Digital Economy Dispatch #203 -- Are We Asking the Right Questions about AI and Digital Transformation?

Digital Economy Dispatch #203 -- Are We Asking the Right Questions about AI and Digital Transformation?
29th September 2024

Every day it seems as though another dozen articles appear telling me how I should be thinking about the future of technology in our AI-driven world. The sheer pace of technological change is both exhilarating and terrifying. We're witnessing a revolution that's reshaping industries, economies, and societies. Yet, why do these articles all sound so familiar and repetitive? Perhaps, amidst all the excitement and uncertainty, there's a fundamental question that we’re overlooking: how do we imagine a future that's likely to be radically different from our present?

There’s no doubt that we tend to view the future through the lens of our past and present experiences. We use our existing knowledge and understanding to frame the way we see the world evolving and extrapolate potential directions. This approach has served us well in many ways, but it can also blind us to the unexpected. Bill Gates comments on this challenge was that we always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. As a result, many of us didn’t foresee the long-term impact of several innovations that now seem obvious: The internet, smartphones, and social media spring to mind. Yet, here we are.

Our inability to reimagine the future is a challenge that Ben Evans highlighted some years ago in one of his excellent observations on technology’s directions and impact. He was exploring how experts can struggle to anticipate the transformative potential of emerging innovations by looking back at a 1964 study from RAND which asked a broad group of world experts their views in an attempt to forecast key developments in fields such as space flight, medicine, and automation. While some predictions were accurate, many others were wildly off-target.

One notable example, the underestimation of the transformative power of general-purpose computing and software platforms, has many parallels with discussions today about the future impact of AI. As Ben notes, from the study in 1964:

“…the interesting thing is how often the order is wrong. What we now know to be the hard problems were going to be solved decades before what we now know were the easy ones.”

For instance, that study predicted that it would take until at least 2020 to "fax" a newspaper to the home. Meanwhile, it also suggested that automatic doctors, radar implants for the blind, and machine translation would be commonplace by 1990. These mis-calibrations are perhaps understandable give the context of what was happening in the world in the 1960s. Existing industries and societal pressures undoubtedly affected the way people saw priorities and drove their expectations for the future pace of change.

However, more obvious in hindsight is the study’s inability to anticipate the development of more radical technology disruption such as the internet, mobile phones, and the widespread adoption of personal computers, which have revolutionized countless aspects of society. At that time, much of the foundation for these advances was starting to come into place. Yet, few people could even conceptualise these technologies to identify their possibilities.

As the RAND study demonstrates, even experts can be trapped by their preconceived notions, leading to inaccurate forecasts and missed opportunities. The challenge of predicting the future isn't so much about defining the applications of known technologies. It's about envisioning the entirely new possibilities that may arise. We're constrained by our current paradigms and assumptions, which limit our capacity to imagine radical departures from the status quo.

As a result, is there a danger that we may miss the most significant questions about AI and digital transformation simply because they fall outside the scope of our current understanding? Take generative GenAI, for example. These tools are already capable of producing human-quality text, images, and even code. Their potential applications are vast and unpredictable. But too much of what I see and hear addresses the same near-term issues and concerns.  Are we spending enough time considering the more radical aspects of such technologies? We see this particularly as we address AI’s broader societal and ethical implications. Many questions must be considered: How will AI-generated content impact the spread of misinformation and disinformation? What are the implications for privacy and data security?

Similarly, the development of autonomous systems, such as self-driving cars and drones, raises a host of questions about safety, regulation, and the future of work. While we're actively exploring these issues, we may be overlooking other, more fundamental questions. How will the widespread adoption of autonomous systems change our relationship with technology and our understanding of agency and control? What are the implications for social equity and economic inequality?

The danger we face today is that we are not focusing sufficient attention on asking the right questions, or we find that we base our response to these key questions on the wrong conceptual frameworks. We see the world through the eyes of the past. To address these challenges, we must force ourselves to break free of these constraints to cultivate a mindset of curiosity and open-mindedness. We must be willing to challenge our assumptions and explore new perspectives.

That’s not easy. It demands that we step out of our comfort zone. It requires a commitment to continuous learning and a willingness to engage with diverse thinkers and disciplines. Yet, it is only by expanding our horizons that we can better understand the potential implications of AI and digital transformation and develop more effective strategies for navigating the future. More fundamentally, it will help us to ask better questions about our digital future – the questions that today are largely unasked.

As digital leaders, we have a unique responsibility to shape the future of technology. Yet, the most important questions about AI and digital transformation may be the ones we are not asking. While we often focus on predicting the future applications of AI and the societal changes they will bring, there is a more fundamental challenge we must confront: our limitations in imagining the future itself.