash from raising funds through debt or equity

How is it arrived at?

This tends to be the most difficult thing for people to understand. The P&L measures how much you sold and how much things cost and how much is left over. Easy enough.

The balance sheet tells you what your assets are worth, how much cash you have in the bank (assets), how much you owe to others (liabilities), and what is left over for shareholders (equity).

The cash flow statement measures the movement of cash. In other words, how much cash did you start and end with, how much cash came in and out of the business, and through which channels.

Let's look at an example.

The Indirect Method: How Most Cash Flow Statements Are Built

Most businesses use what's called the "indirect method" for calculating cash flow. This method starts with net income and makes adjustments to convert accrual accounting figures to cash basis.

For example, if accounts receivable increases by $10,000, that means you've recorded $10,000 in sales that you haven't collected in cash yet. So you'd subtract $10,000 from net income to get a more accurate picture of your operating cash flow.

Similarly, if inventory increases by $5,000, that means you spent $5,000 in cash that isn't reflected as an expense on your P&L yet. So you'd subtract that $5,000 from net income as well.

Do you see how cash will not lie to you?

Once you get the hang of it, these adjustments start to make intuitive sense. They're all about answering a simple question: "Did cash actually change hands?"

Always remember that the cash flow measures the movement of cash and its resulting balance. This is a tricky concept to get the hang of, but the more you study it, the more you'll get the hang of it.

Where do you get a statement of cash flow?

Technically, you can build one yourself using nothing but your bank statements, but I wouldn't recommend it. Your bookkeeper ought to have one handy, and if he or she does not, it's time to get a new bookkeeper.

Your bookkeeper also ought to be able to walk you through the cash flow statement. If he or she cannot walk you through it, it's time to get a new bookkeeper. Also, you and I should talk.

If you use any kind of Enterprise Resource Planning (ERP) software like QuickBooks, it will build you one automatically.

Reading Between the Lines

Sit down and examine your cash flow statement thoroughly. If you look at the entries behind it, you will uncover secrets beyond your wildest dreams about your business.

For example, you might discover:

• Your business is profitable on paper but hemorrhaging cash due to aggressive growth

• You're collecting receivables much slower than you're paying vendors, creating unnecessary cash pressure

• Seasonal patterns in your cash flow that you could plan around better

• Hidden expenses that aren't properly categorized

The power of the cash flow statement isn't just in the numbers themselves, but in the story they tell about how money moves through your business.

What's Next?

In our next installment, we'll discuss how to read your cash flow statement, how to interpret what it is telling you, and how to leverage it to grow (and protect) your business.

Then in parts four and five, we'll dive into cash flow forecasting techniques and effective cash utilization strategies that will keep you on top of your bank balance without sucking up all of your time.

Until then, I'd encourage you to pull up your most recent cash flow statement and simply familiarize yourself with the major categories and numbers. Which direction is your cash flowing? And more importantly - do you know why?

Whenever I sit down with a new Fractional CFO client, the very first thing I address is cash flow. Of the three financial statements it seems to get the short end of the stick. Everyone wants to know if they are profitable (P&L) and what the business is worth (Balance Sheet).

But everyone ignores the king.

Why is Cash King?

There are three principles that make cash king:

1. Cash is Oxygen

2. Cash Does Not Lie

3. More Cash Means More (and Better) Options

Each of these principles supports the key aspect of your responsibility as a business owner: building a secure cash balance and managing it to maximize long-term cash generation.

More on that a little bit later. For now, let's focus on these laws.

Cash is Oxygen

The chart below is called a Sankey Diagram. It represents the movement of cash into and out of a business. Its utility is debatable, but I like it for no other reason that it visualizes the flow of cash and if you think about it, it looks like a cardiovascular system.

Cash flows into the business from left to right, settling in the "chest of the business" before flowing out to various operational areas.

Without cash (and a nice steady flow of it), your business simply dies. It's that straightforward. To stay viable, we must ensure steady cash inflow while carefully managing how it leaves our business.

Cash Does Not Lie

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Making a million dollars in profit last month doesn't guarantee I have a million dollars in the bank.

Take my consulting agency, for instance. When I bill clients, most have 30-day payment terms. This means I won't see payment for today's invoice for at least a month, if I'm lucky.

Meanwhile, I've already spent money to deliver those services. I'm immediately out of pocket for expenses like meals, airfare, and hotel accommodations.

$Income - Cost = Profit$

$1,000 - 750 = 250$

Today +15 Days +30 Days Beginning Cash 5,000 4,250 2,250 Cash Flow -750 -2,000 +1,000 Ending Cash 4,250 2,250 3,250

While I’m waiting to get paid, I have ongoing operational expenses like website hosting, marketing costs, and LinkedIn Premium subscriptions. When that $1,000 payment finally arrives on day 30, I've already covered these additional costs out-of-pocket.

Without careful planning, I could easily find myself in a cash flow crunch.

Your bank account balance reveals the unvarnished truth about your business's financial health. If you don't understand the patterns of cash flowing in and out, you're setting yourself up for dangerous misconceptions.

More Cash Means More (and Better) Options

“Mo money, mo problems” is a lie.

Imagine we own a restaurant and want to expand our dining room. This expansion would allow us to serve more customers during each service period, resulting in increased revenue and improved future cash flow.

Let's say the expansion costs $10,000, and our monthly rent of $2,000 is also due.

How would our options differ with these two cash balances?

$8,000 Cash Balance $50,000 Cash Balance - We could choose not to expand and just pay rent

• We could go into debt by $10K to pay for the expansion | - We could pay cash for the expansion AND pay rent

• We could go into debt by $10K

• We could go into debt by $3K

• We could expand the dining room AND add a staff member to handle the extra volume |

With a lower bank balance, we must choose between paying for the expansion OR paying rent. Alternatively, we could borrow to cover the costs, which adds risk to our business. This pressure intensifies if the contractor exceeds the budget or experiences delays (both of which are virtually guaranteed).

With a strong cash balance, we gain flexibility. We might still finance some or all of the expansion, but if costs exceed estimates, we have cash reserves to fall back on. We even have the option to hire another server to accommodate all those new customers.

The Path Forward: Mastering Your Cash Flow

Understanding that cash is king isn't just business wisdom—it's survival intelligence. As we've explored, cash provides your business with oxygen, reveals the unvarnished truth of your financial position, and creates a landscape of possibilities rather than limitations.

In the next post, I'll share practical strategies for building a robust cash reserve that not only keeps your business secure during uncertain times but also positions you to capitalize on opportunities when they arise. We'll explore how to read a cash flow statement and how to use it to make informed decisions that prioritize long-term cash generation.

Until then, take a moment to examine your current cash position. Is it giving you the oxygen, honesty, and options your business deserves? The throne awaits a ruler who understands its true power.

See you in Part Two, where we'll turn cash flow theory into action.

The team fell so far behind that they ran out of budget, fired the project lead who had scoped the work, and simply disappeared.

This isn't an isolated incident. It happens constantly in transformation projects, and it's one of the primary reasons implementations fail: everyone signs up for the promises made by the future state without ever understanding the underlying operation.

The Partner Problem

Implementation partners are a necessary part of any major software deployment, but they often approach projects with a deeply flawed mindset:

• They've done this "1,000 times before" (their words) so they use a templated approach

• They conduct minimal scoping conversations before charging ahead

• They promise unrealistic timelines and features to win the business

• They lack real understanding of your operational nuances

When things inevitably go sideways, one of two things happens: they get overworked trying to salvage the project, or you're left with such a bad taste that you bring in someone else – who then tells you the first team had no idea what they were doing.

This cycle is devastating to your strategic goals and contributes directly to implementation fatigue.

Choosing Partners That Won't Fail You

After years of rescuing failed implementations, I've developed a framework for selecting partners who won't leave you hanging:

1. Prioritize industry expertise over software expertise: Find partners who understand your specific business operations, not just the software. A Salesforce wizard who knows nothing about your finance processes will create more problems than they solve.

2. Demand proof of similar-sized implementations: Partners who typically work with enterprises will struggle with mid-market companies, and vice versa. Match the scale of their experience to your needs.

3. Insist on meeting the actual implementation team: Don't fall for the bait-and-switch. The impressive team that sells you might not be the one showing up for the work.

