The AI giants, OpenAI, Microsoft, Google, and others, are racing to develop world-dominating AI models. To achieve this, they aim to harness as much data as possible for training. This creates an uneven playing field for other companies trying to build better models, even for niche applications, as these massive AI systems continue to evolve at an astonishing pace.
But that’s not the whole story. There’s still a significant amount of data these tech giants can’t access. In the coming years, we’ll see a surge of AI companies enabling individuals and businesses to leverage their own data alongside public AI models to create better tools tailored to their unique needs.
Take a personal data company, as an example. For years, they’ve championed the idea that “there’s a party that has more data about you than Google, Meta, or Microsoft – and that party is you.” Despite widespread concerns about how much tech giants know about us, this statement holds true. Your life spans so many domains that no single organization (outside of authoritarian regimes) could possibly have a complete picture.
The same applies to businesses. Companies accumulate vast amounts of data from operations, finances, employees, and products.
The Challenge of Data Utilization
The major tech companies have mastered the art of aggregating and utilizing the data they collect, often to our detriment – whether to sell us more, target our profiles, or predict our behavior. While this is usually paired with services that deliver value to users, it often comes at the cost of privacy and control.
On the flip side, individuals and businesses lack the tools to effectively utilize their own data for their own benefit. This challenge stems from several factors:
- Data exists in diverse formats and locations.
- It’s difficult to aggregate and even harder to combine effectively.
- Building models and tools that can fully exploit this data remains complex and costly.
For businesses, tools and services that integrate data from multiple systems have emerged. Yet, these solutions remain expensive and complex, often failing to deliver tangible value. For individuals, the situation is even more nascent. While Apple offers tools for aggregating health data and some finance apps aim to streamline money management, we lack tools that can consolidate all personal data and provide actionable insights for daily life.
AI as the Game-Changer
AI has the potential to transform this landscape. Modern AI models are powerful enough to combine and analyze vast amounts of data without requiring bespoke models for every application. Users can interact directly with their data instead of navigating through specialized dashboards for every use case. This opens up new possibilities for individuals and businesses alike to harness their data.
However, we’re unlikely to see a single, all-encompassing solution for managing personal and business data. Instead, we’ll see AI tools tailored to specific use cases, integrating personal data with external models and public datasets. Crucially, these solutions can keep user data private, enabling sensitive information to be included without third-party exposure.
We’re witnessing the emergence of two main approaches to user-specific AI solutions:
- For Personal Use: These tools help businesses and individuals make better decisions, analyze data, and receive actionable advice. For instance, a company might use AI to analyze operations or financial data, while an individual might use AI to improve productivity, health, or financial management.
- For Representation: These tools act as digital representatives. Companies can deploy AI to communicate with customers, partners, or investors, while individuals can use AI “twins” to share their knowledge and perspectives.
Early Examples of Vertical AI Use Cases
We’re already seeing examples of these approaches in action:
- Legal AI: Companies can integrate their agreements, policies, and goals with external legal data and AI models to receive tailored advice on legal matters.
- Risk Management: Tools are emerging to help businesses analyze risks and navigate geopolitical or economic uncertainties. These AI solutions function like expert team members, combining external knowledge with internal data to offer recommendations and highlight risks.
- Personal AI Tools: Early-stage tools are helping individuals manage health, fitness, and finances, as well as creating digital twins that can represent them – even digitally preserve their presence forever.
The Future of AI-Driven Data Management
The saying “Who manages data, manages AI” rings true. While it’s easy to assume that tech giants have the upper hand, the reality is more nuanced. There’s a growing demand for tools that allow users to combine their own data with external models, creating personalized solutions that work for them.
We’re likely to see diverse approaches emerge, influenced by privacy regulations and technological advancements. What’s certain is that this shift is already underway, offering a massive opportunity for companies creating these solutions and empowering businesses and individuals to finally make their data work for them.
Jouko Ahvenainen
