Why GenAI Stalls Without Strong Governance
As companies grapple with moving Generative AI projects from experimentation to production, many businesses remain stuck in pilot mode. Recent research highlights that 92% of organizations are concerned that GenAI pilots are accelerating without first tackling fundamental data issues. Even more telling, 67% have been unable to scale even half of their pilots to production. This production gap is less about technological maturity and more about the readiness of the underlying data. The potential of GenAI depends upon the strength of the ground it stands on. Today, for most organizations, that ground is shaky at best.
Why GenAI Gets Stuck in Pilot
Although GenAI solutions are certainly mighty, they’re only as effective as the data that feeds them. The old adage of “garbage in, garbage out” is truer today than ever. Without trusted, complete, entitled, and explainable data, GenAI models often produce results that are inaccurate, biased, or unfit for purpose.
Unfortunately, organizations have rushed to deploy low-effort use cases, like AI-powered chatbots offering tailored answers from different internal documents. While these improve customer experiences to an extent, they don’t demand deep changes to a company’s data infrastructure. However, to scale GenAI strategically—whether in healthcare, financial services, or supply chain automation—requires a different level of data maturity.
In fact, 56% of Chief Data Officers cite data reliability as a key barrier to the deployment of AI. Other issues include incomplete data (53%), privacy issues (50%), and larger AI governance gaps (36%).
No Governance, No GenAI
To take GenAI beyond the pilot stage, companies must treat data governance as a strategic imperative. They need to ensure data is up to the job of powering AI models, and to do so, the following questions need to be addressed:
- Is the data used to train the model coming from the right systems?
- Have we removed personally identifiable information and followed all data and privacy regulations?
- Are we transparent, and can we prove the lineage of the data the model uses?
- Can we document our data processes and be ready to show that the data has no bias?
Data governance also needs to be embedded within an organization’s culture. To achieve this, building AI literacy across all teams is essential. The EU AI Act formalizes this responsibility, requiring both providers and users of AI systems to make best efforts to ensure employees are sufficiently AI-literate, understanding how these systems work and how to use them responsibly. However, effective AI adoption goes beyond technical know-how; it demands a strong foundation in data skills, from understanding data governance to framing analytical questions. Treating AI literacy in isolation from data literacy would be short-sighted, given how closely they are intertwined.
In terms of data governance, there’s still work to be done. Among businesses that want to increase their data management investments, 47% agree that lack of data literacy is a top barrier. This highlights the need for building top-level support and developing the right skills across the organization. Without these foundations, even the most powerful LLMs will struggle to deliver.
Developing AI That Must Be Held Accountable
In the current regulatory environment, it’s no longer enough for AI to “just work”; it also needs to be accountable and explainable. The EU AI Act and the UK’s proposed AI Action Plan require transparency in high-risk AI use cases. Others are following suit, with over 1,000 related policy bills on the agenda in 69 countries.
This global movement towards accountability is a direct result of increasing consumer and stakeholder demands for fairness in algorithms. For example, organizations must be able to explain the reasons why a customer was turned down for a loan or charged a premium insurance rate. To do that, they need to know how the model made that decision, which hinges on having a clear, auditable trail of the data used to train it.
Without explainability, businesses risk losing customer trust and facing financial and legal repercussions. As a result, traceability of data lineage and justification of results is not a “nice to have,” but a compliance requirement.
As GenAI expands beyond being used for simple tools to fully-fledged agents that can make decisions and act upon them, the stakes for strong data governance rise even higher.
Steps for Building Trustworthy AI
To scale GenAI responsibly, organizations should look to adopt a single data strategy across three pillars:
- Tailor AI to business: Catalogue your data around key business objectives, ensuring it reflects the unique context, challenges, and opportunities specific to your business.
- Establish trust in AI: Establish policies, standards, and processes for compliance and oversight of ethical and responsible AI deployment.
- Build AI data-ready pipelines: Combine your diverse data sources into a resilient data foundation for robust AI, incorporating prebuilt GenAI connectivity.
When organizations get this right, governance accelerates AI value. In financial services, for example, hedge funds are using GenAI to outperform human analysts in stock price prediction while significantly reducing costs. In manufacturing, supply chain optimization driven by AI enables organizations to react in real-time to geopolitical changes and environmental pressures.
These aren’t just futuristic ideas; they’re happening now, driven by trusted data.
With strong data foundations, companies reduce model drift, limit retraining cycles, and increase speed to value. That’s why governance isn’t a roadblock; it’s an enabler of innovation.
What’s Next?
After experimentation, organizations are moving beyond chatbots and investing in transformational capabilities. From personalizing customer interactions to accelerating medical research and improving mental health to simplifying regulatory processes, GenAI is beginning to demonstrate its potential across industries.
Yet these gains depend entirely on the data underpinning them. GenAI starts with building a strong data foundation through robust data governance. While GenAI and agentic AI will continue to evolve, they won’t replace human oversight anytime soon. Instead, we’re entering a phase of structured value creation, where AI becomes a reliable co-pilot. With the right investments in data quality, governance, and culture, businesses can finally turn GenAI from a promising pilot into something that fully gets off the ground.
