As companies grapple with moving Generative AI projects from experimentation to productionising – many businesses remain stuck in pilot mode. As our recent research highlights, 92% of organisations 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. And today, for most organisations, 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, organisations have rushed to deploy low-effort use cases, like AI-powered chatbots offering tailored answers from different internal documents. And while these do improve customer experiences to an extent, they don’t demand deep changes to a company’s data infrastructure. But 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 are 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 to their business.They need to ensure data is up to the job of powering AI models, and to 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 organisation’s culture. To do this, requires building AI literacy across all teams. The EU AI Act formalises this responsibility, requiring both providers and users of AI systems to make best efforts to ensure employees are sufficiently AI-literate, making sure they understand how these systems work and how to use them responsibly. However, effective AI adoption goes beyond technical know-how. It also 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’re intertwined.
In terms of data governance, there’s still work to be done. Among businesses who 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 organisation is crucial. 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 explained. The EU AI Act and the UK’s proposed AI Action Plan requires transparency in high-risk AI use cases. Others are following suit, and 1,000+ related policy bills are 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, organisations must be able to say the reasons why a customer was turned down for a loan or charged a premium insurance rate. To be able to do that, they would need to know how the model made that decision, and that in turn hinges on having a clear, auditable trail of the data that was used to train it.
Unless there is explainability, businesses risk losing customer trust as well as 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.
And 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
So, what does good look like? To scale GenAI responsibly, organisations 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 baking in prebuilt GenAI connectivity.
When organisations get this right, governance accelerates AI value. In financial services for example, hedge funds are using gen AI to outperform human analysts in stock price prediction while significantly reducing costs. In manufacturing, supply chain optimisation driven by AI enables organisations to react in real-time to geopolitical changes and environmental pressures.
And 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, organisations are moving beyond chatbots and investing in transformational capabilities. From personalising customer interactions to accelerating medical research, improving mental health and 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 strong data governance. And while GenAI and agentic AI will continue to evolve, it 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.