Innovations rooted in artificial intelligence (AI) and data within one area of a business often serve as a catalyst for broader transformation across other parts of the organisation. For instance, innovations in data security are no longer just a defensive measure; they can enhance advances in product development processes, and, in turn, help accelerate a company’s overall long-term growth trajectory.
We see that corporations that employ in-house data engineers, such as those developing antivirus software, are often better equipped to leverage this technology expertise in the creation of new, data-driven business services elsewhere in the organisation. This phenomenon, known as data complementarity, enables firms to repurpose internal capabilities to fuel innovation. This means that these organisations are uniquely positioned to transform operational challenges into strategic opportunities and, as a result, can gain a considerable competitive edge in the digital marketplace.
Recent research that I conducted with colleagues on Data Innovation Complementarity and Firm Growth highlights the transformative impact of these data-feedback loops. These mechanisms enhance productivity, strengthen a firm’s competitive edge, and help solidify its position in an increasingly data-driven economy.
One notable example is Amazon, the U.S. tech giant, which patented its data-protection technology, which was originally developed to securely transmit financial data. This innovation later became the foundation of its groundbreaking ‘1-Click’ ordering system, now a hallmark of Amazon’s operational efficiency and a game-changer in customer experience, setting new standards for seamless online transactions worldwide.
Virtuous cycle
Our research has shown that there is a virtuous cycle in which advances in data security innovation act as a catalyst for broader innovation across multiple areas of the business, ultimately fuelling corporate growth.
Large corporations, particularly tech giants, are increasingly capitalising on this dynamic, leveraging strong data complementarity to accelerate their competitive advantage. In contrast, smaller firms that lack these capabilities risk falling behind, contributing to a widening innovation and competitive gap across the corporate landscape.
Organisational structure
Maintaining a flat organisational structure is essential, as it promotes the cross-fertilisation of ideas, expertise, and problem-solving approaches, along with extensive communication between departments. Importantly, within the realm of data security, individuals in an IT department driving innovation in one area can assist other departments with data-driven innovation projects in areas such as marketing, sales, or customer service. Therefore, a data security programmer can also develop services for other departments within the firm. These benefits are not restricted to tech companies: we also see these positive spillovers in the financial industry and manufacturing, particularly in companies whose boundaries have significantly evolved over the past two decades.
The role of regulation
In many cases, innovation around data has been due to regulation – meaning that where companies have to innovate in order to comply with legislation, in the process they transform how other business services function. As shown in our recent research on AI Investment and Climate Policy, this is particularly true with respect to climate policies and AI investments.2
When climate regulations are clearly defined and robustly enforced, corporations tend to strategically reallocate their AI development operations to regions where clean and renewable energy is readily available. This shift not only enables compliance with environmental standards but also encourages the adoption of more sustainable IT practices. In doing so, these firms inadvertently contribute to the broader decarbonisation of the digital economy. As a result, a powerful synergy emerges: climate policy steers AI towards greener pathways, while advances in AI accelerate the energy transition by optimising clean energy systems and reducing inefficiencies.
This alignment sets the stage for a powerful and positive feedback loop – one in which the expansion of clean energy supports the rapid advancement of artificial intelligence, and AI technologies, in turn, play a critical role in accelerating climate action across industries.
Conclusion
A firm’s organisational structure plays a vital role in amplifying the impact of data innovation across its operations. By facilitating the flow of know-how and expertise across departments, organisations can unlock new opportunities for data-driven innovation. Perhaps, most importantly, these benefits are not exclusive to large tech firms; smaller companies and emerging players can also leverage internal collaboration to strengthen their competitiveness and thrive.
At the same time, well-defined and robust regulations can serve as powerful catalysts for AI innovation as they can encourage companies to transition toward cleaner energy sources and embrace more sustainable digital practices. Finally, in doing so, AI accelerates the broader energy transition — establishing a virtuous cycle in which technological progress and climate action reinforce each other.
