CONTRIBUTOR
While the C-suite has long discussed enterprise AI as a potential game changer, the game actually changed last fall with the release of OpenAI’s ChatGPT. Since then, CxOs down to organizations’ line-of-business managers have been abuzz with possible ways to put large language models to practical use.
Prominent enterprise software vendors have also been thinking about how their customers can leverage generative AI. Earlier this month, Microsoft incorporated ChatGPT into its Bing search engine and announced that ChatGPT is available in an Azure OpenAI managed service, and Microsoft customers will be able to create their own versions of ChatGPT. The vendor also announced its plans to incorporate ChatGPT across all its software services. While during the last week of March, Google announced a limited release of its experimental, conversational AI service, Bard. Adobe also announced its generative AI service, Firefly, which enables customers to create and edit photos with text commands.
Salesforce also announced Einstein GPT for its Commerce Cloud. That capability will help customers to automate custom recommendations in Salesforce Commerce Cloud. Everywhere one looks, there’s a new generative AI announcement. OpenAI even announced it has implemented initial support for plugins.
In a recent survey, Accenture found that roughly 75% of companies had integrated AI into their business strategies and had reworked their cloud plans to optimize their chances of AI success. According to Accenture, “nearly a third (30%) of all AI pilot initiatives are subsequently scaled to deliver wide-ranging outcomes, from accelerating R&D timelines for new products to enhancing customer experiences. 42% said that the return on their AI initiatives exceeded their expectations, while only 1% said the return didn’t meet expectations.”
According to McKinsey, 52% of respondents to one of their surveys said AI is getting more than 5% of their digital efforts.
“The pace of innovation coming out of OpenAI is staggering. Not simply the speed but how the democratization of the technology is enabling swathes of ideation and “what if” questioning from the board level down to the developer (and all points in-between). It’s very rare that an emergent technology has this much impact,” says Christian Reilly, VP, former technology strategist at Citrix.
Enterprises Search for Generative AI Use Cases
“Though we’re still in the early stages of enterprises implementing generative AI, one of the most intriguing use cases is information technology. IT leaders can leverage generative AI tools for coding, writing copy and many other tasks that require text generation. Enterprises that apply these tools correctly can save countless hours and resources on these simple tasks, freeing employees to tackle more value-added services,” says CF Su, VP of machine learning at Hyperscience.
We reached out to numerous experts to get a sense of what projects enterprises have underway. Here’s what they say:
Employee training: CxOs can use generative AI to create virtual assistants to assist employees in training and development. With these bots, the AI will quickly answer employees’ questions and provide tailored guidance. This should help employees to learn more efficiently.
Knowledge management: CxOs can use generative AI to create customized knowledge bases that store and organize information. For the first time, enterprises may soon be able to access all of their organizational knowledge quickly and efficiently.
Decision-making: CxOs can use generative AI to analyze data and provide insights that help decision-making. Generative AI can analyze large amounts of data and provide insights based on the data, assisting CxOs in making more informed decisions. The challenge many organizations face is trusting the quality of their data.
Customer support: Generative AI can be integrated into a company’s customer support channels, such as chatbots, email or voice assistants, to provide 24/7 assistance to customers. This can help reduce the burden on customer support teams and improve customer satisfaction. “Generative AI can improve the capabilities of intelligent virtual assistant software by enabling them to generate more natural and human-like responses. It allows for higher-level customer support, providing personalized recommendations,” says Olha Sypa, marketing manager at Intelliarts AI.
Content Development. “Mediocre writers will not have jobs by the end of this year. ChatGPT is already good, especially when creating SEO-friendly content: A marketer’s dream and a mediocre writer’s worst nightmare,” says Chief Trust Officer at Cerby, Matt Chiodi.
Market specialization and business resiliency: Generative AI can provide personalized insights. For example, ChatGPT can provide customized product recommendations to customers based on their past purchases or to recommend personalized learning paths to employees based on their skill level and job responsibilities. These capabilities already exist, for sure, but to a much lesser extent and effectiveness. “With advanced conversational AI, organizations can rapidly decrease the time taken to deliver fully personalized updates, especially when supply chains get disrupted or potential crises emerge that require providing not just expert advice but also reassurance to clients,” says Bart Schouw, chief evangelist, IoT and Analytics at Software AG.
What happens next? Reilly predicts it will be “a new phase of the API gold rush, both for enterprise and for SaaS providers. Unlocking access to data is key. APIs will become even more strategic and even more valuable.”
As the capabilities of AIs built on large-language models continue to be unleashed, new use cases will reveal themselves. One of the biggest challenges CxOs may face is prioritizing what to do first. And the most critical generative AI use cases for CxOs will depend on their organization’s specific needs. However, customer support, employee training, knowledge management, decision-making and personalization are all areas where these new tools may provide significant value.
