As firms 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. Much 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 in regards to the readiness of the underlying data. The potential of GenAI depends upon the strength of the bottom it stands on. And today, for many organisations, that ground is shaky at best.
Why GenAI gets stuck in pilot
Although GenAI solutions are actually mighty, they’re only as effective as the info 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 which might be 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 an organization’s data infrastructure. But to scale GenAI strategically, whether in healthcare, financial services, or supply chain automation, requires a distinct level of knowledge maturity.
In actual 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 bigger AI governance gaps (36%).
No governance, no GenAI
To take GenAI beyond the pilot stage, firms must treat data governance as a strategic imperative to their business.They should ensure data is as much as the job of powering AI models, and to so the next questions must be addressed:
- Is the info used to coach the model coming from the precise systems?
- Have we removed personally identifiable information and followed all data and privacy regulations?
- Are we transparent, and may we prove the lineage of the info the model uses?
- Can we document our data processes and be ready to point out that the info has no bias?
Data governance also must be embedded inside an organisation’s culture. To do that, requires constructing AI literacy across all teams. The EU AI Act formalises this responsibility, requiring each providers and users of AI systems to make best efforts to make sure employees are sufficiently AI-literate, ensuring they understand how these systems work and the way to use them responsibly. Nonetheless, effective AI adoption goes beyond technical know-how. It also demands a powerful foundation in data skills, from understanding data governance to framing analytical questions. Treating AI literacy in isolation from data literacy can be short-sighted, given how closely they’re intertwined.
When it comes to data governance, there’s still work to be done. Amongst businesses who need to increase their data management investments, 47% agree that lack of knowledge literacy is a top barrier. This highlights the necessity for constructing top-level support and developing the precise skills across the organisation is crucial. Without these foundations, even probably the most powerful LLMs will struggle to deliver.
Developing AI that should be held accountable
In the present regulatory environment, it’s not enough for AI to “just work,” it also must be accountable and explained. The EU AI Act and the UK’s proposed AI Motion 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 accelerating consumer and stakeholder demands for fairness in algorithms. For instance, organisations must give you the chance to say the the explanation why a customer was turned down for a loan or charged a premium insurance rate. To give you the chance to try this, they would wish to know the way the model made that call, and that in turn hinges on having a transparent, auditable trail of the info that was used to coach it.
Unless there’s explainability, businesses risk losing customer trust in addition to facing financial and legal repercussions. In consequence, traceability of knowledge lineage and justification of results isn’t a “nice to have,” but a compliance requirement.
And as GenAI expands beyond getting used for easy tools to fully-fledged agents that could make decisions and act upon them, the stakes for strong data governance rise even higher.
Steps for constructing trustworthy AI
So, what does good appear 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 corporation.
- Establish trust in AI: Establish policies, standards, and processes for compliance and oversight of ethical and responsible AI deployment.
- Construct AI data-ready pipelines: Mix your diverse data sources right 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 instance, 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, firms 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 starting to display its potential across industries.
Yet these gains depend entirely on the info underpinning them. GenAI starts with constructing a powerful data foundation, through strong data governance. And while GenAI and agentic AI will proceed to evolve, it won’t replace human oversight anytime soon. As an alternative, we’re entering a phase of structured value creation, where AI becomes a reliable co-pilot. With the precise investments in data quality, governance, and culture, businesses can finally turn GenAI from a promising pilot into something that fully gets off the bottom.
