Most organisations still don’t have the data foundation their AI ambition requires

Why is AI ambition accelerating faster than data readiness?

The latest Informatica CDO Insights report shows that 69% of businesses are now deploying generative artificial intelligence (AI), up from 48% just a year ago. This acceleration reflects a growing recognition among businesses of AI’s potential to transform operations, enhance customer experiences, and drive innovation.

However, the EDM Association’s 2026 Global Data Management Benchmark reveals that only 31% of businesses surveyed across over 50 countries have advanced data strategy capability.

That means that while many businesses are funding AI initiatives, they are doing so on shaky data foundations and underestimating the critical role it plays in such projects.

This disconnect isn’t a new concern. It’s a widening gap that demands immediate attention. As AI ambition grows, the disparity between what businesses want to do with their data and what their infrastructure can actually support is becoming more pronounced, leading to increased costs and complexities that can undermine innovation efforts.

With 86% of businesses planning to boost their data management investments in 2026, it’s evident that spending on AI is rising with, rather than in reaction to, this capability gap.

This emerging trend signals a fundamental shift in how leaders like you should perceive their data capabilities. As this article will further explore, AI investments are only as robust as the data foundation that supports them. Thus, to fully realise your AI ambitions, you must rethink your data strategy.

So, stop viewing data management as a pre-launch checklist item. Begin considering your data foundation as an ongoing operational necessity to support your AI initiatives in real, day-to-day workflows.

What does an “advanced data strategy capability” actually mean?

Having a genuine and mature data strategy goes beyond purchasing a tool or platform. Instead, it’s a holistic approach composed of several essential components, including:

• Clear data ownership and accountability. By designating data stewards within your business, you clarify who is responsible for managing and governing your data assets. This action promotes data integrity among your teams, ensuring that information remains a trusted resource for decision-making.
• Consistent quality standards. Defining and maintaining high-quality data standards facilitates accuracy and reliability of the information handled. For instance, by implementing rigorous data validation checks, you will ensure that marketing insights based on consumer data are both accurate and actionable.
• Enforced governance frameworks. Move beyond documentation. Implement governance structures that your teams actively monitor and use. This approach is particularly vital in highly regulated industries, such as finance, where data regulations compliance is paramount.
• Technical integration of information. When data is seamlessly integrated and readily accessible across departments, you break down silos and facilitate smooth workflows. This fosters informed decision-making, collaboration, and drives innovation.

In simple words, all those businesses polled by the EDM Association that consider data management an afterthought in their data strategy aren’t facing challenges because they adopted AI tools.

They struggle due to long-standing deficiencies in their data management practices that have gone unaddressed for way too long.

Does AI fix bad data, or amplify it?

Business leaders often assume that once AI technology is implemented, it will solve all their data quality issues. Actually, this isn’t the case because AI isn’t a neutral layer added on top of your existing data.

Understanding the Reality

AI doesn’t question the information you’re feeding it; it processes what you’re giving it. If your data strategies are flawed, AI will amplify those errors rather than correct them. AI tools act as a magnifying glass, exposing and amplifying the current state of your data, whether good or bad.

This dynamic can significantly impact your business, making issues such as poor data quality, inconsistent data governance, and unclear ownership become much more pronounced once you start using AI.

Imagine one of your reporting tools inserting an incorrect figure into a financial report. One of your human employees can catch the mistake and rectify it. But if AI processes that same inaccurate information, it can perpetuate the error. It may use it to build incorrect patterns, make inaccurate predictions, and pass them off to departments and decision-makers as credible insights.

3 Ways AI Exposes Data Issues

When you feed the wrong data to AI, the tool can repeat the same mistake over and over again. Here are three examples showing how AI interacting with your data brings issues to the forefront:

1. Amplification of errors and inaccuracies. AI systems can inadvertently reinforce inaccuracies and misclassifications, leading to bias, incorrect outputs, and flawed recommendations.
   Why it matters: Take a financial services business deploying AI for credit approval. If the underlying data is inconsistent or riddled with mistakes, the AI tool may generate biased decisions or miss fraudulent requests. This may impact customers’ trust and lead to financial loss.
2. Decisions based on old records. Poor data governance can result in duplicated or outdated information being fed into AI systems.
   Why it matters: If you use AI for inventory management, for example, and the data is based on obsolete or inaccurate information, you may end up overstocking items that don’t sell well, making incorrect forecasts, and inflating costs.
3. Security and compliance issues. AI requires a massive amount of information to function effectively. But sensitive data processed by ungoverned AI tools is much more difficult to track, manage, and protect.
   Why it matters: Consider an insurance business that uses AI to analyse sensitive customer data. If they fail to secure that information adequately, they risk non-compliance with regulations such as the EU General Data Protection Regulation (GDPR). That could lead to security breaches, hefty fines, and reputational damage.

The hidden costs of neglecting your data foundations

Weak data foundations can have severe operational and financial consequences for your business. Here is a closer look at the potential pitfalls and their implications.

