Interview with the Author: Dr. Martin De Saulles on AI and the Data Revolution

I had the pleasure of interviewing Dr. Martin De Saulles about his book The AI and Data Revolution: Understanding the New Data Landscape. The interview is below.  

Please introduce yourself to our readers.

I have worked in marketing, consulting, and academia for over 35 years. In the 1990s, when managing a research center for a management consulting firm, I specialized in the rapidly growing telecoms and media sectors as well as completing a PhD on innovation in the retail sector.  

Following some time as a telecoms industry analyst, I moved into academia where I taught modules on information management, marketing, and innovation.  

Since July 2024, I have been a freelance writer and technology analyst based in the UK. My focus is on the ways businesses actually use technology and the impact this has on productivity and innovation. 

Briefly summarize The AI and Data Revolution: Understanding the New Data Landscape.

My latest book, The AI and Data Revolution, chronicles the evolution of the data industry and, more recently, AIs and the ways they have evolved in terms of their use by organizations, monetization by businesses, and regulation by governments. The book applies established business and innovation frameworks to help understand why these changes have occurred and how they might evolve in the coming years.   

It is primarily a textbook for undergraduate and postgraduate students of business, information management, and computing, but written in an approachable style that, I hope, appeals to a broader audience. The changes we are currently witnessing affect us all and understanding what is driving them from a socio-technical perspective is important. 

Why did you decide to write this book?

I have written several books over the previous decade on innovation applied to the internet revolution and its impact on how organizations use data. The current wave of innovation around GenAI and its potential for kick starting a new revolution in information creation and usage seemed like a good opportunity for a book that explores what was going on and how it might play out over the coming years. 

Having researched and written about information and communication technologies (ICTs) over the last 30 years, it now feels to me a lot like the period in the mid to late 1990s when the potential of the internet was exciting a lot of people. Of course, in these situations, there are a lot of unreasonable expectations and hype, particularly from vendors, but at the heart of it there are real and important changes taking place. 

Why is understanding the data landscape important?

The current wave of AI innovation is enabled by the previous 30 years of data creation and distribution brought about by the internet and the development of the World Wide Web (the web). The current frontier models released by OpenAI, Anthropic, Meta, and Google were trained on data harvested primarily from the open web.  

The tensions this is generating with content creators and platforms that rely on advertising to generate revenue are growing—and threatening to undermine their business models. We are seeing some realignment amongst content owners, including news providers as well as social media platforms that have acted as aggregators for content creators. 

Data ownership, as far as it can be protected, will become a key resource for those building large language models (LLMs) and developers creating applications that sit on top of them. 

I suspect that the internet (and how we use it) will look very different in 5 years’ time. Anyone whose work centers on finding and managing information needs to be aware of these changes and consider what they mean for their jobs and organizations.  

How can information professionals use the current data landscape in their work?

I think the changes we are seeing offer lots of opportunities for information professionals. I was a researcher in a consulting firm in the 1990s and saw the impact that the internet had on how we found and used information over that decade. One of the key changes was our end users, i.e., consultants, using the open web to find information themselves rather than solely relying on people like me to search across specialist databases. Part of my role shifted to educating users on how to construct better searches and why proprietary databases were often still the best place to find what they needed. I can see the need for information professionals to continue acting as educators within organizations, to show users how to construct effective prompts when using LLMs, for example, and to help them understand the limitations of these models. 

In this sense, training will be especially important, particularly while the AI models and applications go through the rapid phase of innovation we are currently witnessing. Knowing which models are best suited to specific needs and incorporating corporate data assets into the mix will be key skills for information specialists to develop.  

What are two things you hope all readers take away?

The current wave of AI-driven innovation is part of an evolving set of innovations resulting from the combination of technological advancements, commercial factors, and public policy decisions. There is a tendency amongst many tech-enthusiasts to assume that innovation is driven by the smart graduates of universities like Stanford and supported by the funding ecosystems found in Silicon Valley and other centers of venture capital. There is something in this—but the underpinning technologies powering the business models of Google, Meta, Apple, and Microsoft are built on research and innovations often hatched in public research centers and from government funding. The internet, the web, and many of the open protocols powering them were not created with financial returns in mind. 

Understanding how these factors shaped our current data and AI landscape is key if we are to predict how they might evolve in the future. This is particularly true with GenAI as its underpinning technologies become cheaper and more accessible to developers around the world.  

The second thing I hope readers take away is that we are at the early stages of the current AI and data revolution. New models and ways of manipulating data are released almost every day. The recent release of the Chinese DeepSeek model shook many in the field as it showed what can be done with far fewer resources, both financial and technical.  

While there is a lot of hype around the potential of AI to transform many aspects of our lives, it is not yet clear what this will look like in practice. Understandably, some organizations are cautious about entrusting their data and established business processes to new technologies. As with any new technology, AI systems must be implemented with care, particularly around data integrity and security, to ensure trustworthy outcomes. 

Is there anything else you would like to share?

I strongly believe we are at the beginning of a revolution in how data is created and managed. Organizations with access to unique data resources—and the skills to leverage AI to extract value from these resources—will benefit through substantial competitive advantages. 

Businesses need to start thinking very carefully now about what data assets they currently have and how they might create new ones in the future. This might be looking at analytics they collect from their online activities, as well as customer and supplier data, and data generated internally from business operations. Building the skills internally to apply appropriate AI technologies to this data is the next phase. Experimentation in a controlled environment will be key, but the potential rewards could be substantial.  

It is an exciting time for anyone who manages information. 

If anyone would like to discuss any of these issues, please feel free to contact me on LinkedIn or through my website.