4. Require detailed transition and knowledge transfer plans: What happens when the implementation is "done"? The best partners have robust plans for ensuring your team can maintain what they've built.

Be wary of the upsell.

Many partners will offer run support to squeeze recurring fees from you. There is nothing wrong with them providing run support, but that run support should match your strategy, and you should know exactly what you are paying for.

Subsequently, sales reps - particularly SASS reps - love to sell bolt-on functionality. Don’t let them lead you toward a pre-determined conclusion. You’re in the driver seat. Drive the car.

If a rep is trying to take you out of the driver’s seat, they’re going to be a nightmare to work with long term. Just cut bait and move on.

The Internal Stakeholder Essential

Even with the perfect partner, you still need that internal project lead I mentioned in my previous post. This person's job is to:

• Translate between your business and the implementation team

• Establish clear communication protocols

• Create accountability frameworks with consequences

• Document decisions and challenges throughout the process

This role cannot be outsourced. They must be from your business, understand your operations, and have enough authority to make decisions stick.

In the end, a successful implementation partnership isn't about finding perfect vendors—they don't exist. It's about creating the right structure, setting appropriate expectations, and establishing clear accountability on both sides.

By selecting partners based on industry knowledge rather than just technical skill, verifying their experience with similar organizations, meeting the actual implementation team, and demanding comprehensive transition plans, you dramatically increase your chances of success.

Remember that your internal project lead is the linchpin that holds everything together. Their ability to translate business needs, maintain communication channels, and enforce accountability will ultimately determine whether your implementation succeeds or fails.

This is the core challenge in finance transformation today. Leaders are sold on sleek software solutions without addressing the fundamental operational issues plaguing their teams.

I've seen this pattern repeatedly in my work with finance teams. They're drowning in manual work, so they reach for technological salvation – only to find themselves with the same problems plus a hefty implementation bill.

The Process-First Approach

Before you consider any new finance technology, you need to understand and optimize your current processes. Here's my framework for doing this effectively:

1. Map the entire workflow: Document every step, handoff, and decision point in your current process. Make the invisible visible. This alone often reveals shocking inefficiencies.

2. Identify control points and bottlenecks: Where does work consistently slow down or stop? What checkpoints are truly necessary vs. historical artifacts?

3. Eliminate before automating: Ask the hard question about each step: "Do we actually need this?" Many processes contain unnecessary steps that should be eliminated, not automated.

4. Test manual improvements first: Implement process improvements manually before investing in technology. This validates your approach and builds user buy-in.

Fixing Month-End Close Hell

Want to know why your month-end close takes weeks? It's not because your journal entry system is broken. It's because your vendor rebate process requires four different people to touch the data, starting with someone manually copy-pasting rows into a CSV file.

No amount of sophisticated software can fix that fundamental inefficiency. You need to redesign the process itself, then find the right technology to support it.

A Real Life Example

Years ago, I was a supervisor managing a team of three analysts, and our month-end close took 7 days for a $10M annual business. I was sick of it.

Why did it take so long? One of the analysts was responsible for building the revenue accrual. To do this, he would spend days tracking down sales reps trying to figure out what the monthly billing estimates were going to be. The reps that did answer the phone couldn’t get off the phone with him fast enough.

In the meantime, the other analyst was responsible for booking payroll and commission accruals. The commission accruals were stuck until we knew what the revenue accrual was going to be so this analyst couldn’t start until the first process was finished.

Lastly, the third analyst was responsible for reporting who produced the P&L and delivered it to yours truly. Obviously reporting wasn’t ready until the books were closed.

You can almost visualize the bottlenecks can’t you?

No amount of sophisticated software can fix that fundamental inefficiency. You need to redesign the process itself, then find the right technology to support it.

The Technology Ladder

Once you've optimized your processes, approach technology implementation in stages:

• Level 1: Maximize existing tools (Excel/Google Sheets with proper structure)

• Level 2: Add automation to existing tools (Excel macros, Power Query, Python)

• Level 3: Implement specialized tools for optimized processes (BlackLine, HighRadius, Anaplan, Planful, etc.)

Most finance teams jump straight to Level 3 without mastering Levels 1 and 2. This is a recipe for implementation disaster.

Remember: Excel with a clean, well-designed process will outperform a fancy SaaS solution with a broken process every single time. I've seen Fortune 500 companies successfully use QuickBooks as a sub ledger.

How We Won with this Approach

We ended up taking our close from 7 days to 2 through this approach without a single cent of CapEx that I inevitably would have been turned down for.

We started with process and mapped every single step of our close process across the three swimlanes of our team. Where we needed cross-functional input (sales and payroll), we engaged with them in the spirit of “if you help us, we’ll bug you less.”

We automated our data processing using Power Query (which comes straight out of the box with excel). We even automated the journal entry template creation. When we were all said and done the two days we could not automate was simply due to the timing of data availability in the system.

This worked at a small scale, but it can also be replicated at-scale. The flip side is also true, when I have strayed from this approach, the solution fell apart.

Start Small, Win Big

One final tip: target a single, high-impact process for your first optimization effort. Instead of a company-wide rollout, focus on a specific business unit or function.

For example, instead of implementing a planning system across the entire enterprise, start with revenue forecasting for one business unit. Get that working at 99% accuracy, then expand methodically.

This approach builds credibility, develops your implementation muscles, and creates internal champions for your broader transformation efforts.

In my next post, I'll share how this process works with external implementation partners so that you don’t have to suffer through one more SaaS implementation that falls short.

Until then, remember: process first, technology second.

I remember the first time I saw this play out. As a senior analyst, we were rolling out Anaplan - “the only AI-infused scenario planning and analysis platform.” A great product, one that the market was adopting and our competitors were implementing.

We never made it past User Acceptance Testing.

Years later, as a transformation director, I found myself advocating for the exact same software in a Finance Leadership meeting with executives much wiser than I. The room erupted in knowing chuckles and eye-rolls. "We've been down this road before," they said. "It didn't work then, why would it work now?"

Implementation Fatigue: The Silent Killer

What they were experiencing is what I call "implementation fatigue" – the exhaustion and jadedness teams develop after one or two failed software implementations. The symptoms are easy to spot:

• Teams already operating at or over capacity now asked to attend daily scrums for 8-12 weeks

• Stakeholders siloed across different locations and time zones

• Difficulty obtaining a holistic view of workflows

• A collective eye-roll when any new system is mentioned (harder eye-rolls when you suggest a solution that was already attempted)

The bad news is that this is one of the hardest cultural roadblocks you will face in the Transformation space. The good news is that this cycle is entirely preventable and even reversable.

The Path Forward: Five Keys to Implementation Success

There are five keys to overcoming implementation fatigue. After years in the trenches, I've found these four elements to be non-negotiable for any successful finance transformation:

Recruits are better than Conscripts

The most effective fighting forces in the world are made of volunteers, not people pressed into service. Find team members who are genuinely frustrated with current processes and motivated to fix problems. They will be volunteer to attend and engage in scrums.

Limit the scope

Limit scope either by size (one business unit instead of all) or functionality (revenue forecasting before full P&L forecasting). Most software solutions paint utopian pictures—implement once and everything improves. In reality, the best approaches are pragmatic and incremental. Rather than attempting to perfect everything at once, focus on perfecting a few elements at a time. This creates momentum that builds over time.

Ongoing Support: Don’t let Perfection be the Enemy of Good

There’s an entrepreneurial axiom that I have found to be particularly relevant in digital transformation:

This means the first iteration of any product will almost certainly fall short of fully satisfying customer needs. The same principle applies to transformation solutions. They need testing, adjustment, and retesting before they're truly effective.

This reality can be disheartening if people aren't prepared for it. Set clear expectations that the process won't be perfect immediately—improvement takes time. Additionally, provide consistent support after implementation. Your team will need ongoing assistance and engagement once the system goes live.

Designate internal project management

Project management is all about balancing priorities while ensuring clear communication and accountability. While most generic project managers excel at communication and accountability, they often lack the crucial context that comes from daily involvement in your business. Your project manager (or at least someone partnering with the implementation PM) should come from within your organization, have deep knowledge of your operations, and possess the communication skills to effectively mediate between stakeholders. Without this business context, even the most skilled project managers will struggle to succeed.

