Why your AI investment is only as strong as the data underneath it

Why 2026 Is the Year AI Adoption Outpaces AI Data Readiness

According to Informatica’s 2026 Chief Data Officer Insights report, 69% of businesses in the US, UK, EU and APAC have now integrated generative artificial intelligence (Gen AI) into their business practices.

This is a significant increase compared to the 48% recorded just a year earlier, signalling that AI is no longer just a concept or a pilot project; it’s taking centre stage in the business world.

However, many businesses have rushed their AI deployments, often overlooking AI data foundational elements such as governance and data quality, which are essential to support their AI projects. As a result, while they may have cutting-edge AI tools, they may not be fully equipped to maximise their AI potential.

That’s why the same report shows that 86% of business leaders plan to ramp up their investments in data management this year, primarily to bolster data privacy and security, enhance data and AI governance, and skill up employees.

Business leaders have realised that the strength of their AI investment is directly linked to the quality of the data that fuels it. They have noticed that businesses experiencing tangible AI investment ROI aren’t necessarily those with the most advanced models, but rather those focusing on building a robust and reliable AI data foundation.

AI tools deployment was never the hard part. So, is your AI data foundation strong enough to support your investments?

Why Governance Cannot Be a Pre-AI Checkbox

Businesses often perceive governance as a one-time task to complete before deploying AI tools. They view it as a fixed compliance milestone, or something to tick off during procurement or onboarding.

But the dynamics of data inputs and user interactions with AI systems change constantly. This makes governance a living discipline that needs to adapt and evolve continuously with the daily use of AI.

This approach is essential to ensure that AI systems function effectively, ethically and transparently within your business.

Unlocking AI Potential: 3 Reasons Why You Need Continuous Governance

Without continuous data governance and assessment, the information that guides AI outputs becomes unreliable. Continuous governance should be a top priority for every business leveraging AI, as it allows you to:

• Keep up with constant data change. The information you feed your AI systems is continuously evolving, but so should your processes, algorithms and models driving AI outputs. Imagine a bank that implemented an AI-driven loan approval process by setting governance policies about data usage and model auditing.
• When new data sources emerge (e.g., social media activity for credit scoring), initial policies may become irrelevant, resulting in overlooked biases and compliance issues. By embracing continuous governance, you ensure that your AI tools remain aligned with the latest data and trends.
• Navigate evolving employee interaction. Staff engagement with AI tools isn’t static. For instance, your users may experiment and utilise these systems in ways you might not predict, altering data flows and the outputs generated. Without real-time monitoring of AI usage and data flows, your teams could misinterpret results, leading to incorrect decision-making.
• Adapt to technological advancements and regulatory changes. Technology, ethical standards and regulations (e.g., DORA and AI and data privacy laws) evolve as rapidly as AI capabilities. Regularly updating guidelines is paramount to ensure your governance and data outputs are always up to speed.

The Hidden Cost of Building AI on Weak AI Data Foundations

According to Informatica’s report, 57% of data leaders cite data reliability as a major barrier to transitioning Gen AI from pilot to production. This is mainly because while the adoption of AI technologies can positively impact your business and services, the success of these tools largely depends on the quality of the data they are built upon.

The Real Costs of Poor Data

When AI tools produce inconsistent or inaccurate outputs, teams lose trust in AI-generated recommendations. As a result, users revert to manual processes. When this happens, the financial repercussions of undermining AI with weak data foundations extend way beyond licensing fees, with additional costs arising from:

1. Inconsistent outputs. When AI is trained on poor-quality data, the outputs can become erratic and unreliable. Consequently, your teams may have to deal with inconsistent recommendations, leading to confusion and frustration.
2. Erosion of trust. If AI-generated recommendations are frequently inaccurate, your teams may lose trust in these systems entirely. This lack of confidence can lead to reluctance to use AI, forcing your business to move back to outdated manual processes that are often slower, less efficient and error-prone.
3. Wasted resources. Time is money, and weak AI data foundations can drain both. Your employees are troubleshooting flawed AI implementations and retraining models, instead of focusing on innovation and strategic initiatives. This wastes resources and hampers growth.

The Stakes Are Higher in Regulated Sectors

For businesses operating in highly regulated sectors, the consequences of weak data governance can escalate dramatically. Leveraging untrustworthy AI outputs can lead to:

• Compliance issues. Incorrect decisions based on ungoverned AI recommendations could lead to regulatory violations and costly fines.
• Reputational damage. Trust is paramount. Mistakes caused by flawed data can jeopardise your client relationships and undermine your reputation.

For example, a bank using an AI model with poorly trained data may accidentally deny loans to qualified applicants. As a result, you will lose revenue and experience a wave of customer dissatisfaction that will take months to recover.

Such hidden costs of building AI on weak AI data foundations significantly outweigh initial investments. Thus, to maximise the potential of AI, you must assess, clean and establish robust governance frameworks.

Only then will you enhance trust, mitigate risk and ensure the successful deployment of AI technologies.

The Trust Paradox: Why Confidence in AI Data Can Be a False Comfort

While 65% of data leaders polled by Informatica say that their employees trust AI outputs, 75% still believe that their employees lack the data literacy to question or interpret AI information effectively.

The Implications of the Trust Paradox

This disparity creates a significant gap between perceived and actual data readiness, fostering a culture marked by high data confidence, but poor understanding. For instance, some employees may assume that the data underpinning AI systems is reliable simply because it’s embedded in their workflows.

