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The CFO as the ultimate change agent

Anthony Stevens is the co-author of Chasing Digital: A Playbook for the New Economy (Wiley), and founder and CEO of Digital Asset Ventures, a digital strategy and software development company. Digital Asset Ventures’ technology expertise is concentrated in three key areas: distributed ledger technology, artificial intelligence, and big data and data networks.

A colleague of mine once said, ‘Never let precision exceed accuracy.’What does that mean exactly, and does it relate to you as a CFO?

Let’s look at an example. If you are looking at a softwaredevelopment based investment, future sales and adoption can be exceedingly hard to measure, especially if it’s not your core business, and you are working with a limited set of data or user behaviours to help model growth.

At the same time, you need to determine whether a $1 million technology investment is likely to generate a return (by reducing costs, opening new markets, introducing a new product, and so on). It’s a common problem.

Quite often, a gigantic spreadsheet and modelling team gets rolled out. Big mistake.

The problem is that no matter how much science is ploughed into the spreadsheet, there’s inevitably a massive let-down when the hockey stick graph is never realised.

So how, then, do you ensure investment aligns with some prospect of growth? In short, you need to go big and go small, but ignore the in-between.I’ll explain what I mean by that soon.

But first, allow me to share with you the key focus areas of this article.

It should be noted that not all areas in a business are within the purview of the CFO. However, without the CFO as a change agent for the company, innovation and growth will stall.

With that in mind, there are three main areas I’ll cover in this article:

1. Innovation and its relation to growth
2. Innovation governance
3. Risk management

Innovation and its relationship to growth

It all starts with an understanding of innovation and growth. What drives each of these and how are they related?

Innovation drives growth, but what is innovation?

According to author and innovation expert Clayton Christensen, there are three types of innovation you can use to describe the nature of an investment:

1. Disruptive innovation. This type of innovation is desirable in capital-rich economies as it generates economic growth and job opportunities in exchange for (often risky) investments. Disruptive innovation can unlock huge amounts of value not only at the company level, but also on a global scale. For these innovations to flourish, however, they need capital.

2. Sustaining innovation. In contrast to disruptive innovation, sustaining innovation is the process of fine-tuning your company’s business by making a good product or service better, rather than generating new buyers or new economies. These types of innovations are very important for maintaining or increasing margins relative to the competition.

3. Efficiency-based innovation. Last on the list is efficiency innovation, or doing more with less – a specialty of countries like Japan and China. This type of innovation increases free cash flow but cuts jobs, often dramatically.

A framework for investment governance

Now that you understand the three different types of innovation, and how they drive growth, let’s move on to investment governance itself.

As I stated earlier, to ensure investment aligns with some prospect of growth, you need to go big and go small, but ignore the in-between. Allow me to explain.

Go big

Identify the size of the market, the average margins in the business you are operating, and the current growth rate. This will give you a sense of the size of the market and the likely returns if things go well.

The question at this stage is one of strategy and alignment with your overall investment thesis and parameters. This means asking, ‘Would an investment of this nature be accretive to our business and financial performance?’

Go small

This is the tricky bit and can be unsettling. The question to ask is, ‘What do we need to do in order to prove out the economics of this business or investment case? And what metrics best reflect some success and, ultimately, product-market fit?’

For example, if you are launching a new software app, what investment do you need to support the initial 100 signups? The trick is to keep this very immediate (early on) and very measurable. The point is that if the first, say, 100 signups are too hard to achieve, then the project will fail no matter what.

Ignore the in-between

So here’s the rub and where people get caught. Especially in the world of software, too many finance leaders expect unit economics too early on. It’s the precision exceeding the accuracy.

In reality, people just don’t know – the complexity and drivers of elements like support, rates of growth, which services will be adopted, payment cycles and so on are just too hard to map out. The upshot here is that the unit economics should be developed once there’s some real data to support the modelling effort. Otherwise, it’s just likely to be (bad) guesswork.

A perspective on risk management

The final piece of the puzzle is risk management. Through the process of digital transformation, there’s a fine line between empowering and inspiring confidence, and creating agility and protecting the obvious risks of your company – reputation, financial loss or impairment, or business continuity.

Also, too many CFOs focus on stuff that ultimately doesn’t matter – procurement and vendor selection, and nuanced issues like open-source software licensing agreements.

The overall theme here is that CFOs need to focus on the demand-side of the business, not supply. It’s an overarching theme in today’s economy, and is highly relevant when it comes to investment decision making.

Anthony Stevens is the co-author of Chasing Digital: A Playbook for the New Economy (Wiley), and founder and CEO of Digital Asset Ventures, a digital strategy and software development company. Digital Asset Ventures’ technology expertise is concentrated in three key areas: distributed ledger technology, artificial intelligence, and big data and data networks.

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