Why Data Management Has Become the Real Constraint on AI Scale

Artificial intelligence has moved well beyond experimentation. Across industries, organizations are embedding AI into core operations — from customer engagement and forecasting to automation and decision support. Yet as AI adoption accelerates, a quieter constraint is emerging beneath the surface: data management.

Recent research from Boomi (a Data Management Platform), conducted in partnership with FT Longitude, surveyed 300 data and analytics leaders globally. The findings point to a clear conclusion: most organizations trust their AI far more than the data that powers it — a gap that becomes increasingly dangerous as AI scales.

Confidence in AI Is High — Confidence in Data Is Not

According to the research, 77% of organizations say they trust the reliability and effectiveness of their AI systems, citing gains in productivity, innovation, and customer understanding.

However, that confidence erodes when leaders look beyond AI outputs and examine the broader data environment:

• Only 50% trust the overall quality of their organizational data
• Just 47% trust the completeness of their datasets

This disconnect matters. AI models do not evaluate context or intent — they scale patterns. When data quality is inconsistent, incomplete, or poorly governed, AI accelerates existing problems rather than solving them.

As Joanne Biggadike, Head of Data Governance at Schroders, explains in the report:

“The whole point of data governance — quality-checking, cleansing, keeping humans in the loop — is to build trust. Without it, you don’t know where your data came from or whether it’s accurate, so you’re just guessing.”

Manual Data Management Works — Until It Doesn’t

Many organisations still rely heavily on manual data processes to protect downstream systems and reduce risk. While this approach can work in early AI initiatives, it does not scale.

The report highlights a critical inflection point: as companies introduce generative and autonomous AI, manual oversight becomes impractical and ineffective.

Ranajay Nandy, Vice President of Data and Analytics at Citizen Watch Group, notes:

“When ML models were based on a single algorithm, there was time to review and correct biases. But new models such as generative AI have made this impossible… The only way to avoid that is to use automated data management and governance tools.”

The risks are no longer theoretical. 13% of surveyed organizations report having already suffered business damage due to AI errors caused by poor data management — a figure likely to rise as AI supports more mission-critical processes.

Data Governance Is Advancing — Ethics and Explainability Lag Behind

One notable pattern in the research is where organizations are choosing to focus their efforts.

Companies report higher maturity in:

• Data governance frameworks
• Privacy and security compliance
• Data validation and quality checks

They are significantly less advanced in:

• AI ethics teams
• Bias-detection audits
• AI explainability and transparency tools

This suggests a pragmatic reality: organizations are shoring up data foundations first, while ethical and regulatory frameworks struggle to keep pace with AI’s rapid evolution.

Scaling AI Requires Centralization and Automation

Looking ahead, 83% of organizations expect to integrate more data sources into their AI systems within the next 12 months.

Yet readiness is uneven:

• Only 51% report mature centralized data platforms
• Just 42% report mature automated tools for data integration and cleansing

Kevin Thompson, Director and Digital Consulting Architect at Huron, describes why centralization matters:

“A centralized data platform is critical because it’s the connective tissue that moves data reliably across an organization, allowing people to trust the outputs and act on them immediately.”

Automation plays a complementary role — handling routine validation, cleansing, and monitoring at scale — while allowing humans to focus on exceptions, oversight, and decision-making.

When the Foundations Are Right, AI Delivers Real Value

The report includes a case study where improved data governance and quality enabled AI-driven predictive maintenance. The results were tangible:

• Over 40% reduction in unplanned downtime
• Extended asset lifespans
• Improved operational planning and forecasting

The takeaway is clear: AI produces measurable ROI only when supported by trusted, well-governed data.

The Swinmark Perspective

At Swinmark, we see this pattern repeatedly. Organizations don’t fail at AI because of model selection or tooling — they struggle because their systems, data flows, and governance structures were never designed for AI at scale.

As AI agents, automation, and decision systems become embedded across the business, success will depend less on experimentation and more on foundational discipline:

• Clear ownership of data
• Integrated systems, not silos
• Automation where scale demands it
• Governance that evolves with technology

AI is no longer a future initiative. It is an operational reality. And increasingly, data management is the strategy.

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Source:
Boomi & FT Longitude, “The State of Data Management for AI: Lessons from 300 Data Leaders Scaling AI,” April–May 2025.