As companies work hard to transform generative AI projects from experiments to production – many businesses are still in pilot mode. As highlighted by our recent study, 92% of organizations are concerned that Genai pilots are accelerating without first addressing the basic data problem. More convincing: 67% of people can’t even expand half of the pilots to production. This production gap has nothing to do with technological maturity, but more about the preparation of basic data. Genai’s potential depends on the strength of the ground it occupies. Today, for most organizations, this foundation is shaky at best.
Why Genai is trapped in the pilot
Although the Genai solution is certainly powerful, They are as effective as the data that feeds them. Today’s ancient motto of “garbage, garbage trash” is more true than ever. Without trusted, complete, qualified data, the Genai model often produces inaccurate, biased or unsuitable results.
Unfortunately, organizations are eager to deploy low- and use cases, such as AI-powered chatbots, providing tailored answers from different internal documents. While these do improve the customer experience to some extent, they do not need to make profound changes to the company’s data infrastructure. However, to strategically scale Genai, whether it is health care, financial services or supply chain automation, requires different data maturity.
In fact, 56% of Chief Data Officers regard data reliability as a key barrier to AI deployment. Other issues are incomplete data (53%), privacy issues (50%) and a large AI governance gap (36%).
No governance, no Genai
To get Genai beyond the pilot phase, companies must view data governance as a strategic priority for their business. They need to ensure that the data complies with the work that powers the AI model, so the following issues need to be addressed:
- Is the data used to train models from the correct system?
- Have we deleted personally identifiable information and complied with all data and privacy regulations?
- Are we transparent and can we prove the lineage of the data used by the model?
- Can we record our data process and be prepared to prove that the data is not biased?
Data governance also needs to be embedded in the organization’s culture. To do this, it is necessary to build AI literacy across all teams. The EU AI Act adapts this responsibility to this responsibility, requiring providers and users of AI systems to do their best to ensure that employees have sufficient AI-Literate to ensure that they understand how these systems work and how they use them responsibly. However, effective AI adoption goes beyond technical knowledge. From understanding data governance to architectural analysis issues, it also requires a solid foundation in data skills. Given how intertwined they are, it would be shortsighted to deal with AI literacy in isolation from data literacy.
There is still work to be done in data governance. Among businesses that want to increase their investment in data management, 47% agree that lack of data literacy is the biggest barrier. This highlights the importance of building top support and developing the right skills throughout the organization. Without these foundations, even the most powerful LLM will be difficult to deliver.
Developing a responsible AI
In the current regulatory environment, AI is no longer “just work” enough, and it still needs to be accountable and explained. The EU AI Act and the UK proposed AI action plan require transparency in high-risk AI use cases. Others followed the lawsuit, with more than 1,000 related policy bills on the agenda of 69 countries.
This global responsibility action is a direct result of increased consumer and stakeholder requirements for algorithmic fairness. For example, an organization must be able to say why a client refuses a loan or charges premium premiums. To be able to do this, they need to know how the model makes the decision, which in turn depends on the clear, auditable trail of the data used to train it.
Unless interpreted, businesses may lose customer trust and face financial and legal implications. As a result, the reason for the traceability and results of the data lineage is not “good to have”, but compliance requirements.
As Genai expands for not only simple tools, but also mature agents that can make decisions and take decisions on them, the bets for strong data governance are even higher.
Steps to build a trustworthy AI
So, what does it look like? To responsibly scale Genai, organizations should seek to adopt a single data strategy across three pillars:
- Tailor-made AI to business: Classify your data around key business goals to ensure it reflects the unique context, challenges, and opportunities specific to your business.
- Build trust in AI: Establish policies, standards and processes for compliance and supervision of ethical and responsible AI deployments.
- Build an AI-ready pipeline: Integrate your diverse data sources into the elastic data foundation of robust AI baking in pre-established Genai connections.
Governance accelerates AI value when organizations do this correctly. For example, in financial services, hedge funds are leveraging AI to represent the performance of human analysts who are forecasting stock price while significantly reducing costs. In manufacturing, AI-driven supply chain optimization enables organizations to respond to geopolitical changes and environmental stresses in real time.
These are not only futuristic ideas, but now happening with trusted data.
With a strong data base, the company reduces model drift, limits retraining cycles and increases the speed of value. That’s why governance is not a barrier; it’s the enabler of innovation.
What’s next?
After the experiment, organizations are moving beyond chatbots and investment transformation capabilities. From personalizing customer interactions to accelerating medical research, improving mental health and simplifying regulatory processes, Genai is beginning to demonstrate its potential across the industry.
However, these benefits are entirely dependent on data based on data. Genai first builds a strong data foundation through strong data governance. Although Genai and Agent AI will continue to evolve, it will not replace human supervision anytime soon. Instead, we are entering a stage of structured value creation where AI becomes a reliable secondary pole. With the right investment in data quality, governance and culture, businesses can eventually transform Genai from a promising pilot to something completely stands out.