Such mindset can lead to blind acceptance of AI-generated results, translating into:

• Poor decision-making. When your workforce relies on incorrect data, your business may make ill-informed decisions, negatively impacting performance and outcomes.
• Hampered innovation. When your teams lack the skills to assess AI outputs, they may overlook opportunities for improvement and innovation.

Imagine you deployed AI to optimise your inventory management system. Your stock managers, confident in the system’s recommendations, may fail to notice discrepancies caused by incomplete or outdated sales data. As a result, you may have stock shortages on popular items and overstock of less popular products, significantly impacting revenue.

Furthermore, when businesses’ AI governance hasn’t evolved to match the employees’ everyday use of AI tools, the workforce is often unable to challenge AI outputs, leading to errors in AI-based decision-making.

Bridging the Gap

To address this paradox, opt for a multifaceted approach:

• Invest in data literacy. Prioritise training programs that enhance employees’ understanding of data and AI technologies. You will empower staff to validate and interpret AI outputs.
• Implement robust technical governance. Governance tools are essential, but they need human oversight. Combine technology with training. It will ensure that your staff knows when and how to challenge AI recommendations.

Confidence in data doesn’t always mean it’s trustworthy. Bridge the gap between perceived and actual data readiness by investing in data literacy and enhancing governance practices. By doing so, you will empower your teams to challenge AI-generated outputs, fostering a culture of critical thinking and accountability that ultimately drives better results.

A lack of proper understanding can lead employees to accept potentially erroneous AI conclusions, perpetuating bad data practices and eroding trust over time.

What Separates the Businesses Seeing AI Returns from Those Cleaning Up After the Fact

In 2026, a Gartner study confirmed that businesses reaping the benefits of their AI initiatives invest up to four times more in their analytics and AI data foundations. That shows, contrary to popular belief, that it isn’t the sophistication of AI tools that leads to success. It’s the commitment to data privacy, security, governance and workforce upskilling.

In other words, the way your business approaches data management can significantly affect the outcomes of your AI implementation.

Successful Businesses Focus on a Multifaceted Investment Strategy

Leading businesses are treating data privacy and governance as fundamental pillars of their strategy. They recognise that strong AI data foundations provide a reliable base upon which effective AI solutions can be built. Therefore, they prioritise investments in:

• Data privacy. Regular data audits allow them to evaluate their data quality and privacy compliance posture.
• Data security. Investing in advanced security frameworks empowers them to protect sensitive information and prevent breaches.
• Data governance. By establishing clear data usage and privacy policies, they ensure that stakeholders can easily understand them.
• Workforce upskilling. They regularly train their employees to ensure they are up to date with data governance best practices and security protocols.

For highly regulated businesses, this approach is especially powerful. By implementing strong data governance measures, you won’t only minimise the risk of regulatory fines. You will also be able to leverage high-quality data to create more effective AI-driven fraud detection systems. This approach will lead to increased operational efficiency and enhanced customer trust in the long term.

Businesses Struggling With Their AI Initiatives Focus Solely on AI Tools

Conversely, some businesses opt to emphasise immediate AI adoption without underpinning it with necessary data governance, as if they were merely reacting to technological trends. They concentrate their efforts on:

• State-of-the-art AI tools. They purchase the latest features-rich AI tools, neglecting to invest in key components, such as compliance and security measures, putting the business at risk of data breaches.
• Fast AI deployment. They implement AI tools as fast as possible, before building a strong AI data foundation. But if your AI-driven marketing campaign is built on inaccurate customer data, it can lead to wrong targeting and ineffective strategies.

The consequences of such an approach can be disastrous. Your teams will have to continuously engage in firefighting, responding to and rectifying recurring data problems. Your entire business will suffer from reduced AI investments ROI due to persistent data inaccuracies.

The Devil Is in the Details

The contrast between these two business strategies isn’t merely a matter of performance. It reflects a fundamental structural difference. Weak data frameworks breed inefficiencies, with each new AI initiative inheriting and amplifying existing data problems.

On the other hand, strong AI data foundations let you generate long-term business value, compounding positive outcomes through deeper insights and reliable predictions. Acora demonstrated it in the first half of 2026, when it helped several global customers balance AI innovation with governance and risk, turning the businesses’ AI ambitions into measurable success.

Building AI-Ready Data Foundations With Acora

Ultimately, the businesses that will thrive with AI in 2026 are those that embrace robust data governance, security practices, enhanced data quality and continuous employee development as a continuous process, not a one-off project.

At Acora, we understand the challenges faced by IT Directors, CISOs and Heads of IT. Our team of experts can help you:

• Evaluate your current AI data foundation posture by conducting comprehensive assessments.
• Identify potential governance and security gaps by highlighting the vulnerabilities that could undermine your AI initiatives.
• Establish frameworks for consistent data quality management to proactively address issues and ensure your AI investment ROI translates into true business value rather than costly reworks.

Prioritise data governance and security today to ensure your business can successfully navigate the AI landscape of tomorrow by turning potential challenges into opportunities for growth and innovation.

Fixing your AI data foundations rather than simply switching on AI tools is easier than it may seem. Speak to our experts to discover how we can assess your data readiness and help you lay the groundwork for a successful AI journey.