Secure executive support with accountability

Leadership must stand behind their team, clear obstacles, and be ready to go to bat with implementation partners when needed. In most cases, executive support roars out of the gate and then peters out after the third steering committee meeting. Their job is to ensure that the initiative has the proper resources to be successful and to provide the proper level of air support for the implementation team to get the job done. Inside the room, they should be listening to what the team needs and then delivering and guiding with the proper sense of direction. Outside of the room, they should be protecting the team from becoming derailed by lesser priorities and cross-functional flak.

This approach isn't magic, and it won't eliminate all implementation challenges. But it will dramatically increase your odds of success and break the cycle of implementation fatigue that's plaguing your finance transformation efforts.

In my next post, I'll dive deeper into how to create SOWs that actually work and share a case study of how one team broke their implementation curse using this exact framework.

Until then, remember: fix the process first, then worry about the software.

The Uncomfortable Truth

Executive teams, driven by earnings pressure, are actively seeking ways to optimize costs. FP&A departments are prime targets for these optimization efforts, with leaders looking to bridge productivity gaps through AI and automation. Harsh? Perhaps. But it's a reality we need to face.

Two Paths Forward

Standing at this technological crossroads, FP&A professionals have two choices:

1. Become a tech-savvy "super analyst" who leverages AI to multiply their impact

2. Find a new career path

The Path to Becoming a Super Analyst

There is mounting evidence in recent weeks that while LLMs are fantastic at automating tasks and enhancing workflows, their ability to reason is not as far along as many of the AI developers are claiming.

Why? To train AI, there has to be some judge of the accuracy of the outputs which come either in the form of objective truth (e.g. 2+2=4) or from a human (e.g. this is a picture of an apple).

As a result, AI excels at factual domains (dates, coding, mathematical projections, etc.), but struggles with philosophical domains (reasoning through the infinite meaning of life).

So how does this apply to FP&A? Well, you basically have the smartest mathematical mind in the universe at your fingertips, but that mathematical genius cannot think itself out of a paper bag left to its own devices.

1. Embrace AI as Your Power Tool

Start with the tools already at your disposal. Modern Excel and other financial platforms come with powerful AI capabilities. Here's how to leverage them effectively:

• Ask AI to review your existing processes and suggest efficiency improvements. Example: "Review this variance analysis file and suggest ways to automate the repetitive steps using Power Query."

• Utilize AI to optimize your calculations and formulas Example: Using Copilot to build a multiple linear regression model that predicts sales receipts based on historical data, unit prices, and payment patterns.

• Leverage AI to automate routine tasks through Power Query and other tools Example: Create automated data cleaning workflows that standardize incoming data formats, remove duplicates, and consolidate multiple sources into a single analysis-ready table.

2. Evolve from Analyst to Developer

To evolve from an analyst to a hybrid developer-decision maker, FP&A professionals should focus on three key areas:

• Develop Technical Capabilities

  • Build and maintain predictive models

  • Create automated systems that support decision-making

  • Master AI-powered automation and predictive modeling tools

• Enhance Problem-Solving Skills

  • Proactively identify business challenges

  • Create custom solutions using AI tools

  • Transform complex data into clear insights for decision-makers

• Focus on Value Creation

  • Bridge the gap between raw data and business decisions

  • Build efficient processes without relying on expensive external software

  • Actively seek out problems rather than waiting for assignments

Remember that while AI excels at mathematical and factual tasks, it still needs human guidance for reasoning and strategic thinking. This creates an opportunity for FP&A professionals to position themselves as skilled operators who can harness technology to deliver unprecedented value.

3. Become a Problem Solver, Not Just an Analyzer

The most valuable FP&A professionals will be those who:

• Proactively identify business problems

• Leverage AI tools to develop custom solutions

• Create efficient processes without relying on expensive external software

4. Start Tactically, Scale Effectively

AI solutions are inherently messy and they take a long time perfect. I am still tinkering with AI workflows that help me do everything from curate my content to predict sales for my clients.

However, it is not complicated to spin something up. I show exactly how I accomplish this on my YouTube channel.

You also have to remember that AI at this point is mostly hype. That’s not to say

The New FP&A Playbook

Success in this new landscape requires a fundamental shift in how we approach our role:

• Stop waiting for problems to be assigned - actively seek them out

• Build solutions using AI tools already at your disposal

• Focus on transforming chaos into clarity for decision-makers

• Develop expertise in AI-powered automation and predictive modeling

The Bottom Line

Companies are increasingly willing to invest heavily in AI-powered solutions while trimming human headcount. The key to survival isn't fighting this trend - it's positioning yourself as the person who can harness these tools to deliver unprecedented value.

Action Steps

• Start experimenting with AI tools in your current workflows

• Identify manual processes that could benefit from automation

• Build expertise in predictive modeling and data analysis

• Focus on solving business problems, not just analyzing them

The future of FP&A belongs to those who can evolve from pure analysts into tech-savvy problem solvers. The tools are available - the question is, are you ready to use them?

The Automation Trap

When I sit down with clients, I always start by asking about their pain points. Without fail, they describe inefficient processes that consume too much time and resources. Their instinctive solution? Automation.

But no one stops to think deeper about the process to begin with. This belies an even greater risk - optimizing the wrong thing.

A Real-World Example

Consider an accounting team struggling with accounts receivable reconciliation. They manually match invoice data with cash receipts that lack direct links to invoices. When leadership wants to automate this process, they envision eliminating the manual work entirely.

However, when implementation begins, they discover that automating a broken process merely produces automated errors. The underlying issue isn't the manual work - it's the disconnected data and poorly designed workflow.

The Hidden Costs of Premature Automation

• Perpetuating inefficient processes at scale - garbage in / garbage out automation just makes the pile of garbage bigger

• Creating technical debt that becomes harder to fix - I liken this to trying to untangle the wires behind your television

• Wasting resources on solutions that don't address root causes - who amongst us has ever worked on a system implementation that just fell flat upon go-live?

• Building automated systems around temporary workarounds - not every process is worth automating

A Framework for Automation Readiness

Before pursuing automation, ensure you can answer these critical questions:

1. Can you explain the process step-by-step to someone else? If not, you don't understand it well enough to automate it.

2. Can you facilitate the process through a simple system like Excel? If not, you're trying to solve process problems with technology.

3. Do the key stakeholders agree on how the process should work? Lack of consensus will lead to automated chaos or worse an utter lack of adoption.

4. Can you effectively test the automation? Without clear success criteria, you're flying blind.

The Path Forward

Instead of rushing to automate, follow this approach:

1. Map out your current processes in detail

2. Identify and eliminate unnecessary steps

3. Redesign workflows to address root causes

4. Test and refine the improved process manually

5. Only then consider automation

A Word of Caution

Be especially wary of vendors promising to automate your processes without thoroughly understanding them first. Any solution that claims to work without detailed process analysis is likely to create more problems than it solves.

The path to effective automation isn't always glamorous. It requires careful analysis, process improvement, and sometimes the courage to stop doing things that no longer add value. Above all, it undoubtedly will take longer than you expect. But this methodical approach will save you from the costly trap of automating inefficiency.

My dad always told me “If you have time to do it over, you have time to do it right the first time.”

Nowhere is that more critical or true than it is with process automation.

But, what happens to all those people when AI is crunching the numbers in seconds rather than days? Who is translating it all for leadership if AI is crafting the notes? "AI is going to take all our jobs" was my knee-jerk reaction.

But, I’ve started to come around. It’s not so much that I think all of these jobs will be done by robots. Alternatively, I think it’s going to make the best analysts 100 times better.

The Truth About AI in Finance

First, let's address the elephant in the room: AI requires skill to use effectively. It’s a skill anyone can learn, but it is a skill nonetheless.

Just like a financial model is only as good as its assumptions, AI output is directly correlated to the quality of your prompts. If you can't articulate what you need clearly and provide proper context, AI will serve up garbage. The old adage "garbage in, garbage out" hasn't gone anywhere.

Well, the "M" in LLMs stands for "models" – and as we know, models provide frameworks for reality, they don't replace it. You still need human judgment to manage these models, even if they're hyper-efficient.

So, it’s not so much that AI is going to replace you and your job. It’s more like a person who knows how to leverage AI is going to take.

