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Why digital resilience is critical to success with AI at scale

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Why digital resilience is critical to success with AI at scale

Anyone who has been involved in digital transformation for a long time has witnessed the ups and downs of technology trends. However, nothing has captured the attention – and concern – of business leaders, technologists, and the public quite like artificial intelligence (AI).

In recent years, AI has emerged as a transformative force across industries, promising to revolutionize business practices and drive significant economic benefits. While grandiose claims from technology leaders are not uncommon, the support from economists, social commentators, and politicians indicates that AI is poised to make a profound impact.

Despite the excitement surrounding AI, a concerning reality is beginning to surface as organizations worldwide grapple with a growing disconnect between the theoretical potential of AI and its practical implementation. This gap is increasingly being acknowledged by business leaders and technology experts. Is this a temporary hurdle for AI, or are we facing yet another setback in large-scale digital transformation?

Delivering AI at scale

To delve deeper into these issues, it is crucial to recognize that AI adoption is occurring within a broader context of digital transformation. The progress made with AI-based tools and technologies follows decades-long efforts in digital transformation across most organizations. Numerous digital solutions have been implemented, necessitating significant changes across all facets of the organization.

While some changes involve minor adjustments to existing processes, the adoption of digital technologies has also led to fundamental shifts in all business activities. By promoting a more disciplined approach to digital transformation, organizations are striving for long-term systemic changes aimed at revolutionizing their structure, strategy, skills, and systems.

“It takes no more than a cursory review of large-scale digital transformation efforts to recognize that managing change is hard”

Alan Brown

For many organizations, adapting to digital change is not a new concept. In fact, it can be argued that all management is essentially change management. Leaders like Robert Schaffer suggest that change should not be viewed as an occasional disruption but as a fundamental aspect of management.

Traditional change management approaches often treat disruption as a separate process from “normal” management tasks, guiding an organization from one stable state to another. In the realm of digital transformation, where change is constant, this perspective can be limiting. Change must be considered a fundamental aspect of management, impacting all activities within an organization.

However, a brief examination of large-scale digital transformation initiatives reveals that managing change is indeed challenging. Recent efforts to digitally transform key aspects of UK government services highlight the difficulty of coping with the broad impacts of change, even with well-designed strategies in place.

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How can organizations establish a meaningful approach to change that enables them to adapt to current AI-driven changes and prepare for the unexpected? The answer may lie in prioritizing and enhancing digital resilience.

Perspectives on digital resilience

While having a solid plan is essential, as the saying goes, no plan survives first contact with the enemy. Therefore, resilience plays a crucial 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 in the face of unforeseen challenges, disruptions, or changes in the digital landscape.

But what does it mean to be resilient in the face of disruptive digital change, particularly with AI? The foundation of AI lies in data, which serves as its fuel. The effectiveness of AI is directly correlated to the quality, accuracy, and availability of data. Therefore, a resilient approach to data collection, storage, management, and maintenance is imperative.

Data resilience

Smarter approaches to data-driven decision-making require organizations to develop the necessary capabilities to integrate multiple data sources, identify errors in data, extract valuable insights from patterns, and more. Establishing a comprehensive approach to data resilience facilitates the data-driven insights essential for machine intelligence (MI).

MI combines capabilities that turn vast amounts of data into valuable sources of innovation. It is a fundamental capability for the digital economy, offering the potential to make sense of extensive data through machine learning and AI, leading to new sources of value. MI encompasses various techniques such as natural language processing, image recognition, algorithmic design, and more to extract patterns, interpret their meaning, and take action based on the insights gained.

As inherently disruptive as MI is, it is crucial to acknowledge that it may present significant challenges, which can be addressed through the following strategies:

  • Transitioning from localized databases tied to specific applications to larger data lakes that support new layers of intelligence crucial for MI success.

  • Establishing a flexible, scalable technology infrastructure across the organization to integrate various applications using open, component-based techniques and connected platforms offered by leading providers.

  • Overcoming cultural barriers within the organization that have historically hindered progress and encouraged adherence to outdated business models and processes.

