Data essentials in the age of generative AI

Generative AI can help companies reinvent themselves

Generative AI can help companies reinvent themselves and drive growth in many ways. However, to unlock the value of AI at scale across an organization, a digital core that’s built for both machines and humans is required.

The good news is that most organizations already own the most valuable asset in the era of generative AI: their proprietary data. But is that data ready for generative AI?

With the rapid advancement of generative AI changing the sheer amount and types of data companies need…the road to readiness can seem complex. But there is a clear path forward.

We’ve identified six key essentials you need to know about how data is changing in the era of generative AI— and what it means for your business—so that you can pull ahead of the pack.

1. Your proprietary data is your competitive advantage

Generative AI foundation models become relevant— and therefore useful—when combined with your company’s proprietary data. This combination unlocks high value insights into your customers, products, and operations, providing a competitive edge.

Tapping historical and real-time institutional knowledge can improve internal decision-making, reduce risks and identify new efficiencies, as well as open up attractive monetization opportunities.

2. Your unstructured data holds untapped potential

Unstructured data—encompassing formats like text, images, audio and video—is rich with contextual information.

Generative AI excels at processing this data type, transforming it into valuable business insights and applications. Like turning a how-to video into a list of product features, summarizing a voice call or spinning up marketing content.

When combined with structured data, it adds the context needed to enable more human-like communication: it contains signals for tone and personality, look and feel that drive much richer interactions.

3. Synthetic data is key to filling in data gaps

AI is hungry for data—and the more complex the task or output, the more data is required. Synthetic data addresses the scarcity of specialized datasets, enabling companies to explore multiple scenarios without the extensive costs associated with real data collection.

For example, a company might use synthetic product and customer data during market-testing to save time and resources. It can also be used for risk-management, designing “what-if” scenarios, and even to remove bias.

Synthetic data also addresses certain data risks. It can be used to train AI models without transgressing privacy if the data is sensitive. In cases where data is regulated, keeping copies of synthetic data rather than the original reduces risk in case of a breach.

4. Connected data is key to context for generative AI

So much of today’s data is locked in silos and functional domains, limiting potential and collaboration. Generative AI facilitates the use of cross-functional data, enabling the reinvention of end-to-end business processes that cut across functions and value chains.

Businesses can apply generative AI to break down data silos and discover more efficient ways of working. To achieve this, every part of the organization must make data accessible and treat it as a valuable product—reliable, secure and easy to use.

Think, how much better would life be if customer service could “see” the required updates based on exact specifications from product R&D. Or marketing could know right away that supply chain can keep up with their promotion.

Access to cross-functional data breaks down boundaries and opens up the organization to new ways of working.

5. Generative AI accelerates data risks

Most new opportunities come with new risk, and generative AI is no exception. It introduces new challenges, particularly when it comes to data governance and security. There are a number of common blind spots that organizations must address to mitigate new risks like — new data types, greater access, increased attacks and maintaining data quality.

To mitigate these risks, companies should adopt robust data governance—something that is often baked into Responsible AI programs. Accenture’s own internal Responsible AI program, for instance, has four main components — establish AI governance; conduct an AI risk assessment; enable a systematic RAI testing program; and ongoing monitoring and compliance of AI.

6. Generative AI, applied to data, jumpstarts data readiness

It’s not just about what your data can do for generative AI, it’s also about what generative AI can do for your data. Applying generative AI to your current data processes can enhance various aspects of the data supply chain, from capture and curation to consumption.

Generative AI can help summarize and classify business data requirements; automatically generate design documents, test cases and data; and generate runbooks and deployment scripts. It can be used to help users find, contextualize and use data.

It also provides opportunities to leap-frog legacy systems and slow ways of working. For example, generative AI supports the reverse-engineering of an existing system prior to migration and modernization.

Mining your data’s full potential

Many companies are sitting on a goldmine in potential generative AI value in the form of their proprietary data. It’s time to dig in. The journey to data-readiness can be accelerated by keeping these six essentials in mind. Now that you’ve got this information, how do you move forward and ready your data?

We’ve identified key actions companies need to take to ensure their data is ready for generative AI.

Read the full report to understand key considerations and how to strengthen your data capabilities to build data readiness.