As AI reshapes our industry, I'm seeing a fundamental shift in how finance teams operate. The traditional hierarchical structure is flattening, and the nature of our work is evolving.

The Rise of the Super-Analyst

Consider this: Why maintain separate analysts for sales, OpEx, margins, and EBITDA when one analyst, supported by specialized AI agents, could manage an entire P&L? Or, why couldn't a single analyst handle multiple business units or clients?

This isn't science fiction. It's already happening.

The New Finance Organization

The transformation is already underway. Here's how I see it playing out:

• Data preparation and routine analysis will be automated through data pipelines and ETL workflows. We won't need armies of junior analysts maintaining spreadsheets.

• Senior analysts will shift from manually maintaining forecasts to managing predictive algorithms. Their value won't come from being good Excel model builders– it'll come from their ability to tune and interpret AI models (even within Excel).

• The middle management layer will thin considerably. The best managers will evolve into directors, while others might find themselves caught in no man's land as algorithms take over quality control.

• Directors will need to be exceptional. They'll need to understand the details while delivering crystal-clear insights to leadership. The competition for these positions will intensify as organizations flatten.

The Future is Already Here

As technology continues to advance, this trend will only accelerate. But there's a catch: it's not a simple matter of replacement. The nature of finance work is fundamentally changing. Success will increasingly go to those who can harness technology most effectively.

And here's the crucial point: while models can suggest countless good decisions, humans will remain responsible for making the most important ones. Leadership skills, business acumen, and strategic thinking aren't going away – they're becoming more valuable than ever.

A Word of Caution

While this AI-driven transformation is exciting, it's not without its challenges and limitations:

• Not Every Organization is Ready: This vision works best in companies with clean data structures and mature processes. Early-stage companies or those with messy data environments may still need traditional full-time analysts who build institutional knowledge⁠⁠. That said, AI-driven automation and predictive tools are becoming more accessible by the day, so even at smaller scales, bootstrapped teams have tons of tools at their fingertips.

• Managers Aren't Obsolete, They're Evolving: While some managerial tasks will be automated, we still need humans to ensure AI outputs align with strategic goals. The manager's role is shifting from spreadsheet oversight to AI governance and cross-functional enablement⁠⁠.

• Data Quality and Ethics Matter More Than Ever: Just like traditional financial models, AI systems are only as good as their inputs. Organizations need robust data quality processes and ethical governance frameworks to ensure AI-driven decisions are both accurate and responsible⁠⁠.

The key is to approach this transformation thoughtfully, ensuring we're not just chasing efficiency at the expense of effectiveness.

Need Help?

AI adoption has allowed me to deliver hundreds of thousands of dollars in cost savings for my clients, and I leverage these tools today to manage my own business without a staff. If you want help deploying these practices, schedule a free consultation, or become a paid member of my Substack to get exclusive guides and tools that will help you make the most out of these solutions.

I recently published a long form guide on how FP&A professionals can navigate this crazy new world.

Even if you don’t subscribe, make sure you’re constantly assessing the challenges in your workflow that would benefit from these solutions.

The Bottom Line

The only constant in our industry is change. AI isn't an enemy to feared (at least not yet) – it's a tool that will separate the great finance professionals from the good ones. The question isn't whether AI will take your job. The question is: are you ready to become 100 times better at what you do?

Read more

Hell, I use AI in everything. I used it to help me write this article. AI doesn’t even have clean data - it’s trained on the internet, and if you think the internet is clean, I’ve got a bridge in Brooklyn to sell you.

The Universal Truth About Data

Now, I'm not saying clean data isn't important—it's vital, and you'll eventually need it. But data doesn't start out clean, and if you're waiting for perfect data instead of rolling up your sleeves and getting dirty with what you have, you'll never make progress.

Data, by its very nature, is messy. It's imperfect. It lies. It contradicts itself. This isn't a flaw in your systems or a failure of your team - it's just the reality of capturing information about the messy, imperfect, real world we operate in.

Using "bad data" as an excuse to avoid transformation is, at best, lazy thinking. At worst, it's a complete abdication of your responsibility to drive insights and value for your business partners.

People always think that data is holding them back, when in reality their broken processes are what is leading to horrible data.

A Better Approach: Start with Decisions

Instead of obsessing over perfect data, start with the decisions you need to make:

• Need to forecast sales? Start with the sales data you have, even if it's incomplete. Unpacking what you have tends to lead to getting cleaner data.

• Planning inventory? Focus on demand patterns, even if they're noisy. Bringing attention to the noise tends to hone the process which in turn hones your data.

• Managing cash flow? Begin with your balance sheet, even if reconciliations aren't perfect. Work your reconciliation process and the cash flow process gets cleaner.

The Elephant Strategy

You don't eat an elephant all at once - you take it one bite at a time. The same is true for data transformation:

• Identify one critical business decision

• Map the minimum data needed for that decision

• Clean and organize that data and ONLY that data

• Build processes around maintaining that specific dataset

• Move to the next priority

The Clean Enough Fallacy

There's a persistent myth in business that "if our data was just cleaner, everything would be easier." While clean data does make life easier, it's a moving target. There's no magical threshold where data becomes "clean enough." Your data can always be cleaner, more detailed, more current.

I've worked with companies of all sizes - while some had cleaner data than others, they all believed their data sucked. Bottom line: there's no such thing as perfectly clean data.

Consider this: Your competitors likely have better data than you in some areas and worse data in others. Some of your business units probably have cleaner data than others. Yet somehow, successful businesses still make decisions every day with imperfect information. Your job is to arm them with the most valuable data in the right context and timeframe to make sound, actionable decisions.

The Path Forward

Here's how to break free from the "bad data" paralysis:

• Start with your goal - what decision needs to be made?

• Take inventory of available data - what do you have vs. what do you need?

• Focus on efficiency - how can you clean and maintain just the critical data points?

• Build iterative improvements - create processes that make the data incrementally better over time

The Bottom Line

Perfect data is a myth. Waiting for perfect data before taking action is a recipe for stagnation. Start with the decisions you need to make, use the data you have, and build processes to make it better over time.

Remember: The goal isn't perfect data - it's better decisions. Don't let the pursuit of perfection prevent you from making progress.

Ready to transform your data approach?

Subscribe to my newsletter. It’s full of insights on how to turn messy data into actionable business decisions. You can also book a session with me directly where we’ll assess your current challenges and develop a practical roadmap for making progress today!

The Three Horizons of Financial Planning

Traditionally, finance teams look at three time horizons:

• Short-term (e.g. 13-week cash forecasts)

• Mid-term (e.g. 12-18 month financial statement forecasts)

• Long-term (e.g. 3-5 year strategic plans)

While all these horizons matter, I made a classic mistake: I treated our startup like a mature business instead of the scrappy venture it was.

I had fallen into a trap.

The Over-Engineering Trap

I was building across all three time horizons because that’s what finance does. But here’s the problem, the startup was in business model verification mode, which made instituting these practices difficult for a number of reasons:

• Things were changing so rapidly that when we tweaked the business model, those changes rippled through three planning timeframe, making the models more challenging to manage than they needed to be.

• This pace of change also increased the opportunities for errors

• Cash management was paramount because when you run out, the gig is up anyway, so who cares about three year P&L projections with a nine month runway?

Of course, long-term planning has its place in startups—after all, you need growth projections to pitch your vision to investors. But this shouldn't be your primary focus.

Picture this: I'm sitting there with a team of five people and 50 customers, implementing:

• Sophisticated forecast policies

• Investor pitch decks

• 18-month cash flow projections

• Regular expense reviews

• Monthly financial deep-dives

I had brought my Fortune 500 playbook to a pickup basketball game. The team didn't need - and frankly couldn't sustain - these enterprise-level processes.

I was also obsessed with promoting AI solutions for our processes. Despite our small team and limited resources, I believed AI tools could help us bypass traditional constraints. I spent time meeting with AI vendors, discussing ways to streamline our sales cycle, speed up lead qualification, and improve lead quality.

Sounds great right??

Not really. These B2B platforms were far too expensive for our limited budget, especially with our burn rate concerns. Plus, we hadn't even validated our business model yet.

What good would an AI-powered sales team do for our billion-dollar aspirations when we were only handling five leads per week? The costs and effort simply didn't justify the potential benefits.