While these changes may be ongoing, innovations driven by MI will undoubtedly strain existing organizational structures. Effective progress can be achieved when the corporate culture is open to new ideas, as demonstrated by technology providers, consumer service companies, and industrial solutions providers that have already embraced such changes.

The six faces of resilience for AI

Although data resilience is crucial, it alone is not sufficient for successful AI delivery at scale. Digital transformation requires a complex array of technologies and practices to facilitate change across the enterprise. In practice, there are six distinct faces of resilience that must be addressed to ensure the successful implementation of AI at scale.

  1. System resilience. Designing systems and solutions to be fault-tolerant, adaptable, and capable of graceful failure when operating incorrectly or compromised.

  2. Cyber resilience. Safeguarding systems and data from external threats and ensuring information is only exposed through secure mechanisms.

  3. Information resilience. Establishing governance and management processes for data to guarantee accuracy, appropriateness, and responsible sourcing of information.

  4. Organizational resilience. Implementing management practices that enable swift decision-making while adhering to relevant laws, standards, and guidelines.

  5. Operational resilience. Maintaining expected performance amid changing operating conditions, degraded systems, or expanding stakeholder demands.

  6. People resilience. Supporting employees and stakeholders to perform optimally in the short term while preserving their well-being in the long run.

All six perspectives on resilience are critical considerations when transitioning to AI at scale, collectively forming a framework for organizations to evaluate their ability to manage change and sustain high performance in the digital transformation era driven by AI. By integrating these six facets, organizations gain a comprehensive view of the challenges they must address, considering the broad impact of digital transformation in the AI age.

Bend, don’t break

Based on these insights, resilience emerges as a central component of a successful AI delivery strategy at scale. To enhance the resilience of digital transformation initiatives, organizations can use the six perspectives outlined above to pose five key questions regarding any digital strategy.

How prepared are we to adapt to change?

A resilient digital strategy enables organizations to quickly adapt to evolving trends, technologies, and customer expectations by being flexible, agile, and responsive. It allows businesses to seize opportunities, reallocate resources as needed, and effectively mitigate risks.

How well do we manage risks associated with change?

Resilience helps organizations identify and mitigate risks related to their digital projects, including vulnerabilities, security measures, and contingency plans for disruptions like cyber attacks or system failures. A resilient digital strategy integrates risk management as a core aspect of its implementation.

What processes ensure continuity and recovery from disruptions?

Resilience ensures business continuity by enabling swift recovery from disruptions, utilizing backup systems and redundancies to minimize downtime and data loss. A resilient strategy includes disaster recovery plans, backup solutions, and proactive monitoring to swiftly address disruptions and restore normal operations.

How can we enhance customer trust and satisfaction in change management?

Resilience is essential for maintaining customer trust across digital channels. Providing uninterrupted services enhances reputation and fosters customer loyalty. Resilience ensures that customer expectations are met even during unforeseen circumstances, crucial in today’s interconnected digital landscape.

How do we promote positive change for innovation and growth?

Resilience empowers organizations to experiment, innovate, and pursue digital transformation initiatives confidently. It fosters a culture of learning from failures, encouraging continuous improvement. A resilient strategy encourages exploration of new technologies, business models, and growth opportunities while swiftly recovering from setbacks.

Toward an AI perspective on digital transformation

In today’s digital economy, characterized by disruption and uncertainty, resilience is a critical component of a successful AI-at-scale strategy. It enables organizations to navigate uncertainties, adapt to change, manage risks, ensure continuity, build customer trust, and drive innovation.

As AI adoption accelerates, prioritizing data resilience is a crucial initial step. Additionally, digital strategies should be evaluated through at least six resilience perspectives: system, cyber, informational, organizational, operational, and people. By incorporating resilience into digital initiatives, organizations can position themselves for long-term success in delivering AI in a rapidly evolving digital landscape.

Alan W. Brown is the author of “Surviving and Thriving in the Age of AI – A Handbook for Digital Leaders” published by LPP. Alan is a professor in digital economy, an experienced business executive, and a strategic advisor with over 30 years of experience driving large-scale software-driven programs in the US, Europe, and the UK. He is a fellow of the British Computer Society and recently completed a fellowship at the Alan Turing Institute, the UK’s national institute for data science and AI.

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