The 80% reality: Why data quality insecurity is the silent killer of GBS AI strategy

When asked to name the biggest challenge standing in the way of AI adoption and expansion, a striking 80% of global business services (GBS) leaders pointed to the same issue: perceived poor data quality. 

Despite the excitement surrounding AI, most organizations can’t move forward. They are held back by their own information. The study¹, conducted with Quadrant Strategies,  surveyed 330 GBS leaders from medium to large enterprises around the world and included in-depth interviews, revealed several key findings, including:

• Workflows in financial operations and compliance reveal the largest gaps in data intelligence and automation.
• The greatest technology needs exist within finance functions, followed by HR, procurement, legal, and facilities.
• AI-enabled platforms are increasingly seen as a solution to drive cost savings, increase growth, create consistency, and generate high-quality data.

In the sections that follow, we explore what these findings mean for GBS decision-makers and examine practical solutions for improving data quality insecurity – quickly and sustainably.

The reality is clear

Leaders increasingly recognize that AI success depends on trusted, well-governed data. This realization is shaping boardroom conversations and driving greater awareness of data quality insecurity.

There are good reasons for this concern, as many organizations are confronting governance gaps and limited visibility. They don’t know where their data comes from, how it’s managed, or how it’s integrated across systems. Without that clarity, deploying AI and intelligent automation becomes difficult to scale and trust.

To move forward, GBS leaders must address data quality challenges directly and establish strong governance foundations.

The mishmash of modern data

One GBS leader surveyed explained the challenge this way: “Having an integrated technology landscape becomes difficult because the data is disjointed and separated across multiple sources.”

In other words, at the root of data quality insecurity is data fragmentation. Data is scattered across multiple systems, formats, teams, and locations. Records are split among CRM, ERP, email, and shared drives. Documents are stored in PDFs, spreadsheets, images, and legacy systems with no common metadata. Different departments maintain their own versions of the same data. Cloud, on-premises, and third-party platforms throughout the enterprise don’t integrate well.

Another GBS leader referred to the problem as “a mishmash of random datasets that exist in different platforms” and noted the “quality of that data is inconsistent.” 

Instances of mishmash data are most prevalent in  financial operations—precisely where clean, connected data is essential for automation, AI, and enterprise-wide efficiency. The problem persists across the US, India, and EMEA region with data intelligence gaps in operations support, asset management, and financial operations. 

With data streaming in from so many disparate sources, leaders say it’s also “difficult to know which data to filter out.” This lack of clarity amplifies data quality insecurity, further delaying AI progress.

Why AI stalls without governance

Globally, GBS leaders cited several technology challenges, including AI adoption readiness and application integration, as well as a lack of internal skills and budget constraints. However, data quality emerged as the leading concern, with 58% identifying it as their top challenge.

However, data quality is not a standalone initiative. It is the direct result of strong data governance. One EMEA leader specifically called out the crux of the issue as “data governance, which is the data ownership ultimately, and then how you manage and make sure that data quality is where it needs to be.”

Data quality is the outcome. Governance is the discipline that produces and sustains it.

Governance establishes common definitions, ownership, standards, controls, and accountability. It ensures data is accurate, consistent, secure, and usable at scale. Governance builds data quality into workflows, systems, and service delivery models. In short, governance defines what “good data” means.

It can be tempting to bypass enterprise-wide governance and rely on piecemeal interventions. That approach creates a cycle of stopgap interventions that delay meaningful progress. Research shows that organizations must “fix these foundational data issues before AI works.” 

From data quality insecurity to confidence

GBS leaders now acknowledge the need to “build the foundational infrastructure,” seeing it as a necessary investment for growth and scale. The current landscape for many looks like a fragmented mix of legacy systems. 

“We are a federation of entities … thirty different ERPs,” said one GBS leader in India. “The only way forward is investment into more modern platforms.”

Motivated by their primary objective—cost reduction and efficiency—these leaders recognize it’s time to move from disjointed data and systems to decisive information. Connected, well-governed data is the only way to enable downstream automation and other AI implementations that will drive those sought-after savings and business value.

As such, they are prioritizing solutions that can deliver standardization and data integrity. The research indicates that investing in consolidated technology is the only way to move past the “random datasets” and “fill in some of the process and automation gaps.”

Earning the right to automate

GBS leaders are increasingly called to meet the mandate of “balancing efficiency and value creation,” yet they cannot achieve this if they are insecure about their data. Though the pressure to adopt AI is high, the 80% reality proves that skipping the data quality step creates significant risk and limits long-term success.

By finding the right technology partner, GBS leaders can prioritize what one research respondent called “standardizing and improving data integrity,” ensuring that the information feeding their AI models is accurate, secure, and accessible.

As a proven and trusted leader in information management, Iron Mountain provides a digital backbone for GBS with solutions and services that support data governance. Our experts are prepared to help you overcome data quality insecurity and build an AI-ready foundation with confidence. 

It’s time to tackle data governance as an essential step in reaching an automated future, and Iron Mountain can help you get there. Learn more about the digital backbone for GBS or book a demo today.

¹ _{Global Business Services Buyer Research conducted by Iron Mountain with independent research specialist, Quadrant Strategies}