Any long-time participant in the world of digital transformation will have seen the ebb and flow of technology trends many times. But nothing has quite captured the imagination – and anxiety – of business leaders, technologists, and the public like artificial intelligence (AI).
In recent years, we’ve seen AI emerge from the shadows as a transformative force across industries, promising to revolutionise business practices and drive significant economic benefits. Wild claims by technology leaders come as no surprise. However, when these are supported by economists, social commentators, and politicians, then it is time to sit up and take note.
Yet, despite the enthusiasm from AI advocates, a troubling reality with broader AI adoption is emerging. When engaging with organisations worldwide, you’ll notice a growing disconnect between the initial excitement surrounding the theory of AI’s impact and the realities of its implementation in practice. It’s a gap we’re increasingly seeing highlighted by business leaders and technology commentators. Is this a temporary stumbling block facing AI, or are we experiencing another disappointment for large-scale digital transformation?
Delivering AI at scale
To dig deeper into these issues, an important starting point is to recognise that advances in AI adoption are taking place within a much broader digital transformation context. The recent progress with AI-based tools and technologies is occurring after decades-long digital transformation efforts in most organisations. A wide variety of digital solutions have been brought into play requiring significant upheaval across every aspect of the organisation.
Many of these changes involve small adjustments to current ways of working. However, as organisations began to adopt digital technologies to improve their core operating processes, they also have been forced to make more fundamental shifts across all their business activities. By encouraging a more disciplined approach to digital transformation, they have sought longer-term systemic change aimed at revolutionising the organisation’s structure, strategy, skills and systems.
“It takes no more than a cursory review of large-scale digital transformation efforts to recognise that managing change is hard”
Alan Brown
For many organisations, adapting to digitally-driven change is nothing new. Indeed, it can be argued that all management is change management. Commentators such as Robert Schaffer believe that leaders should view change not as an occasional disruptor but as the very essence of their management job.
However, traditional change management approaches often considered coping with disruption as being detached from “normal” management tasks, treating it as a separate process that takes an organisation 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 organisation’s activities.
Yet, it takes no more than a cursory review of large-scale digital transformation efforts to recognise that managing change is hard. Recent experiences such as those aimed at digitally transforming key aspects of UK government services reveal that the struggle to stay on top of the broad impacts of change can overwhelm even the most well-designed strategies.
How can organisations define a meaningful approach to change that allows them to adapt to current AI-fuelled 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 organisation to adapt, recover and thrive in the face of unexpected challenges, disruptions or changes in the digital landscape.
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 organisations 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 recognise 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 localised 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 organisation. 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 Amazon Web Services, Google, Microsoft, IBM and others.
- Tackling cultural barriers across the organisation. 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 organisational structures. Leadership is always a critical element of any major organisational 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.
The six faces of resilience for AI
However important, data resilience on its own is insufficient. Digital transformation relies on a complex stack of technologies and practices to support change across the enterprise. In practice, we can identify an additional six distinct faces of resilience that must be addressed to ensure the success of delivering AI at scale.
- System resilience. Architecting systems and solutions to be fault tolerant, adaptive and able to fail gracefully when operating incorrectly or compromised.
- Cyber resilience. Ensuring that systems and data are protected from external threats and that information is exposed only through appropriate secure mechanisms.
- Information resilience. Creating governance and management processes for data to ensure that all information is accurate, appropriate and responsibly sourced.
- Organisational resilience. Establishing management and decision-making practices that enable rapid actions to be taken while conforming to all necessary laws, standards and guidelines.
- Operational resilience. Continuing to perform as expected as the operating environment changes, systems are degraded or stakeholder demands expand.
- People resilience. Supporting all employees and other stakeholders to perform at their best in the short term while sustaining their health and wellbeing over the longer term.
All six of these perspectives on resilience are important considerations when moving to AI at scale, and together they form a framework for organisations to review their ability to manage change and sustain high performance in the context of the kinds of digital transformation that are being experienced with AI. Bringing these six angles together provides a holistic view of the challenges to be addressed, taking into account the broad impact of digital transformation in the age of AI.
Bend, don’t break
Based on such experiences, there are many ways in which resilience is found to be central to a successful AI-at-scale delivery strategy. To improve how digital transformation activities can become more resilient to change, the six perspectives listed above can be used to ask five key questions of any digital strategy.
How prepared are we to adapt to change?
The digital landscape constantly evolves, with new technologies, market trends and customer expectations emerging all the time. A resilient digital strategy enables organisations to quickly adapt to these changes by being flexible, agile and responsive. It allows businesses to seize new opportunities, reorganise resources to adapt to changing circumstances, and mitigate risks effectively.
How well do we manage the risks associated with change?
Resilience helps organisations identify and manage the risks associated with their digital initiatives. This includes assessing potential vulnerabilities, implementing robust security measures, and establishing backup plans in case of disruptions such as cyber attacks, system failures or natural disasters. A resilient digital strategy considers risk mitigation as an integral part of its implementation.
What processes do we have in place to ensure continuity and recovery from disruptions?
Resilience ensures business continuity by enabling an organisation to recover swiftly from disruptions. It involves having backup systems and redundancies in place to minimise downtime, data loss or customer impact. A resilient digital strategy incorporates disaster recovery plans, backup solutions and proactive monitoring to swiftly address any disruptions and restore normal operations.
Where can we improve customer trust and satisfaction in how we manage change?
Resilience is crucial for maintaining customer trust and satisfaction across all digital channels. When organisations provide uninterrupted services or products to their customers, it enhances their reputation and fosters customer loyalty. Resilience ensures that customer expectations are met even during unforeseen circumstances, which is crucial in today’s interconnected and competitive digital landscape.
How do we encourage positive change to drive innovation and growth?
Resilience empowers those within an organisation to experiment and innovate, and to pursue digital transformation initiatives with confidence. It encourages a culture of learning from failures and setbacks, fostering a mindset of continuous improvement. A resilient digital strategy promotes exploration of new technologies, business models and opportunities for growth while enabling organisations to recover quickly from any setbacks along the way.
Toward an AI perspective on digital transformation
With no end in sight to the disruption and uncertainty we face in today’s digital economy, resilience is an essential component of every successful AI-at-scale strategy. It enables organisations to navigate uncertainties, adapt to change, manage risks, maintain continuity, build customer trust and foster innovation.
As adoption of AI accelerates, ensuring data resilience is an essential first step. In addition, digital strategies must be tested against at least six perspectives on resilience: system, cyber, informational, organisational, operational and people. By incorporating resilience into digital initiatives, organisations can position themselves for long-term AI delivery success 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. He has spent more than 30 years in the US, Europe and the UK driving large-scale software-driven programmes with commercial high-tech companies, leading R&D teams, building state-of-the-art solutions and improving software product delivery approaches. He is a fellow of the British Computer Society and recently completed his role as a fellow at the Alan Turing Institute, the UK’s national institute for data science and AI.
