The GenAI Readiness Gap: Why Most Enterprises are Unprepared for AI-Driven Transformation

It’s no secret GenAI is quickly becoming a defining force in enterprise transformation. In fact, 79% of business leaders believe GenAI will give their organisation a competitive advantage within the next 18 months.

This insight is according to The Pathway to GenAI Competitive Advantage, a landmark research report delivered by EncompaaS in partnership with the Business Performance Innovation (BPI) Network and the Growth Officer Council.

However, belief and readiness are two different things. The same study reveals a sobering truth: 60% of business leaders lack confidence in their organisation’s data readiness to support GenAI. Only 13% say they feel “extremely confident”.

This disconnect between high expectations and limited preparedness is what the report defines as the GenAI readiness gap, and it’s the greatest barrier to realising the competitive advantage GenAI promises.

The readiness gap: What’s holding enterprises back?

There’s a stark difference between deploying a GenAI interface and delivering value from it. Many organisations have experimented with GenAI tools, but when projects underdeliver (or fail outright), it’s usually due to the same underlying issue: inadequate data foundations.

The report highlights the biggest obstacles business leaders are encountering as they work to realise the full value of GenAI:

• 69% cite data accuracy and reliability
• 68% struggle with AI integration and implementation
• 58% identify AI ethics, governance, and trust
• 50% cite data security and privacy

These findings reveal that most GenAI failures stem not from the AI itself, but from data that is fragmented, unstructured, inaccurate or insecure.

Even seemingly simple GenAI applications like querying a contract or analysing a trend depend on data that is:

• Classified and enriched
• Normalised and organised
• Governed with proper privacy and compliance controls

Unfortunately, data silos, legacy data practices and a limited understanding of data quality versus data readiness remain persistent barriers.

As the report explains, “Data quality doesn’t equal data readiness. Businesses are applying structured data governance techniques to unstructured data, creating a ‘square peg in a round hole’ scenario when preparing data for AI”.

What sets AI-ready organisations apart?

The 13% of business leaders who feel “extremely confident” in their AI readiness offer a blueprint for success. Their advantage doesn’t come from adopting GenAI first; it comes from laying the groundwork before adoption.

According to the report, AI-ready organisations are those that:

• Invest in automated data discovery, classification, and enrichment
• Break down data silos to create a holistic view of enterprise information
• Implement robust governance frameworks to de-risk sensitive content
• Prioritise explainability and transparency in their AI workflows

These organisations understand that GenAI is only as powerful as the data it’s fed. Rather than rushing to implement GenAI at the interface level, they’ve focused on preparing their information environment to ensure the accuracy, reliability, and security of AI-generated outputs.

Consider this insight from the report:

“Among business leaders confident in their data-AI readiness, 81% expect to see AI agents improving customer experience… Among respondents who lack confidence, only 21% said the same thing, highlighting the contrast in value realisation between leaders who’ve shored up their data supply chain and those who haven’t.”

In other words, data readiness is a strategic differentiator, not simply a technical matter. The ability to harness agentic AI, for example, depends on whether organisations have prepared their data in ways that GenAI can actually use.

Bridging the GenAI readiness gap

The GenAI readiness gap isn’t insurmountable, but it does require deliberate action. The organisations seeing early success with GenAI aren’t necessarily the ones who adopted it first. They’re the ones who prepared their data environment before bringing AI into the equation.

Here are seven practical steps to help close the gap

1. Conduct a full data assessment and inventory – Connect to all relevant systems to map structured, unstructured and semi-structured data. Capture key characteristics, relationships, permissions and formats across repositories to create an enterprise-wide view of the data landscape.
2. Analyse and classify content using AI-driven discovery – Leverage tools like EncompaaS to automatically analyse data assets and classify sensitive information (such as PII, PCI, PHI, sentiment, ownership and criticality) across documents, emails, images, video, audio and more.
3. Enrich metadata to build a unified, context-rich data layer – Use document cognition to extract and apply metadata that reflects the content’s business purpose. This creates a consistent metadata structure to support accurate GenAI outputs and unified governance.
4. Improve data quality through automated cleansing – Apply cleansing rules to address inconsistencies, errors and duplicates, ensuring GenAI is fuelled by complete, accurate and trustworthy information.
5. Strengthen data access governance – Highlight and remediate incorrect permissions, enforce least privilege access, and apply best practice encryption to safeguard sensitive data from unauthorised access.
6. Automate policy enforcement across systems – Configure and execute automated retention, disposal and archiving policies across all repositories. Ensure disposals are hermetic and duplicate items are removed to maintain regulatory compliance and reduce legal risk.
7. Monitor security and assess privacy risks continuously – Leverage real-time monitoring to identify vulnerabilities, compliance gaps and data breaches. Conduct privacy impact assessments to align AI usage with GDPR, CCPA and other regulatory frameworks.

Why the time to act is now

GenAI will not wait for enterprises to catch up. As a recent McKinsey paper says:

“Most companies are pursuing efficiency gains with GenAI, but leaders believe the real value of the technology will accrue from applications that transform the effectiveness of business functions.”

Yet organisations that jump ahead without preparing their data will find themselves stuck with unreliable results, compliance risks, and failed initiatives. Already, Gartner predicts that 30% of GenAI projects will be abandoned after proof of concept due to poor data quality and governance issues.

To avoid becoming part of that statistic, enterprises must shift their focus from GenAI excitement to AI data enablement. Data preparation is no longer just an IT concern; it’s a business-critical priority that will determine who gains the advantage and who falls behind.

How EncompaaS closes the readiness gap

At EncompaaS, we help enterprises transform their data landscape to unlock GenAI’s full potential. Our intelligent information management platform leverages next-generation AI to:

• Classify data in-place: Automatically discover and organise data across repositories to ensure trusted provenance
• Protect sensitive content: Identify and anonymise personal and confidential information to meet privacy requirements.
• Improve data quality: Cleanse and enrich data to create reliable, AI-ready datasets.
• Ensure traceability: Build in auditability, versioning and data lineage across AI pipelines
• Maintain accuracy: Continuously monitor data quality to sustain trustworthy GenAI outputs.

As our CEO Jesse Todd explains in the report’s foreword, “Without AI-ready data, even the most promising GenAI use cases will fail to deliver.” It’s an entirely preventable challenge, but only if organisations take a proactive, strategic approach to their data.

By acting now to prepare your data for AI, you can position your organisation for transformational success.

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