I had lost sight of our immediate needs, fixating instead on processes and systems we might need three to five years down the road.

What Early-Stage Companies Actually Need

In retrospect, I should have focused on just three core areas:

1. Cash is king - reporting should’ve revolved around cash flow

Basic reporting through QuickBooks was working fine. While this could be automated for monitoring, our key focus needed to be answering three critical questions: "What's our cash balance?", "What's our burn rate?", and "How can we either slow the burn or raise more cash?"

1. Customer metrics - focus on LTV and core billing/onboarding analytics

Like most startups, the company was obsessed with growth, but the systems managing that growth needed to be robust. We onboarded customers that we analyzed would be profitable based on their profiles. We effectively vetted these customers after launch to verify they met our model's expectations—and when they didn't, we adjusted the model to reflect reality.

Beyond that, our systems needed to be focused on three critical aspects: ensuring customers were billed on time, confirming they paid on time, and alerting us when either process went off track. I wish we would have spent more time on this.

1. Investment requirements - but only through the lens of the above two areas

Every spending decision was essentially a strategic investment from our cash reserves. While these decisions were typically based on gut feelings from the heads of sales and operations, the finance department should have provided these stakeholders with a framework to ensure our investments would:

• Improve the cash position

• Make customer acquisition, onboarding, billing, and collections more efficient and profitable

Without clear proof (or at least a compelling argument) that the investment would accomplish one or both of these goals, we should have passed. Finance's responsibility was to establish these decision-making frameworks.

Everything else was just window dressing. Those beautiful 3–5 year projections? The startup didn't survive long enough to validate them. That sophisticated agent force planning? It was a solution in search of a future problem.

The Reality Check Framework

Finance's job is to drive value-adding decisions for the business through timely, accurate information. That's it.

To do this effectively, you need to build support systems that serve the business where it is while keeping an eye on where it's going.

When I work with clients, I ask myself (and leadership) two key questions:

1. Am I (FP&A) meeting the business where it is?

2. Are our processes and systems robust enough to support where the business will be in three to five years?

For younger, less mature businesses, the first question is crucial. As businesses grow and scale, the second question becomes increasingly important.

Now, this framework isn't perfect—it suggests a tradeoff between these two priorities. These considerations exist within a broader set of responsibilities (i.e., capital raising, risk management, etc.). Additionally, "meeting the business where it is" remains more ambiguous than my analytical mind would prefer. I'll explore what this means in practice in future articles.

For now, it helps to conceptualize the importance of focusing your efforts in the right place given the situation.

The Art of Ad Hoc Analysis

Here's another subtle but crucial lesson: When the same "ad hoc" question keeps coming up, it's time to standardize that reporting. But don't mistake regular reporting needs for ad hoc exercises.

Common ad hoc scenarios might include:

• "What if we cut half the staff?"

• "What if we hire an outside marketing agency?"

Your job isn't to avoid these questions - it's to identify which ones deserve to graduate into your standard reporting package. Be picky because the business can change quickly if you don’t strike the right balance between ad hoc exercises and regularly needed insights, you’ll build the wrong platforms.

There's also a flip side to this lesson: It's perfectly fine (especially when the business model is rapidly evolving) for much of the FP&A support to be ad hoc.

I know this sounds sacrilegious to finance professionals, but the truth is ad hoc analysis never truly goes away. If you try to implement frameworks that are too rigid for your business's maturity level, you'll waste precious time building systems that will need to be completely revised within weeks.

The Bottom Line

Want to build a finance function that actually serves your business? Start small, stay focused on cash, and let your infrastructure grow with your actual needs - not your aspirational ones. You can always build more sophisticated systems later, but you can't get back the time you waste building systems you don't need yet.

If you're facing these challenges in your business and want expert guidance on right-sizing your finance function, I'm here to help. Drawing from my experience as both a Fortune 500 FP&A leader and startup CFO, I can work with you to build exactly what your business needs today while positioning it for tomorrow's growth. Schedule a consultation to discuss how we can optimize your finance operations together!

Since 2017, I've heard the same refrain at every finance conference: "Excel is dying." Vendors pitch their solutions in two flavors:

• "We'll help you transform without abandoning spreadsheets"

• "We'll help you completely kill spreadsheets"

Here's the truth: Excel isn't the problem. The processes these spreadsheets support are the problem.

Today, I’m going to show you how to drive efficiency and time savings NOW without having to spend a dime on professional services or brand new, fancy software.

This Isn’t Your Daddy’s Excel

Today's Excel would be unrecognizable to an analyst from a decade ago:

• You can automate data preparation and transformations with Power Query

• Python integration allows you to build advanced analytics directly into your workbook

• Built-in AI with Copilot is adding functionality faster than anyone can keep up

• Data modeling is now possible directly in excel with Power Pivot

This opens a world of opportunities:

• Accurate, automatically updating cash flow forecasts

• Beautiful, dynamic dashboards

• Complex predictive analytics models

The Process Paradox

When organizations struggle with their Excel-based processes, they often blame the tool. But here's what I've learned as a fractional CFO and as an FP&A Solutions Consultant: The fanciest SASSiest solution on planet earth cannot fix your broken process, but a functional process can be automated and seamlessly integrated using nothing but Excel.

Bottom line - it's not about the spreadsheet.

The AI Advantage

Consider this: I know enough Python to read a script and understand what it does, but I'm not great at writing code from scratch. In the past, this would have been a major limitation. Today? I can have AI be my coder while I supervise the results. I need to know enough to be dangerous, I don’t need to be able to build robust code from scratch.

When I started learning to code, building my own projects, it would be a long and tedious process that involved lots of Googling and Youtubing.

Now, with AI, what used to take a month of manual coding can now be accomplished in thirty minutes or less. This isn't about replacing Excel - it's about making Excel more powerful than ever.

The Real Solution

Instead of trying to "kill" Excel, try this instead:

• First, focus on and fix one of your broken processes. Map out your steps end to end and look for waste and rework. If you’re doing anything manually, flag it.

• Then, leverage Excel's modern capabilities to automate and enhance those processes. If you don’t know how, that’s ok, leverage Co-Pilot. Practice prompting to enhance the results from the model.

• Finally, once your process is working - or better yet - you have a collection of processes that are working well, consider more specialized tools to scale those now-working processes. If the solution cannot improve on what you already have, move on. It’s not worth the cost and pain of trying to fix something that isn’t broken.

Ready to Transform?

This practice has helped me to rapidly deploy real solutions that have delivered substantial Type I and Type II savings with minimal to zero investment dollars.

If you’re looking to radically improve your workflow with existing tools already in your tool belt, subscribe to this newsletter for even more insights. Each week, I share practical tips, AI prompts, and real-world examples that you can implement immediately.

Need hands-on guidance? I offer targeted consulting sessions where we can review your specific processes and create an action plan for automation and improvement. Book a complimentary 30-minute discovery call to explore how we can work together.

Even if you don’t subscribe, make sure you give the approach above a try. You will be surprised at how far it will take you.

The Bottom Line

Excel isn't dying - it's evolving. The tools we have available today within Excel are more powerful than ever before. The problem isn't your spreadsheets; it's how you're using them. Fix your processes first, then let modern Excel capabilities amplify your success.

Stop looking for software salvation. Your existing tools - yes, even Excel - are probably sufficient to start improving today. The hard work isn't in implementing new systems; it's in the unglamorous task of fixing your broken processes.

“If I have to sit through one more shitty transformation roadmap session from another consultant who has never set foot in the building, I might jump out the window.”

Not the most…productive start to a meeting with an executive I’ve ever had.

I had just been hired by a client to drive transformation initiatives within the accounting and finance functions and over the course of the year we had start to stack successes, each one larger and more impactful from the last, but it was a grind (with a lot of frustrating setbacks).

“Yeah,” I added with a chuckle that probably sounded more nervous that I would’ve liked, “I can’t stand consultants.”

In that moment, I counted my lucky stars that I had taken the approach I did: meet the business where it is and engrain yourself in the operation before you try to make any suggestions on what to change.

See, I’ve sat on every side of the table - I’ve been the prospective client, the client, the consultant, the sub-contractor for the consultant, and the individual contributor who is tasked with working with the consultant. I’ve seen this love-hate relationship from every single angle.

