Ir al contenido principal

Complete los detalles para desbloquear el seminario web

Al continuar, aceptas nuestros Términos de uso, nuestra Política de privacidad y que tus datos se almacenen en los EE. UU.

Altavoces

Empresas

¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más
Pruebe DataCamp para empresasPara obtener una solución a medida, reserve una demostración.

Calculate the Value of Machine Learning Projects

November 2025
Webinar Preview

Click here for the accompanying hands-on access to Gooder AI, the session slides, and other resources

Access the hands-on trial of Gooder AI

Summary

Calculating the business value of machine learning projects is essential for data scientists and business stakeholders to ensure effective deployment. The session, led by Eric Siegel, CEO of Gooder AI, highlights the importance of predictive AI and the need to estimate value before initiating machine learning projects. Predictive AI is key for improving operations across various sectors, yet many models fail to deploy due to a lack of business value estimation. The session introduces Gooder AI, a platform designed to link technical metrics with business KPIs, enabling data scientists to plan, sell, and approve AI deployments effectively. By focusing on business metrics like profit and savings, the session emphasizes aligning machine learning projects with organizational goals to maximize impact and drive successful deployments.

Key Takeaways:

  • Predictive AI is essential for enhancing large-scale operations across industries.
  • Most machine learning models fail to deploy due to a lack of business value estimation.
  • Gooder AI helps connect technical metrics with business KPIs.
  • Estimating business value before deployment is crucial for project success.
  • Data scientists need to adopt a business-oriented approach to maximize impact.

Deep Insights

The Importance of Predictive AI

Pred ...
Leer Mas

ictive AI is a transformative technology that enhances operations across various sectors, from sales and fraud prevention to healthcare and manufacturing. Eric Siegel points out that predictive AI is distinct from generative AI, focusing on improving existing operations rather than creating new content. "Predictive AI is the technology you turn to improve your existing large-scale operations," Siegel notes. Despite its potential, many predictive AI projects fail to deploy due to a lack of business value estimation. Siegel argues that companies should invest equally in predictive and generative AI, as they address different needs and can complement each other.

Challenges in AI Deployment

A significant challenge in deploying AI models is the disconnect between technical metrics and business value. Many data scientists focus on technical metrics like precision and recall, which are often meaningless to business stakeholders. Siegel highlights that only 22% of data scientists report their initiatives usually deploy, primarily due to this disconnect. The session stresses the need for data scientists to adopt business metrics, such as profit and savings, to evaluate model performance. This shift enables better communication with stakeholders and increases the likelihood of project deployment.

Gooder AI: Connecting the Dots

Gooder AI is introduced as a solution to connect technical metrics with business KPIs. The platform allows users to estimate the business value of AI projects before deployment, addressing a critical gap in the industry. By focusing on business metrics, Gooder AI empowers data scientists to plan and sell AI deployments effectively. Siegel explains, "You can't say this model is worth a million bucks without considering the business context." The platform provides an interactive UI that helps users visualize potential deployment options and their business impact, making it easier to communicate with stakeholders.

Estimating Business Value

Estimating the business value of AI projects is vital for successful deployment. Siegel emphasizes that this estimation should be done before deployment to guide project decisions and ensure alignment with business goals. The session introduces the concept of using profit curves and savings curves to visualize the business impact of AI models. By moving from technical evaluation to business valuation, data scientists can better plan and communicate the potential value of their projects. This approach not only improves deployment rates but also enhances the overall impact of AI initiatives within organizations.

Relacionado

webinar

A Playbook For Getting Value from AI

Lisa Weaver-Lambert, Digital Transformation and AI Advisor, will share a practical playbook for driving value from AI across your business.

webinar

Getting ROI from AI

In this webinar, Cal shares lessons learned from real-world examples about how to safely implement AI in your organization.

webinar

Scaling Enterprise Value with AI: How to Prioritize ChatGPT Use Cases

Learn to navigate privacy and security concerns, the ethical and compliance considerations, and the human factors to safely incorporate generative AI in your organization.

webinar

Transforming AI Into Value: Driving Business Growth and ROI

Industry experts explore the strategies and frameworks needed to harness AI effectively. Discover how to drive adoption of AI, build clear alignment with business goals, and unlock the ROI of your AI investments.

webinar

Assessing Your Organization's AI Maturity

Eryn Peters, Co-founder at AI Maturity Index, will share a comprehensive approach to evaluating your organization’s data and AI maturity.

webinar

Driving ROI with AI: Build AI Strategies That Scale

Industry leaders share techniques to define and implement AI strategies that scale. You’ll learn how to identify high-impact AI projects, use AI to enhance strategy and operations, and apply best practices for managing AI initiatives.