Part 2: Use cases and benefits
We have a 40-year history of telling the weather, and we're one of the most trusted brands and the most accurate weather forecaster. And so generative AI allows us to hit different parts of the market segments with more accurate information that's just in time for their use case. Weather forecast is useless unless it reaches the person it's supposed to reach. We take more than 100 different forecasts and we window those down to make the most accurate forecast for the application. Generative AI allows us to describe a forecast, a context, some inputs, in a way that's hyper-personalized to the audience. When a customer goes to weather.com, that experience is delivered on AWS, and with 26 billion forecasts daily, you know, there's a lot of traffic. The weather changes constantly and the weather change creates a lot of data. When you move data to a place, y- you need to deliver all of your insights and all of your value from that place. Building a solid data foundation on AWS lets us get ancillary use cases from that data. An example is in the enterprise space, the Weather Engine, which is one of our enterprise offerings, lets businesses subscribe to custom AI models that give them insights about the weather. When we first started, we had a very clumsy container-based workflow, which was very difficult to scale and manage. We worked with the Generative AI Innovation Center to establish MLOps, which let our business be more agile and efficient with its development of ML and AI models. MLOps is basically a pipeline-based delivery, you know, using SageMaker and other A- AWS technologies to provide models and inference and training, all in a way that lets us focus on data science and weather forecasting as opposed to computer science. We saved 90% reduction in our cost and overhead, with a 20% increase in model creation because of this adoption of MLOps. By using AWS, we're guaranteed, you know, regional parity, AZs within those regions, and then a number of different services that all are compliant with the standards and protocols of the AWS Foundation. AWS lets us scale to planet-sized scale for a planet-sized problem. The weather is getting more chaotic, and as it gets more chaotic, it produces more data, there's more impact on human and civilization, so getting the most accurate forecast specifically to the customer that's desiring it or around their business needs is important to us.
This weather forecasting testimonial demonstrates key principles of effective AI implementation and sales positioning in action.
The organization smartly anchors their AI story in their established identity—"40 years of telling the weather" as "one of the most trusted brands." This perfectly illustrates our earlier point about AI's power: not creating new products but elevating existing tools to fulfill their original vision. For sales representatives, the lesson is clear: position AI as an amplifier of existing strengths, not a disruptive replacement.
Their data approach highlights why contextual, domain-specific information is essential for enterprise AI. By taking "100 different forecasts and windowing those down," they create value through proprietary data combined with meteorological expertise—something generic AI couldn't replicate.
When they describe using "generative AI to deliver hyper-personalized" forecasts, they're implementing Retrieval Augmented Generation in practice. They retrieve structured weather data and transform it into tailored natural language outputs. This exemplifies what we discussed about making specialized information accessible through natural language interaction.
Their evolution from "clumsy container-based workflow" to streamlined MLOps reflects the typical journey from AI experimentation to production implementation. Sales representatives should recognize where prospects are in this journey and position solutions accordingly.
The outcomes they achieved—"90% reduction in cost with 20% increase in model creation"—provide the quantifiable business impact that effective AI sales narratives require. These metrics translate technical capabilities into business value stakeholders can appreciate.
Their observation about weather becoming "more chaotic" producing "more data" with "more impact" reinforces AI's role as a sense-making tool in an increasingly complex world. This connects to our discussion about how LLMs allow companies to leverage growing data volumes while lowering the technical threshold for employees to extract value.
For sales professionals, this testimonial shows how to effectively position AI by:
Connecting capabilities to specific business challenges
Demonstrating value through concrete outcomes
Framing AI as enhancing rather than replacing existing expertise
Focusing on business needs rather than technology for technology's sake
This example brings our AI value principles to life, showing how thoughtful implementation can transform even established industries when applied to well-defined business challenges.