And I think I’ve found a way to make the relationship better…

The Consulting Paradox

Picture this: You're an executive trying to keep your metaphorical airplane on the runway while simultaneously building it. Every decision has a butterfly effect, and your stressed-out team is already maxed out. Then, a consultant parachutes in, promising a perfect view of the future - for a price - without knowing the first thing about your reality on the ground.

Months later, the solution isn't what was promised, everyone's frustrated, and you're stuck with an outsider making waves. Sound familiar?

Why Everyone Hates Consultants

• From the client's perspective:

  • You hire highly recommended experts who promise to fix your problems in X time for Y money

  • Your team burns out explaining the same issues they've explained to countless consultants before

  • Two weeks into a twelve-week project, you're behind schedule

  • Suddenly, you're told it'll cost 20% more, take an extra month, and only solve 80% of the problem

As one of my favorite clients once said: "I want to get a bunch of consultants in a room, throw a dollar in the middle, and see which one walks out with it."

The Hard Truth About Making an Impact

The idea that anyone can walk in and deliver on a million-dollar project without proper due diligence is ludicrous. Here's why:

• Making real impact takes time and usually starts small

• You're working with people who have lived these problems for years maybe even decades

• Even the perfect solution is worthless without buy-in from the team

• You're battling both consultant prejudice and organizational inertia

A Different Approach: Honesty

My pitch is harder, but more honest. I tell clients:

"I'm a problem solver, but I cannot promise I can solve all your problems. What I can promise is that we'll move fast, understand your challenges deeply, put effective solutions in place, and scale what works."

Here's how I approach consulting differently:

• I get in the mud with your team to understand what's really happening and why

• I focus on building a culture of continuous improvement rather than promising quick fixes

• I become a soldier in the trenches with you, not a mercenary who disappears when things get tough

• I maintain enough distance to stay objective while getting close enough to understand the real issues

The Secret to Effective Consulting

Success in consulting isn't about being the smartest person in the room or having all the answers. It's about earning trust at every level of the organization and showing that you're there as reinforcements, not as a threat.

By the time you're three or four weeks into an engagement, you should feel like part of the team. If you don't, you're probably not going to make the impact you're hoping for.

The Local Nature of Change

Here's the fundamental truth I've learned: All culture is local. A company is made up of individual people, and you won't help the whole unless you help the sum of its parts. This means starting small, making high impact changes, and gradually expanding to adjacent processes - building trust and momentum along the way.

Yes, most people hate consultants. But by being honest about what we can and can't do, by getting our hands dirty alongside our clients, and by focusing on real, sustainable improvements rather than quick fixes, we can change that perception one engagement at a time.

One of the many (many, many, many, many, many) core responsibilities for Finance teams is connecting human behavior to numerical performance metrics. While Key Performance Indicators (KPIs) have traditionally served as our measuring stick, Objectives and Key Results (OKRs) represent an evolution in how we approach performance measurement and management.

Understanding the Basics: KPIs vs. OKRs

KPIs tell the story behind your business results. Take sales, for instance: traditional KPIs might focus on quantity sold and price points. These metrics provide valuable insights, but OKRs take this framework a step further by creating actionable, measurable objectives tied to specific outcomes.

Building Effective OKRs: A Practical Example

Let's break down how to structure OKRs using a sales growth scenario:

• Objective: Increase overall sales volume

• Key Result 1: Enter and establish presence in new territory X

• Key Result 2: Generate Y units of sales volume from new territory

What makes this OKR structure powerful is its measurability and direct attribution. Success or failure becomes clearly visible: either we see orders flowing from the new territory, or we don't.

We’ve also connected upstream behavior (our ability to penetrate a new market) with direct downstream business results (Sales on the P&L).

The Process-First Approach

Before implementing OKRs, it's crucial to have a solid operational foundation. This means:

Understanding all process ingredients and components

Understand all process ingredients and components

Nothing can be measured or attributed to results if we don't first develop a thorough understanding of the underlying process and its fundamental components. Without this crucial foundation, any attempt to measure performance or establish accountability becomes an exercise in futility. I talk about this at length in a previous post. Process is first, always - it forms the bedrock upon which all meaningful metrics and measurements must be built.

How many prospects need to be contacted to generate a sale? What is the typical conversion rate at each stage of the funnel? What factors influence these conversion rates?

Identify which variables we can and cannot influence

Now that we know how the process flows, we can determine which process ingredients we can influence, and which ones we cannot. We can influence how many prospects we contact, we need to make sure we are bringing prospects in and establish goals around how many and how quickly we contact them.

We cannot influence the fact that market conditions are currently forcing a particular prospect out of business because their factory burned down.

An extreme example, but it shows that our focus should be on prospect volume, not limiting the number of textile factories that burn down monthly.

Map how these elements interact within the broader business operation

Finally, we can map these variables to the broader process that drives the business operation:

• Our conversion rate is 5%

• If we contact 100 prospects, 5 of them will become customers

• Avg Sales per customer is $50

Bottom Line: For every 100 prospects contacted, we can generate $250 in incremental sales.

Creating a Working Model

Once we understand our process and its components, we can develop OKRs that:

• Target specific, controllable variables—prospects in new territory X

• Connect directly to measurable outcomes—number of prospects contacted

• Drive desired behavioral changes—sales team must contact 100 prospects per month

• Support overall business objectives—5 × $50 × 12 months = $3,000 in annual incremental sales

This creates a cohesive strategy that drives toward an executive-level goal (increased sales) and makes it tangible and measurable at a tactical level (did we contact the right number of prospects this month).

Now What?

Now it's time to execute and measure the results. Here's what we need:

1. An efficient (ideally automated) data source to track the number of prospects contacted in territory X by date

2. A simple visualization that clearly shows whether we've hit our target

3. A regular check-in schedule to review our progress and engage with key personnel to understand why we're hitting or missing our targets

There is a wonderful data visualization example on Tableau Public from Nir Smilga that shows what this looks like in practice.

Receipts Predictive Model

483KB ∙ XLSX file

Download

Download

Every time I sit down with a new client, they want to know how to start building predictive analytics.

Now, it used to be that you would need a ton of infrastructure to get up and running with AI and advanced predicted algorithms. I have sat on both sides of the client / vendor table and heard about all of the heavy IT lifting, and long lead time and capital investment that would need to happen to forecast sales.

I’m going to show you how to do it with an Excel spreadsheet and a little help from Copilot.

Want to see this in action? Check out a YouTube playlist that covers this end-to-end:

Setting the Table

We’re going to use data from Maven analytics that comes from a hypothetical company called “Northwind Traders.” Our task is simple: our current sales receipts forecast process sucks, and we need to fix it.

Their current approach of using simple moving averages to project sales receipts has created problems in their cashflow forecasting process. Their data lacks consistency, and the moving average method proves too inaccurate for their forecasting needs.

For simplicity's sake, I dumped the prepared data into a clean and tidy Excel table, but I'll probably do another installment around how to prep your data using Power Query.

Feature Engineering

The next part will consume 80% of your time and effort. In the world of data science, we call it feature engineering. In the finance world, I call it “picking the best inputs that will predict the best outputs.”

In a real-world scenario, we’d want to find the right mix of internal and external data inputs (x-variables) that are a) highly correlated with the output we are trying to predict (y-variable) and b) collectively explain as much of the variability in our output as possible (measured by r-squared).

The beauty of accomplishing this means that we have what all FP&A professionals crave - easily articulated variance drivers.

To do this in Excel, we simply start pulling data for the available variables:

• Payment Date: the unique payment dates available in our data sorted from oldest to newest using a SORT(UNIQUE(FILTER))) formula

• Order Date: the date when the order was placed based on the payment date using a conditional XLOOKUP formula

• Receipts: the cash receipts from based on the payment date using a SUMIFS formula; this is the y-variable we are trying to predict

• Shifted Sales: the net sales of the cash receipts based on the order date using a SUMIFS formula

• Avg Discount: the aggregate average percent that was applied on gross sales for a given order date using an AVERAGEIFS formula

• Avg Unit Price: the aggregate average price that was charged for a given date using an AVERAGEIFS formula

• Days of Sales Outstanding (DSO): the total number of days it took the customer to pay (Payment Date less Order Date) using a SUMIFS formula

Using the correl() function in Excel, we can easily determine the relationship between the inputs (Shifted Sales, Avg Unit Price, Days of Sales Outstanding) and the output (Receipts):

• Shifted Sales: 0.556

• Avg Unit Price: 0.532

• DSO: 0.512

Now, these correlations are relatively weak. We would rather see factors closer to 1 or -1. To dive a bit deeper, if we square each of these figures, we can determine how much of the variation in Receipts is explained by each independent variable (31%, 28%, and 26% respectively).

