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Lessons on integrating generative AI into the enterprise
At Generative AI World 2023, various industries convened to explore existing and potential generative AI use cases. Review insights from one company's implementation experience.
As generative AI continues to grow rapidly, many organizations are trying to determine where and how to best integrate the technology into their existing systems. Organizations know they can benefit from using generative AI, but many don't know where to start, or they fear the associated risks.
At Generative AI World 2023 in Boston, industry experts came together to discuss real-world use cases for generative AI in the enterprise in the hopes of equipping teams with the knowledge to approach this technology from an offensive -- rather than a defensive -- position.
The consensus among the majority of speakers was that it is still too early to say how enterprise use cases will take shape, but that the time to get started is here.
"Implementing generative AI is a leadership imperative," said Rachel Catanach, general manager at FleishmanHillard, during the presentation "How GenAI is Transforming Marketing." Catanach explained that change will only occur if organizations take a people-centered approach rather than focusing on processes only. To get started, she recommended organizations assemble a diverse, multidisciplinary team to assess potential areas for implementation and outline an organization's plans.
In the presentation "View form the C-Suite and the Boardroom," Ajita Rajendra, executive chairman of the board at A. O. Smith Corporation, had similar advice for organizations looking to implement generative AI. He stated that the first step is to simply start using and experimenting with the technology and put together cross-functional teams that report directly to the CEO, so everyone understands its impact. Rajendra explained that it is important to start small and take a slow, measured approach, such as incorporating it into tasks and functions before jumping to business models.
The relationship between AI and weather forecasting
Weather prediction is one industry where generative AI has made waves. As weather patterns become more erratic, accurate predictions are becoming fewer and farther between, causing potentially lasting negative effects.
In the presentation "Case Study: Transforming the Weather Industry," chief marketing officer of Tomorrow.io Dan Slagen discussed how his organization is using generative AI to accelerate weather forecasting efforts to provide more accurate predictions.
Tomorrow.io is a climate adaptation platform that focuses on translating weather data and intelligence into forecasts for customers. To provide accurate predictions, Tomorrow.io constructed its own proprietary models, built on private modeling, blending, observations and machine learning, and runs them in the cloud.
According to Slagen, most forecasts come from public data sources, which are often not helpful for day-to-day operations. Instead of only using public data, Tomorrow.io runs its own models and has its own proprietary data -- including historical and real-time data -- which gives the model the information to predict up to two weeks out. "Regardless of how good your AI is, it definitely is going to depend on what the data set is pulling from," said Slagen.
Implementing generative AI across the organization
Tomorrow.io has incorporated generative AI into its platform, and it has also worked to include the technology in other facets of the business -- something many organizations are just beginning to explore.
Generative AI was instated as a focus at the management level to ensure consistent adoption across the entire organization, said Slagen. From there, the company formed nucleus teams to help solve strategic organizational problems with generative AI, such as lead generation, improving efficiencies and cost reduction. This also involves constant self-evaluation to determine what works and what doesn't and continued exploration and experimentation to find other processes to streamline with generative AI, explained Slagen.
But despite being on board with implementing generative AI, lack of applicable AI skills in the hiring pool is still a prevalent issue. To overcome this challenge, Slagen explained that Tomorrow.io has put its company's mission at the forefront of its hiring to attract the right applicants. "Making sure that we find the absolute best talent that wants to come in and put their effort and time toward using gen AI is really the types of people that we focus on," said Slagen. Moving forward, many organizations will expect applicants to be open and have experience with generative AI.
Moving forward with generative AI
Regardless of where organizations stand in their generative AI journey, the technology must be taken seriously because the benefits can help drive positive organizational change.
Shikhar Ghosh, professor of management at Harvard Business School, said during the presentation "View from the C-Suite and the Boardroom," that the consequences of ignoring generative AI could be compared to the effects of termites rather than a tornado. Instead of wiping out businesses and industries quickly, it will gradually eat away and weaken existing business models unless organizations create a strong foundation to counter and grow with it.
Without the addition of generative AI across the company, Slagen said Tomorrow.io would not be where it is or as hopeful for the future. "As we move forward as a company, this is a huge, huge part of our organization," Slagen explained. " I don't see any hyper-growth startup out there that's not going to make gen AI a huge part of [its] product, data science [or] go-to market [strategy]. It's an incredible competitive advantage for us."