1. Operational Inefficiencies

IBM reported that 90% of enterprise data is locked in unstructured silos. Without a well-defined governance plan, your teams will have to deal with:

• Duplicated efforts. When your data isn’t centralised, the same information is entered multiple times across different systems. That may cause confusion, inaccuracies, and wasted time and resources. For example, your marketing team’s campaign may repeatedly target the same customers, wasting budget and time.
• Siloed reporting. If your departments use information stored in isolated systems that don’t communicate with one another, they may generate inconsistent reports. For instance, a manufacturing business using separate, unintegrated software for production and inventory management may get conflicting data, leading to production delays and supply chain issues.
• Slow or inconsistent decision-making. Relying on inaccurate or incomplete data can lead to slow and risky decision-making. Imagine your finance team waiting days for the marketing department to provide customer insights only to discover that the data provided is outdated. Such delays can result in missed opportunities and slower responses to market changes.

2. Compliance Exposure and Security Issues

As regulations such as GDPR, DORA, and the EU AI Act evolve, business leaders are increasingly required to demonstrate how data and AI are used and controlled together. Failing to do so will expose your business to:

• Legal consequences. Under GDPR, your business can be fined up to €20 million or 4% of its global revenue if it fails to comply.
• Reputational damage. Data breaches and non-compliance with regulations and industry standards can severely damage your reputation, eroding trust among customers and partners.
• Higher security risks. When your data access and usage policies are inconsistent or unenforced, your business is more vulnerable to cyber security attacks and data leaks.

3. Commercial Costs

In the context of mergers and acquisitions, data governance has become a critical point for dealmakers who have begun to:

• Scrutinise data quality. Investors are increasingly requiring thorough assessments of AI data quality and governance during due diligence. Furthermore, if your business lacks robust AI data governance, potential investors might withdraw from transactions or seek to renegotiate terms.
• Include data maturity in their valuation calculation. When your AI data maturity level can affect your business valuation, poor AI data governance can considerably reduce your business’s market value.

Ultimately, security risks, compliance exposure, and deal risks are interconnected symptoms stemming from a single underlying issue: a weak data foundation that hasn’t been built to last.

Should data governance be a one-off project, or an ongoing discipline?

Many leaders fall into the trap of viewing data readiness as a one-time task to be completed ahead of significant initiatives, such as AI rollouts. They run a single data quality audit or governance review before launch and believe that the job is done.

However, this short-sighted approach doesn’t take into consideration that data is dynamic. As such, it requires continuous attention and oversight.

Data Decay: The Quiet Threat

Data isn’t static; it continuously changes over time. When you introduce new systems, for example, or your teams adopt new unapproved AI tools, or regulatory requirements evolve, your initial data foundation quickly becomes obsolete.

Thus, if left unmonitored, what was once a robust data foundation may actually have become inadequate. This could lead to issues such as data breaches, compliance violations, or unreliable AI outputs that can severely impact your business’s reputation and bottom line.

Your AI Data Foundation Needs Ongoing Governance Practices

To address these challenges and move away from the one-off project mindset, give ongoing data governance, quality monitoring, and access management the same level of attention and commitment as security monitoring or financial controls.

A 2026 report from Gartner underscores the effectiveness of this solution. According to the report, businesses that prioritise foundational areas such as data quality, change management, and governance are the ones reporting the most successful AI initiatives, dramatically outperforming those relying on one-off initiatives.

Their maturity stems from repetition and continuous oversight rather than a single successful audit.

3 Key Strategies That Elevate Your Data Governance to an Ongoing Commitment

To thrive in an increasingly complex and regulated data landscape, treat data governance as a perpetually ongoing discipline by investing in:

1. Regular audits and reviews. Conduct regular audits to assess your business’s data integrity and governance compliance. It will minimise the risk of non-compliance and ensure that your information remains accurate, consistent, and complete.
2. Continuous education and training. Regularly train your employees about the importance of data governance. It will ensure that all teams understand their role in maintaining data quality.
3. Adaptive frameworks. Create AI data governance frameworks that can evolve alongside regulatory changes and new technologies. This agility will help you keep your data practices relevant and effective.

What are businesses with advanced data capability doing differently, and where should I start?

Those businesses that answered the EDM Association’s survey and have set themselves apart through their advanced data capabilities aren’t just leveraging data; they are mastering it. They have implemented several key practices that effectively integrate data management with their operational strategies, such as:

• Prioritise clear ownership of data assets to ensure accountability at every level.
• Maintain and adapt their governance frameworks instead of merely documenting them, creating living resources that evolve with their needs.
• Have quality and security processes in place that run alongside AI adoption and enhance data integrity rather than hindering AI initiatives. Ensure that governance runs parallel to technological adoption.

As a business leader aiming to improve your data strategy and foundations, start with an honest assessment of the current state of data ownership and quality across your business.

This self-reflection is essential before making substantial investments in AI. Because simply spending more money on AI without first strengthening your foundational data infrastructure is unlikely to yield better outcomes.

As we have just learned, the tension between AI ambition and capability remains significant, but those who prioritise their data foundation and view it as an ongoing infrastructure to maintain will unlock genuine, sustainable value from AI in the years to come.

So, if your AI ambitions are rising, don’t let your data readiness fall behind; make it part of your strategy. Connect with Acora to start a conversation about data and AI governance. Together, we can bridge this capability gap so that your AI investments translate into successful adoption and continuous value.