Artificial intelligence is rapidly becoming an integral part of modern enterprise operations, with 92 percent of IT leaders actively investing in AI to advance their data and analytics initiatives. The sharp uptick in these types of initiatives means that the need for high-quality data is higher than ever.
Organisations looking to fuel their AI and advanced analytics must be able to identify and leverage their most valuable datasets. For many, this means tapping into their long-standing core transactional systems which were never designed with these newer technologies in mind. As enterprise operations evolve, the ability to harness data is also growing in complexity, presenting a new challenge for organisations on their innovation journeys.
The symptoms of this challenge often materialise in poor data integrity, fragmented governance and data silos, all of which create roadblocks to efficient data processing. These are some of the most common reasons behind AI and analytical solutions failing to translate into meaningful business outcomes.
The data accessibility challenge
For many enterprises, their mainframes house decades’ worth of critical, high-quality data, functioning as a repository of historical operational and customer interactions. However, only a small fraction of IT leaders extensively leverage this valuable resource in their data-driven initiatives. The reason behind this is that integrating this data with modern AI systems presents significant accessibility challenges. Indeed, 44 percent of enterprises surveyed by IDC reported issues with technology gaps or incompatibilities when considering migration or modernisation of mainframe applications.
Even when challenges are resolved, there are still questions to be answered around the provenance of the data. Where does it come from? Has it been altered or manipulated in any way? Is it maintained in line with the appropriate data governance practices? AI systems are only as trustworthy as the data that powers them, so IT leaders must be able to answer these questions with confidence, otherwise they risk the data used to fuel the decision-making capabilities of the AI solution being unreliable.
A new era of data governance
In the face of quickly evolving regulations, the role of governance has expanded well beyond compliance to become a strategic necessity. Every business that handles sensitive data needs to contend with the reality of potential data breaches and cyberattacks. This necessitates a strong security posture and risk management practices which are all built around robust data governance. Static controls and “one and done” reviews are no longer enough. These new frameworks demand continuous visibility into how data flows across the enterprise.
Effective data governance requires a framework that safeguards data integrity at every stage of the lifecycle. That includes ensuring accuracy, consistency, and reliability, while also providing end-to-end lineage tracking, metadata management, and routine validation. As data moves across systems and is transformed, synchronised, duplicated, and moved to where AI applications need it, it must remain accessible, trusted, and actionable.
Modern data management solutions embed these safeguards directly into enterprise workflows. Automated lineage, embedded access controls, audit logging, and compliance frameworks help organisations meet regulatory requirements and strengthen confidence in AI-driven insights.
By treating data protection as the foundation of AI adoption rather than an afterthought, enterprises can scale their AI initiatives while maintaining the trust and security essential for long-term success.
Taking analytics to the next level with mainframe data
Advanced analytics and AI solutions feed off the same thing: data. In order to both reduce the risk associated with innovation and maximise the ROI of their shiny new tools, organisations must leverage their mainframe data with a clear, strategic mindset. The potential benefits are many, ranging from improved decision-making capabilities to increased visibility into operational efficiency, and real-time competitive insights. On the tactical level, this means finding those modern integration tools that help address the challenges around data governance and boost visibility within enterprise environments.
Part of the challenge lies in the reality that no two organisations or modernisation strategies are the same. Some leaders prefer pre-built solutions, while others attempt to adapt existing systems, but there is no one-size-fits-all approach. And while many business leaders understand the critical importance of their mainframe data, navigating through the process remains a sticking point.
Practical challenges such as data retrieval, security, compliance, and scalability frequently complicate integration efforts. At the same time, perceived obstacles often amplify the hesitation, making the process daunting and even risky. As a result, organisations may delay or abandon initiatives altogether, even as some competitors push ahead with multiple projects in parallel.
Overcoming these challenges requires not just technical expertise, but also a shift in mindset. By approaching integration strategically and adopting modern tools that bridge mainframe and cloud environments, enterprises can move beyond roadblocks and begin fully leveraging their data. There is much to gain: more accurate AI models, richer analytics that meaningfully inform business decisions, and insights that drive a stronger competitive advantage.
