Building Trust in AI: What’s Holding Us Back?

AI holds significant opportunities for enterprises with large-scale data management requirements. It has the potential to streamline operations, improve accuracy and tighten security while minimising reliance on manual processes.

Yet, many organizations remain hesitant to entrust AI with such a critical function.

Concerns surrounding accuracy, transparency and control are at the heart of AI’s trust gap. Enterprises often focus on AI’s potential inaccuracies and the risk of data misuse, limiting their ability to fully capitalise on its capabilities.

However, establishing trust in AI is increasingly important. By adhering to ethical frameworks, ensuring transparency and implementing robust data governance, organizations can confidently and securely integrate AI into their data management strategies, unlocking its potential while maintaining control and compliance.

Why trust in AI matters

AI’s trust challenge is not about its capabilities but rather how it is perceived. Organizations have long relied on humans to manage data, despite well-known challenges like human error, inconsistency and bias.

Yet when it comes to AI, expectations are different; many assume it must be flawless before it can be useful.

Jaimie Tilbrook, Chief Product Officer at EncompaaS, says this need for perfection is preventing many organizations from taking full advantage of AI’s capabilities:

“These issues are artificially holding back the necessary trust that is required to successfully utilise AI,” Tilbrook says. “Trust is required to dramatically escalate the success of information management and see it become normalised in this space.”

Ironically, businesses have long accepted the risks of data breaches and over-retention, yet hesitate to adopt AI: a solution capable of addressing these challenges. No technology is entirely without risk, but with the right governance and transparency, AI presents a powerful opportunity to reduce exposure and future-proof data at scale.

Three key barriers to trusting AI systems

1. AI still feels like a novelty
   For many, their initial exposure to AI came through rudimentary applications: chatbots generating whimsical stories, AI-created artwork with noticeable distortions, or document summaries with inconsistent accuracy.
   
   While these experiences showcased AI’s capabilities, they also reinforced skepticism when the outcomes were flawed.
   
   When AI is perceived as a novelty rather than a sophisticated business tool, organizations may be reluctant to entrust it with critical functions such as data governance and security. Shifting this perception requires demonstrating AI’s reliability, precision and ability to enhance human decision-making rather than replace it.
   
2. The flawed expectation of 100% accuracy
   A prevailing misconception is that AI must achieve absolute accuracy before it can be deemed trustworthy. Ironically, human decision-making, upon which organizations rely daily, is inherently flawed, prone to errors, inconsistencies and biases. These imperfections present significant risks in data management, yet AI is often held to an unattainably high standard.
   
   In practice, AI already surpasses human reliability in many enterprise applications, particularly in automating compliance, detecting anomalies and managing large-scale information ecosystems.
   
   AI-driven information management platforms can identify and classify sensitive data at a scale no human team could replicate. By reducing compliance risks and minimising human error, AI enhances data security rather than compromising it.
   
   The issue, therefore, is not AI’s capability but rather the flawed assumption that human judgment represents the ultimate benchmark for accuracy and reliability.
   
3. The fear that AI retains and shares data
   One of the biggest trust barriers is the belief that once AI processes data, it retains that information and uses it to answer future queries, even for different users or organizations. While this can be true in some public AI models, it is not an inherent function of enterprise AI systems designed for secure information management.
   
   This myth has led many decision-makers to err on the side of caution, delaying AI adoption despite its security benefits.
   
   AI-driven governance platforms like EncompaaS allow organizations to maintain complete control over their data. This ensures that sensitive information is handled responsibly, securely and in compliance with strict regulatory standards.

Explainability and transparency must pave the way for trust

One of the biggest steps toward building trust in AI is explainability: the ability to understand, audit and validate AI decisions. Black-box AI models, where decisions are made without clear reasoning, create uncertainty and risk.

For AI to gain widespread trust, organizations need systems that provide:

• Clear audit trails showing how AI decisions are made.
• Transparent models that allow human oversight.
• Governance frameworks ensuring AI remains aligned with business policies and compliance regulation

Without explainability, AI adoption will remain slow and cautious. But when organizations can see how and why AI-driven decisions are made, confidence grows.

Accountability can be established through AI governance

Trust goes beyond how AI functions. It stems from who is responsible for its outcomes. Robust governance frameworks ensure that AI is:

• Used ethically and in compliance with regulatory requirements.
• Monitored continuously to detect and prevent unintended bias or risk.
• Aligned with organizational policies to maintain security and transparency.

Building trust and reputation in AI through responsible use is the key for organizations. By implementing clear accountability structures, enterprises can ensure AI remains a trusted tool, not an unpredictable risk.

Transparency builds stakeholder confidence

Customer trust in AI is just as important as building internal confidence. Customers, regulators and partners also need to know that AI is being used responsibly. Transparency in AI governance helps:

• Regulators verify compliance with laws like GDPR and CCPA.
• Stakeholders trust that AI-powered decisions are fair, unbiased and explainable.
• Customers feel confident that their data is protected and not being misused.

The path to responsible AI adoption

Hesitations around AI adoption are understandable, but they are also preventable. As EncompaaS CEO Jesse Todd explains, “There is no reason to wait to embrace the competitive advantages AI can bring to your organization, as long as you prepare and de-risk your data in readiness for AI adoption.”

By implementing intelligent information management solutions, policies and governance frameworks, enterprises can eliminate risk while maximising AI’s potential

EncompaaS: The solution for trusted AI governance

AI has the potential to transform enterprise data management, but without the right governance, it introduces risk and uncertainty.

With EncompaaS, enterprises gain full visibility and control over AI-driven processes, maintaining oversight of how data is classified, enriched and governed. Automated compliance capabilities ensure regulatory adherence, reducing the complexity of evolving legal requirements. At the same time, advanced security measures safeguard sensitive information, mitigating risks and reinforcing trust in AI-powered decision-making.

By integrating AI with a structured governance framework, organizations can move from hesitation to confidence, unlocking AI’s full potential while maintaining security, compliance and operational integrity.

To explore how EncompaaS can help establish a trusted AI governance framework, download our guide on responsible AI implementation, or contact us to discuss how we can support your enterprise data strategy.