As a Fractional Finance Director, Understanding History Matters

Are we watching history repeat itself?

The recent fall in AI-related shares got me thinking about the dot-com boom of the late 1990s and its subsequent collapse.

Back then, the internet was going to change everything. Investors poured billions into technology companies, telecommunications infrastructure and businesses that promised extraordinary growth.

And they were right about the internet.

It did change everything.

But they weren’t necessarily right about the businesses they invested in, the valuations they placed on them or how quickly those businesses would make money.

Between March 2000 and October 2002, the Nasdaq fell almost 78%.

Some businesses disappeared. Others survived and went on to become enormously successful.

The technology was revolutionary. That didn’t make every investment a good one.

Are We Seeing the Same Thing With AI?

There are certainly similarities.

Huge amounts of money are being invested in AI infrastructure, particularly data centres, chips and computing capacity.

The expectations are enormous.

But investors are now beginning to question whether the financial returns will justify the investment.

In early November, the Nasdaq experienced a significant correction, with concerns about the scale and profitability of AI investment contributing to the sell-off.

Of course, there are differences. Many of today’s leading technology companies are established, profitable businesses, unlike some of the speculative internet companies of 2000.

And the recent correction is nowhere near the scale of the dot-com collapse.

But the underlying question is familiar.

Just because something is a good idea doesn’t mean it makes financial sense at any price.

It is also perfectly possible that the biggest beneficiaries of AI will ultimately be the businesses that use it to improve productivity, rather than those spending billions developing the infrastructure.

We won’t know for some time.

Which brings me to something much closer to home.

Why I Spend So Much Time Looking Backwards

As a Fractional FD, I’m generally appointed to help a business move forwards.

Improve profitability. Manage cash. Support growth. Raise funding. Prepare for a sale.

All forward-looking activities.

But one of the first things I do is spend a considerable amount of time understanding what has already happened.

Not simply looking at last year’s profit and loss account.

I want to understand the underlying financial performance.

How have sales developed? Which customers and products have driven growth? What has happened to margins? How have overheads moved? What has happened to working capital?

And, most importantly, why?

A business might have grown sales by 20%, which sounds excellent.

But what if margins have fallen, stock has doubled and cash has deteriorated?

The headline looks good. The underlying story may be rather different.

The same principle applies to AI investments. Enormous growth in demand is impressive, but it doesn’t automatically establish that the investment will generate an adequate return.

Understanding the Numbers, Not Just Reporting Them

One of the themes running through my book is that producing accurate numbers is only the starting point.

You need to understand what those numbers are telling you.

I use a simple approach to variance analysis:

Identify. Understand. Act.

If sales are £200,000 below forecast, that tells me very little.

Why?

Was an order delayed? Did we lose a customer? Has demand weakened? Was the original forecast simply too optimistic?

The answers have very different implications for the future.

Equally, if gross margin improves, I want to understand whether that came from better pricing, lower purchasing costs, improved product mix or something temporary.

Otherwise, how can we sensibly forecast what happens next?

Don’t Just Extrapolate the Past

There is an important distinction between understanding history and assuming it will repeat itself.

A business that has grown 20% annually for three years won’t necessarily grow 20% next year.

That’s not a forecast. It’s an assumption.

In my book, I explain why I prefer to start with the underlying performance of the business, adjust for known changes and then build a forecast around the specific actions management intends to take.

New customers. Price increases. Product launches. Recruitment. Investment. Cost savings.

What will happen? When? Who is responsible? What will it cost?

Then test the assumptions.

What happens if growth is slower? Margins are weaker? Customers pay later? Investment costs more?

A forecast should help management understand the risks and make better decisions, not simply produce a reassuring number.

The Past Doesn’t Predict the Future

I don’t know whether AI shares will fall further or whether the recent correction will prove to be a buying opportunity.

But history gives us some useful questions to ask.

Are expectations realistic? Is investment running ahead of returns? Are we confusing growth with profitability? What happens if the assumptions are wrong?

Exactly the questions I want management teams to ask about their own businesses.

Understanding history doesn’t mean assuming it will repeat itself.

It means learning from what has happened, recognising patterns, questioning assumptions and making better-informed decisions about what comes next.

That’s why I believe understanding historic financial performance is so important.

After all, if you don’t properly understand how you got to where you are today, how confident can you really be about your plan for tomorrow?

If you would like to find out more, please contact me, Jason Soars, Fractional FD, [email protected], 07894641231, www.linkedin.com/in/jasonsoars/