But in the midst of all this buzz, we need to take a step back and acknowledge a critical aspect that often remains in the shadows: Data Quality. While AI may be the hero of the moment, data is the foundation on which it stands – and, more importantly, it’s the next big frontier for innovation.

CEOs and the Shift Toward Data Quality

It’s no longer just about creating impressive AI models. In conversations with CEOs from major organizations—companies managing vast volumes of data like Visa, Carglass, General Motors, and more—one consistent theme emerged: Data Quality is the future. They expressed a collective realization that without clean, consistent, and reliable data, even the best AI models can lead to unreliable outputs and costly missteps.

Interestingly, regions such as Dubai and Qatar are not only embracing AI’s transformative potential but are also putting a sharp focus on consistent data quality. They understand that successful AI is built on a foundation of high-quality, dependable data—essential for driving trust, reliability, and long-term innovation.As data complexity grows, so does the need for integrity. AI's success depends on its foundation: high-quality data.

One CEO put it bluntly: “We spend too much on data management, and the inconsistency in our data has been a roadblock. The future is all about data quality.” This sentiment was echoed again and again. The leaders of these large enterprises understand that as the complexity of data increases, so too does the challenge of maintaining its integrity.

Impact startups in the Web Summit

In this AI-driven innovation at Web Summit, several companies stood out for their unique contributions and meaningful impact that we really liked their approach:

• Tabia Health: Transforming chronic disease care through customized, data-driven clinical pathways that enhance patient outcomes and cost efficiency.
• ReveniQ: Simplifying SaaS billing with flexible, usage-based solutions, optimizing revenue and integrating seamlessly with major platforms.
• The Sofa Activist: Engaging users in sustainability with a playful yet impactful approach to personal environmental responsibility.
• Vinesia: Offering secure, transparent wine investment, leveraging blockchain for authenticity and real-time value tracking.
• Nutripy: Holistic wellness measurement tools that track essential health metrics, enabling personalized health journeys.
• Kenzu: Personalized digital wealth management, providing real-time portfolio analysis and tailored investment strategies.
• ByeBnk: Revolutionizing wealth management with tokenized investment solutions.
• Peqal: Simplifying global investments.
• Alba Health: Improving child health through groundbreaking gut microbiome analysis.
• Stryde: Democratizing venture capital by making private market investments accessible and secure for all investors.
• Tersa Earth: Innovating mining remediation with carbon-neutral solutions that eliminate toxic tailings and recover valuable metals.

These companies exemplify the innovative spirit of Web Summit, demonstrating that transformative solutions are built on a foundation of purpose, creativity, and precise execution.

The Hidden Costs of AI's Data Dependency

AI thrives on data. But have we stopped to ask: What is the real cost of managing and maintaining this data? Every CEO we spoke to who works with large volumes of data echoed the same sentiment: “This is exactly the future. We spend too much, and data is not consistent. The future is data quality.”

AI thrives on data, but managing and maintaining data isn't simple – it’s complex, resource-intensive, and costly. Beneath the glittering AI promises lie these realities:

• Expensive Data Handling: Storing, processing, and curating data for AI solutions comes with steep costs. The need for accurate, reliable data is constant and demanding.
• Inconsistent Data = Poor Results: Fragmented or inconsistent data leads to AI outputs that are unreliable, creating potential for costly errors and misinformed decisions.
• Data Overload and Noise: AI relies on vast data sets, but when this data is not high-quality, it becomes little more than noise – and can undermine trust in AI’s capabilities.

These hidden costs aren’t just financial; they also impact trust, reliability, and the long-term effectiveness of AI solutions. Businesses eager to adopt AI must confront these data challenges head-on – or risk being led astray by inaccurate outputs.