Embracing GenAI is a huge step forward for those who have integrated it into their daily routines and leveraged its potential for improved productivity. However, when it comes to enterprise-specific projects, the landscape changes. Here, the demand for accuracy and precision reaches new heights. Despite its promising potential, statistics show that nearly 80% of GenAI projects currently encounter setbacks or fail outright. This is often due to misalignment with business goals, data issues, high costs, and integration issues. In a highly competitive market and a challenging economic backdrop, organizations simply cannot afford to let disappointing technology rollouts hinder their progress.
Many leaders, eager to reap the benefits of GenAI, are actively exploring its potential. However, to ensure successful outcomes, it is essential to recognize the importance of creating the necessary conditions for success. GenAI requires the right data context and architecture to produce effective insights that inform decision-making. Yet, in the rush to catch up, some companies are accelerating projects alongside a complex data landscape that leads to a web of problems where AI provides misleading information, biased responses, and devalued claims.
Going forward, companies must create the right data foundation to take advantage of GenAI and maintain their competitive edge.
President and Chief Product Officer for SAP HANA Database & Analytics.
Exposing the Reality of Flawed GenAI
According to a recent McKinsey report, GenAI has the potential to add between $2.6 and $4.4 trillion in GDP value across all industries. Within 18 months, it has become fundamental to long-term business strategy and growth. Yet the dangers of skipping key steps toward adoption, such as the lack of a unified and transparent data strategy, have never been clearer. Recent high-profile cases have seen brands taken to court over issues with GenAI solutions and chatbots.
This illustrates the consequences for those who do not take the appropriate measures to ensure the accuracy and reliability of GenAI output. It further emphasizes that organizations will be held accountable for the way their AI models share and provide business intelligence.
The value of a strong data foundation
To avoid such consequences, it is essential that companies consider whether they have the right data strategy in place to ensure GenAI models are trained rigorously with the right business context to deliver accurate, reliable, and high-quality output. Currently, too many companies fall at the first hurdle, operating without a strong data foundation that becomes the bedrock upon which GenAI can build.
To tackle a complex data landscape and avoid the consequences of flawed GenAI, it’s essential to have a data strategy that provides a holistic view of key insights from across the business. Bringing together disparate data with a data fabric approach ensures that context remains intact, providing a picture of the meaning and relevance of how data was generated, where it typically resides, when it was created, and who it’s associated with. This means businesses have full visibility into their operations to understand the origins and value of data, providing a single source of truth for decision-making.
Many companies often find themselves with data that is stored in disparate systems or data lakes, as a result of efforts to consolidate data sources. A strategy that incorporates a data fabric approach can significantly accommodate this complexity and adapt to each unique technology landscape. This eliminates the need for costly and timely data transfers. It also means that it is suitable for companies at all stages of modernization.
The gateway to smart working and resilient operations
Once this foundation is in place, organizations can begin to practice better data governance, adhering to data access rights, data security, and data privacy. With strict standards, businesses can expect vastly improved AI insights with the ability to understand why and how it produced a particular response. This is crucial when it comes to eliminating biases or testing and improving GenAI models to better suit business requirements.
Employees can then leverage AI to work smarter and reap productivity benefits. With access to real-time insights, businesses can streamline and automate processes and reduce the need for timely, repetitive manual work. At the same time, the technology can support better quality control of results by providing the infrastructure to run tests and simulate certain scenarios, whether that’s for product prototyping or process optimization. This best practice ensures that organizations remain resilient and compliant with future regulations. As responsible and trustworthy AI use becomes a central focus for governments worldwide, strict regulations are on the horizon and businesses can’t afford to get caught out. By putting the right data architecture in place now, businesses can accelerate their decision-making with confidence and certainty, and prepare for a future where they are accountable for GenAI and its decision-making.
Taking advantage of the GenAI opportunity
GenAI has quickly become a critical element of day-to-day business operations. When done correctly, combined with the right data foundation, organizations can use immediate, accurate, real-time insights from their operations to inform decision-making and strategy. It can also create a more efficient workforce, boost productivity, and future-proof the business against looming regulations. However, many leaders overlook the importance of getting their data right first. They must embrace a strategy that simplifies their data landscape, leverages data across silos, preserves context, and informs GenAI models to deliver positive outcomes—to stay competitive and offset market pressures.
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