Businesses should not fall victim to data incompetence in the age of GenAI

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Embracing GenAI represents a significant leap forward for individuals who have integrated it into their daily routines, leveraging its capabilities for enhanced productivity. However, when transitioning to enterprise-specific projects, the landscape shifts. Here, the demand for accuracy and rigor reaches new heights. Despite the promising potential, statistics indicate that nearly 80% of GenAI projects currently encounter setbacks or outright fail. This is often due to misalignment with business goals, data issues, high costs and integration problems. In a market of fierce competition and against a tough economic backdrop, organizations simply cannot afford to have underwhelming technology roll-outs hinder their advancement.

Many leaders, eager to harness the benefits of GenAI, are actively exploring its potential. However, to ensure successful outcomes, it is essential to recognize the importance of establishing 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 avoid getting left behind, some businesses are accelerating projects alongside a complex data landscape that leads to a web of issues whereby AI provides misleading information, as well as biased responses and debased claims.

Moving forward, businesses must establish the right data foundation to benefit from GenAI and maintain their competitive edge.

Irfan Khan

President & Chief Product Officer for SAP HANA Database & Analytics.

Exposing the realities of flawed GenAI

According to a recent report by McKinsey, GenAI can potentially create between $2.6 and $4.4 trillion in value to GDP across industries. Within 18 months, it has become fundamental to long-term business strategy and growth. Yet, the dangers of skipping important steps to adoption, such as lacking a unified and transparent data strategy, have never been more apparent. Recent high-profile cases have seen brands taken to court for issues with GenAI solutions and chatbots.

This illustrates the consequences for those who fail to take the appropriate action to ensure the accuracy and reliability of GenAI outputs reiterating that organizations will be held accountable for how their AI models share and provide business information.

The value of a strong data foundation

To avoid such consequences, it is essential for businesses to consider whether they have the right data strategy in place to ensure GenAI models are thoroughly trained with the appropriate business context, to provide accurate, reliable and high-quality output. Currently too many fall at the first hurdle, operating without a strong data foundation that becomes the basis for GenAI to build on.

To address a complex data landscape and avoid the ramifications of flawed GenAI, it is essential to have a data strategy that provides a holistic view of key insights from around the business. Bringing together disparate data with a data fabric approach ensures that context is kept intact, providing a picture of meaning and relevance of how data was generated, where it typically resides, when it was created and who it relates to. This means businesses will have complete visibility into their operations to understand the origins and value of data, providing a single source of truth for decision-making.

Many businesses often deal with data stored across disparate systems or in data lakes, as a result of attempts to consolidate data sources. A strategy that incorporates a data fabric approach can importantly accommodate this complexity and adapt to every unique technology landscape. This eliminates the need for costly and timely data transferal. It also means that it is suitable for businesses at all stages of modernization.

The gateway to working smart and operating with resilience

Once this foundation is in place, organizations can start to practice better data governance, adhering to data access rights, data security and data privacy. With rigorous standards in place, businesses can expect vastly improved AI insights with the ability to understand why and how it produced a certain response. This is critical when it comes to eliminating bias or testing and improving GenAI models to better reflect business requirements.

Employees can then leverage AI to work smarter and capitalise on 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 outcomes by providing the infrastructure to run tests and simulate certain scenarios, whether that’s for product prototyping or process optimisation. This best practice sets organisations up to remain resilient and compliant to future regulation. With responsible and reliable AI use becoming a central focus for governments globally, rigorous regulatory guidance is on the horizon and businesses cannot afford to be caught out. Setting up the correct data architecture now will ensure businesses have the ability to accelerate their decision-making with trust and confidence, and prepare for a future where they will be accountable for GenAI and its decision-making.

Capitalizing on the GenAI opportunity

GenAI has fast become a critical element of day-to-day business operations. When executed correctly, in combination with the appropriate data foundation, organizations can use instant, accurate, real-time insights from across their operations, to inform decision making and strategy. It can also create a more efficient workforce, drive productivity and future-proof the business against impending regulations. However, many leaders overlook the importance of getting their data in order first. They must embrace a strategy that simplifies their data landscape, leverages data from across the siloes, keeps its context intact, and informs GenAI models to drive positive outcomes. That’s how they’ll remain competitive and offset market pressures.

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Irfan Khan is President & Chief Product Officer for SAP HANA Database & Analytics.