The share of businesses scrapping most of their AI initiatives increased to 42% this year, up from 17% last year. Even more concerning, according to IBsolution many data challenges are related to privacy and data protection (50%), the ethical use of AI (50%) and data quality (41%).

A lack of technical maturity and interoperability (48%) and deficits in the quality, completeness and readiness of data (42%) also play a role in unsuccessful AI projects.

These statistics reveal a troubling reality: organisations are discovering that having the latest AI models isn’t enough. The real determining factor for AI success? The quality and reliability of the data engineering foundation supporting those models.

It’s a simple truth that’s proving complicated in practice: AI is only as good as the data it’s trained on. And getting that data right requires serious data engineering expertise.

Behind the 42%

Many enterprises are hitting unexpected roadblocks as they move AI from proof-of-concept to production. The challenge isn’t finding the right AI model—it’s ensuring they have robust data infrastructure to make AI work effectively.

Consider the difference between traditional IT systems and AI: when conventional systems process flawed data, the errors stay contained. But AI systems learn from data, meaning any inconsistencies, biases, or gaps become amplified and baked into the model’s decision-making process. It’s the classic “garbage in, garbage out” principle, but with far more serious consequences.

This creates a compounding effect where poor data quality doesn’t just impact individual transactions—it systematically undermines the entire AI system’s reliability. The stakes are higher, and the margin for error is smaller.

The three essentials for building AI-ready data infrastructure

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Scalable platforms

Modern AI applications have an insatiable appetite for data and computational resources. They need continuous feeds of high-volume, high-velocity information from diverse sources. This isn’t just about having more storage—it’s about building systems that can handle the complexity of real-world data environments. The scalability challenge extends beyond volume. AI systems need to process everything from structured database records to unstructured social media posts, IoT sensor data, and legacy system outputs. All of this needs to work together seamlessly, adapting to changing demands without compromising performance.

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Data integration and quality

Here’s the reality: organisational data is messy. It lives across multiple systems—decades-old legacy databases, modern cloud platforms, IoT devices, external APIs, and everything in between. This diversity creates integration challenges that, if not properly addressed, introduce exactly the inconsistencies that make AI unreliable. Effective data engineering means implementing robust validation processes, automated quality checks, and comprehensive lineage tracking. It’s about transforming that messy, diverse data landscape into something AI can actually learn from and trust.

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Governance and compliance

AI governance isn’t just traditional data governance with a new label—it presents fundamentally different challenges. Research shows that 64% of organisations have deployed at least one generative AI application with critical security vulnerabilities. Most concerning, 31% of these organisations weren’t aware of these vulnerabilities until after an incident occurred. AI systems can inadvertently expose sensitive information through their outputs or decision processes in ways that traditional systems simply don’t. This requires governance frameworks that can adapt to the dynamic nature of AI systems while maintaining strict compliance standards.

The strategic opportunity

As we move into 2026, the focus is shifting toward maximising the value of unstructured data—documents, images, video, and audio that traditional systems couldn’t effectively process. According to IDC, unstructured data is set to account for 80% of all data collected globally by 2025, and it’s growing at an astounding rate of 55-65 percent annually. Organisations that can harness this data will have significant competitive advantages.

This capability requires sophisticated data engineering that can handle the complexity of unstructured data while maintaining the quality and governance standards necessary for AI success. It’s not just about having more data; it’s about having better data processes.

The potential ROI for AI in the UK is significant; a Microsoft-commissioned report indicates that prompt investment in digital technologies and skills could have an average societal ROI of more than 5:1 for every £1 spent by companies in the next decade. These returns are overwhelmingly realized by organizations that prioritize a strong data foundation. What’s becoming clear is that successful AI implementation hinges on data engineering excellence. Organisations that recognise this and invest accordingly will capture the full value of AI. Those that focus solely on AI models while neglecting the underlying data infrastructure will likely struggle to move beyond experimental implementations.

The foundation you build today will determine your AI success tomorrow.

Ready to build your AI-ready data foundation? Through solutions such as Regulated Product Remediation and Customer Redress Analytics, we combine deep technical expertise with business insight to help organisations turn data challenges into strategic advantage.

Contact us to discuss how we can help accelerate your AI journey through better data engineering.

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