Clearly I wouldn’t stake my job on the strength of these relationships, but they’re strong enough to demonstrate how to do this in practice. In a real-world scenario, we would want stronger relationships and we’d even be pulling external data points (inflation, market growth, weather patterns, etc.), but this is made-up data and I have shit to do so these are good enough.

Setting up the Model(s)

A quick note about granularity

Now it’s time to bring our data together in a summarized way. It’s important when it comes to these models, the less dimensions the better. In this case, our only dimension payment week.

This can be frustrating if you want to forecast by category, but if you go that route you’d ideally want to treat each category as a forecast point. It’s also important for you to ask “Why do we need to forecast at a more granular level if it does not yield better results?” More often than not, the answer is some form of “well, we always forecast by product category.” I don’t have a ton of patience for the “it’s the way we’ve always done it” explanation.

That said, if you’re a sales analyst supporting a particular product category or you need to plan commissions for sales personnel, that’s a completely different story. Maybe instead of forecasting every category, you only forecast the most important categories.

In the world of finance, we love the details. It’s critical that you parse out when the details are important and when they are not. Plus, just because a forecast is performed at a higher level doesn’t mean that the forecast is not detailed or less informative.

Setting up the time horizon

We start by setting up model parameters based on our imported data:

• We find the minimum week in the data set (C7): =MIN(orders_training[paymentWeek])

• We find the maximum week in the data set (C8): =MAX(orders_training[paymentWeek])

• We find the number of actual weeks (C9): =((C8+7)-C7)/7

• We determine the Last week of the year (C10): =EOMONTH(DATE(2015,12,1),0)-(WEEKDAY(EOMONTH(DATE(2015,12,1),0))-1)

  • you could also simply input the last week of the year, but I’m all about the automation

• We determine the number for weeks in the forecast (C11): =ROUND((C10-C8)/7,0)

This gives us a total of 126 weeks (87 weeks of actuals and 39 weeks to forecast). Typically, you shouldn’t get too far over your skis with how many forward-looking periods you are trying to forecast because the further you go into the future the more unreliable the results (the world tends to change after all).

So again, in practice, we would either limit the number of weeks we’d forecast or we’d keep what we have but only publish a shorter-term forecast (e.g. 13 weeks).

Generate the Dates

With these model parameters, I can establish our time horizon and determine whether those dates are actual (AC) dates or forecast (FC) dates.

• AC / FC: If the payment week is less than or equal to the Payment week, then it’s an actual date (AC) otherwise it’s a forecasted date (FC)

• Payment Week: We generate a sequence of weeks based on the minimum week in the data set and the total number of weeks defined by the model parameters

• Order Date: For actual payment dates, we can establish the week in which the original order took place

With our time horizon set, we can bring in the actual data points from our data set.

Time to Predict

To make our predictions we are going to use two different models:

1. Exponential Trend Smoothing

2. Multiple Linear Regression

3. Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX)

Why do we use three different models? For one, not all models are created equal, they all have pros can cons, so it’s better to be armed with multiple that are targeted at the same variable you’re trying to predict.

Secondly, averaging predictions from multiple models often leads to better performance than any single one.

In general, I aim to build out 3-5 predictive models (sometimes more) when I’m trying to forecast a given data point. The beauty of python is that it is fully open-source so if you want to integrate a new model, you can build it directly into excel and you can use Copilot to help you with model selection and implementation.

So, the goal here is to make three stabs at the same prediction and then measure each one’s performance along with the ensemble (average) prediction.

Exponential Trend Smoothing

This one is easy and it comes right out of the box. Exponential Trend Smoothing (ETS) algorithms are really useful when you have a bunch of historical data points of the variable you’re trying to predict and it follows are relatively stable historical pattern.

This will also come in handy when we go to craft forward-looking independent variables.

Here’s the formula: =FORECAST.ETS(Target_Date, Values, Timeline, Seasonality, Data_Completion)

=FORECAST.ETS($B$15#,FILTER(D$15#,$A$15#="AC"),FILTER($B$15#,$A$15#="AC"),1,1)

• Target_Date is the date you’re trying to forecast (we’ll do this for both historical periods and future periods)

• Values are the historical values for the variable we are trying to predict (in this case receipts)

• Timeline is the list of dates that those actual historical values correspond with

• Seasonality is optional and I mostly use the default which tells the algorithm to automatically detect seasonality patterns

  Data_Completion is also optional but I always use 1 which tells the algorithm to fill in missing values

In our excel file, here is what is looks like:

Multiple Linear Regression with NumPy

Before we build the prediction using linear regression, we need to create forecasted values for our independent variables. To do this, we simply repeat the ETS function we did above for each of the independent variables (Shifted_Sales, Avg_Unit_Price, and DSO):

Now we need to get into Python. To initiate python, click where you want the values to start and enter =py into the cell / formula. This will initialize an environment for python that will allow you build scripts directly into cells.

If you aren’t comfortable with python, don’t worry copilot is unbelievable at acting as a coder. I have learned more in the last year with AI helping me build code than I did in the previous four when I started to learn the language.

I wrote a separate post on exactly how I came up with this final algorithm but here is the cliff notes version. That’s right, Copilot wrote the script you see below, and I was using it in five mins.

import pandas as pd
import numpy as np

# Load dynamic data
data_dict = {
    "AC_FC": xl("A15#", headers=False).iloc[:, 0],       # AC/FC (single column)
    "Order_Date": xl("B15#", headers = False).iloc[:, 0],  # Dates
    "Shifted_Sales": xl("E15#", headers=False).iloc[:, 0], # Historical Gross_Sales
    "Avg_Price": xl("F15#", headers=False).iloc[:, 0],   # Historical Net_Sales
    "DSO": xl("G15#", headers=False).iloc[:, 0],         # Historical DSO
    "Receipts": xl("D15#", headers=False).iloc[:, 0],    # Historical Receipts
    "Shifted_Sales_Forecast": xl("H15#", headers=False).iloc[:, 0],  # Forecasted Gross_Sales
    "Avg_Price_Forecast": xl("I15#", headers=False).iloc[:, 0],    # Forecasted Net_Sales
    "DSO_Forecast": xl("J15#", headers=False).iloc[:, 0]           # Forecasted DSO
}

# Create DataFrame
data = pd.DataFrame(data_dict)

# Convert Order_Date to datetime and calculate Days_Since
data["Order_Date"] = pd.to_datetime(data["Order_Date"], errors='coerce')

# Split into historical (Actual) and future (Forecast) data
historical_data = data[data["AC_FC"] == "AC"].copy()
future_data = data[data["AC_FC"] == "FC"].copy()

# Train the model on historical actuals
X_train = historical_data[["Shifted_Sales", "Avg_Price", "DSO"]]
Y_train = historical_data["Receipts"]
X_train_with_intercept = np.c_[X_train, np.ones(X_train.shape[0])]
coefficients, _, _, _ = np.linalg.lstsq(X_train_with_intercept, Y_train, rcond=None)

# Prepare X variables for all periods
# Use actual X for AC periods, forecasted X for FC periods
data["X_Shifted_Sales"] = np.where(data["AC_FC"] == "AC", data["Shifted_Sales"], data["Shifted_Sales_Forecast"])
data["X_Avg_Price"] = np.where(data["AC_FC"] == "AC", data["Avg_Price"], data["Avg_Price_Forecast"])
data["X_DSO"] = np.where(data["AC_FC"] == "AC", data["DSO"], data["DSO_Forecast"])

# Predict Receipts for all periods
X_all = data[["X_Shifted_Sales", "X_Avg_Price", "X_DSO"]]
X_all_with_intercept = np.c_[X_all, np.ones(X_all.shape[0])]
data["Linear_Receipts"] = X_all_with_intercept.dot(coefficients)

# Output results without index
data["Linear_Receipts"].values

What does this do? The script produces an output called data[“Linear_Receipts”].values. This output is the series of predictions for receipts from the first date in the dataset (8/4/13) through the last date (3/29/15).

