Digital transformation brings constant change to all aspects of an organization. Addressing this requires resilience. The starting point for AI-driven transformation is to ensure data resilience. In addition, success requires adopting a six-point ‘digital resilience’ framework to address uncertainty and thrive in a rapidly evolving digital landscape.
Surrounded by the instability and uncertainty that is prevalent in many sectors of the digital economy, organizations are forced to accept that an ability to recognize and manage change is more important than ever. Many of these changes involve small adjustments to current ways of working. However, as organizations adopt digital technologies to improve their core operating processes, they also look to make more fundamental shifts across all their business activities. By encouraging a more disciplined approach to digital transformation, they seek longer term systemic change aimed at revolutionizing the organization’s structure, strategy, skills and systems.
For many organizations this is nothing new. To a large degree, all management is change management. And as Robert Schaffer has argued, leaders should view change not as an occasional disruptor but as the very essence of their management job. However, traditional change management often considered change as being detached from ‘normal’ management tasks, treating it as a separate process that takes an organization from one stable state to another. In digital transformation, where change is constant, such a perspective on change management can be very limiting. Instead, it must be considered the essence of management, with implications on all of an organization’s activities.
Yet, it takes no more than a cursory review of large-scale digital transformation efforts to recognize that managing change is hard. Recent experiences such as those aimed at digitally transforming the UK’s tax system reveal that the struggle to stay on top of the broad impacts of change can overwhelm even the most well-designed strategies. How can organizations define a meaningful approach to change that allows them to adapt to current changes and prepare for the unexpected? An answer may be found by placing a fundamental focus on understanding and strengthening digital resilience.
Perspectives on digital resilience Creating a strong plan is all very well. But, as often quoted, no plan survives first contact with the enemy. Hence, resilience plays a critical role in the success of any digital strategy. In the context of digital transformation, it is this resilience that determines the ability of an organization to adapt, recover and thrive in the face of unexpected challenges, disruptions or changes in the digital landscape. Its importance was highlighted throughout the Covid crisis, when companies that had invested in a robust digital infrastructure, cloud-based tools and secure remote access capabilities experienced smoother transitions to remote work setups and were able to continue serving customers and generating revenue. Throughout those difficult times, the importance of resilience for business continuity was clear, and this has not diminished. In fact, it has grown with the continued pressure to find new ways of serving customer needs in an era disrupted by significant economic, political and financial instability. In December 2022 this was recognized by McKinsey in an extensive survey that showed that digital resilience was a priority for the majority of the organizations who responded. Rapid deployment of solutions based on generative AI is only serving to increase this focus. But what does it mean to be resilient in the face of the kind of disruptive digital change we are experiencing with AI? The starting point for responding to this question is to examine the role of data as the foundation for AI. Data is the fuel for AI, and the utility of AI is directly related to the quality, accuracy and availability of that data. A resilient approach to the way data is gathered, stored, managed and maintained is essential.
Data resilience Smarter approaches to data-driven decision-making require organizations to build the capabilities needed to bring together multiple data sources, filter out errors in the data, extract meaningful insights from repeated patterns, and so on. Establishing a broad approach to data resilience enables the data-driven insight at the heart of machine intelligence (MI). It is the combination of capabilities provided by MI that transforms so much data into genuine sources of new value. It can be seen as a core capability for the digital economy. MI holds out the promise of being able to make sense of large volumes of data by exploiting a combination of machine learning and AI to yield entirely new sources of value. It encompasses natural language processing, image recognition, algorithmic design and other techniques to extract patterns, learn from these by assessing what they mean, and act upon them by connecting information together.
MI is inevitably disruptive by nature. Hence, it is essential to recognize that MI and its associated digital business models may pose significant challenges, which can be addressed in the following ways.
Changing the way data is collected and processed. It is important to move away from localized databases associated with specific applications and form larger data lakes that can be exploited by new layers of intelligence essential to MI success.
Ensuring a flexible, scalable technology infrastructure across your organization. Business success requires integrating the many applications that constitute a complex set of workflows by using open, component-based techniques as well as connected platforms such as those provided by AWS, Google, Microsoft, IBM and others.
Tackling cultural barriers across the organization. Previous technology investment often constrained thinking and encouraged business leaders to cling on to ageing business models and supporting processes. New thinking is required. While many of these changes will be ongoing, MI-based innovations will inevitably put stress on existing organizational structures. Leadership is always a critical element of any major organizational change, and until the key business leaders are convinced of the need for radical change, little progress will be made.
Companies as diverse as major technology providers, large-scale business-to-consumer services providers and industrial business-to-business solutions providers are already seeing the impact of such changes, illustrating that effective progress can be made when the corporate culture is receptive to new ideas.
Further progress requires a clear plan. Business leaders and management should consider exploiting current technology-driven developments through three categories of activity: research, experimentation and execution. These three prongs are central to a robust data resilience strategy, and a range of different approaches to them are adopted by the most successful organizations.