Unlocking GenAI’s Full Potential: Overcoming the Top 3 Barriers to Success

Overcoming the Top 3 Barriers to GenAI Success

The excitement and potential surrounding GenAI is undeniable, but for many enterprises, the reality of implementation is far more complex than anticipated. As GenAI moves from experimentation to enterprise adoption, many organisations are discovering that value isn’t guaranteed.

Research from our latest report, The Pathway to GenAI Competitive Advantage, reveals a divide: while some companies are beginning to see real returns, others are struggling to extract meaningful impact from GenAI.

Three barriers consistently stand in the way: data accuracy, AI integration and governance.

Barrier 1: Data accuracy and reliability

The problem:

GenAI is only as effective as the data behind it and that’s where many organisations stall. In fact, 69% of leaders say data accuracy and reliability is their biggest challenge when it comes to GenAI performance.

This concern is well-founded. When data is incomplete, inconsistent or poorly classified, the resulting outputs reflect those deficiencies. The consequences include flawed insights, compliance risks and decisions that lack a reliable foundation.

Nearly half of business leaders report dissatisfaction with the accuracy of their GenAI results. The issue isn’t with the models themselves, but rather the readiness of the data feeding them.

Key contributing factors include poor-quality training data, difficulty curating relevant datasets and insufficient or missing metadata. As the report notes:

“Most organisations are only scratching the surface of their unstructured content. Most of this data is missing from AI pipelines because it’s undiscovered, lacking context and not trusted.”

The solution:

Consistent, high-quality GenAI outcomes rely on robust data preparation and management. The organisations leading in this space are those prioritising data enrichment, classification and governance from the outset.

These organisations are assessing both structured and unstructured data, identifying inconsistencies and gaps that could undermine AI performance. Many are leveraging AI to automate tagging and classification, ensuring GenAI can not only access information but interpret it with the necessary context.

Our findings showed that these businesses are also applying active metadata strategies, which go well beyond basic file names and timestamps to generate metadata that is meaningful for AI use cases. EncompaaS Chief Customer Officer David Gould points to passive vs active metadata as a major player in the success of GenAI:

EncompaaS Chief Customer Officer David Gould points to passive vs active metadata as a major player in the success of GenAI:

“Traditional or Passive metadata – such as file name, creator and date – offers little value when trying to extract meaningful insights,” says David. “Active metadata – created specifically for AI use cases – enables AI models to discover, extract and utilise content effectively.”

When data is accurate, complete and enriched with context, GenAI delivers. More importantly, it scales, enabling reliable and repeatable outcomes for more informed decisions across the enterprise.

Barrier 2: AI integration and implementation

The problem:

GenAI platforms may appear simple on the surface (enter a prompt and receive an answer), but behind the scenes, integration is complex. According to our report, 68% of respondents cited AI integration and implementation as their top challenge.

Many enterprises are grappling with fragmented IT ecosystems, legacy infrastructure and siloed data. Successfully embedding GenAI into these environments demands not only technical capability, but a deep understanding of data flows, business logic and organisational processes.

“Far too many business leaders focus on GenAI’s interface layer. They’re fascinated by the speed of an answer, but this is only the tip of the iceberg,” the report cautions.

If GenAI cannot access the right data, or is fed inconsistent or incomplete information, its outputs will be flawed, limiting its usefulness and increasing risk.

The solution:

Effective GenAI integration begins with rethinking your data architecture. The organisations seeing real value are the ones investing in modern, scalable infrastructure that supports AI from the ground up.

Key considerations include:

• Unified data access: Eliminate silos by connecting systems and repositories, enabling GenAI to operate with a complete and consistent dataset.
• Cloud-native infrastructure: Leverage platforms that support agility, automation and scalability as AI requirements evolve.
• Governed data pipelines: Ensure GenAI is connected to secure, well-managed data flows that uphold quality, consistency and compliance.
• Business-led use cases: Identify high-impact opportunities where GenAI can deliver meaningful value, focusing on areas such as process automation, risk mitigation, knowledge discovery and decision support. Map the supporting systems and data required to enable each use case effectively.

Modernising your environment is not an overnight transformation, but it is a strategic investment. Done right, it allows GenAI to move beyond experimental pilots and deliver sustained, enterprise-wide impact.

Barrier 3: AI governance and trust

The problem:

As GenAI adoption accelerates, so too do concerns around ethics, bias, privacy and regulatory compliance. Our research shows that 58% of business leaders identify governance as one of their top challenges, while half express concerns about privacy and security.

These concerns are not theoretical. When AI systems produce opaque results or access sensitive data without adequate safeguards, trust is eroded, internally and externally. Customers, regulators, partners and stakeholders all expect transparency and accountability. Without it, the reputational and regulatory risks are significant.

Governance can no longer be an afterthought or a set of static policies. It must be operationalised and embedded into every layer of the AI lifecycle.

The solution:

Trustworthy GenAI outcomes depend on strong, scalable governance frameworks. Leading organisations are embedding governance into their data environments and AI workflows to mitigate risk and build trust from day one.

Key practices include:

• Automating governance at scale with platforms that apply compliance, retention and privacy policies consistently across all data so nothing slips through the cracks.
• Following established responsible AI frameworks like Microsoft’s Responsible AI Standard (v2), which covers everything from fairness and transparency to accountability and safety.
• Building explainability into AI workflows, so teams can trace how GenAI came to a conclusion and verify that it’s using the right data, the right way.

Without robust governance, GenAI initiatives are unlikely to scale and may never progress beyond proof of concept.

How leading enterprises are overcoming the barriers

In our research, we found that those organisations succeeding with GenAI shared key shifts in mindset and strategy.

First, they elevate data preparation to a business-wide priority. Rather than relegating it to an IT task, they treat it as a foundational step and critical to the performance and reliability of future GenAI initiatives.

They also prioritise transparency and trust, knowing that the credibility of AI outputs depends on both. That means making sure the data is accurate, the sources are traceable and the governance is robust.

Finally, they’re investing in the right platforms to make this possible at scale. Platforms like EncompaaS help these organisations discover, classify and govern data (both structured and unstructured) so GenAI can access high-quality information in a secure and compliant way.

One global professional services firm worked with EncompaaS to classify and manage more than 500 million documents. They were able to delete over 10 million over-retained files, significantly reducing risk and laying the groundwork for trustworthy, enterprise-wide GenAI adoption.

Overcome GenAI barriers more easily with EncompaaS

EncompaaS supports organisations in addressing the most critical barriers to GenAI success, starting with data readiness.

Our intelligent information management platform automatically discovers, enriches, normalises and de-risks all enterprise content, ensuring no valuable data is overlooked. It applies governance policies in place and at scale, embedding compliance into every stage of the data lifecycle.

With EncompaaS, teams gain the visibility and control needed to manage risk without slowing down innovation. There’s no need to choose between agility and accountability—you get both.

Read the full report: The Pathway to GenAI Competitive Advantage.