Skip to main content

Fill in the details to unlock webinar

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Speakers

For Business

Training 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp For BusinessFor a bespoke solution book a demo.

Make AI Work More Than 5% of the Time

November 2025
Webinar Preview

Session Resources

Summary

The session "Make AI Work More Than 5% of the Time" focuses on improving AI project success rates for data professionals and business leaders. It addresses the concerning statistic from an MIT study that claims 95% of AI pilots fail to reach production. The discussion features insights from Lexi Reese, CEO of Lanai, Pratyush Mulukutla, Chief Data Officer at MediaMint, and Jon Welch, VP of AI at Albert Invent. They explore common pitfalls in AI projects, share success stories, and provide guidance on building the right infrastructure and skills to enhance AI adoption. The session emphasizes the importance of aligning AI initiatives with clear business objectives and measuring their impact effectively.

Key Takeaways:

  • AI success requires clear business objectives and measurable outcomes.
  • Understanding the limitations and scope of AI is essential for effective implementation.
  • Building a strong data infrastructure is foundational to AI success.
  • Translating data insights into business value is a critical skill.
  • AI projects should start with practical, achievable goals and user-centric design.

Deep Insights

Understanding AI Project Failures

The session begins by addressing the startling statistic from MIT that 95% of AI pilots fail to reach production. Lexi Reese dismisses the study as overly pessimistic, suggesting that many AI projects do provide value, but their impact is often unmeasured. Pratyush Mulukutla agrees, noting that AI's success is often hindered by unclear objectives and inadequat ...
Read More

e data infrastructure. Jon Welch adds that unrealistic expectations can lead to perceived failures, as companies often set overly ambitious goals without understanding the incremental nature of AI improvements. The consensus is that AI projects need clear business objectives and realistic expectations to succeed.

Common Mistakes in AI Implementation

A recurring theme is the importance of aligning AI projects with business goals. Pratyush highlights the mistake of pursuing AI for its own sake, without a clear business problem to solve. This often leads to pilots that never progress to production. Instead, companies should identify specific workflows where AI can provide measurable efficiency gains. Lexi emphasizes the need for strategic leadership in AI adoption, suggesting that businesses should integrate AI into their core strategy rather than treating it as a side project. Jon stresses the importance of understanding the limitations of AI and setting achievable goals.

Success Stories and Practical Applications

The speakers share various success stories to illustrate effective AI use. Jon discusses a project with Nureon, where AI was used to enhance search capabilities for raw materials, demonstrating the importance of domain-specific AI applications. Lexi mentions T-Mobile's 30% efficiency gain across business functions after building a unified AI data factory. These examples highlight the need for specific AI solutions that address business needs. The speakers agree that successful AI projects often start with practical, achievable goals and involve close collaboration with end-users to ensure the solutions meet their needs.

Building the Right Infrastructure and Skills

The discussion emphasizes the necessity of a strong data infrastructure as a foundation for AI success. Lexi points out that every successful AI story begins with a solid data architecture. Pratyush adds that having a clear understanding of the data environment and the right technical skills, such as data engineering and MLOps, are essential. The speakers also highlight the importance of translating data insights into business value, a skill that is in high demand. They stress that AI initiatives should be driven by business needs and supported by a comprehensive understanding of both the technology and the business context.

Related

webinar

Generative AI in Practice: 100+ Amazing Ways Generative AI is Changing Business & Society

World-renowned author & futurist Bernard Marr, will outline how Generative AI is being used in practice today.

webinar

Generative AI in Practice: 100+ Amazing Ways Generative AI is Changing Business & Society

World-renowned author & futurist Bernard Marr, will outline how Generative AI is being used in practice today.

webinar

The High Cost of AI Hype

With new breathtaking capabilities from generative AI released every several months—and AI hype escalating at an even higher rate—it’s high time we differentiate most of today’s practical ML projects from those research advances.

webinar

The AI Agent Strategic Playbook

AI leaders and enterprise practitioners, will share how organizations are deploying AI agents today—and what executives need to know to lead those efforts effectively.

webinar

Artificial Intelligence in 2025: Will the Rocket Continue Climbing or Will the Bubble Burst?

Three industry experts discuss the state of AI hype, whether this wave of innovation is going to plateau, what to expect from GenAI in 2025, and a lot more.

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.