Implementing digital transformation strategy presents many challenges. These can range from having to manage massive revamping of business and work processes to implementing completely new models for customer engagement. Some challenges are understood but still difficult, such as dealing with legacy technology and infrastructure. Many other challenges simply can’t be foreseen, yet the organization must be prepared to deal with them.
This dynamic of modernizing and future-proofing in parallel is difficult, and traditional methods of strategizing are not holding up well. Due to this, many organizations are making missteps as they work through their digital transformation journeys. ARC has identified three common contributors to poor outcomes in digital transformation strategy development and execution.
Misstep #1: Digital Possibilities Without Direction
When considering digital transformation, one must start with a seemingly straightforward question: what is it? ARC defines it as the transformation of industrial products, operations, value chains, and aftermarket services that are enabled through the augmentation of people and knowledge, through the expanded use of sensors, data and analytics, automated and crowdsourced.
When viewed through that definition, there are many ways to achieve digital transformation. A few examples include employing:
- New ways to maintain operating assets
- New operating tools, techniques, and procedures
- Digital simulation and AI-based product design
- Different sourcing mechanisms and procedures
- Dynamic digital knowledge bases and virtual experts
- Selling/buying outcomes instead of products
- Dynamic relationships with customers, instead of one-time sales
The myriad opportunities involved require some means to filter decision making on which path(s) to take, why, and in what order. Without cohesive direction, leadership lacks the ability to galvanize the organization around what change means. Unfortunately, that often leads to the digital transformation strategy devolving into the pursuit of technology.
Misstep #2: Implementing Digital Transformation Strategy Becomes Pursuit of Technology
As organizations contemplate approaches to digital transformation, many of those tasked with implementing the transformation are asked to explore concepts, technologies, and methods outside their areas of expertise. A natural line of thinking arises: a technology or set of technologies (e.g., platform) can be identified and purchased to drive all or most aspects of change.
As a result, conversations become technology-centric. Digital transformation turns into the pursuit of the silver-bullet solution or a proof of concept of the latest-and-greatest technology. Striving to get a potentially high-risk decision right, organizations look to compare solution techniques, tools, and technology architectures in “apples-to-apples” ways even when that’s not possible.
Misstep #3: Focusing on the Business Need Without Addressing Cultural Barriers
If the organization moves beyond the non-productive, “hamster wheel” pursuit of technology, it ends up in a better place. It figures out, often with the help of vendors, how to view digital transformation in terms of a defined, and usually narrow business need.
This defined business need becomes the stepping-off point for digital transformation, which leads to organizations pouring energy into figuring out how to scale from this starting point. ARC is now seeing a large segment of the market in this state.
The business need focuses on specific use cases, processes, people, data, measurement, and return. While this is certainly a step in the right direction, it still contains a critical flaw. As the business need is designed into a pilot, inevitable cultural barriers arise. They could be around data, people and working groups, scope, governance, etc. Rather than deal with them, organizations simply avoid them, thus constructing artificial limits on innovation.
Digital Transformation Strategy Begins by Assessing Capacity for Change
If the organization focuses too early on the business need in the digital strategy process, it limits its ability to confront organizational barriers that impede sustainable change. If it doesn’t address them up front, it simply becomes too easy to brush aside or work around entrenched cultural barriers. By first assessing the organization’s capacity for change, the cultural barriers will become evident to all prior to the business need being defined.
As any business need is then surfaced and addressed, the organization has a clearer view as to whether the cultural barrier must first be dealt with or whether it can be handled effectively downstream. The main benefit of this approach is that executives will have a much informed view of the operational realities of executing the digital transformation.
For example, the impact that digital transformation will have on the workforce is likely to be the most far-reaching and sustained, yet resistance to change is naturally human-centered. In developing a digital transformation strategy, the organization must assess and consider all the many issues associated with engaging its workforce. How can the workforce be positively integrated into change? Are key aspects of the workforce limiting how the company can transform? Should those be dealt with before a specific business need is addressed? In first answering these questions in the initial step of the strategy process, the organization can then inform the business case with these workforce constraints/opportunities in mind.
About ARC Advisory Group (www.arcweb.com): Founded in 1986, ARC Advisory Group is a Boston based leading technology research and advisory firm for industry and infrastructure.
For further information or to provide feedback on this article, please contact RPaira@arcweb.com
About the Author:
Michael’s expertise is in analysis, positioning, and strategy development for companies facing transformational market drivers. At ARC, he applies his expertise to developments related to Industrial Internet of Things (IIoT) and advanced analytics, including machine learning.