How we can bridge the AI divide with accessible AI data scientists

By 2025, the world will create 175 zettabytes of data annually, equivalent to one-and-a-half times the length of the Great Wall of China if the wall were filled with one terabyte hard drives. Yet, this data explosion is not translating into universal progress.

The exponential growth of data is far outpacing the linear increase in data science talent – this scarcity has inflated the cost of artificial intelligence (AI) talent to prohibitive levels.

A critical divide is emerging where only large corporations and well-funded institutions can fully leverage AI, while smaller businesses, non-profits and public sector organizations are left behind. This growing chasm affects economic competitiveness and our collective ability to address pressing global challenges.

At the World Economic Forum’s 2024 Annual Meeting in Davos, the AI Governance Alliance made inclusive AI a priority for 2025. Through my work with the alliance, one reality stands clear – we need breakthrough solutions to make AI’s benefits accessible to all – I believe we have found one.

What if we could bridge the data divide by democratizing access to AI innovation? Imagine a world where every organization, regardless of size or resources, can access brilliant AI data scientists at almost no cost.

AI data science agents represent a promising solution to the exploding data challenge. These aren’t mere analytical tools – they represent a paradigm shift in how we approach complex problem-solving on a global scale.

These agents automate the entire data-to-decision pipeline, allowing human data scientists to oversee it, ensure their governance and validate insights to maintain trust. Here’s a closer look at their operational framework.

At the heart of their operation is automated data processing – the most time-consuming aspect of data science. These agents intelligently connect to various data sources – from databases to application programming interfaces (APIs) and internet of things devices – automatically handle data quality issues and prepare data for analysis – a process that typically consumes up to 80% of a human data scientist’s time.

They then discover what makes the data valuable, uncovering meaningful patterns and relationships that might take human analysts weeks to find.

What will set apart data science AI agents is their ability to use a variety of quantitative and advanced reasoning skills in analyzing data. For example, the AI agents could construct a causal model, representing the cause-and-effect relationships within the data and then use that model to predict the impact of future actions.

This deeper understanding, combined with large language models’ ability to interpret and explain complex patterns, enables these agents to communicate their findings in human-like ways.

By combining sophisticated analysis with clear communication, these AI data scientists make advanced AI accessible to organizations that previously couldn’t afford data science teams.

This transformation has far-reaching implications for businesses, governments and society – let’s explore this with two example use cases.

In many underprivileged communities, access to clean water remains a critical challenge. While these communities collect valuable data about their water systems, they lack the resources to leverage it effectively. A single data scientist’s salary could exceed the entire operational budget of a local water authority.

An AI data scientist could transform water management in these communities by leveraging data they already have: water point status records, usage patterns, basic sensor readings and local weather data.

What makes this approach particularly powerful is its use of causal reasoning. Rather than just identifying patterns, it can uncover why certain water points fail more frequently than others. For instance, it might discover that failures in specific areas are primarily caused by usage patterns rather than infrastructure age, enabling more targeted and cost-effective interventions.

The impact would be both immediate and practical. The AI could:

Most importantly, these insights would be delivered in clear, actionable terms that local teams can understand and trust.

The backbone of global manufacturing isn’t giant factories, but small and medium-sized manufacturers operating on thin margins in fierce competition. While leading manufacturers have progressed in digital transformation, small and medium-sized enterprises face significant challenges due to financial constraints.

An AI data scientist could transform manufacturing operations by:

The real innovation lies in how these AI data scientists make complex processes accessible by communicating in the language of manufacturing. Plant managers can simply ask questions such as: “Why did we see more defects last week?” They could also receive clear explanations linking specific production conditions to outcomes and practical recommendations for improvement.

With AI data scientists, small manufacturers could achieve the same operational sophistication as large corporations – without the massive investment in data science teams – enabling a more resilient manufacturing sector where businesses of all sizes can innovate and thrive.

AI data scientists have the potential to level the playing field between resource-rich organizations and those who currently can’t afford data science expertise. However, realizing this vision requires more than just powerful automation – it requires trust.

This trust is an essential anchor in our AI-driven future. While artificial intelligence may provide a majority of analytical capabilities in the future, human intelligence will remain the critical backstop. As AI data scientists automate complex workflows, human experts provide the essential oversight and judgment that ensures these systems remain reliable and trustworthy.

AI data scientists will leverage large language models to write and execute data science code, while their causal reasoning capabilities allow them to explain their work clearly.

Business users can interact with them in natural language to uncover the fundamental relationships in their data and understand why things happen, not just what happened. This combination of human oversight and explainable AI transforms them from black boxes into trusted partners.

The future we envision is one where every organization, regardless of size or resources, can harness the power of data. AI data scientists will work alongside human experts – not to replace their judgment, but to augment their capabilities. This isn't a distant vision. We’re currently working with leading enterprises using AI data scientists to tackle challenges beyond human analytical capacity while maintaining human strategic control.

As AI advances, providing an ever-greater share of the world's analytical capabilities, human oversight will remain the cornerstone of trust. This balance of trust and automation will make AI truly accessible to all, driving innovation and progress across every sector of society.