As pressure mounts to deliver on the promise of Generative AI, business leaders are learning that technology alone isn’t enough. Our recent research report, The Pathway to GenAI Competitive Advantage, revealed that 79% of leaders believe GenAI will give their organisation a competitive edge within 18 months.

Yet only 13% feel extremely confident in their data’s readiness to support GenAI adoption. This confidence gap highlights what the research defines as the data-AI readiness gap, a major barrier to scaling Generative AI applications effectively.

Key challenges in AI readiness and adoption

Organisations face several persistent barriers as they attempt to move from GenAI experimentation to enterprise-scale impact:

  • Data quality and reliability remain the top concern, with 69% of leaders citing them as the primary obstacle to realising GenAI value.
  • Siloed systems and legacy infrastructure make it difficult to access and govern data consistently across the organisation.
  • Lack of cross-functional alignment hinders adoption. Without shared understanding across IT, compliance, legal and business teams, initiatives struggle to scale.
  • Insufficient governance and trust frameworks increase the risk of bias, inaccuracy, or misuse—especially as AI outputs are integrated into decision-making.

To address these challenges and unlock the full benefits of Generative AI applications, organisations must take a more deliberate, strategic approach to readiness.

This means moving purposefully along the AI maturity curve – a model that reflects how prepared an enterprise is to activate, govern and scale GenAI use cases responsibly and sustainably.

How to scale GenAI with confidence

Based on insights from our research and hands-on experience supporting enterprise AI transformation, we’ve identified five strategic imperatives that separate the leaders from those remaining stagnant.

1. Build a modern data foundation

Most enterprises hold petabytes of untapped data, but much of it is inaccessible, locked in file shares, outdated platforms or disconnected systems. To realise the full potential of GenAI, this content must be discovered, classified and governed at scale, regardless of format or location.

A modern data foundation allows organisations to enrich metadata, apply consistent data governance policies, and create a unified, high-quality data layer that supports responsible Generative AI applications. This foundation is essential for producing accurate outputs, mitigating compliance risks, and achieving sustainable AI adoption across the enterprise.

2. Prioritise use cases with measurable impact

Organisations making real progress along the AI maturity curve focus their resources on strategic, high-impact GenAI use cases. Rather than defaulting to the most visible or novel applications, they choose use cases that deliver clear value, such as automating highly manual and repetitive processes, enhancing analytics, or improving customer experience.

These initiatives are built on trusted data and demonstrate tangible Generative AI benefits, from increased efficiency to faster decision-making. When aligned with business priorities, these use cases help to justify further investment and build internal momentum around AI adoption.

3. Strengthen cross-functional literacy

Maturing AI capabilities depends on more than tools and models. Organisations showing strong progress are those that invest in AI and data literacy across their workforce, particularly IT, legal, compliance and executive teams.

When stakeholders understand how GenAI functions, how data governance underpins its reliability, and where the risks lie, they are better positioned to collaborate and scale responsibly. A shared understanding of trust in AI helps ensure that adoption efforts are sustainable and well-aligned with organisational goals.

4. Balance innovation with discipline

The Generative AI landscape is evolving quickly. However, scaling too quickly without the right foundations often results in failure. Organisations that advance along the AI maturity curve apply discipline and foresight to their AI adoption strategies.

These organisations align GenAI with existing workflows, invest in technologies that support both legacy and modern environments, and maintain strong oversight from pilot to production. This also reduces the risk of regulatory or reputational exposure.

Gartner predicts that 30% of GenAI projects will be abandoned after proof of concept, often due to challenges with data governance, integration and lack of measurable outcomes. A focused, strategic approach helps mitigate these risks from the outset.

5. Embed trust into every layer of AI deployment

AI trust remains one of the most important yet underestimated elements of successful deployment. In our research, 58% of business leaders cited ethics, governance and trust in AI as a top concern.

Building trust in AI systems requires:

  • Policy-based data governance (retention, disposal, access control)
  • Privacy impact assessments and responsible handling of sensitive content
  • Ongoing monitoring and auditability across AI data pipelines

Embedding these practices from the outset helps organisations demonstrate responsible use of Generative AI applications, protect sensitive information, and reinforce AI trust among stakeholders, regulators and customers.

Maturity starts with data readiness

The ability to scale GenAI and deliver meaningful business outcomes is shaped by the quality, accessibility and governance of the data behind it.

The Pathway to GenAI Competitive Advantage report explores how leading organisations are overcoming barriers to adoption and building a foundation for responsible, enterprise-wide GenAI adoption. Drawing on insights from 171 global business leaders, the report outlines the four essential pillars of AI-data maturity: data quality, accuracy & reliability, security & privacy, and cost & ROI.

At the centre of these pillars is a consistent theme: organisations must be able to discover, organise and govern their data at scale to unlock GenAI’s full potential.

EncompaaS’s AI-powered information management platform transforms fragmented, unstructured content into a secure, compliant and context-rich foundation for GenAI. By enabling in-place discovery, enrichment and governance, EncompaaS equips enterprises to move beyond pilots and scale with confidence.

Download the report to benchmark your organisation’s readiness and explore what sets GenAI leaders apart.