When OpenAI launched ChatGPT in late 2022, it ushered in the age of generative artificial intelligence for the masses and introduced a whole new world of innovation, opportunity and risk.

Because of the open nature of ChatGPT and the other generative AI models that soon followed, many companies, especially those in big data and information technology, very quickly had to assess the risk to their operations that these new tools presented and institute policies with their team members regarding the responsible use of generative AI. The tremendous productivity gains that emerged from AI-enabled innovation had to be balanced against concerns of data privacy, information technology security and ethnical use.

Looking back at the past year or so of generative AI’s global impact, here are five considerations that every company should entertain regarding its use:

Generative AI drives grassroots innovation

Arguably one of the most promising aspects of generative AI is how much more efficiency and productivity that software engineers and development teams gain in the coding process. Results from generative AI can accelerate product development, testing and quality assurance, enabling software engineers to focus on the areas that matter most and broaden their skill set.

This capability dramatically fast-tracks the exploration of new ideas, business models, experimentation and internal decision-making. This type of grassroots innovation must be embraced, nurtured and operationalized at some level to remain competitive and activate the full potential of your team.

Focus on solving real customer or internal problems

Like any new shiny thing, it can be easy to become distracted and spend too much time in research and not enough time actually developing products and services that drive productivity or profitability. Another real danger is going down the proverbial development rabbit hole, exerting time and resources on efforts that will not pay off.

To avoid these traps, businesses must identify problems that their customers or internal operations are experiencing, work backward to ascertain how technology can be applied to solve those problems, then make targeted investments in time and resources to develop solutions. One prime example of an application of generative AI to enhance internal operations is customer service, where the technology can support answering questions from consumers and educating them on business-related topics or processes.

A measured, responsible approach to data privacy is required

All companies use or hold data on their customers or team members that must remain secure to ensure trust in the brand. This safeguarding starts with a company’s team members, who must be trained on the proper use of generative AI tools and – until they are – corporate access to those tools should be limited.

If generative AI enables corporate grassroots innovation, it gives fraudsters a powerful tool to carry out more sophisticated attacks more rapidly. This reality makes the need to protect corporate and consumer data an absolute requirement. Companies should strategically and seamlessly assemble a variety of detection capabilities that include authenticated identity data, device-risking, email and mobile risking, behavioral biometrics, document verification and other risk signals to spot early indicators of potential fraud instantly.

Address the ethical use of AI

Although this aspect of generative AI may be less discussed, it is critical that team members understand the ethical use of AI, how and when to represent something as AI-derived, and employ the right guardrails to prevent mis-use of the tools or wrong behavior. Ongoing news coverage has shone a bright light on the use of copywritten materials that are curated by a generative AI prompt.

As the new version of ChapGPT further enhances multi-modal capabilities in the areas of images, graphics, and other creative elements, the use of personal or proprietary materials can become a legitimate liability for a company if misused. To address this emerging issue, many forward-looking companies are forming risk councils to handle ongoing challenges that generative AI creates and document guidelines on the ethical use of any materials derived from AI.

Maintain a flexible approach to AI-related investments

Generative AI continues to drive innovation of new products and services at hyper-speed. In a fast-evolving marketplace, an inherent risk for companies can be making long-term investments in developing offerings that employ AI that, as the technology continues to advance, may lead to future obsolescence. A prudent approach is to make short-term investments that easily migrate into long-term ones, such as investment in partnerships and productivity software that can easily be deployed without massive switching costs, or investment in focused tuning of open-sourcing models for specific use cases versus a large investment in foundational models.

Make no mistake: Generative AI is a disruptive technology that will forever affect how companies manage their data and develop their products and services. However, by employing practices to ensure the responsible use of the tools, companies can realize the full promise of grassroots innovation that generative AI enables and mitigate the risks associated with it.

Shri Santhanam is an executive vice president and general manager of global analytics and AI in Experian’s Global Decision Analytics business unit. He wrote this article for SiliconANGLE.

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