If you’re not conducting business experiments with generative AI right now, you’re probably at least thinking about it. Some businesses are exploring hundreds of use cases—and even identifying 5 or 10 that have outsize impacts on the business can be enough to drive real revenue growth, reduce costs, or both.

Those kinds of results make it easy to see why there’s so much hype around generative AI. While it’s important to have guidelines in place to counter the risks—concerns about bias, transparency, and security are valid—generative AI helps businesses interact with data in ways that were unimaginable a few years ago.

For example, medical professionals can leverage generative AI to access the latest research and diagnostic protocols to treat patients with rare conditions. Educators can generate learning materials that are customized to the needs and levels of individual students to make experiences more engaging and effective. Retailers can gather product or policy information to answer complex customer questions and ultimately improve their brand loyalty. Legal professionals can gather large quantities of legal precedents and case-relevant information to work more efficiently and accurately. The opportunities are endless—and still being discovered.

If you’re an early adopter of generative AI, you can gain significant market share against competitors as long as the technology is right for your business. Once you decide to test the waters, the question then becomes: How can you get the best possible value from this technology? Here are four ways to do that.

1: APPROACH GENERATIVE AI AS A FEATURE OF YOUR LARGER ARTIFICIAL INTELLIGENCE STRATEGY—NOT AS A SEPARATE SOLUTION.

2: TAP EXISTING KNOWLEDGE BASES TO EXTRACT EARLY VALUE FROM GENERATIVE AI.

A key ingredient of extracting generative AI value will be ensuring that organizations have a strong knowledge management strategy, starting by leveraging your internal data and industry knowledge bases. Progressive organizations will fine-tune existing large language models (LLMs) by injecting industry domain knowledge into generative AI workflows. The integration of internal knowledge—everything from customer support documents to corporate policies to FAQs to company reports—will become a repeating pattern as organizations from every industry recognize its importance.

3: DON’T THINK OF GENERATIVE AI AS YOUR “GET OUT OF JAIL FREE CARD” FOR POOR DATA MANAGEMENT.

Generative AI requires strong knowledge management, which demands vigilant data management. If you have neglected the quality of data in your enterprise or have not defined a proper data strategy, your generative AI outcomes could be limited—or, worse, negatively impacted by inaccurate answers. Generative AI experiences have lowered the barriers to human interaction with data and systems, but generative AI is not a “get out of jail free card” for poor data management and data governance. A strong data management discipline is the foundation of your competitive advantage; therefore, your technology must work from accurate, consistent data.

4: CONDUCT A COST VERSUS VALUE ANALYSIS TO BE SURE YOU’RE GETTING ENOUGH VALUE. 

Like the cloud and its consumption costs, generative AI is another consumption-driven business meter. Businesses investing significant funds in generative AI must conduct value versus cost analyses and quickly shut down projects that have zero or minimal return to the bottom line. As a business leader, you should feel empowered to demand results from generative AI and challenge projects that are not delivering outcomes.

It’s important that you don’t implement generative AI just because it’s the next big thing—it’s too costly for that. Generative AI must solve a human need by delivering results that address real-world problems, impact lives, or improve business. If you invest in a long-term, persistent AI strategy, generative AI has the potential to make your organization more efficient and productive—and ultimately gain a competitive advantage.