What (Almost) Everybody Gets Wrong About Digitalisation

David Fullerton
November 24, 2021

Everyone is talking about digitisation, but very few can tell you precisely what the term means. Fewer still can tell you of their success stories. With very good reason.

Imagine this scenario: A company decides to work out a digital way of registering how many crates are found throughout its seven locations at a given time. The containers should be registered by size and content. The digital developers hired to do the job is given a minimal budget and told to complete the task within six months. The result: an app that barely works, and none of the employees like using.

The result: an app that barely works, and none of the employees like using.

Another company goes through the same process, but instead of developing everything from scratch, it deploys an intuitive, visual platform that can be adapted to various uses and is as simple to use as taking a photo with a smartphone.

This example might have some readers wincing because it brings back painful memories of similar digitisation projects in their companies or organisations.

Time and time again, we see digitisation projects either miss their goals or fail completely. Why? A big part of the reason is a lack of clarity about what digitisation means – and how the wrong approaches force people to adapt to technology instead of the other way around.

Failure From the Starting Point

In a 2019 note, McKinsey details how up to 70% of transformation projects fail. Similar statistical success rates (or more currently failure rates) apply to digital transformation and digitalisation projects.

Core reasons include a lack of leadership and employee ability to drive their transformation. Furthermore, if the capabilities are found in-house, it often resides in people’s heads – people who have other tasks or even other jobs. Finally, a lack of buy-in can seriously hamper the efforts.

I would argue that this argument gets things front to back, as it were. The problem is more often that companies and organisations start at the wrong point. I am often reminded of a quote from the Japanese robot scientist, Dr. Ishiguro: “Humans’ speed of evolution cannot keep up with technology’s, so it must be the technology that changes.”

“Humans’ speed of evolution cannot keep up with technology’s, so it must be the technology that changes.”

By that, I mean that digitisation all too often makes things too complex and asks employees to adapt their behaviour to new technologies instead of the other way around. Instead, we should expect digitisation efforts to make the best possible use of existing technology.

Keep it Simple (But Not Stupid)

I would argue that to have REAL digitalisation, companies need to keep things as simple as possible and make as good use as possible of existing equipment.

One example is when looking at measurements and quality documentation. Some approaches need new, specialised hardware and other equipment to function. More often than not, all of this requires training and oversight to ensure that it is used correctly.

If you are going to be doing documentation, why not have a solution built around pictures? Today, everyone has a smartphone. Simply asking them to snap some shots when they complete a particular task takes them no time and is already a familiar task.

The same applies to processing the data that digitisation projects generate. To stay with quality documentation, many people will know the pain of having PDF-files, JPG-images, Excel spreadsheets and Word documents flying around in a mess of emails and text messages.

As elsewhere, I think that companies need to pursue a mix of automation and platforms. Data should be processed automatically, or at least semi-automatically. Furthermore, it needs to happen through a centralised platform that is easily accessible.

This focus on simplicity does not mean that you end up with a ‘dumb’ system. On the contrary, once data gathering is streamlined and simplified, and all data is in a central platform, it can be explored to create insights, understanding and efficiencies. This is where, for example, technologies like AI and big data analytics can shine.

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