In a world where AI is increasingly shaping the future of industries, the key to gaining a competitive edge lies not just in the algorithms that give rise to the AI, but in the quality, diversity, and integrity of the data that powers those algorithms.
The world is not short of data. Thanks to the rapid digitization of our economies and growing prevalence of smart devices, the amount of data produced globally is large and rapidly increasing. This represents a huge economic opportunity. The European Commission estimates that the EU’s data economy alone will be worth €829 bn in 2025, accounting for around 6% of regional GDP.
Yet many firms struggle to gain access to the high-quality data that is needed to train generative AI models. Those that rely on off-the-shelf AI solutions find themselves at a disadvantage since these generic models lack the tailored insights and specific nuances necessary for a true competitive edge.
Thus, organizations looking to get ahead need to train their AI with proprietary, unique data that will reveal insights that none of their competitors can gain (simply because they don’t have that proprietary data). In other words: those who control proprietary, high-quality data will control the AI systems that generate the most value.
How to train your algorithm
The training of an AI with proprietary, unique data can be done either by fine-tuning existing models or by developing proprietary ones from scratch. Fine-tuning involves adjusting pre-trained models on specific data relevant to an organization’s unique needs, allowing for more accurate, business-specific outcomes. Developing proprietary AIs from the ground up with specialized datasets implies building the AI exclusively within the boundaries of the organization, using proprietary data and algorithms.
However, the amount of data required to fine-tune an existing AI, let alone to train a proprietary one, is non-negligible, and thus beyond the reach of many small, medium, or even average-sized organizations. This represents a challenge for companies willing to harness the power of AI to gain a competitive advantage, as they often find themselves in a position where they simply cannot get the data they need to better compete. To use a mechanical analogy, they may have the car, but they lack the fuel to be able to drive it.
Even some of the world’s most prominent generative AI companies like OpenAI, the company behind ChatGPT — which until now have acquired large parts of their data from scraping the web — may find it increasingly hard to get their hands on this valuable resource. OpenAI is facing a slew of copyright challenges, while owners of intellectual property are starting to take proactive steps to protect their content.
Monetize the insight, not the data
Against this backdrop, a new approach is emerging that allows companies to improve their AI capabilities through collaborative efforts, while complying with strict data privacy standards. Instead of amassing reams of data to feed the algorithm, you can bring the algorithm to the data.