This ouput is the result of a linear regression algorithm that uses Shifted_Sales, Avg_Price, and DSO to predict receipts. For historical periods, we are using actual values for these data points. For forecast periods, we are using forecasted values for these data points.

Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX)

Autoregressive Integrated Moving Average (ARIMA) isn’t all that much different than ETS. In both scenarios, we are fitting historical receipts to a trend line and then extrapolating that trend over a period of time into the future. The nuance is simply in how the mathematical models function underneath the hood.

We are going to take ARIMA a step farther by also including “Exogenous Inputs” which influence the trend of receipts. We’ve already established a relationship between Receipts and Shifted Sales, Avg Unit Price, and DSO.

This helps to better inform the trend of Receipts that we are attempting to forecast.

With this data set, each produced results that were in the same ballpark.

Here is the script we punch into excel:

import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA

# Load dynamic data
data_dict = {
    "AC_FC": xl("A15#", headers=False).iloc[:, 0],       # AC/FC
    "Order_Date": xl("B15#", headers=False).iloc[:, 0],  # Dates
    "Receipts": xl("D15#", headers=False).iloc[:, 0],    # Historical Receipts
    "Shifted_Sales": xl("E15#", headers=False).iloc[:, 0], # Historical Shifted_Sales
    "Avg_Price": xl("F15#", headers=False).iloc[:, 0],    # Historical Avg_Price
    "DSO": xl("G15#", headers=False).iloc[:, 0],          # Historical DSO
    "Shifted_Sales_Forecast": xl("H15#", headers=False).iloc[:, 0],  # Forecasted Shifted_Sales
    "Avg_Price_Forecast": xl("I15#", headers=False).iloc[:, 0],      # Forecasted Avg_Price
    "DSO_Forecast": xl("J15#", headers=False).iloc[:, 0]             # Forecasted DSO
}

# Create DataFrame
data = pd.DataFrame(data_dict)

# Convert Order_Date to datetime and set as index
data["Order_Date"] = pd.to_datetime(data["Order_Date"], errors='coerce')
data = data.set_index("Order_Date")

# Split into historical (Actual) and future (Forecast) data
historical_data = data[data["AC_FC"] == "AC"].copy()
future_data = data[data["AC_FC"] == "FC"].copy()

# Prepare historical Receipts and exogenous variables for ARIMAX
receipts_series = historical_data["Receipts"].asfreq("W-SUN", method="ffill").dropna()
exog_train = historical_data[["Shifted_Sales", "Avg_Price", "DSO"]].reindex(receipts_series.index).dropna()

# Ensure receipts_series aligns with exog_train after dropna
receipts_series = receipts_series.loc[exog_train.index]

# Fit ARIMAX model (order=(1,1,1) as a starting point) with exogenous variables
model = ARIMA(receipts_series, order=(1, 1, 1), exog=exog_train)
model_fit = model.fit()

# Predict in-sample Receipts for AC periods
ac_predictions = model_fit.predict(start=receipts_series.index[0],
                                  end=receipts_series.index[-1],
                                  exog=exog_train)

# Forecast out-of-sample Receipts for FC periods
n_forecast = len(future_data)
exog_future = future_data[["Shifted_Sales_Forecast", "Avg_Price_Forecast", "DSO_Forecast"]]
fc_forecast = model_fit.forecast(steps=n_forecast, exog=exog_future)

# Assign predictions to the original DataFrame
data.loc[historical_data.index, "ARIMA_Receipts"] = ac_predictions.reindex(historical_data.index)
data.loc[future_data.index, "ARIMA_Receipts"] = fc_forecast

# Output ARIMA_Receipts for all periods without index
data["ARIMA_Receipts"].values

The script is taking the actual Receipts values, fitting those values to a trend, and then applying that trend into the future.

Judging the Outcomes

Now it’s time to evalulate how accurate our models are. Granted, no model is going to be 100% accurate, but we’d obviously prefer outcomes with a lower rate of forecast error.

I mentioned at the top that I’m using features that are weaker predictors than we would prefer simply for demonstration purposes, but now we get to see why that makes a difference.

Here are the forecasted values that each model produced:

If we average the three models together we can compare the ensemble results to actual receipt values:

Honestly that’s not a horrendous picture, and it gets even better when we remove outliers from the actual results:

Here are how each of the models shook out:

Notice that the avg of all three models had a lowest error rate of all. This demonstrates the value of using multiple models to do your forecasting. That said, 67.1% forecast accuracy isn’t necessarily something to write home about, but it’s a good start.

We could drastically improve the results by going back to the drawing board in regards to feature engineering. What independent variables might be better predictors? What other models would be use? How can we tweak those models to better fit our use case?

All questions that Copilot is more than capable to help us address.

What’s Next?

We’ve done something extremely powerful:

• We’ve built a number of predictive analytics models

• In doing so, we’ve built relationships between a key number of inputs and their impact on the output we are trying to predict

• They way we’ve structured the data (using nothing but out-of-the-box excel functionality) makes the model fully automated and will run with updated data.

This opens the door for a new world of possibilities:

• Automated rolling forecasting

• Automated Variance analysis

• OKRs based on the inputs

• Ready-Made Scenario Analysis

We’ll get into each of these areas over the next several weeks!

And then it failed spectacularly.

Fast forward a few years. I'm now the Director of Transformation sitting in a finance leadership team meeting, advocating for the exact same software. The room erupts in knowing chuckles and eye-rolls. "We've been down this road before," they say. "It didn't work then, why would it work now?"

Here's the Plot Twist

The software wasn't the problem. In fact, our competitors were using it successfully. The same system that had "failed" us was transforming other organizations. The difference? They hadn't made our critical mistake: attempting to automate broken processes.

The Real Problem

When organizations struggle with their financial processes, they often blame their tools:

• "Our ERP system is outdated."

• "Excel can't handle our complex needs."

• "We need better automation."

Here's the reality: QuickBooks alone can power a successful business. I've seen startups thrive on it, and I've even seen Fortune 500s consolidate results from subsidiaries using QuickBooks as their subledger. The issue isn't that your tools are inadequate - it's that your processes are fundamentally broken.

The Month-End Close Example

Want to know why your month-end close takes weeks? It's not because your journal entry system is broken. It's because your vendor rebate process requires four different people to touch the data, starting with someone manually copy-pasting rows into a CSV file. No amount of sophisticated software can fix that fundamental inefficiency.

The AI Automation Trap

Today, everyone's rushing to implement AI and automation solutions, imagining a world where robots will magically make their existing processes more efficient. This thinking is flawed for two crucial reasons:

1. AI is smart enough to recognize that your current process sucks. If you asked it to design a process from scratch, it would look nothing like what you're doing today.

2. Automation amplifies everything - including mistakes. If you automate a broken process, you're just creating errors faster and more consistently.

The Path Forward

Instead of searching for the perfect software solution, start with these steps:

1. Map your processes at the atomic level. Understand every input, transformation, and output.

2. Trace your inputs upstream. Where do they come from? How could they be cleaner?

3. Follow your outputs downstream. Who uses them? What are their actual needs?

4. Start small. Perfect one process at a time.

Once you've cleaned up your processes, then - and only then - should you consider more sophisticated tools to scale them.

A Better Approach

For example, instead of implementing a comprehensive planning solution, start by perfecting your sales forecast for your most critical business unit in Excel. Today's Excel would be unrecognizable to my former self as an entry-level analyst ten years ago. You can now handle predictive analytics, reporting automation, and business intelligence dashboards in Excel—all in a fraction of the time it used to take.

Once your sales forecast is working smoothly, add a direct cost model. Then a margin calculator. Before you know it, you'll have the building blocks of a sophisticated system—one that's based on proven processes rather than wishful thinking.

The Bottom Line

Stop looking for software salvation. Your existing tools - yes, even Excel - are probably sufficient to start improving today. The hard work isn't in implementing new systems; it's in the unglamorous task of fixing your broken processes.

Remember: The best system in the world can't fix a broken process, but a solid process can succeed even with basic tools.