Why finance models are outdated in an age of digital transformation

Finance departments like certainty – forecasts, plans, and smooth lines. But in business and software development, it’s impossible, even damaging, to be tied to certainty. This has been evident time and time again in 2020, with the coronavirus pandemic forcing many companies to adapt.

Now is the time for finance teams to change their outlook to support broader agility. Software development is key to innovation and change, but the only thing certain about the process is its uncertainty.

 

How finance typically functions today

Finance forces IT to focus on costs. The cost of staff, running costs, the cost of hardware and software licenses.

When IT focuses only on costs, it focuses on getting a good buy above all else. Eventually, this results in cutting costs, building up a debt of work that should have been done but was too expensive at the time. Think of all those “legacy” applications that are now on out of date software and costly to modernize. While this might help cut costs and balance the books for the finance team, it has the negative effect of creating slow-moving, or completely stalled IT, disrupting plans and timescales for innovation.

 

Why business cases are often so wrong

Annual finance cycles also lead to less informed decision making. . Modelling a ‘business case’ a year out requires many predictions about how customer needs and competitor abilities will evolve over the year. Each year, the Chief Information Officer (CIO) prepares a case for a year’s worth of budget. This task trickles down all the silos of IT, which have to predict what will be needed. To calculate what it requires, IT will need to know what it’s building – all those apps, the infrastructure to support it, and the people to develop and run them. This is predicting what your software deliverables will be, and now we’re right back to a fatal misunderstanding software: thinking that you can predict what the software should be with acceptable accuracy.

Hopefully, those predictions will be right! When your business has to fit into an annual planning cycle, you really only get the chance to learn and adapt from what’s actually happening in your business once a year. Sure, you’ll have  indicators of how things are going each quarter. But “going back to finance” to adjust numbers isn’t an easy, and often not even a possible task. task Finance misses out on the chance to get smarter and more accurate in their numbers as weekly software releases are used by customers.

Over the past 25 years, so many software projects have failed to deliver on expectations that it’s worth looking at a different approach.

 

Starting ignorant can be an asset

Many organizations are finding that much shorter cycles are resulting in better software and finance planning. A smaller cycle means that you can learn from real market validation more frequently, getting smarter each time. In financial planning, this means frequently adjusting the numbers instead of sticking to annual estimates. The goal is to make numbers adjust to reality as you discover it – as you fail your way to success – getting a better idea of what customers want, what they’ll pay, and how to defend against competition

In business and software development, each week when you release your software you become smarter. German financial services company Allianz, for example, found success with 100-day cycles to discover and validate new businesses. Instead of one chance every 365 days to get it right, it has three, almost four chances. As each week goes by, it becomes smarter, there’s less waste and risk, and finance planning gets more accurate.

 

Business cases focused on growth

You truly don’t start learning until you deliver something to market. With more traditional delivery methods, businesses could spend nine months or more defining and then delivering the perfect solution before getting it to market – well, what they think is the perfect solution. That’s time wasted when you could be learning from customers and improving how you do business with those customers. Shorter cycles allow you to make real-world, data-driven business decisions and begin getting a return on investment sooner.

This approach also reduces the risk of the project being a bad market fit. As you experiment with each small-batch release, you learn what customers will and won’t buy, course correcting along the way. By contrast, long release cycles mean you won’t know if the idea is good for at least 12 months – a long time to carry an ever-growing pile of risk.

 

The small-batch imperative

Creating software products in small batches means carrying less risk, getting faster pay outs on investments, and taking advantage of data-driven business development. Finance teams must support this fail-to-succeed cycle. And it should want to: the rich stream of data makes it more responsible in its investments and provides data to support decisions.

The alternative is shuffling back to an old model of IT financing. Business and software development will constantly be driven to be the cheapest provider, likely outsourced, handing over the fate of the business to a third party that profits from finding the cheapest way to fulfil its contractual obligations.

As we’ve seen, industrial-era finance processes are a harmful bottleneck for digital transformation programs at many organisations. The bottleneck can be cleared by understanding the true nature of software, and changing funding models to take advantage of digital-era planning and execution.

 

 

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