The Importance of Digital Resilience for Achieving Scalable AI Success

The Rise of AI: Transforming Digital Landscapes

Any long-time participant in the world of digital transformation will have witnessed the ebb and flow of technology trends many times. Yet, nothing has quite captured the imagination—and anxiety—of business leaders, technologists, and the public like artificial intelligence (AI). In recent years, AI has emerged from the shadows as a transformative force across industries, promising to revolutionize business practices and drive significant economic benefits. While wild claims by technology leaders are not surprising, the endorsement of these claims by economists, social commentators, and politicians compels us to take notice.

However, despite the enthusiasm from AI advocates, a troubling reality regarding broader AI adoption is emerging. Engaging with organizations worldwide reveals a growing disconnect between the initial excitement surrounding AI’s theoretical impact and the realities of its implementation. This gap is increasingly highlighted by business leaders and technology commentators alike. Is this merely a temporary stumbling block for AI, or are we witnessing yet another disappointment in large-scale digital transformation?

Delivering AI at Scale

To delve deeper into these issues, it’s essential to recognize that advances in AI adoption are occurring within a much broader digital transformation context. The recent progress with AI-based tools and technologies follows decades-long digital transformation efforts in most organizations. A wide variety of digital solutions have been introduced, necessitating significant upheaval across every aspect of the organization.

Many of these changes involve minor adjustments to existing workflows. However, as organizations began to adopt digital technologies to enhance their core operating processes, they were also compelled to make more fundamental shifts across all business activities. By fostering a more disciplined approach to digital transformation, organizations have sought long-term systemic change aimed at revolutionizing their structure, strategy, skills, and systems.

Alan Brown aptly notes, “It takes no more than a cursory review of large-scale digital transformation efforts to recognize that managing change is hard.” For many organizations, adapting to digitally-driven change is nothing new. Indeed, it can be argued that all management is change management. Commentators like Robert Schaffer suggest that leaders should view change not as an occasional disruptor but as the very essence of their management role.

Traditional change management approaches often treat disruption as a separate process, taking an organization from one stable state to another. However, in digital transformation, where change is constant, such a perspective can be limiting. Change must be considered the essence of management, with implications for all organizational activities.

Perspectives on Digital Resilience

Creating a robust plan is crucial, but as the saying goes, 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, resilience determines an organization’s ability to adapt, recover, and thrive amid unexpected challenges, disruptions, or changes in the digital landscape.

But what does it mean to be resilient in the face of the disruptive digital change we are experiencing with AI? The starting point for this inquiry 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 data gathering, storage, management, and maintenance is essential.

Data Resilience

Smarter approaches to data-driven decision-making require organizations to build capabilities that integrate multiple data sources, filter out errors, and extract meaningful insights from repeated patterns. Establishing a broad approach to data resilience enables the data-driven insights at the heart of machine intelligence (MI).

MI transforms vast amounts of data into genuine sources of new value, serving as a core capability for the digital economy. It promises to make sense of large data volumes by leveraging machine learning and AI to yield entirely new sources of value. MI encompasses natural language processing, image recognition, algorithmic design, and other techniques to extract patterns, learn from them, and act upon them by connecting information.

However, MI is inherently disruptive. Therefore, it is crucial to recognize that MI and its associated digital business models may pose significant challenges, which can be addressed in several ways:

1. Changing Data Collection and Processing: Move away from localized databases tied to specific applications and create larger data lakes that can be leveraged by new layers of intelligence essential for MI success.

2. Ensuring Flexible, Scalable Technology Infrastructure: Business success requires integrating various applications that constitute a complex set of workflows using open, component-based techniques and connected platforms provided by major tech companies like Amazon Web Services, Google, Microsoft, and IBM.

3. Tackling Cultural Barriers: Previous technology investments often constrained thinking and encouraged business leaders to cling to outdated business models and processes. New thinking is essential.

While many of these changes will be ongoing, MI-based innovations will inevitably stress existing organizational structures. Leadership is a critical element of any major organizational change, and until key business leaders are convinced of the need for radical change, little progress will be made. Companies across various sectors are already experiencing the impact of such changes, illustrating that effective progress can be achieved when corporate culture is receptive to new ideas.

The Six Faces of Resilience for AI

While data resilience is vital, it is insufficient on its own. Digital transformation relies on a complex stack of technologies and practices to support change across the enterprise. In practice, we can identify six distinct faces of resilience that must be addressed to ensure the successful delivery of AI at scale:

1. System Resilience: Architecting systems and solutions to be fault-tolerant, adaptive, and capable of failing gracefully when operating incorrectly or compromised.

2. Cyber Resilience: Protecting systems and data from external threats and ensuring that information is exposed only through appropriate secure mechanisms.

3. Information Resilience: Creating governance and management processes for data to ensure that all information is accurate, appropriate, and responsibly sourced.

4. Organizational Resilience: Establishing management and decision-making practices that enable rapid actions while conforming to all necessary laws, standards, and guidelines.

5. Operational Resilience: Ensuring continued performance as the operating environment changes, systems degrade, or stakeholder demands expand.

6. People Resilience: Supporting all employees and stakeholders to perform at their best in the short term while sustaining their health and well-being over the long term.

These six perspectives on resilience are crucial considerations when moving to AI at scale. Together, they form a framework for organizations to review their ability to manage change and sustain high performance in the context of the digital transformation experienced with AI. By integrating these six angles, organizations can gain a holistic view of the challenges they face, taking into account the broad impacts of digital transformation in the age of AI.

Bend, Don’t Break

Based on various experiences, resilience is central to a successful AI-at-scale delivery strategy. To enhance how digital transformation activities can become more resilient to change, the six perspectives can be used to ask five key questions of any digital strategy:

1. How prepared are we to adapt to change?
   The digital landscape is constantly evolving, with new technologies, market trends, and customer expectations emerging. A resilient digital strategy enables organizations to quickly adapt by being flexible, agile, and responsive.

2. How well do we manage the risks associated with change?
   Resilience helps organizations identify and manage risks tied to their digital initiatives, including assessing vulnerabilities and implementing robust security measures.

3. What processes do we have in place to ensure continuity and recovery from disruptions?
   Resilience ensures business continuity by enabling swift recovery from disruptions, incorporating disaster recovery plans and proactive monitoring.

4. Where can we improve customer trust and satisfaction in how we manage change?
   Maintaining customer trust and satisfaction across digital channels is crucial. Resilience ensures that customer expectations are met even during unforeseen circumstances.

5. How do we encourage positive change to drive innovation and growth?
   Resilience empowers individuals within an organization to experiment and innovate, fostering a culture of learning from failures and setbacks.

Toward an AI Perspective on Digital Transformation

In today’s digital economy, where disruption and uncertainty are constants, resilience is an essential component of every successful AI-at-scale strategy. It enables organizations to navigate uncertainties, adapt to change, manage risks, maintain continuity, build customer trust, and foster innovation.

As AI adoption accelerates, ensuring data resilience is a critical first step. Additionally, digital strategies must be tested against the six perspectives of resilience: system, cyber, informational, organizational, operational, and people. By embedding resilience into digital initiatives, organizations can position themselves for long-term AI delivery success in a rapidly evolving digital landscape.

Alan W. Brown, the author of Surviving and Thriving in the Age of AI – A Handbook for Digital Leaders, brings over 30 years of experience in driving large-scale software-driven programs across the US, Europe, and the UK. His insights into digital economy dynamics are invaluable for leaders navigating the complexities of AI and digital transformation.