3 strategies for using generative AI to responsibly extract data insights

In an increasingly complex and connected world, many are keeping a keen eye on artificial intelligence (AI) to understand its role in building high-performing, resilient and responsive enterprises.

A key challenge for many organizations today is the large gap between the explosion of content we have seen in recent years and the individual who needs decision support. According to the International Data Corporation (IDC), the amount of data being created, captured, replicated and consumed will hit 175 zettabytes by next year – a nearly 300% increase compared with 2019.

Generative AI can help us navigate this data surge by making mass data quantities accessible on a human scale, enabling us to extract the insights we need from otherwise unmanageable volumes of data. However, if not used responsibly, AI can create a hall of mirrors that obscures our ability to discern truth from falsehood.

We've already seen the consequences of unregulated AI use in various arenas: the proliferation of misinformation and disinformation that affects elections, copyright violations that lead to legal disputes, and the provision of false or discriminatory legal advice that threatens the integrity of justice systems.

To harness generative AI effectively and responsibly, organizations can implement the following three strategies to derive valuable insights from a sea of data, without exposing themselves or others to the harmful consequences of unchecked AI deployment.

While knowledge is power, knowledge visibility is a superpower. For centuries, people have imagined knowledge visibility through the ability of a person to understand all human languages or even speak with animals. It is finally within our grasp to deploy such a new orchestration layer above our existing enterprise software stack.

This knowledge fabric will be agnostic to the human languages in which ideas are authored, captured, or recorded. It does not care what underlying. systems of record content reside in, and it will file type content is authored in, whether it be audio, image, text, or video.

AI is the catalyst that can finally fulfil this concept. It can tap into disparate assets – whether semi-structured, structured or unstructured – and merge them into a cohesive whole. It can be easily argued that the organizations that sit at the top of the Fortune 500, due to their economies of scale, are amongst the pinnacle brands that have been able to better predict the way the world operates because of their vanguard use of these technologies.

What happened over the last few years is a wider distribution of knowledge visibility based on lowered barriers to entry. This democratization of knowledge now enables organizations of all sizes to identify insights, patterns and trends that might otherwise remain hidden, unlocking new opportunities and exposing previously unidentified risks. This empowers leaders with decision advantage, delivering the information they need to act in a fast-paced world.

With the influx of unchecked generative content, the internet can become a hall of mirrors where we will not know what is real and what is not. So how do we safeguard our intelligence?

To overcome uncertainties, organizations need to create their own knowledge fabrics for their value chains. This construct serves as a repository of reliable information, ensuring you always have attributable, trustworthy, and verifiable content you can rely on. This could include:

We have the opportunity to enter a new era of information discovery where we can fall back to trusted sources and put up guardrails to protect our data from disinformation and unreliable input.

Not all content is created equal, and the same is certainly true for AI tools. To get started with a responsible AI framework, you need to identify how much control you will have over your content and your AI model and then scale your usage accordingly.

To do this, you can use a simple matrix that represents control over your content on one axis and control over your AI technology on the other, as outlined with possible actions to take below.

Advancements in cognitive computing have made knowledge fabrics a reality, offering unprecedented levels of accuracy, scale, security and speed. By selecting high-value information retrieval use cases, curating trusted content collections and understanding the technologies’ control planes, even the most sensitive workflows can finally be addressed.

Amidst the current data deluge, AI presents a transformative opportunity to build more intelligent organizations by reducing the distance between knowledge and people. If Steve Jobs considered the personal computer the bicycle for the mind, then AI – as another landmark in technological development – becomes the rocket ship for it.

With the proper AI safeguards in place, organizations can make more informed decisions, navigate new challenges in a dynamic world, and shape a more sustainable future for all of us.