The adoption of new digital technologies in response to the pandemic has reached levels that weren’t expected for many year, - and those changes are likely be long lasting. A few months ago, for example, a McKinsey survey of global executives found that the pandemic has accelerated the digitization of their customer and supply-chain interactions, their internal operation, and the share of digitally-enabled products and services in their portfolios by roughly three to four years.
“Faced with the pandemic, firms did things that once seemed impossible - sometimes overnight,” noted Forrester Research in an online event that took place earlier this year. “Under the relentless pressure of new customer realities, the future came into focus: The value of your company depends on how customer-obsessed, resilient, creative, and adaptive you are in jumping to the next growth curve in your industry. … 2021 will be the year that every company - not just the 15% of firms that were already digitally savvy - doubles down on technology-fueled experiences, operations, products, and ecosystems.”
But, what should the vast majority of less digitally savvy companies do to embrace the changes they were forced to make in record time to deal with the immediate crisis? How to Speed Up Your Digital Transformation, a recent Harvard Business Review article by Benjamin Mueller and Jens Lauterbach, provides some answers to this question.
“The pandemic has given many organizations an unexpected crash course in digitalization,” wrote the authors. “While much progress has been made - from hardware and infrastructure to updated work processes and a rejuvenated corporate culture - many organizations are confronting the question of how to integrate fragmented and often makeshift digitalization efforts in a way that’s sustainable. … This is particularly pertinent for small and medium-sized organizations that need to be more targeted in their efforts and may not have the resources to engage in the ‘fail fast’ approach often heralded by the larger poster children of the digitalization movement.”
To understand the difficulties faced by companies going through such a crash course in digital transformation, Mueller and Lauterbach conducted a two-year research study at a leading bank in Europe which was replacing its 30-year old custom-built core banking system with a state-of-the-art, SAP-based system. They closely followed the users and departments most impacted by the transition to the new system, analyzed the challenges these users faced while learning how to work with new digital tools, and conducted over 60 interviews with stakeholders at various levels of the organization, - from employees that had long been working with the bank’s legacy system to the executives managing the transition to the new system.
All the users received identical training and support. But, while users in some departments found that the transition was relatively easy and were able to learn how to work with the new system in a matter of weeks, others struggled for a prolonged period, needing over six months to adapt to the new system.
What accounted for these differences? “We were particularly interested in the contrast between departments that managed to use the new system effectively and quickly and those that struggled for a prolonged period,” wrote the authors. Not surprisingly, the missing piece of the puzzle was complexity, in particular, a kind of so called complexity-in-use that explained why learning a new digital tool was straightforward for some departments while difficult for others, even though they were all using the same system.
Let me briefly summarize the key elements of complexity-in-use. A detailed explanation of the concept can be found in this research paper by the authors.
An IT system aims to capture a digital model or representation of the world, - whether it’s a business like a retailer or a bank, or an application like inventory management or loan processing. Users interact with the various tasks of the system to get their work done. Understanding what a user does requires understanding how a task is structured and how it’s represented in the system.
Tasks and systems consist of surface and deep structures. A task’s surface structure are its interfaces and dependence on the underlying system. The higher the number of tasks in the system, and the more data and algorithms are required to implement each task, the greater the dependence on the overall system, which then leads to increased complexity-in-use.
The tasks deep structure reflect the semantics of the system, that is, the real-world application domain that the system is intended to represent. Semantic dependency increases the more users are required to understand how the business logic is represented in the system to get their work done. System and semantic dependencies complement and reinforce each other. The impact of semantic dependency will be higher the higher the system dependency.
The HBR article illustrates the practical impact of these concepts on users with concrete examples from their bank study.
“For example, one group of clerks used the new SAP-based loan management system to enter new contracts. For them, learning how to do their work with the new system was easy. … the data entry clerks’ task only requires the loan contract data to be represented in the system. Understanding the deeper logic of a loan contract is not required to enter data successfully, nor is understanding how loan contracts are represented or processed in the system. Therefore, learning the system for that specific task is relatively straightforward.”
“In stark contrast, clerks who needed to make edits to loans in stock had a much harder time learning how to work with it. … Beyond just the loan contract data, a significant number of their tasks rely on additional business concepts (e.g., loan status or certain calculation rules) that are represented in the system. These clerks also need to understand what the data means and how it’s being processed in order to make correct edits to the loan. In effect, learning the system is much more complex and effortful.”
What should companies do to speed up their digital transformation initiatives and make their outcomes more predictable? Based on the findings from the bank’s study and feedback from its executives, the author’s recommend three specific actions.
Develop a complexity heatmap that identifies the different degrees of complexity-in-use across the organization. To do so, first determine the relevant tasks and the key features of the new system; then decide which tasks are to be digitalized, their properties and systems dependence and how much business logic is involved; finally, the heatmap can be drawn, including the relative levels of complexity-in-use in the various tasks that are to be digitalized.
Such a heat map would reveal that the low complexity entry clerk’s tasks would show low system dependence and no semantic dependence; and that the high complexity loan-editing clerk’s tasks would show high system and semantic dependence.
Design a step-by-step transformation plan. It’s particularly important to first direct attention and resources toward “quick-win-projects”, that is, projects with relatively low complexity-in-use. These are areas where digitalization investments are likely to pay off quickly, and such quick wins have an important psychological effect. Successful early projects “serve as guiding and motivating lighthouses, enabling a lean approach to transformation management that can be adapted and improved,” helping to recoup early investments and build momentum for more complex efforts later on.
Develop tailor-made transformation measures. One-size-fits-all digital transformations don’t work. Complexity heatmaps help design and direct training efforts based on actual needs. Transformation measures need to be calibrated to the complexity and needs of different areas of the organization, enabling them to direct resources to where they’re needed most to reap the benefits of the new technologies, and successfully transform the company for the digital age.
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