As organisations look to scale their AI solutions, many are discovering a growing gap between ambition and execution, which is often caused by the lack of data readiness. This is a growing trend and Gartner predicts that through 2026, 60 percent of AI projects unsupported by AI-ready data will be abandoned.
With AI readiness topping the C-suite agenda, the real, urgent challenge is clear: progress must be shaped less by the latest AI model, and more by the state of organisations’ data estates. How then can enterprises most effectively transform their data management processes for AI success?
What is data readiness and why does it drive AI ROI?
Enterprise data is often found scattered across mainframes, cloud applications, legacy systems and third-party platforms. It may lack the governance needed to make it trustworthy and is almost certainly growing exponentially in both volume and complexity.
To power AI tools that can identify anomalies, personalise experiences and deliver predictions, organisations need more than just access to data. They need contextual, connected and synchronised data that teams can trust and that AI models understand.
Data preparation is a crucial first stage
Organisations must ensure they unify siloed data, that it is synchronised in real-time, while mapping and tagging metadata. Metadata – described in simple terms as ‘data about data’ – provides context, by tracking the origin, structure, ownership, and usage rules of the data, making it more discoverable, reusable, traceable and governed.
It plays a key role in helping AI systems interpret data optimally. Indeed, organisations that have worked on metadata collection for the past few years will be at an advantage when it comes to AI adoption.
Once data is prepared, the next step is to prepare the architecture for use. A resilient data architecture supports both existing hardware, such as mainframes, and software, through either a migrated or hybrid strategy, that can be evolved and reshaped over time, providing a future-proof solution.
The mainframe data integration challenge
This architectural challenge is particularly apparent in the backbone of enterprise data: the mainframe. The long-predicted decline of the mainframe hasn’t materialised; in fact, it continues to serve as a reliable, high-performance foundation for critical workloads that works in harmony with modern technologies to support evolving business needs.
Indeed, for industries handling vast quantities of data, such as financial services and the public sector, mainframes are well placed to play a crucial role in driving innovation by integrating AI. In fact, IBM research found that 79% of IT executives believe mainframes to be essential for enabling AI-driven innovation and value creation.
Yet unlocking this potential requires seamless integration, and many organisations face challenges when transitioning from mainframe systems to the hybrid cloud environments needed for AI, particularly when it comes to data silos and the complexity of translating mainframe data formats.
Data silos are arguably the biggest challenge in transitioning to a hybrid cloud environment from a mainframe system. Translating mainframe data formats to modern data formats is extremely complex, which often leads organisations to postpone mainframe integration altogether.
This separation results in mainframe data being treated differently from other data, leading to inconsistent access and governance across the hybrid environment.
Unlocking unstructured data safely
Unlocking hidden unstructured data offers a transformative opportunity to boost workforce productivity and customer experience. However, many organisations face a significant hurdle: applying AI to operational content without violating strict compliance and privacy protocols, including related workflows that often disrupt legacy systems.
Forward-thinking enterprises are overcoming this by utilising tools that connect existing data sources and honour existing compliance policies, allowing teams to leverage modern AI safely, without ever moving sensitive data or compromising security controls.
The combination of structured and unstructured data for AI applications is an integration that is widely seen as delivering a competitive advantage for businesses today.
Strategies for data management evolution
Organisations operating in financial services and banking, for example, are increasingly focused on centralising data and creating ‘data products’ to address challenges in data access and quality.
To succeed in a hybrid environment, it is important for organisations to adopt forward-looking strategies for data management evolution, such as implementing reusable data products with proper metadata and traceability.
Change can be difficult, often requiring executive support or significant pain points to drive modernisation efforts.
Encouraging modernisation alignment between stakeholders
A common challenge is the misalignment between data stakeholders and those driving AI projects. It’s important to understand that different stakeholders or business units will have varying priorities or ‘pillars’ of focus.
While data management should serve as the foundation for all pillars of modernisation, it is often sidelined in daily operations. To secure buy-in, data leaders must reframe data initiatives as the direct enablers of their peers’ strategic objectives. This will ensure better alignment and support within the business.
Common AI integration misconceptions and challenges
Successful AI readiness requires three key components. Currently, the rapid pace of AI development is colliding with poor documentation and neglected data quality. True readiness requires:
- High-quality data: complete, trusted and contextualised data
- Appropriate technology solutions that can bridge legacy solutions and the cloud
- Skilled personnel aligned on both data management and AI execution
Common misconceptions about data modernisation, particularly around hybrid cloud environments and AI implementation, are often caused by organisations underestimating the complexity and cost of data projects, with many simultaneously running multiple AI projects without proper data foundation in place.
The key takeaways
Modernisation is driven by enterprise data quality, accessibility and AI readiness. It is vital to first lay the foundations when it comes to data before embarking on AI initiatives, given that incomplete or untrusted data can render them ineffective.
The road to scalable and reliable AI runs through an organisation’s data architecture, requiring data be unified, synchronised, tagged and governed. After all, AI is only as good as the data you feed it.
